{"paper_id":"45b32d94-ee9a-4465-9fb6-c1a67f6c8353","body_text":"Forecasting Of Large-Scale Manufacturing (LSM) Quantum Index & Its Role in Economic Development of Sindh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Forecasting Of Large-Scale Manufacturing (LSM) Quantum Index & Its Role in Economic Development of Sindh Shahab Uddin, Hafsa Unar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9515017/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the role of large-scale manufacturing (LSM) in the economic development of Sindh and forecasts the Quantum Index of Manufacturing (QIM) using an ARIMA time-series approach. The study uses secondary data for the period 2021–2023, collected from official statistical sources, including the Bureau of Statistics Sindh and related government publications. In addition to forecasting QIM, the research analyzes the relationship between QIM and selected development-related variables, namely industrial electricity consumption, industrial gas consumption, coal production, and limestone production. Descriptive statistics and correlation analysis reveal that electricity consumption and limestone production have strong and statistically significant positive associations with QIM, while gas consumption and coal production show weak and statistically insignificant relationships. For forecasting, several ARIMA specifications were estimated and compared using Akaike Information Criterion (AIC). The ARIMA (1,0) model was selected as the most suitable model because it produced the lowest AIC value. Forecast results indicate a declining trend in QIM over the next four months, suggesting possible short-term weakness in manufacturing performance in Sindh. The findings highlight the importance of reliable electricity supply and raw material availability for industrial productivity. The study concludes that strengthening energy support and resource utilization is essential for sustaining manufacturing growth and informing policy decisions aimed at improving industrial performance in Sindh. Agricultural Economics and Policy Development Economics Large-scale manufacturing Quantum Index of Manufacturing ARIMA forecasting Industrial electricity consumption Sindh economy Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Manufacturing is one of the main parts of the industry sector, and it consists of two main parts; large scale manufacturing (LSM) and small-scale manufacturing (SSM) industries. The large-scale manufacturing industries (LSM) are based on 10 or more employees, although the survey of small-scale manufacturing (SSM) focuses on small manufacturing industries where fewer than 10 employees work. The role of Sindh province in large-scale manufacturing industries is imperative. The manufacturing sector share its 65% in the industry according to the Pakistan Economic Survey (Lewis Jr, 2026 ). Performance of the Industrial sector is based on large-scale manufacturing industries (LSM), as it holds 74.0% share in the industry (Jafri & Unar, 2025 ). Large scale of manufacturing industries has many sectors, but the Monthly Industrial Production and Employment Survey (MIPE) used 18 major sectors of LSM Table 1 Selected large manufacturing major sectors S. No Sectors S. No Sectors 1 Food products 10 Chemicals & chemical products 2 Beverages 11 Pharmaceutical products 3 Textiles 12 Rubber and plastic products 4 Wearing apparel 13 Other non-metallic mineral products 5 Leather & related products 14 Basic iron & steel metals 6 Wood and woodworks 15 Fabricated metals products 7 Paper and paper products 16 Electrical equipment 8 Printing and recorded media 17 Motor vehicles & parts 9 Coke & refined petroleum Products 18 Furniture This section provides information about items and reporting units of manufacturing production. Some of the items with their reporting units are discussed below in the form of a table. This table shows that the manufacturing item, food product measured in M. Tone unit, such as coke & refined petroleum products, beverages and pharmaceuticals in “000” litters, whereas lather and refined petroleum products in “000” square meters. M. Table 2 Manufacturing Production Items and Reporting Units Sr. No Manufacturing items unit Weight Production index 1 Food product M. Tons 12.3 114.3 2 Beverages “000” litter 0.5 125.7 3 Lather & related product “000” sq. M. 0.4 70.9 4 Coke & refined petroleum products “000” litter 12.4 124.8 5 Pharmaceutical products “000” litter 8.5 81.9 The census of (large-scale) manufacturing industries (CMI) is conducted/published by the Pakistan Bureau of Statistics (PBS). PBS is also called national statistical organization (NSO). The first CMI was conducted in 1954, and the last was in 2015–2016 (PBS, 2022 ). A census is the complete count of the overall population in which each and every individual can be counted. CMI 2015-16 manage the number of establishment manufacturing in Sindh, 62,299 (Pakistan Bureau of Statistics, 2016). CMI is a major source of industrial statistics. Census manufacturing industries collect major measures such as production, investment, and structural changes of the large manufacturing industries (LSM) (Bureau of Statistics, 2023b). The census manufacturing industries CMI is very essential because it has enormous role in assembling GDP. CMI is conducted every five years through mailing questionnaires. CMI provide data on aggregate and assess of inputs and outputs, census value added, non-aggregate DP, fixed assets, stocks, employment and employment cost and industrial taxes. The CMI report provides the particular data at provincial and national basis. The objective of managing CMI is to assess the changes in the composition, formation, and growth of the manufacturing sector associated with medium at provincial levels. The census manufacturing industries (CMI) 2015–2016 was conducted in the execution of the provision section of 39 general statistics (Re-organization) Act 2011 (Finance Division, 2023 ).CMI is the key source for updating the business Register (BR). The monthly industrial production and employment survey (MIPE) is regularly conducted by the provincial bureau of statistic Sindh (BOS) since the 1970s. It is survey basis report. (Bureau of Statistics, 2023b). The crucial statistics with reference to industrial production and employment of large-scale manufacturing (LSM) industries in Sindh are provided by MIPE. (Pakistan Bureau of Statistics, 2016). An essential characteristic of the monthly industrial production and employment survey is the gauging of the quantum index of manufacturing (QIM), which measures the changes in manufacturing sectors. QIM is an index based on the amount of units of goods, such as weights, and it is calculated on a monthly and cumulative basis. MIPE updated regarding the General Index and Quantum Index of Manufacturing (QIM) to the base period 2015–2016, and an analysis of the current ranking of production and employment in the manufacturing sector (Bureau of Statistics, 2023b). MIPE basically carries information on 65 items manufactured in 17 selected large-scale industrial sectors covering 641 industrial establishments (selection based on CMI 2015–2016) (Pakistan Bureau of Statistics, 2016). These industries account for 84.0% of the total value added of the large manufacturing sector in Sindh. Total number of employees in selected large-Scale Manufacturing (LSM) industries according to the MIPE, April 2023, of Sindh are 188, 197, and the production workers are 141,158 (Finance Division, 2023 ). However, that have define Employment’s quantity of LSM in three categories by different sectors, according the MIPE report huge amount of employees lies in Textile sector that is 88, 563, and the medium ratio of employees 24, 430 are working in the Food sector, in Furniture sector there are very low ratio of employee 53 are working, in term of gender wise employees and production worker can be shows in percentage as the total percent of Female employees is 5.5%, male employees 94.5%, word employee states that entire persons give their time in the firm or any other working place and get enumeration, and production worker are engaged in work directly linked with production like manufacturing, compiling, packing, repairing etc (Desk, 2023 ). CMI 2015-16 shows that the total number of establishments is 42, 578, and in these establishments, the number of employed persons is 2,340,966. According to the CMI 2015-16, the total value of production at producer price was Rs 11,181 billion, depicting an increase of 244.76% over Rs 3,243 billion in CMI 2005-06 (Pakistan Bureau of Statistics, 2016). Economic development is a program, policy, or activity that seeks to improve the economic well-being and quality of life for the community. Manufacturing has a crucial role in the economy, and Pakistan's manufacturing sectors contribute 12.4% in gross domestic product (GDP), and the sector employs 16.1 percent of the country’s labour force. (Federal Bureau of Statistics, 2010). This benefaction of large-scale manufacturing LSM increase the growth rate of the economy. CMI 2015-16 report shows that the total contribution to GDP at fundamental prices stands at Rs 2,946 billion (Bureau of Statistics, 2023b). The raising frame in Sindh is 473 units. There is a highly significant role of education, health, electricity, gas, and mining. (Katoch et al., 2024 ). If the education status of students is good, it can positively affect industrial production. Sindh is considered rich in mining, and our industrial production is developing. In such a way, health, electricity, and gas are also very essential sectors in industrial production. (Hussain et al., 2022 ). In this report that check the impact of these mentioned variables with quantum index. These all sector linked with industrial production. The main motive of this research report is to forecast the large-scale manufacturing quantum index of Sindh. It has an essential role in economic development. Manufacturing is one of the main parts of the industry sector. It is broken down into two main areas large scale manufacturing, small-scale manufacturing and household manufacturing industries, this paper considered as the forecasting of LSM, and it is based on 10 or more employees. (Khan et al., 2023 ). Through the quantum index that measures the changes in manufacturing sectors, QIM is an index based on the amount of units of goods, such as weights, and it is calculated on a monthly and cumulative basis. The behaviour of production and investments of large-scale manufacturing industries LSMI measure by the census manufacturing industries. LITERATURE REVIEW Haraguchi (2017) examined that there is enormous role of manufacturing in the economic development of developing countries (Haraguchi et al., 2017). It has been debated in current years that the significance of manufacturing has decreased over the last 20-25 years. It is also displayed in this paper that it is not confirmed argument, that resulting in premature de-industrialization or non-industrialization in developing countries (Lautier, 2024). In the world of GDP, the components of manufacturing sectors and employment contribution have not changed since long time ago (Török, 2022). This study concentrated that in recent years, China is developing country because it’s manufacturing value added MAV share is 30% or above (Gao & Rong, 2023). The study results show that. This paper concluded with recommendation that the developing countries do not turn aside from manufacturing disclaim path of economic. Large scale manufacturing sector of Pakistan contributes its share 15% or above it increased at a compound rate since 1949/50 to 1970/71. 