The Role Of Forecasting In Enhancing Supply Chain Performance: A Literature Review

Supply Chain

Discuss About The Journal Of The Operational Research Society?

In the recent years, the importance of the supply chain in the performance of the business organizations has drastically increased. There are several reasons such as the economic globalization, technology development, growing consumer power and the global focus on the sustainability. The supply chain forecasting and its accuracy has become essential in increasing the performance of the supply chain. With the growing importance of the supply chain in increasing the performance of the supply chain, the interest of the researchers has also grown in this area. The present literature review will discuss the views pertinent to different authors who have discussed role of forecasting in enhancing supply chain performance, the importance of the forecasting accuracy, the impact of adopting structured quantitative and qualitative forecast techniques in forecast accuracy and finally review benefits and challenges associated with forecasting in manufacturing environment.

The supply chain refers to the activities, processes and relationships which are present in the manufacturing process and includes the material sourcing, product manufacturing and storing through the process of logistics and manufacturing, and finally delivering the manufactured products to the end consumer. In the manufacturing process, the supply chain is not a linear set of activities; however, it comprises a complex set of processes, activities or relationships which are essential in the manufacturing process (Rai, PAtnayakuni & Seth, 2006).

The forecasting is an act which predicts the business activities of the demand of a particular commodity in the near future. The prediction is conducted based in the information available at the present time. The supply chain is dependent on relationships which are developed during the manufacturing of a specific product or service. The forecasting process works as a guidance for the future business activities (Hyndman & Athanaspopolos, 2014). The forecasting can be conducted by analyzing the previous years or the historic data. It is called quantitative method of forecasting as it uses the previous year data or statistics to predict the changes in future. Other than that, there are qualitative methods of forecasting too in which the experts use their knowledge to predict the future trends. In order to attain accurate forecasting, a combination of both the methods will be used. It can be used to plan the activities and establishing a link between the upstream and the downstream activities.

In the perspective of Gunasekaran, Patel, & McGaughey (2004), forecasting and the product development lifecycle are important part of the supply chain. Forecasting is the method of meeting the customer’s needs and demands in a timely fashion which impacts the supply chain performance measures as they are all linked to the perceived customer value of the product. Rotemberg & Saloner (1989) have discussed that the forecasting methods warrant that that there is constant monitoring by the management and there is improvement in the performance measures. Accurate forecasting prediction requires that there are cross-functional teams, rapid prototyping and engineering approaches. According to the Hsu & Chen (2003), there are several alternative methods which are used in the forecasting process; however, to maintain the forecasting accuracy feedback of the previous activities must be used to modify the forecasting instrument. Gunasekaran, Patel & McGaughey, (2004) has stated that the accuracy of the forecasting methods can be improved by benchmarking them with the other methods. Other than that, by integrating different production schedules, an organization can increase the demand forecasting for different links in the supply chain. In the perspective of Taylor (2003) is also important to increase the accuracy of the supply chain forecast as the accuracy is directly linked with the performance of the supply chain. In the views of Chen, Drezner, Ryan & Simchi-Levi, (2000) forecasting methods can also remove the uncertainties in the supply chain. The benchmarking technique integration with other forecasting methods can give a better understanding and accuracy.

Forecasting

According to McCarthy and Golicic (2001), strategic competitive advantage can be gained by the business organizations if the forecasting techniques are integrated with the supply chain performance. According to Taylor & Buizza (2003); it is important to create collaborative relationships with the trade partners and other tiers in the supply chain to improve the forecast accuracy. According to Lee, Padmanabhan & Whang, (1997), forecasting is a pivotal business function which can improve the performance of the organization by disrupting the activities related to planning, order and replenishing of the products.

The collaborative forecasting has the potential to increase the performance of the firms. The literature of Lockamy & McCormack (2004) has discussed the importance of collaborative forecasting by integrating customers’ planning into the manufacturing process and developing supply chain metrics to increase the supply chain performance. Cachon & Lariviere (2001) has highlighted the importance of a tool named, CPRF in the forecasting method. It combines forecasting and collaboration between different members of the supply chain. CPRF (Collaborative Planning, Forecasting and Replenishment) enhances the performance of supply chain by supporting and assisting joint practices between different sections of supply chain. In the views of Aburto & Weber (2007) forecasting tools can increase the efficiency, increase the sales, reduce the assets, working capital and decrease the inventory associated with the supply chain. However, Cachon & Fisher (2000) have stated that this forecasting method demands reliance with other supply chain partners and requires timely and detailed information with the trading partners. In the views of Lee & Billington (1992) forecasting tools can improve the performance of the supply chain; however, it requires substantial investment in human and technological resources. Recently, the alternative approaches can increase the responsiveness and product availability of assurance of the organization.