1.5 percent to 9.4 percent share of gross national product expanded at that time, most of the current studies put question to the meaning of the Sectoral shares of manufacturing sectors and the growth rates at the time when different sectors produced goods, valued at prices and then various policies such as subsidy and trade restricting policies distorted them (Wizarat, 2026). This study come to an end in such a way that contribution sector’s measure be better if could acquire by GNP value not at domestic prices but at world prices (Akaike, 1974). Due to low performance of industrial sectors overall economic downtrend across different sectors in the ongoing (fiscal year from July 1 to June 30). Government’s current year’s measure due to dollar shortage has diminished production, because there is powerlessness of industries to secure letters of credits (Bureau of Statistics, 2023a). QIM sows that the LSMI output declined by 9.09% in March 2023 as compared to February 2023. Production of LSM major sectors has grew such as wearing apparel, furniture and other manufacturing (football) whereas decreased in food, tobacco, textile, coke & petroleum products and other in the month of (BR, 2023). In accordance with the Pakistan Bureau of statistics (PBS) rebasing of QIM from 2005-06 to 2015-16, released report shows that PBS states that the weights that are currently used for QIM were derived from CMI, 2005-2006. It also came up that weights for the QIM industries are used as a proxy indicator for large-scale manufacturing industries to measure growth on a monthly basis. It is also presented that the LSM sector is considered a major contributor in national economy, and the contribution of LSM is 10.7 percent. Further stated that the total amount that is used for the computation of QIM for 2005-06 items was 112 with cumulative weight of 70.3 percent, whereas it increased to 123 items and 78.37% in QIM 2015-16 (Staff, 2022). Abbas G, (2023), studied on negative growth of LSM for five consecutive months, it is explained that recent fiscal year shows that throughout the industrial output economic growth will further fall in the next quarter, in term of electricity/gas the third quarter will be more inconvenient and it is also expected by the experts that in the winter supplies of gas will be interrupted to industrial units. (Abbas, 2023). It is further inspected by the economists that a downtrend is caused by record energy and raw material prices. Furthermore, export-based manufactures productions have already indicated a decline due to the higher cost of energy and other inputs. Meyler (1998) used ARIMA modelling for forecasting Irish inflation. It is suggested that more focus be placed on minimizing out-of-sample forecast errors than on maximizing the in the sample ‘goodness of fit. For the forecast performance, the ‘model mining’ approach is used. Further, it is illustrated in the study that the multivariate models are well-specified and generally perform better than the ARIMA model. Author windup his study in such a way that the feasible method to better performance of forecasting is to attempt to fit an ARIMA model to a ‘noiseless’ version of the HICP. (Meyler et al., 1999). Ali (2024) explained that Pakistan is facing many problems regarding high inflation rate, unemployment, decreasing foreign investment and very nominal economic growth (Ali, 2024). For the stability and rapid economic growth, Pakistan needs precise literature for evidence-based planning of industrial input and output for making policy of GDP, export and import, etc. Analysis A. R. Kemal in the international literature, research on economic and regional development is not new (Kemal, 1974). Researchers have become increasingly interested in the uneven spatial distribution of income, economic opportunities, and activities in recent years. The studies used statistical analysis techniques to examine the spatial disparities between the regions and how they changed over time. The present a thorough analysis of the prior research on economic and regional development in this paper using quantitative techniques and social network analysis. These provide an overview of the various research methodologies through research questions, as well as a discussion and recommendations for additional study. Emergence of Karachi as the Largest Industrial Region of Pakistan Iqbal M., & Amp; et al (2017) Karachi is the largest industrial centre of the country (Iqbal & Ullah, 2017). The industrial development is expanded in the other district of Karachi by stabling of new industries. In the future, Karachi will become a more efficient industry in the region. According to analytical data based on CMI (2005–2006). The growth of its industrial sector is sweeping through other neighbourhoods. Through its opportunities and potentials, Karachi is steadily growing into the world's largest manufacturing region. Additionally, as other regions of the Karachi division are being developed for export purposes, its growth will accelerate in the near future. Karachi has developed into Pakistan's premier industrial region. METHODOLOGY Pakistan Standard Industrial Classification (PSIC) A prime official agency of the Pakistan Federal Bureau of Statistics (FBS) has developed the Pakistan standard industrial classification (PSIC), it is obtained by united nation UN. It is an industry classification system, the first classification of economic activities, PSIC, came into existence in 1970 based on ISIC Rev-2. Time to time configuration of economic activities updated, and now the fourth version is PSIC Rev-4 (2010), which is an updated version of ISIC Rev-4 released by UN in 2008. Its main objective is to provide a set of activity categories that can make use of the collection and reporting of statistics according to such activities. All developments of ISIC rev-4 have been incorporated into it. It resulted in a more detailed structure, responsive to the services and the latest kind of economic activities. Now it is a much-improved tool for classification and international data comparison. Quantum Index of Manufacturing (QIM) Quantum index manufacturing QIM is said to be an index based on the quantity of goods, such as the number or weight. A decreasing trend is showing in the four-month forecasting of QIM. The graph in Figure 1 shows the Quantum Index Manufacturing (QIM) trend of LSM industries from 2021 to early 2023. It starts with a gradual rise in 2021, followed by a sharp increase reaching a peak around mid-2022. Afterward, fluctuations occur with a noticeable decline toward late 2022 and early 2023, indicating slowing industrial growth and reduced manufacturing momentum. Research data Research variables: In our research report, the main variable is the quantum index manufacturing QIM variable, and other variables are used as proxy variables ( a copy of indirect variables ). The main variable is quantum index manufacturing, such as the health, education, electricity, gas and mining, which perform various statistical tests among QIM and these proxy variables. The consider these variables because they are highly linked with industrial production such as health is indirectly correlated with industrial production, good education positively effect on our industrial production, whereas electricity and gas also have key role in manufacturing, the use of mine materials is very vital they are used to construct roads and hospitals, they are require to build automobiles and houses, and for the many goods and services. Period of time: This report covers the period of time from 2021 to 2022 and from 2022 to 2023. Sources of data collection: To investigate the production of large-scale manufacturing data of time series for analysis of considered variables, data was collected from various published data sources, such as the data of electricity and gas is provided by the energy sector, development of statistics of Sindh (DS), mining data from the industrial sector, and the Minerals Statistics Bureau of Statistics (BOS) Sindh. Forecasting Method Time series: A set of values of a variable collected at a regular interval. The observations in a time series, denoted by with the equal interval of time (t). ARIMA: Usually, ARIMA is the compilation of AR, MA and I. The objective of ARIMA is to backcast and forecast the given series. AR Process A model that uses the dependent relationship between an observation and some number of lagged observations. Auto-regressive AR models predict future values based on past values. The autocorrelations of a pure AR (p) process should decay gradually at increasing lag length. Hence, using an autocorrelogram, it is not possible to differentiate between a pure AR (3) model and a pure AR (4) model. However, the partial autocorrelations of a pure AR (p) process do display distinctive features. The partial autocorrelogram should ‘die out’ after p lags. Thus, the partial autocorrelogram of a pure AR (3) process should die out after 3 lags, whereas that of a pure AR (4) process would die out after 4 lags. Hence, for a pure AR (p) process, the theoretical ACF and PACF are as follows: where i denotes the number of lags. I: it stands for the integrated order of I (d), which reports the minimum number of differences required to obtain a covariance- stationary series. The use of differencing of raw observations to make the time series stationary MA Process A model that uses the dependency between an observation and a residual error from a moving average model applied to lag observations. Moving average MA terms mean the values of the time series “q” times depend upon the error terms. The behaviour of the correlogram and partial autocorrelogram for pure MA (q) processes is the reverse of that for pure AR processes. The autocorrelogram of a pure MA (q) process should ‘die out’ after q lags. The partial autocorrelogram of a pure MA process, on the other hand, only decays slowly over time (similar to the behaviour of the autocorrelogram of a pure AR process). Thus, it should be impossible to distinguish between the PACF of an MA (3) and MA (4) process, whereas the ACF of the MA (3) process should decay to zero after 3 lags and the MA (4) process after 4lags. Hence, for a pure MA (q) process, the theoretical ACF and PACF are as follows: Stationery and integration of time series It is most probable that the time series data contain a unit root; therefore, the unit root tests are used to identify the rank at which a time series becomes stationary. Different series become stationary at different levels with constant and constant with trend. Unit root testing Time series has the problem of a unit root (non-stationary). In other words, the mean of a time series does not remain constant over time due to trending. Unit root tests are performed to check the stationarity of the time series. Non-stationary time series gives spurious results. In this regard, augmented (Dickey & Fuller, 1979; Phillips, 1987) tests are applied for stationarity testing. Flowchart of ARIMA Modelling The process of ARIMA modelling is described in the flow chart below: This methodology was designed by Box and Jenkins (1978) to find the best fit of a time series in order to make forecasts. ARIMA modelling process described by the flow diagram, it shows that first of all plot the time series data and consider the specific ARIMA model, renovate the data into stationary and recognize the degree of difference, to make the data stationary take the difference of the observation, in the next step with the help of autocorrelation function ACF and partial autocorrelation function PACF, Akaike information criterion AIC find the different ARIMA models until get least value of AIC then the model will be preferred as good for forecasting, parameter estimation p,d and q parameters of a model estimated, AR(p) which is dependent relationship between actual and lagged observation in which can take the lagg of the observations, likewise MA(q) residual error, after estimation of parameter can check these orders that where it stopped to suggest the forecasting, discover that ARIMA model is fit, execute the residual analysis on best ARIMA model. After that, forecast the best ARIMA model. RESULT AND FINDINGDescriptive Statistic In three fiscal years from July 2019 to June 2022, the average of QIM is 284.5333 with 48.60993 of standard deviation, Min QIM is 172.1, and the maximum QIM is 350.6. Sindh produced coal on average 638904.8 M. Ton, the minimum production of coal is 124889 M. Ton, and the maximum production of coal is 482428 M. Ton. Similarly, the average production of limestone in Sindh during the specified time duration is 638904.8 M. Ton, along with minimum and maximum production of 117940 and 982968 M. Ton, respectively. Table 03: Deceptive Statistics Variable Unit Mean SD Min Max QIM (at base 2005-06 for correlations) Index 284.5333 48.60993 172.1 350.6 Limestone Production M. Ton 638,904.8 263,759.6 117,940 982968 Electricity (Industrial Consumption) MN.KWH 485.6667 69.57302 306 587 Gas (Industrial Consumption) MCFT 532,474.9 73,174.1 391,637 624,202 Coal Production M. Ton 390,310.6 83,288.27 124,889 482428 As shown in Table 03, on average, industrial consumption in Sindh is 485.6 million Kilowatt whereas the minimum and maximum industrial consumption are 306 and 587 million kilowatt-hours. In Sindh, 532474.9 million