It has been discussed by Devaraj, Krajewaki, & Wei (2007) that in the forecasting procedure, several different processes are required. The companies must audit their internal forecasting process before collaborating with different trading partners and better to work jointly on demands planning. Bacchetti & Saccani (2012) have discussed that there are four components of the trading partners, such as management, systems, techniques and the performance measurement. In the forecasting process, the involvement of the senior management is important. The training is important in boundary-spanning personnel forecasting. In the forecasting process, the market intelligence is obtained. Stadtler (2005) have discussed market intelligence is obtained from different sources, primary of which are the salesperson, purchasing managers and the buyers. In the perception of Makridakis & Wheelwright (1977) marketing mix activities and the perception of the customers, suppliers are important to understand the shaping of demand in the near future. The information sharing between the trading partners can reduce the demand and supply uncertainty in the future. The forecasting gives information regarding the future demand, supply or the price of the manufactured products. Therefore, it is essential in the management of the supply chain.

Role of forecasting in Enhancing Supply Chain Performance

In the perception of Fildes, Goodwin, Lawrence and Nikolopoulos (2009), forecasting accuracy is very important in the planning process of supply-chain companies. In the supply chain companies, the forecasting demand involves the computerized forecasting system which can produce initial forecast and these forecasts can adjust the demand planning of the company. It increases the accuracy of the forecasting system. The accuracy of the statistical forecasting system can be enhanced when the experts adjust the forecast according to their judgment and takes into consideration special events and changes in the statistical model. Stadtler (2005) have discussed the judgmental adjustments can improve the accuracy of the forecast in the manufacturing firms; however, it may introduce the bias in the forecasting. In the views of Nenni, Giustiniano & Pirolo, (2013) forecasters make unnecessary adjustments in the absence of reliable information which may hinders the accuracy of the forecast. In the views of Acar, Yavuz, Gardner (2012) forecast adjustments made by the experts can yield better results. The large judgmental adjustments in improving the accuracy of the forecast. Ali, Mohammad & Boylan, John (2010) have discussed that there are several reasons for the efficacy of the large adjustments such as large adjustments are applied when there is reliable information. In the perception of Aviv (2001) small adjustments are usually less effective as the information on which these adjustments are carried is considered as unreliable. According to Baumann (2010/2011), human decision making is as such that they ignore the good advice and the computer mediated advice and have excessive trust on their personal judgment. Boylan (2010) has discussed that the many times, the users make adjustments to the predictions which decreases the accuracy of the forecasting.

In the perception of Cachon & Lairiviere, (2001) forecasting accuracy is important in the supply chain management and other organizational functions such as scheduling, resource planning and the marketing depends on the accuracy of the organization forecast. According to Chen & Wolfe (2011), the forecast accuracy is an important part in the delivery of the supply chain. Datta & Christopher (2011) have discussed that the forecasting tools must capture the hard data as well as the judgmental data to achieve accurate results. It is important to maintain accuracy in the forecasting predictions as the organization will have to make orders to the suppliers or manufacture the products according to the results of the forecasts.

In the perception of Derrouiche, Neubert, & Bouras (2008) quantitative forecasting methods are widely adopted to support the company’s operations in the supply chain activities. According to Durango-Cohen & Yano (2011), there are several techniques used for the quantitative forecasting such as trend analysis, seasonal adjustments, decomposition, graphical methods, econometric modelling and life cycle modeling. Ebrahim-Khanjari, Hopp & Iravani, (2012) have discussed that the trend analysis is the method of forecasting the data when there is definite upward or downward pattern for the forecast. In the perception of Ellinger, Shin, Northington & Adams, (2012), uses several models for the forecasting such as exponential smoothing, regression and the triple smoothing. According to Fildes & Kingsman (2011), seasonal adjustment refers to the model in which the variation in demand in different seasons can be identified. The adjustments are made in the baseline forecast so that the impact of the seasonal demand can be identified. Fildes & Goodwin (2007) have discussed that the decomposition in another method of forecasting in which the data is separated into three different sections, namely, trend, seasonal and the cyclic data. Fildes, Goodwin, Lawrence & Nikolopoulos (2009) have stated trend refers to the horizontal upward or downward movement with time. According to Fildes & Hastings, (1994), trend can be a recurring demand pattern with some or no repetition. The random is another set of data which comprises of the data in which no pattern can be identified. Fildes, Goodwin & Lawrence (2006) have stated that forecast method can project the patterns and can combine them to generate some relevant information. Forslund & Jonsson (2007) that the quantitative forecasting method can be used to represent an objective picture of the actual sales. The quantitative forecasting relies on the statistics and the sales or the demand patterns in the previous years. Franses & Legerstee (2011) have stated that the quantitative forecasting methods helps the business managers to focus on the recent data and the company can spot trends which provide accurate sales and market forecast. In the perception of Ho & Ireland (2012), there are several benefits of employing quantitative forecast methods in the sales or the demand forecast. Huang, Hsieh & Farn (2011) have discussed that it can also temper unwanted enthusiasm or falsified numbers provided by the employees. It can show the realistic numbers and establish a reality check for the organization. It can also be used to generate or find patterns for making more accurate projections with the help of number. In the perception of Jonsson & Gustavsson (2008) quantitative forecasting methods are also beneficial in attracting external stakeholders within the organization. The external stakeholders rely on accurate numbers more than the enthusiasm of the people. The potential investors will also feel comfortable with the forecast process.