cubic feet on average are used in the industrial sector, with a standard deviation of 73174.1. Relationship between QIM and Supporting Development Variables The relationship of Industrial Electricity Consumption and production of Limestone with the Quantum Index of Manufacturing is found to be significant and positively related. Hence, the bi-directional relationship between the variables can be observed, and the increase in the industrial electricity consumption and the production of Limestone may have a positive impact on the QIM of Sindh. Table 04: Correlation Results between QIM Development Variables Variables Correlation T-Value P-Value Coal Production 0.3220 1.59 0.1248 Electricity (Industrial Consumption) 0.7076 4.69 0.0001 Gas (Industrial Consumption) 0.0543 0.2552 0.8009 Limestone Production 0.5992 3.5107 0.002 The relationship between QIM and the production of Coal is found to be weak (i.e. 32%), which is statistically insignificant in Table 04. Similarly, the variable of industrial gas consumption is also not significantly correlated with QIM. Thus, the association between the production of Coal and Industrial gas consumption has no significant impact on the industrial change of Sindh. However, the sign of associations of all the study’s variables with QIM is positive, according to the economic theory. Estimated ARIMA models This graph performs the four-month forecasting. The red trend line indicates the actual observations, basically, which are the lagged observations, whereas the forecasting values show that the quantum index manufacturing of LSM may be decreased in the coming four months in Figure 3. The above table shows the values of different models. The (1,0) model at the top of the table, which has the lowest value of AIC =7.1, shows that the model is adequate for forecasting. Table 5: Estimated ARIMA Model Variable Coefficient Std. Error t-Statistic Prob. C 128.7888 6.0512 21.28318 0.0000 AR (1) 0.828046 0.082045 10.09261 0.0000 SIGMASQ 55.70173 17.00637 3.275345 0.0036 R-squared Adjusted R-squared F-statistic Prob. (F-Statistic) AIC D-W stat 0.610550 0.573459 16.46109 0.000050 7.156108 1.985375 As consider the (1,0) model for the forecast, now check the output of that model in this table. The adjustment sigma is 55%, this table shows that the model has R 2 = 61%, Adj R 2 = 57% , a lowest value of AIC = 7.1, DW = 98% it also shows the different outputs of the model as show in Table 5. Usually, this AIC graph considers the order of AR(1) and MA(0) , as we can see that at the point of (1,0)(0,0) AR (1) we get the lowest value of AIC = 7.1, which shows that the estimated model is good to forecast, as shown in Figure 4. DISCUSSION The findings of this study provide important insights into the behavior and determinants of large-scale manufacturing (LSM) performance in Sindh, particularly through the analysis of the Quantum Index of Manufacturing (QIM). The observed trend in QIM reveals a period of growth followed by volatility and eventual decline toward the end of the study period (2022–2023) (Bhutta et al., 2024 ). This pattern reflects the broader industrial instability highlighted in prior literature, where external constraints such as energy shortages, inflation, and supply chain disruptions have negatively influenced manufacturing output. A key contribution of this study lies in examining the relationship between QIM and selected development-related variables, including electricity consumption, gas usage, and mining outputs (coal and limestone). The empirical results demonstrate that industrial electricity consumption has a strong and statistically significant positive relationship with QIM (Li & Yuan, 2021 ). This finding aligns with economic theory and existing studies, which emphasize that energy availability is a fundamental driver of industrial productivity. The high correlation (0.7076) suggests that uninterrupted and efficient electricity supply is critical for sustaining manufacturing growth in Sindh. Similarly, limestone production also shows a significant positive association with QIM. This result highlights the importance of raw material availability in supporting industrial activities, particularly in sectors such as construction, cement, and infrastructure development. The strong linkage indicates that mineral resource utilization plays a complementary role in enhancing manufacturing performance. In contrast, coal production and gas consumption exhibit weak and statistically insignificant relationships with QIM (Dai et al., 2023 ). Although these variables are theoretically relevant, their limited impact in this study may reflect inefficiencies in resource utilization, supply constraints, or structural issues within the industrial sector. Another important aspect of this study is the application of the ARIMA modeling approach for forecasting QIM. The selection of the ARIMA (1,0) model based on the lowest Akaike Information Criterion (AIC) confirms its suitability for short-term forecasting. The model explains approximately 61% of the variation in QIM, indicating a reasonably good fit for time series data. The forecasting results suggest a declining trend in QIM over the next four months, signaling potential challenges for industrial growth in the near future. This projected decline is consistent with recent economic conditions in Pakistan, where rising energy costs, limited access to raw materials, and macroeconomic instability have constrained industrial output. The declining forecast of QIM also raises concerns for policymakers and industrial planners. Since manufacturing plays a crucial role in economic development and employment generation, a sustained decrease in industrial output could adversely affect economic growth (Naudé & Szirmai, 2012 ). The findings suggest that immediate attention should be given to strengthening key supporting sectors, particularly energy and mining. Ensuring a reliable electricity supply and improving the efficiency of resource extraction and utilization can significantly enhance industrial productivity (Zhang & Dilanchiev, 2022 ). Moreover, the study reinforces the importance of adopting data-driven approaches, such as time series forecasting, in economic planning. The use of ARIMA modeling provides a practical tool for anticipating future trends and enabling proactive policy interventions. However, it is important to acknowledge that the model is based on historical data and may not fully capture sudden structural changes or external shocks, such as policy shifts or global economic crises. In comparison with existing literature, the results of this study are consistent with the view that manufacturing remains a critical engine of economic development in developing regions. The positive linkage between infrastructure-related variables and industrial output supports earlier arguments that industrial growth depends heavily on supportive economic conditions (Ahumada et al., 2025 ). At the same time, the weak relationships observed for some variables indicate that not all theoretically relevant factors translate into practical significance, highlighting the need for context-specific analysis. Overall, this study contributes to the understanding of industrial dynamics in Sindh by integrating correlation analysis with forecasting techniques. The findings emphasize that while manufacturing performance is influenced by multiple factors, energy availability and raw material supply remain the most critical determinants. Future research may expand this analysis by incorporating additional variables, such as technological innovation, investment levels, and policy interventions, to provide a more comprehensive understanding of industrial growth patterns. LIMITATIONS This study has several limitations that should be considered while interpreting the results. First, the analysis is based on a relatively short time period (2021–2023), which may limit the robustness and generalizability of the findings, particularly for long-term forecasting. Second, the study uses a limited number of proxy variables (electricity, gas, coal, and limestone), which may not fully capture all determinants of industrial performance, such as technological advancement, policy changes, inflation, and external trade conditions. Third, the ARIMA model relies solely on historical data and does not incorporate structural breaks or unexpected economic shocks, which may affect forecast accuracy. Additionally, data quality and availability from secondary sources may introduce measurement errors. Finally, the study focuses only on Sindh province, restricting the applicability of findings to other regions of Pakistan with different industrial structures and economic conditions. FUTURE RECOMMENDATIONS Future research should expand the scope of analysis by incorporating a longer series to improve the reliability and accuracy of forecasting results. Including additional explanatory variables such as inflation, exchange rates, technological innovation, foreign direct investment, and policy interventions would provide a more comprehensive understanding of the determinants of industrial performance. Moreover, advanced econometric and machine learning models, such as VAR, ARDL, and hybrid forecasting techniques, can be applied alongside ARIMA to enhance predictive performance and capture complex relationships. Researchers should also consider structural breaks and external shocks to better reflect real economic conditions. From a policy perspective, future studies may explore sector-specific analysis within LSM to identify high-growth industries. Additionally, comparative studies across provinces can offer broader insights into national-level planning. Improving data quality and frequency, particularly for industrial and energy sectors, will further strengthen empirical analysis and support evidence-based decision-making. CONCLUSION The province of Sindh is the second-largest province of Pakistan as our study belongs to the area of manufacturing industries so the Karachi which is the largest city of Sindh and Pakistan great importance in the area of industrial economy. Due to Karachi city, which is the economics hub of Pakistan, the province of Sindh contributes to the economy with high shares. In Sindh for the measurement of the industrial change, the Bureau of Statistics, Govt of Sindh is regularly particularly the MIPE report and computing QIM (quantum index of manufacturing) in the barometer of the indicator change of province economy. This study is designed to framework the QIM of Sindh for the prediction of next four months ARIMA technique is applied in this study, the learned in ARIMA modelling technique for forecasting as while run the ARIMA technique in EViews, in which AR and MA different order and at the AR(1) get the lowest value of AIC in estimated model and preferred that it is adequate model for forecasting, also plot the AIC graph which forecast that QIM decrease in next four months which around 129.8, as compare previous month QIM of Sindh. After that compute the correlations among the QIM and development variables, as mentioned in report economic development and also discussed that consider the proxy variables to check the association among them, because they linked with the economic development, so the proxy variables are, electricity ,gas and mining(choal, limestone) and the association between QIM and choal is 32%, and p-value is 0.1248 which is highly significant and the may say that it is failed to reject that there is association between them, correlation between electricity and QIM is 70%, according to the p-value that is 0.0001which is less than 0.05 can say that these two variables are highly associated, and the association between gas and QIM is 5% and the p-value is 0.8009, which tell that it is also failed to reject that there is association between gas and QIM, whereas 59% association between limestone and QIM and the p-value 0.002 which also show that null hypothesis is failed to reject. Now may conclude that the variable electricity is beneficial in our industrial sector because it has 70% association with quantum index of manufacturing, and the limestone has also positive effect on our industrial production there is 59% association between them, and it is noted that the QIM of four months decrease according our forecasting that is, 129.6,129.5,129.3,129.6 these are the QIM values of next four months, and may suggest that the industrial production should concentrate on these two variables, this research study