Forecasting Accuracy

According to Klatch (2007), qualitative forecasting methods is another reliable method of forecasting for the demand and the sales. The qualitative forecasting methods are based on the judgment and the opinion of the managers and the executives of the business organizations. There are several methods which are used in qualitative forecasting methods, namely, executive opinion, Delphi technique, Sales force polling and the consume surveys. Lau, Ho, & Zhao, (2013) have discussed that the choice of the forecasting impacts on the product life cycle and the decision-making of the organization.

LeBlanc, Hill, Harder & Greenwell (2009) have stated quantitative models are only applicable if there is little to no systematic change in the environment. When the patterns or relationships between different factors change, there is little to no systematic change in the environment. Liao & Chang (2010) have stated that the objective models are of little use if there is a changing relationship between different entities. However, the qualitative approach can be applied in these cases. The qualitative approach is the approach which is based on the human judgment. According to Mishra, Raghunathan & Yue (2009), the judgmental forecasting base the forecasting on the existing trends and they are also possess a number of shortcomings. However, the advantage of these forecasting methods is that they can identify the systematic changes more quickly and can interpret the impact of these changes in a better manner. Morlidge (2014) has discussed that judgmental forecasting tools are useful in short-term forecasting methods and can supplement or support the projections which is established with any of the quantitative method. According to Nikolopoulos & Fildes (2013) executive opinions refers to the forecasting approach in which the executives from sales, production, finance or administration can generate an accurate forecast about the future sales. The qualitative forecasting method can is feasible when there is lack of feasible historic data (Require rephrasing). In the perception of Olhager (2013), the Delphi method is a structured communication technique which establishes a forecasting method involving interaction between different forecasting approaches and relying on a panel of experts. The Delphi method is dependent on the principle that forecasting from a structured group of individuals is more efficient than forecasting from unstructured group.

It can be summarized that a combination of both qualitative and quantitative forecasting methods can be used to enhance the accuracy of the forecasting. Both of the methods are complementary and can be used in combination to enhance the accuracy of the forecasting process.

Oliva & Watson, (2009) have discussed that there are several benefits of forecasting in the supply chain of manufacturing organizations. In the forecasting process in the manufacturing companies, there are three types of forecasting, namely, demand forecasting, supply forecasting and the price forecasting. Parks (2012), the demand forecasting, the companies search investigate the demand of an object by the industry and the end users. In the supply forecasting, the companies collects the data about the current producers and the suppliers. In the perception of Ali, Mohammad & Boylan, John (2010), the supply demands are evaluated according to the technological and the political trends which might affect the supply of the organization. The manufacturing companies manufacture a product which is sold to the end users. Therefore, determining the price of the manufactured products is also essential.

In the perspective of Aviv (2001), price forecast should provide a prediction of the short and the long term prices of the products. There are several benefits of the forecasting in the manufacturing industries such as increase in the customer satisfaction, reducing the stock-out in the inventories and scheduling the production of the organization in a better and productive manner. It has been discussed in the literature of Baumann (2010/2011) the manufacturing industries, it is important to keep the customers satisfied, it is important to provide them, the product or the services that they want. The forecasting in the business helps in the prediction of demand so that the customer demands can be fulfilled in the shortest lead time. Another benefit of the demand forecasting is reduction in the inventory stock-out.  It has been discussed by Cachon & Lairiviere (2001) manufacturing organizations, the companies work with different suppliers and have a long lead time. If a business organization is buying from the companies with the longer lead time, then demand forecast is important so that the suppliers can arrange raw materials for the manufacturing process. It also reduces the requirement of the safety stocks in the inventory. Chen & Wolfe (2011) have discussed that good forecasting process can lead to proper inventory arrangements and links. It identifies the production requests in the future. It is also important in the new product launch and examining the seasonal variations in the demand.