may be helpful for planners and policy makers. References Abbas, G. (2023, 2023/01/17). LSM posts negative growth for 5th consecutive month. https://profit.pakistantoday.com.pk/2023/01/17/lsm-posts-negative-growth-for-5th-consecutive-month/ Ahumada, H., Navajas, F. H., & Espina Mairal, S. (2025). Productivity growth and infrastructure related sectors. Económica , 71 . Akaike, H. (1974). A new look at statistical model identification. IEEE Transactions on Automatic Control , 19 (6), 716-723. https://doi.org/10.1109/TAC.1974.1100705 Ali, I. (2024). Investigating the inflation-economic growth nexus in Pakistan from 1990 to 2020. International Journal of Economics and Business Administration , 12 (2), 71-90. Bhutta, M. A., Sheikh, M. R., & Hussain, I. (2024). Trade openness, market capitalization and entrepreneurial ventures: implications for business and industrial growth of Pakistan. Journal of Entrepreneurship and Business Venturing , 4 (1). Bureau of Statistics, S. (2023a). Monthly Industrial Production (MIP) 2023 . https://sbos.sindh.gov.pk Bureau of Statistics, S. (2023b). Monthly Industrial Production and Employment (MIPE) April 2023 . https://sbos.sindh.gov.pk/files/SBOS/MIPE/2023/MIPE%20Report%20Apr-2023.pdf Dai, L., Lei, H., Cheng, X., & Li, R. (2023). Prediction of coal seam gas content based on the correlation between gas basic parameters and coal quality indexes. Frontiers in Energy Research , 10 , 1096539. Desk, B. R. W. (2023). Economic Distress. https://www.brecorder.com/news/40246686 Federal Bureau of, S. (2010). Pakistan Standard Industrial Classification (PSIC) Rev.4 (2010) . https://www.pbs.gov.pk/sites/default/files/other/documents/PSIC_2010.pdf Finance Division, G. o. P. (2023). Pakistan Economic Survey 2022-23 . https://www.finance.gov.pk/survey/chapters_23/Overview.pdf Gao, Y., & Rong, J. (2023). Research on the upgrading of China’s manufacturing under the value-added capability and correlation effect of global value chain. African and Asian Studies , 23 (1-2), 122-159. Haraguchi, N., Cheng, C. F. C., & Smeets, E. (2017). The Importance of Manufacturing in Economic Development: Has This Changed? World Development , 93 , 293-315. https://doi.org/https://doi.org/10.1016/j.worlddev.2016.12.013 Hussain, A., Jat, Z. G., Hassan, M., Hafeez, A., Iqbal, S., & Imran, M. (2022). Curriculum Reforms In School Education Sector In Sindh; What Has Changed? Journal of Positive School Psychology , 6 (9), 2675-2687. Iqbal, M. J., & Ullah, A. (2017). Emergence of Karachi as the Largest Industrial Region of Pakistan. Journal of Basic & Applied Sciences , 13 , 392-398. Jafri, M. K., & Unar, H. (2025). FORECASTING OF LARGE SCALE MANUFACTURING (LSM) QUANTUM INDEX & ITS ROLE IN ECONOMIC DEVELOPMENT OF SINDH. “All papers published in the PROCEEDINGS were accepted after formal peer review by the experts in the relevant field. , 79. Katoch, O. R., Sharma, R., Parihar, S., & Nawaz, A. (2024). Energy poverty and its impacts on health and education: a systematic review. International Journal of Energy Sector Management , 18 (2), 411-431. Kemal, A. R. (1974). The Contribution of Pakistan's Large Scale Manufacturing Industries Towards Gross National Product at World Prices. The Pakistan Development Review , 13 (1), pp.1-12. https://doi.org/10.30541/v13i1pp.1-12 Khan, M. M., Singh, K. P., & Khan, W. U. (2023). A critical study on the implementation of operation, control and maintenance techniques for flexible manufacturing systems in small scale industries. Materials Today: Proceedings . Lautier, M. (2024). Manufacturing still matters for developing countries. Structural Change and Economic Dynamics , 70 , 168-177. Lewis Jr, S. R. (2026). Economic policy and industrial growth in Pakistan . Taylor & Francis. Li, K., & Yuan, W. (2021). The nexus between industrial growth and electricity consumption in China–New evidence from a quantile-on-quantile approach. Energy , 231 , 120991. Meyler, A., Kenny, G., & Quinn, T. (1999). Forecasting irish inflation using ARIMA models. Naudé, W., & Szirmai, A. (2012). The importance of manufacturing in economic development: Past, present and future perspectives. Pakistan Bureau of, S. (2016). Census of Manufacturing Industries (2015-16) . https://www.pbs.gov.pk/sites/default/files//industry_mining_and_energy/publications/cmi_2015-16/CMI_2015-16_report.pdf PBS. (2022). Rebasing of quantum index of large-scale manufacturing industries from 2005-06 to 2015-16. Staff, P. (2022, 2022/01/20). Govt rebases quantum indices of large scale manufacturing industries. https://propakistani.pk/2022/01/20/govt-rebases-quantum-indices-of-large-scale-manufacturing-industries/ Török, L. (2022). The contribution of the Visegrad four automotive industry to economic growth. Journal of International Studies , 15 (1), 90-103. Wizarat, S. (2026). Revisiting the Rise and Fall of Industrial Productivity in Pakistan . Cambridge Scholars Publishing. Zhang, Y., & Dilanchiev, A. (2022). Economic recovery, industrial structure and natural resource utilization efficiency in China: effect on green economic recovery. Resources Policy , 79 , 102958. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9515017\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":628875754,\"identity\":\"478c0cbc-d52a-4282-95f8-6ec58fbe74a7\",\"order_by\":0,\"name\":\"Shahab Uddin\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"https://orcid.org/0009-0004-5683-6226\",\"institution\":\"Shah Abdul Latif University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Shahab\",\"middleName\":\"\",\"lastName\":\"Uddin\",\"suffix\":\"\"},{\"id\":628881378,\"identity\":\"5cab156a-8bfa-4c52-8aa2-171c1dd7f974\",\"order_by\":1,\"name\":\"Hafsa Unar\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of 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1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":14716,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTrend of Quantum Index Manufacturing of LSM Industries at Base Year 2015-16\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9515017/v1/8a95a7ee0186a8a57221025a.png\"},{\"id\":107934620,\"identity\":\"818141e2-bd33-498c-9f82-21cc31c2a88a\",\"added_by\":\"auto\",\"created_at\":\"2026-04-27 17:35:04\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":136399,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eARIMA Modelling Procedure\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9515017/v1/0e063c025d4120ad3c4553c6.png\"},{\"id\":108180887,\"identity\":\"3faf1fae-7e4a-484a-b7c8-0c61380d80a2\",\"added_by\":\"auto\",\"created_at\":\"2026-04-30 08:54:43\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":20692,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFour-Month Forecasting of Quantum Index Manufacturing\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9515017/v1/3703438eef2be172db555a09.png\"},{\"id\":107934623,\"identity\":\"fa7c5953-edcb-4f26-a93c-4a25c75d8ed8\",\"added_by\":\"auto\",\"created_at\":\"2026-04-27 17:35:04\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":14490,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAkaike Information Criteria (AIC)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9515017/v1/abb6b394e150e08961145b12.png\"},{\"id\":108183681,\"identity\":\"ade028f4-6eb5-4940-8574-7c5d5910994a\",\"added_by\":\"auto\",\"created_at\":\"2026-04-30 09:02:29\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":608422,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9515017/v1/3bf0b0b8-c145-40a1-9c5f-782d2ee8005f.pdf\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003eForecasting Of Large-Scale Manufacturing (LSM) Quantum Index \\u0026amp; Its Role in Economic Development of Sindh\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eManufacturing is one of the main parts of the industry sector, and it consists of two main parts; large scale manufacturing (LSM) and small-scale manufacturing (SSM) industries. The large-scale manufacturing industries (LSM) are based on 10 or more employees, although the survey of small-scale manufacturing (SSM) focuses on small manufacturing industries where fewer than 10 employees work. The role of Sindh province in large-scale manufacturing industries is imperative. The manufacturing sector share its 65% in the industry according to the Pakistan Economic Survey (Lewis Jr, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2026\\u003c/span\\u003e). Performance of the Industrial sector is based on large-scale manufacturing industries (LSM), as it holds 74.0% share in the industry (Jafri \\u0026amp; Unar, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Large scale of manufacturing industries has many sectors, but the Monthly Industrial Production and Employment Survey (MIPE) used 18 major sectors of LSM\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e Selected large manufacturing major sectors\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eS. No\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSectors\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eS. No\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSectors\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFood products\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eChemicals \\u0026amp; chemical products\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBeverages\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePharmaceutical products\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTextiles\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eRubber and plastic products\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWearing apparel\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eOther non-metallic mineral products\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLeather \\u0026amp; related products\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eBasic iron \\u0026amp; steel metals\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWood and woodworks\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eFabricated metals products\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePaper and paper products\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eElectrical equipment\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrinting and recorded media\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMotor vehicles \\u0026amp; parts\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCoke \\u0026amp; refined petroleum\\u003c/p\\u003e \\u003cp\\u003eProducts\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eFurniture\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis section provides information about items and reporting units of manufacturing production. Some of the items with their reporting units are discussed below in the form of a table. This table shows that the manufacturing item, food product measured in M. Tone unit, such as coke \\u0026amp; refined petroleum products, beverages and pharmaceuticals in \\u0026ldquo;000\\u0026rdquo; litters, whereas lather and refined petroleum products in \\u0026ldquo;000\\u0026rdquo; square meters. M.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eManufacturing Production Items and Reporting Units\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSr. No\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eManufacturing items\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eunit\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eWeight\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eProduction index\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFood product\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eM. Tons\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e114.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBeverages\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026ldquo;000\\u0026rdquo; litter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e125.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLather \\u0026amp; related product\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026ldquo;000\\u0026rdquo; sq. M.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e70.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCoke \\u0026amp; refined petroleum products\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026ldquo;000\\u0026rdquo; litter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e124.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePharmaceutical products\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026ldquo;000\\u0026rdquo; litter\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe census of (large-scale) manufacturing industries (CMI) is conducted/published by the Pakistan Bureau of Statistics (PBS). PBS is also called national statistical organization (NSO). The first CMI was conducted in 1954, and the last was in 2015\\u0026ndash;2016 (PBS, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). A census is the complete count of the overall population in which each and every individual can be counted. CMI 2015-16 manage the number of establishment manufacturing in Sindh, 62,299 (Pakistan Bureau of Statistics, 2016). CMI is a major source of industrial statistics. Census manufacturing industries collect major measures such as production, investment, and structural changes of the large manufacturing industries (LSM) (Bureau of Statistics, 2023b). The census manufacturing industries CMI is very essential because it has enormous role in assembling GDP. CMI is conducted every five years through mailing questionnaires. CMI provide data on aggregate and assess of inputs and outputs, census value added, non-aggregate DP, fixed assets, stocks, employment and employment cost and industrial taxes. The CMI report provides the particular data at provincial and national basis. The objective of managing CMI is to assess the changes in the composition, formation, and growth of the manufacturing sector associated with medium at provincial levels. The census manufacturing industries (CMI) 2015\\u0026ndash;2016 was conducted in the execution of the provision section of 39 general statistics (Re-organization) Act 2011 (Finance Division, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).CMI is the key source for updating the business Register (BR).\\u003c/p\\u003e \\u003cp\\u003eThe monthly industrial production and employment survey (MIPE) is regularly conducted by the provincial bureau of statistic Sindh (BOS) since the 1970s. It is survey basis report. (Bureau of Statistics, 2023b). The crucial statistics with reference to industrial production and employment of large-scale manufacturing (LSM) industries in Sindh are provided by MIPE. (Pakistan Bureau of Statistics, 2016). An essential characteristic of the monthly industrial production and employment survey is the gauging of the quantum index of manufacturing (QIM), which measures the changes in manufacturing sectors. QIM is an index based on the amount of units of goods, such as weights, and it is calculated on a monthly and cumulative basis. MIPE updated regarding the General Index and Quantum Index of Manufacturing (QIM) to the base period 2015\\u0026ndash;2016, and an analysis of the current ranking of production and employment in the manufacturing sector (Bureau of Statistics, 2023b). MIPE basically carries information on 65 items manufactured in 17 selected large-scale industrial sectors covering 641 industrial establishments (selection based on CMI 2015\\u0026ndash;2016) (Pakistan Bureau of Statistics, 2016). These industries account for 84.0% of the total value added of the large manufacturing sector in Sindh.\\u003c/p\\u003e \\u003cp\\u003eTotal number of employees in selected large-Scale Manufacturing (LSM) industries according to the MIPE, April 2023, of Sindh are 188, 197, and the production workers are 141,158 (Finance Division, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). However, that have define Employment\\u0026rsquo;s quantity of LSM in three categories by different sectors, according the MIPE report huge amount of employees lies in Textile sector that is 88, 563, and the medium ratio of employees 24, 430 are working in the Food sector, in Furniture sector there are very low ratio of employee 53 are working, in term of gender wise employees and production worker can be shows in percentage as the total percent of Female employees is 5.5%, male employees 94.5%, word employee states that entire persons give their time in the firm or any other working place and get enumeration, and production worker are engaged in work directly linked with production like manufacturing, compiling, packing, repairing etc (Desk, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). CMI 2015-16 shows that the total number of establishments is 42, 578, and in these establishments, the number of employed persons is 2,340,966. According to the CMI 2015-16, the total value of production at producer price was Rs 11,181\\u0026nbsp;billion, depicting an increase of 244.76% over Rs 3,243\\u0026nbsp;billion in CMI 2005-06 (Pakistan Bureau of Statistics, 2016).\\u003c/p\\u003e \\u003cp\\u003eEconomic development is a program, policy, or activity that seeks to improve the economic well-being and quality of life for the community. Manufacturing has a crucial role in the economy, and Pakistan's manufacturing sectors contribute 12.4% in gross domestic product (GDP), and the sector employs 16.1 percent of the country\\u0026rsquo;s labour force. (Federal Bureau of Statistics, 2010). This benefaction of large-scale manufacturing LSM increase the growth rate of the economy. CMI 2015-16 report shows that the total contribution to GDP at fundamental prices stands at Rs 2,946\\u0026nbsp;billion (Bureau of Statistics, 2023b). The raising frame in Sindh is 473 units. There is a highly significant role of education, health, electricity, gas, and mining. (Katoch et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). If the education status of students is good, it can positively affect industrial production. Sindh is considered rich in mining, and our industrial production is developing. In such a way, health, electricity, and gas are also very essential sectors in industrial production. (Hussain et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). In this report that check the impact of these mentioned variables with quantum index. These all sector linked with industrial production.\\u003c/p\\u003e \\u003cp\\u003eThe main motive of this research report is to forecast the large-scale manufacturing quantum index of Sindh. It has an essential role in economic development. Manufacturing is one of the main parts of the industry sector. It is broken down into two main areas large scale manufacturing, small-scale manufacturing and household manufacturing industries, this paper considered as the forecasting of LSM, and it is based on 10 or more employees. (Khan et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Through the quantum index that measures the changes in manufacturing sectors, QIM is an index based on the amount of units of goods, such as weights, and it is calculated on a monthly and cumulative basis. The behaviour of production and investments of large-scale manufacturing industries LSMI measure by the census manufacturing industries.\\u003c/p\\u003e\"},{\"header\":\"LITERATURE REVIEW\",\"content\":\"\\u003cp\\u003eHaraguchi (2017) examined that there is enormous role of manufacturing in the economic development of developing countries (Haraguchi et al., 2017). It has been debated in current years that the significance of manufacturing has decreased over the last 20-25 years. It is also displayed in this paper that it is not confirmed argument, that resulting in premature de-industrialization or non-industrialization in developing countries (Lautier, 2024). In the world of GDP, the components of manufacturing sectors and employment contribution have not changed since long time ago (Török, 2022). This study concentrated that in recent years, China is developing country because it’s manufacturing value added MAV share is 30% or above (Gao \\u0026amp; Rong, 2023). The study results show that. This paper concluded with recommendation that the developing countries do not turn aside from manufacturing disclaim path of economic.\\u003c/p\\u003e\\n\\u003cp\\u003eLarge scale manufacturing sector of Pakistan contributes its share 15% or above it increased at a compound rate since 1949/50 to 1970/71. 1.5 percent to 9.4 percent share of gross national product expanded at that time, \\u0026nbsp;most of the current studies put question to the meaning of \\u0026nbsp;the Sectoral shares of manufacturing \\u0026nbsp;sectors and the growth rates at the time when different sectors produced \\u0026nbsp;goods, \\u0026nbsp; valued at prices \\u0026nbsp;and then various policies such as subsidy and trade restricting policies distorted them (Wizarat, 2026). This study come to an end in such a way that contribution sector’s measure \\u0026nbsp; be better if could acquire by GNP value not at domestic prices but at world prices (Akaike, 1974).\\u003c/p\\u003e\\n\\u003cp\\u003eDue to low performance of industrial sectors overall economic downtrend across different sectors in the ongoing (fiscal year from July 1 to June 30). Government’s current year’s measure due to dollar shortage has diminished production, because there is powerlessness of industries to secure letters of credits (Bureau of Statistics, 2023a). QIM sows that the LSMI output declined by 9.09% in March 2023 as compared to February 2023. Production of LSM major sectors has grew such as wearing apparel, furniture and other manufacturing (football) whereas decreased in food, tobacco, textile, coke \\u0026amp; petroleum products and other in the month of (BR, 2023).\\u003c/p\\u003e\\n\\u003cp\\u003eIn accordance with the Pakistan Bureau of statistics (PBS) rebasing of QIM from 2005-06 to 2015-16, released report shows that PBS states that the weights that are currently used for QIM were derived from CMI, 2005-2006. It also came up that weights for the QIM industries are used as a proxy indicator for large-scale manufacturing industries to measure growth on a monthly basis. It is also presented that the LSM sector is considered a major contributor in national economy, and the contribution of LSM is 10.7 percent. Further stated that the total amount that is used for the computation of QIM for 2005-06 items was 112 with cumulative weight of 70.3 percent, whereas it increased to 123 items and 78.37% in QIM 2015-16 (Staff, 2022).\\u003c/p\\u003e\\n\\u003cp\\u003eAbbas G, (2023), studied on negative growth of LSM for five consecutive months, it is explained that recent fiscal year shows that throughout the industrial output economic growth will further fall in the next quarter, in term of electricity/gas the third quarter will be more inconvenient and it is also expected by the experts that in the winter supplies of gas will be interrupted to industrial units. (Abbas, 2023). It is further inspected by the economists that a downtrend is caused by record energy and raw material prices. Furthermore, export-based manufactures productions have already indicated a decline due to the higher cost of energy and other inputs. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eMeyler (1998) used ARIMA modelling for forecasting Irish inflation. It is suggested that more focus be placed on minimizing out-of-sample forecast errors than on maximizing the in the sample ‘goodness of fit. For the forecast performance, the ‘model mining’ approach is used. Further, it is illustrated in the study that the multivariate models are well-specified and generally perform better than the ARIMA model. Author windup his study in such a way that the feasible method to better performance of forecasting is to attempt to fit an ARIMA model to a ‘noiseless’ version of the HICP. (Meyler et al., 1999).\\u003c/p\\u003e\\n\\u003cp\\u003eAli (2024) explained that Pakistan is facing many problems regarding high inflation rate, unemployment, decreasing foreign investment and very nominal economic growth (Ali, 2024). For the stability and rapid economic growth, Pakistan needs precise literature for evidence-based planning of industrial input and output for making policy of GDP, export and import, etc.\\u003c/p\\u003e\\n\\u003cp\\u003eAnalysis A. R. Kemal in the international literature, research on economic and regional development is not new (Kemal, 1974). Researchers have become increasingly interested in the uneven spatial distribution of income, economic opportunities, and activities in recent years. The studies used statistical analysis techniques to examine the spatial disparities between the regions and how they changed over time. The present a thorough analysis of the prior research on economic and regional development in this paper using quantitative techniques and social network analysis. These provide an overview of the various research methodologies through research questions, as well as a discussion and recommendations for additional study.\\u003c/p\\u003e\\n\\u003cp\\u003eEmergence of Karachi as the Largest Industrial Region of Pakistan Iqbal M., \\u0026amp; Amp; et al (2017) Karachi is the largest industrial centre of the country (Iqbal \\u0026amp; Ullah, 2017). The industrial development is expanded in the other district of Karachi by stabling of new industries. In the future, Karachi will become a more efficient industry in the region. According to analytical data based on CMI (2005–2006). The growth of its industrial sector is sweeping through other neighbourhoods. Through its opportunities and potentials, Karachi is steadily growing into the world's largest manufacturing region. Additionally, as other regions of the Karachi division are being developed for export purposes, its growth will accelerate in the near future. Karachi has developed into Pakistan's premier industrial region.\\u003c/p\\u003e\"},{\"header\":\"METHODOLOGY\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003ePakistan Standard Industrial Classification (PSIC)\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA prime official agency of the Pakistan Federal Bureau of Statistics (FBS) has developed the Pakistan standard industrial classification (PSIC), it is obtained by united nation UN. It is an industry classification system, the first classification of economic activities, PSIC, came into existence in 1970 based on ISIC Rev-2. Time to time configuration of economic activities updated, and now the fourth version is PSIC Rev-4 (2010), which is an updated version of ISIC Rev-4 released by UN in 2008. Its main objective is to provide a set of activity categories that can make use of the collection and reporting of statistics according to such activities. All developments of ISIC rev-4 have been incorporated into it. It resulted in a more detailed structure, responsive to the services and the latest kind of economic activities. Now it is a much-improved tool for classification and international data comparison.\\u003c/p\\u003e\\n\\u003cp id=\\\"_Toc140677809\\\"\\u003e\\u003cstrong\\u003eQuantum Index of Manufacturing (QIM)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eQuantum index manufacturing QIM is said to be an index based on the quantity of goods, such as the number or weight.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg 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\\\"\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA decreasing trend is showing in the four-month forecasting of QIM.\\u003c/p\\u003e\\n\\u003cp\\u003eThe graph in Figure 1 shows the Quantum Index Manufacturing (QIM) trend of LSM industries from 2021 to early 2023. It starts with a gradual rise in 2021, followed by a sharp increase reaching a peak around mid-2022. Afterward, fluctuations occur with a noticeable decline toward late 2022 and early 2023, indicating slowing industrial growth and reduced manufacturing momentum.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResearch data\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eResearch variables: In our research report, the main variable is the quantum index manufacturing QIM variable, and other variables are used as proxy variables ( a copy of indirect variables ). The main variable is quantum index manufacturing, such as the health, education, electricity, gas and mining, which perform various statistical tests among QIM and these proxy variables. The consider these variables because they are highly linked with industrial production such as health is indirectly correlated with industrial production, good education positively effect on our industrial production, whereas electricity and gas also have key role in manufacturing, the use of mine materials is very vital they are used to construct roads and hospitals, they are require to build automobiles and houses, and for the many goods and services.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003ePeriod of time: \\u0026nbsp;This report covers the period of time from 2021 to 2022 and from 2022 to 2023.\\u003c/p\\u003e\\n\\u003cp\\u003eSources of data collection: To investigate the production of large-scale manufacturing data of time series for analysis of considered variables, data was collected from various published data sources, such as the data of electricity and gas is provided by the energy sector, development of statistics of Sindh (DS), mining data from the industrial sector, and the Minerals Statistics Bureau of Statistics (BOS) Sindh.\\u003c/p\\u003e\\n\\u003cp id=\\\"_Toc140677811\\\"\\u003e\\u003cstrong\\u003eForecasting Method\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTime series: A set of values of a variable collected at a regular interval. The observations in a time series, denoted by\\u0026nbsp;\\u003cimg width=\\\"113\\\" height=\\\"28\\\" src=\\\"data:image/png;base64,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\\\" v:shapes=\\\"_x0000_i1025\\\" alt=\\\"image\\\"\\u003e\\u0026nbsp;with the equal interval of time (t).\\u003c/p\\u003e\\n\\u003cp\\u003eARIMA: Usually, ARIMA is the compilation of AR, MA and I. The objective of ARIMA is to backcast and forecast the given series.\\u003c/p\\u003e\\n\\u003cp id=\\\"_Toc140677812\\\"\\u003e\\u003cstrong\\u003eAR Process\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA model that uses the dependent relationship between an observation and some number of lagged observations.\\u0026nbsp;Auto-regressive AR models predict future values based on past values. The autocorrelations of a pure AR (p) process should decay gradually at increasing lag length. Hence, using an autocorrelogram, it is not possible to differentiate between a pure AR (3) model and a pure AR (4) model. However, the partial autocorrelations of a pure AR (p) process do display distinctive features. The partial autocorrelogram should \\u0026lsquo;die out\\u0026rsquo; after p lags. Thus, the partial autocorrelogram of a pure AR (3) process should die out after 3 lags, whereas that of a pure AR (4) process would die out after 4 lags.\\u003c/p\\u003e\\n\\u003cp\\u003eHence, for a pure AR (p) process, the theoretical ACF and PACF are as follows: where \\u003cem\\u003ei\\u0026nbsp;\\u003c/em\\u003edenotes the number of lags.\\u003c/p\\u003e\\n\\u003cp\\u003eI: it stands for the integrated order of I (d), which reports the minimum number of differences required to obtain a covariance- stationary series. The use of differencing of raw observations to make the time series stationary\\u0026nbsp; \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp id=\\\"_Toc140677813\\\"\\u003e\\u003cstrong\\u003eMA Process\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA model that uses the dependency between an observation and a residual error from a moving average model applied to lag observations.\\u0026nbsp;Moving average MA terms mean the values of the time series \\u0026ldquo;q\\u0026rdquo; times depend upon the error terms. The behaviour of the correlogram and partial autocorrelogram for pure MA (q) processes is the reverse of that for pure AR processes. The autocorrelogram of a pure MA (q) process should \\u0026lsquo;die out\\u0026rsquo; after q lags. The partial autocorrelogram of a pure MA process, on the other hand, only decays slowly over time (similar to the behaviour of the autocorrelogram of a pure AR process). Thus, it should be impossible to distinguish between the PACF of an MA (3) and MA (4) process, whereas the ACF of the MA (3) process should decay to zero after 3 lags and the MA (4) process after 4lags. Hence, for a pure MA (q) process, the theoretical ACF and PACF are as follows:\\u003c/p\\u003e\\n\\u003cp id=\\\"_Toc140677814\\\"\\u003e\\u003cstrong\\u003eStationery and integration of time series\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIt is most probable that the time series data contain a unit root; therefore, the unit root tests are used to identify the rank at which a time series becomes stationary. Different series become stationary at different levels with constant and constant with trend.\\u003c/p\\u003e\\n\\u003cp id=\\\"_Toc140677815\\\"\\u003e\\u003cstrong\\u003eUnit root testing\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTime series has the problem of a unit root (non-stationary). In other words, the mean of a time series does not remain constant over time due to trending. Unit root tests are performed to check the stationarity of the time series. Non-stationary time series gives spurious results. In this regard, augmented (Dickey \\u0026amp; Fuller, 1979; Phillips, 1987) tests are applied for stationarity testing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFlowchart of ARIMA\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eModelling\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe process of ARIMA modelling is described in the flow chart below:\\u003c/p\\u003e\\n\\u003cp\\u003eThis methodology was designed by Box and Jenkins (1978) to find the best fit of a time series in order to make forecasts. ARIMA modelling process described by the flow diagram, it shows that first of all plot the time series data and consider the specific ARIMA model, \\u0026nbsp;renovate the data into stationary and recognize the degree of difference, to make the data stationary take the difference of the observation, in the next step with the help of autocorrelation function ACF and partial autocorrelation function PACF, Akaike information criterion AIC find the different ARIMA models until get least value of AIC then the model will be preferred as good for forecasting, parameter estimation p,d and q parameters of a model estimated, AR(p) which is dependent relationship between actual and lagged observation in which can take the lagg of the observations, likewise MA(q) residual error, after estimation of parameter can check these orders that where it stopped to suggest the forecasting, discover that ARIMA model is fit, execute the residual analysis on best ARIMA model. After that, forecast the best ARIMA model.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"RESULT AND FINDINGDescriptive Statistic\",\"content\":\"\\u003cp\\u003eIn three fiscal years from July 2019 to June 2022, the average of QIM is 284.5333 with 48.60993 of standard deviation, Min QIM is 172.1, and the maximum QIM is 350.6. Sindh produced coal on average 638904.8 M. Ton, the minimum production of coal is 124889 M. Ton, and the maximum production of coal is 482428 M. Ton. Similarly, the average production of limestone in Sindh during the specified time duration is 638904.8 M. Ton, along with minimum and maximum production of 117940 and 982968 M. Ton, respectively.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 03: Deceptive Statistics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 37.5%;\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 13.5417%;\\\"\\u003e\\n \\u003cp\\u003eUnit\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 12.5%;\\\"\\u003e\\n \\u003cp\\u003eMean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 15.625%;\\\"\\u003e\\n \\u003cp\\u003eSD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 10.4167%;\\\"\\u003e\\n \\u003cp\\u003eMin\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 10.4167%;\\\"\\u003e\\n \\u003cp\\u003eMax\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 37.5%;\\\"\\u003e\\n \\u003cp\\u003eQIM\\u003c/p\\u003e\\n \\u003cp\\u003e(at base 2005-06 for correlations)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 13.5417%;\\\"\\u003e\\n \\u003cp\\u003eIndex\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 12.5%;\\\"\\u003e\\n \\u003cp\\u003e284.5333\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 15.625%;\\\"\\u003e\\n \\u003cp\\u003e48.60993\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 10.4167%;\\\"\\u003e\\n \\u003cp\\u003e172.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 10.4167%;\\\"\\u003e\\n \\u003cp\\u003e350.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 37.5%;\\\"\\u003e\\n \\u003cp\\u003eLimestone Production\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 13.5417%;\\\"\\u003e\\n \\u003cp\\u003eM. Ton\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 12.5%;\\\"\\u003e\\n \\u003cp\\u003e638,904.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 15.625%;\\\"\\u003e\\n \\u003cp\\u003e263,759.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 10.4167%;\\\"\\u003e\\n \\u003cp\\u003e117,940\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 10.4167%;\\\"\\u003e\\n \\u003cp\\u003e982968\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 37.5%;\\\"\\u003e\\n \\u003cp\\u003eElectricity\\u003c/p\\u003e\\n \\u003cp\\u003e(Industrial Consumption)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 13.5417%;\\\"\\u003e\\n \\u003cp\\u003eMN.KWH\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 12.5%;\\\"\\u003e\\n \\u003cp\\u003e485.6667\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 15.625%;\\\"\\u003e\\n \\u003cp\\u003e69.57302\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 10.4167%;\\\"\\u003e\\n \\u003cp\\u003e306\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 10.4167%;\\\"\\u003e\\n \\u003cp\\u003e587\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 37.5%;\\\"\\u003e\\n \\u003cp\\u003eGas\\u003c/p\\u003e\\n \\u003cp\\u003e(Industrial Consumption)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 13.5417%;\\\"\\u003e\\n \\u003cp\\u003eMCFT\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 12.5%;\\\"\\u003e\\n \\u003cp\\u003e532,474.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 15.625%;\\\"\\u003e\\n \\u003cp\\u003e73,174.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 10.4167%;\\\"\\u003e\\n \\u003cp\\u003e391,637\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 10.4167%;\\\"\\u003e\\n \\u003cp\\u003e624,202\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 37.5%;\\\"\\u003e\\n \\u003cp\\u003eCoal Production\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 13.5417%;\\\"\\u003e\\n \\u003cp\\u003eM. Ton\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 12.5%;\\\"\\u003e\\n \\u003cp\\u003e390,310.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 15.625%;\\\"\\u003e\\n \\u003cp\\u003e83,288.27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 10.4167%;\\\"\\u003e\\n \\u003cp\\u003e124,889\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 10.4167%;\\\"\\u003e\\n \\u003cp\\u003e482428\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eAs shown in Table 03, on average, industrial consumption in Sindh is 485.6 million Kilowatt whereas the minimum and maximum industrial consumption are 306 and 587 million kilowatt-hours. In Sindh, 532474.9 million cubic feet on average are used in the industrial sector, with a standard deviation of 73174.1.\\u003c/p\\u003e\\n\\u003cp id=\\\"_Toc140677823\\\"\\u003e\\u003cstrong\\u003eRelationship between QIM and Supporting Development Variables\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe relationship of Industrial Electricity Consumption and production of Limestone with the Quantum Index of Manufacturing is found to be significant and positively related. Hence, the bi-directional relationship between the variables can be observed, and the increase in the industrial electricity consumption and the production of Limestone may have a positive impact on the QIM of Sindh.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 04: Correlation Results between QIM Development Variables\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 26.2626%;\\\"\\u003e\\n \\u003cp\\u003eVariables\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 29.2929%;\\\"\\u003e\\n \\u003cp\\u003eCorrelation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 23.2323%;\\\"\\u003e\\n \\u003cp\\u003eT-Value\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 21.2121%;\\\"\\u003e\\n \\u003cp\\u003eP-Value\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 26.2626%;\\\"\\u003e\\n \\u003cp\\u003eCoal Production\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 29.2929%;\\\"\\u003e\\n \\u003cp\\u003e0.3220\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 23.2323%;\\\"\\u003e\\n \\u003cp\\u003e1.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 21.2121%;\\\"\\u003e\\n \\u003cp\\u003e0.1248\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 26.2626%;\\\"\\u003e\\n \\u003cp\\u003eElectricity\\u003c/p\\u003e\\n \\u003cp\\u003e(Industrial Consumption)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 29.2929%;\\\"\\u003e\\n \\u003cp\\u003e0.7076\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 23.2323%;\\\"\\u003e\\n \\u003cp\\u003e4.69\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 21.2121%;\\\"\\u003e\\n \\u003cp\\u003e0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 26.2626%;\\\"\\u003e\\n \\u003cp\\u003eGas\\u003c/p\\u003e\\n \\u003cp\\u003e(Industrial Consumption)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 29.2929%;\\\"\\u003e\\n \\u003cp\\u003e0.0543\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 23.2323%;\\\"\\u003e\\n \\u003cp\\u003e0.2552\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 21.2121%;\\\"\\u003e\\n \\u003cp\\u003e0.8009\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 26.2626%;\\\"\\u003e\\n \\u003cp\\u003eLimestone Production\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 29.2929%;\\\"\\u003e\\n \\u003cp\\u003e0.5992\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 23.2323%;\\\"\\u003e\\n \\u003cp\\u003e3.5107\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd nowrap=\\\"\\\" valign=\\\"top\\\" style=\\\"width: 21.2121%;\\\"\\u003e\\n \\u003cp\\u003e0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eThe relationship between QIM and the production of Coal is found to be weak (i.e. 32%), which is statistically insignificant in Table 04. Similarly, the variable of industrial gas consumption is also not significantly correlated with QIM. Thus, the association between the production of Coal and Industrial gas consumption has no significant impact on the industrial change of Sindh. \\u0026nbsp;However, the sign of associations of all the study\\u0026rsquo;s variables with QIM is positive, according to the economic theory.\\u003c/p\\u003e\\n\\u003cp id=\\\"_Toc140677824\\\"\\u003e\\u003cstrong\\u003eEstimated ARIMA models\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis graph performs the four-month forecasting. The red trend line indicates the actual observations, basically, which are the lagged observations, whereas the forecasting values show that the quantum index manufacturing of LSM may be decreased in the coming four months in Figure 3.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg 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\\\"\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe above table shows the values of different models. The (1,0) model at the top of the table, which has the lowest value of AIC =7.1, shows that the model is adequate for forecasting.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003eTable 5: Estimated ARIMA Model\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003eCoefficient\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003eStd. Error\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003et-Statistic\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 17px;\\\"\\u003e\\n \\u003cp\\u003eProb.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003eC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e128.7888\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e6.0512\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e21.28318\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 17px;\\\"\\u003e\\n \\u003cp\\u003e0.0000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003eAR (1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e0.828046\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e0.082045\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e10.09261\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 17px;\\\"\\u003e\\n \\u003cp\\u003e0.0000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003eSIGMASQ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 22px;\\\"\\u003e\\n \\u003cp\\u003e55.70173\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e17.00637\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e3.275345\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 17px;\\\"\\u003e\\n \\u003cp\\u003e0.0036\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" align=\\\"left\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003eR-squared\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003eAdjusted R-squared\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003eF-statistic\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 17px;\\\"\\u003e\\n \\u003cp\\u003eProb.\\u003c/p\\u003e\\n \\u003cp\\u003e(F-Statistic)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003eAIC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003eD-W stat\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e0.610550\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 15px;\\\"\\u003e\\n \\u003cp\\u003e0.573459\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e16.46109\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 17px;\\\"\\u003e\\n \\u003cp\\u003e0.000050\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\n \\u003cp\\u003e7.156108\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e1.985375\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eAs consider the (1,0) model for the forecast, now check the output of that model in this table. The adjustment sigma is 55%, this table shows that the model has R\\u003csup\\u003e2\\u0026nbsp;\\u003c/sup\\u003e= 61%, Adj R\\u003csup\\u003e2\\u003c/sup\\u003e= 57%\\u003csup\\u003e,\\u003c/sup\\u003e a lowest value of AIC = 7.1, DW = 98% it also shows the different outputs of the model as show in Table 5.\\u003csup\\u003e\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUsually, this AIC graph considers the order of AR(1) and MA(0) , as we can see that at the point of (1,0)(0,0) AR (1) we get the lowest \\u0026nbsp;value of AIC = 7.1, which shows that the estimated model is good to forecast, as shown in Figure 4.\\u003c/p\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eThe findings of this study provide important insights into the behavior and determinants of large-scale manufacturing (LSM) performance in Sindh, particularly through the analysis of the Quantum Index of Manufacturing (QIM). The observed trend in QIM reveals a period of growth followed by volatility and eventual decline toward the end of the study period (2022\\u0026ndash;2023) (Bhutta et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). This pattern reflects the broader industrial instability highlighted in prior literature, where external constraints such as energy shortages, inflation, and supply chain disruptions have negatively influenced manufacturing output.\\u003c/p\\u003e \\u003cp\\u003eA key contribution of this study lies in examining the relationship between QIM and selected development-related variables, including electricity consumption, gas usage, and mining outputs (coal and limestone). The empirical results demonstrate that industrial electricity consumption has a strong and statistically significant positive relationship with QIM (Li \\u0026amp; Yuan, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). This finding aligns with economic theory and existing studies, which emphasize that energy availability is a fundamental driver of industrial productivity. The high correlation (0.7076) suggests that uninterrupted and efficient electricity supply is critical for sustaining manufacturing growth in Sindh.\\u003c/p\\u003e \\u003cp\\u003eSimilarly, limestone production also shows a significant positive association with QIM. This result highlights the importance of raw material availability in supporting industrial activities, particularly in sectors such as construction, cement, and infrastructure development. The strong linkage indicates that mineral resource utilization plays a complementary role in enhancing manufacturing performance. In contrast, coal production and gas consumption exhibit weak and statistically insignificant relationships with QIM (Dai et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Although these variables are theoretically relevant, their limited impact in this study may reflect inefficiencies in resource utilization, supply constraints, or structural issues within the industrial sector.\\u003c/p\\u003e \\u003cp\\u003eAnother important aspect of this study is the application of the ARIMA modeling approach for forecasting QIM. The selection of the ARIMA (1,0) model based on the lowest Akaike Information Criterion (AIC) confirms its suitability for short-term forecasting. The model explains approximately 61% of the variation in QIM, indicating a reasonably good fit for time series data. The forecasting results suggest a declining trend in QIM over the next four months, signaling potential challenges for industrial growth in the near future. This projected decline is consistent with recent economic conditions in Pakistan, where rising energy costs, limited access to raw materials, and macroeconomic instability have constrained industrial output.\\u003c/p\\u003e \\u003cp\\u003eThe declining forecast of QIM also raises concerns for policymakers and industrial planners. Since manufacturing plays a crucial role in economic development and employment generation, a sustained decrease in industrial output could adversely affect economic growth (Naud\\u0026eacute; \\u0026amp; Szirmai, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). The findings suggest that immediate attention should be given to strengthening key supporting sectors, particularly energy and mining. Ensuring a reliable electricity supply and improving the efficiency of resource extraction and utilization can significantly enhance industrial productivity (Zhang \\u0026amp; Dilanchiev, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eMoreover, the study reinforces the importance of adopting data-driven approaches, such as time series forecasting, in economic planning. The use of ARIMA modeling provides a practical tool for anticipating future trends and enabling proactive policy interventions. However, it is important to acknowledge that the model is based on historical data and may not fully capture sudden structural changes or external shocks, such as policy shifts or global economic crises.\\u003c/p\\u003e \\u003cp\\u003eIn comparison with existing literature, the results of this study are consistent with the view that manufacturing remains a critical engine of economic development in developing regions. The positive linkage between infrastructure-related variables and industrial output supports earlier arguments that industrial growth depends heavily on supportive economic conditions (Ahumada et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). At the same time, the weak relationships observed for some variables indicate that not all theoretically relevant factors translate into practical significance, highlighting the need for context-specific analysis.\\u003c/p\\u003e \\u003cp\\u003eOverall, this study contributes to the understanding of industrial dynamics in Sindh by integrating correlation analysis with forecasting techniques. The findings emphasize that while manufacturing performance is influenced by multiple factors, energy availability and raw material supply remain the most critical determinants. Future research may expand this analysis by incorporating additional variables, such as technological innovation, investment levels, and policy interventions, to provide a more comprehensive understanding of industrial growth patterns.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLIMITATIONS\\u003c/h2\\u003e \\u003cp\\u003eThis study has several limitations that should be considered while interpreting the results. First, the analysis is based on a relatively short time period (2021\\u0026ndash;2023), which may limit the robustness and generalizability of the findings, particularly for long-term forecasting. Second, the study uses a limited number of proxy variables (electricity, gas, coal, and limestone), which may not fully capture all determinants of industrial performance, such as technological advancement, policy changes, inflation, and external trade conditions. Third, the ARIMA model relies solely on historical data and does not incorporate structural breaks or unexpected economic shocks, which may affect forecast accuracy. Additionally, data quality and availability from secondary sources may introduce measurement errors. Finally, the study focuses only on Sindh province, restricting the applicability of findings to other regions of Pakistan with different industrial structures and economic conditions.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFUTURE RECOMMENDATIONS\\u003c/h2\\u003e \\u003cp\\u003eFuture research should expand the scope of analysis by incorporating a longer series to improve the reliability and accuracy of forecasting results. Including additional explanatory variables such as inflation, exchange rates, technological innovation, foreign direct investment, and policy interventions would provide a more comprehensive understanding of the determinants of industrial performance. Moreover, advanced econometric and machine learning models, such as VAR, ARDL, and hybrid forecasting techniques, can be applied alongside ARIMA to enhance predictive performance and capture complex relationships. Researchers should also consider structural breaks and external shocks to better reflect real economic conditions. From a policy perspective, future studies may explore sector-specific analysis within LSM to identify high-growth industries. Additionally, comparative studies across provinces can offer broader insights into national-level planning. Improving data quality and frequency, particularly for industrial and energy sectors, will further strengthen empirical analysis and support evidence-based decision-making.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"CONCLUSION\",\"content\":\"\\u003cp\\u003eThe province of Sindh is the second-largest province of Pakistan as our study belongs to the area of manufacturing industries so the Karachi which is the largest city of Sindh and Pakistan great importance in the area of industrial economy. Due to Karachi city, which is the economics hub of Pakistan, the province of Sindh contributes to the economy with high shares. In Sindh for the measurement of the industrial change, the Bureau of Statistics, Govt of Sindh is regularly particularly the MIPE report and computing QIM (quantum index of manufacturing) in the barometer of the indicator change of province economy.\\u003c/p\\u003e \\u003cp\\u003eThis study is designed to framework the QIM of Sindh for the prediction of next four months ARIMA technique is applied in this study, the learned in ARIMA modelling technique for forecasting as while run the ARIMA technique in EViews, in which AR and MA different order and at the AR(1) get the lowest value of AIC in estimated model and preferred that it is adequate model for forecasting, also plot the AIC graph which forecast that QIM decrease in next four months which around 129.8, as compare previous month QIM of Sindh.\\u003c/p\\u003e \\u003cp\\u003eAfter that compute the correlations among the QIM and development variables, as mentioned in report economic development and also discussed that consider the proxy variables to check the association among them, because they linked with the economic development, so the proxy variables are, electricity ,gas and mining(choal, limestone) and the association between QIM and choal is 32%, and p-value is 0.1248 which is highly significant and the may say that it is failed to reject that there is association between them, correlation between electricity and QIM is 70%, according to the p-value that is 0.0001which is less than 0.05 can say that these two variables are highly associated, and the association between gas and QIM is 5% and the p-value is 0.8009, which tell that it is also failed to reject that there is association between gas and QIM, whereas 59% association between limestone and QIM and the p-value 0.002 which also show that null hypothesis is failed to reject.\\u003c/p\\u003e \\u003cp\\u003eNow may conclude that the variable electricity is beneficial in our industrial sector because it has 70% association with quantum index of manufacturing, and the limestone has also positive effect on our industrial production there is 59% association between them, and it is noted that the QIM of four months decrease according our forecasting that is, 129.6,129.5,129.3,129.6 these are the QIM values of next four months, and may suggest that the industrial production should concentrate on these two variables, this research study may be helpful for planners and policy makers.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eAbbas, G. 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The importance of manufacturing in economic development: Past, present and future perspectives.\\u003c/li\\u003e\\n \\u003cli\\u003ePakistan Bureau of, S. (2016). \\u003cem\\u003eCensus of Manufacturing Industries (2015-16)\\u003c/em\\u003e. https://www.pbs.gov.pk/sites/default/files//industry_mining_and_energy/publications/cmi_2015-16/CMI_2015-16_report.pdf\\u003c/li\\u003e\\n \\u003cli\\u003ePBS. (2022). Rebasing of quantum index of large-scale manufacturing industries from 2005-06 to 2015-16.\\u003c/li\\u003e\\n \\u003cli\\u003eStaff, P. (2022, 2022/01/20). Govt rebases quantum indices of large scale manufacturing industries. https://propakistani.pk/2022/01/20/govt-rebases-quantum-indices-of-large-scale-manufacturing-industries/\\u003c/li\\u003e\\n \\u003cli\\u003eT\\u0026ouml;r\\u0026ouml;k, L. (2022). The contribution of the Visegrad four automotive industry to economic growth. \\u003cem\\u003eJournal of International Studies\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;15\\u003c/em\\u003e(1), 90-103.\\u003c/li\\u003e\\n \\u003cli\\u003eWizarat, S. (2026). \\u003cem\\u003eRevisiting the Rise and Fall of Industrial Productivity in Pakistan\\u003c/em\\u003e. Cambridge Scholars Publishing.\\u003c/li\\u003e\\n \\u003cli\\u003eZhang, Y., \\u0026amp; Dilanchiev, A. (2022). Economic recovery, industrial structure and natural resource utilization efficiency in China: effect on green economic recovery. \\u003cem\\u003eResources Policy\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;79\\u003c/em\\u003e, 102958.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"Shah Abdul Latif University\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Large-scale manufacturing, Quantum Index of Manufacturing, ARIMA forecasting, Industrial electricity consumption, Sindh economy\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9515017/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9515017/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study examines the role of large-scale manufacturing (LSM) in the economic development of Sindh and forecasts the Quantum Index of Manufacturing (QIM) using an ARIMA time-series approach. The study uses secondary data for the period 2021\\u0026ndash;2023, collected from official statistical sources, including the Bureau of Statistics Sindh and related government publications. In addition to forecasting QIM, the research analyzes the relationship between QIM and selected development-related variables, namely industrial electricity consumption, industrial gas consumption, coal production, and limestone production. Descriptive statistics and correlation analysis reveal that electricity consumption and limestone production have strong and statistically significant positive associations with QIM, while gas consumption and coal production show weak and statistically insignificant relationships. For forecasting, several ARIMA specifications were estimated and compared using Akaike Information Criterion (AIC). The ARIMA (1,0) model was selected as the most suitable model because it produced the lowest AIC value. Forecast results indicate a declining trend in QIM over the next four months, suggesting possible short-term weakness in manufacturing performance in Sindh. The findings highlight the importance of reliable electricity supply and raw material availability for industrial productivity. The study concludes that strengthening energy support and resource utilization is essential for sustaining manufacturing growth and informing policy decisions aimed at improving industrial performance in Sindh.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Forecasting Of Large-Scale Manufacturing (LSM) Quantum Index \\u0026amp; Its Role in Economic Development of Sindh\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-27 17:34:58\",\"doi\":\"10.21203/rs.3.rs-9515017/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"2644db05-877e-4904-a125-a07c5a78bedc\",\"owner\":[],\"postedDate\":\"April 27th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":67105085,\"name\":\"Agricultural Economics and Policy\"},{\"id\":67105086,\"name\":\"Development Economics\"}],\"tags\":[],\"updatedAt\":\"2026-04-27T17:34:59+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-27 17:34:58\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9515017\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9515017\",\"identity\":\"rs-9515017\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}