Similarly, there are certain challenges in the demand forecasting process of an organization. In the perspective of Berbain, Bourbannais & Vallin (2011), in the present times, there has been a significant change in the consumer behavior as they are looking for diversity in the consuming process as well as they want the consumer products which set them apart from the public. As a result, certain products have a very short product life cycle which led to increasing difficulty in sales forecasting within manufacturing organizations. Datta & Christopher (2011) have stated that due to lack of available history makes it difficult to implement the classical sales forecasting methods for the analysis of the previous data. In the present times, it is also important for the organization to increase the safety stocks so that unexpected variations in demand can be compensated. The classical methods of demand forecasting are unable to yield results as they are dependent on the time series analysis.

As per the discussion of LeBlanc, Hill, Harder, & Greenwell (2009), production decisions are based on the demand forecast of the organization. There is always a lead time or time gap between the earlier forecast and the final receipt of the order. The manufacturers have to start the production as soon as the demand forecast is received. The decision regarding when to start production and how to produce is dependent upon the forecast accuracy and the production cost. The production in the manufacturing organizations is dependent upon the initial forecast which has high uncertainty and low accuracy. Mishra Raghunathan & Yue (2009) have discussed that although the accuracy in these decisions is less, these decisions have a long lead time which can assist the companies in taking advantage of cheap material cost, labor cost and can result in overproduction. LeBlanc, Hill, Harder & Greenwell (2009) have stated that the production done at the later state is backed by strong forecast, but due to the short lead time it demands high cost to obtain raw materials and the products should be manufactured at a tight capacity and schedule. An ideal situation is one in which there is early forecast with high level of accuracy.

The literature review debated above discusses the importance of qualitative and the quantitative data in enhancing the performance of the supply chain. Most of the literature have stated that both the methods have their unique importance in the forecasting process. The quantitative methods can be used when there is abundant historical data available in the same essence. However, the qualitative methods of forecasting can be used when there is little information of the past.

References

Aburto, L., & Weber, R. (2007). Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing, 7(1), 136-144.

Acar, Yavuz, Gardner Jr., E. S. (2012). Forecasting method selection in a global supply chain. International Journal of Forecasting. v. 28, issue 4, 842-848.

Ali, Mohammad M., & Boylan, John E. (2010). The value of forecast information sharing in supply chains. Foresight: The International Journal of Applied Forecasting. Issue 18, 14-18.

Aviv, Y. (2001). The effect of collaborative forecasting on supply chain performance. Management Science. Vol. 47 Issue 10, 1326-1343.

Bacchetti, A., & Saccani, N. (2012). Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice. Omega, 40(6), 722-737.

Baumann, F. (2010/2011). The shelf-connected supply chain: strategically linking CPFR with S&OP at the executive level. Journal of Business Forecasting. Winter2010/2011, Vol. 29 Issue 4, 21-28.

Boylan, J. (2010). Choosing levels of aggregation for supply chain forecasts. Foresight: The International Journal of Applied Forecasting. Issue 18, 9-13.

Cachon, G. P. & Lairiviere, M. A. (2001). Contracting to assure supply: how to share demand forecasts in a supply chain. Management Science. Vol. 47 Issue 5, 629-646.

Cachon, G. P., & Fisher, M. (2000). Supply chain inventory management and the value of shared information. Management science, 46(8), 1032-1048.

Cachon, G. P., & Lariviere, M. A. (2001). Contracting to assure supply: How to share demand forecasts in a supply chain. Management science, 47(5), 629-646

Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management science, 46(3), 436-443.

Chen, P.C. & Wolfe, P. M. (2011). A data quality model of information-sharing in a two-level supply chain. International Journal of Electronic Business Management. Vol. 9 Issue 1, 70-77.

Datta, P.P. & Christopher, M. G. (2011). Information sharing and coordination mechanisms for managing uncertainty in supply chains: a simulation study. International Journal of Production Research. Vol. 49 Issue 3, 765-803.

Derrouiche, R., Neubert, G. & Bouras, A. (2008). Supply chain management: a framework to characterize the collaborative strategies. International Journal of Computer Integrated Manufacturing. Vol. 21 Issue 4, 426-439.

Devaraj, S., Krajewaki, L., & Wei, J.C. (2007). Impact of eBusiness technologies on operational performance: The role of production information integration in the supply chain. Journal of Operations Management, 25, 1199-1216.

Durango-Cohen, E. J. & Yano, C. A. (2006). Supplier commitment and production decisions under a forecast-commitment contract. Management Science. Vol. 52 Issue 1, 54-67.

Durango-Cohen, E. J. & Yano, C. A. (2011). Optimizing customer forecasts for forecast- commitment contracts. Production & Operations Management. Vol. 20 Issue 5, 681- 698.

Ebrahim-Khanjari, N., Hopp, W. & Iravani, S. M. R. (2012). Trust and information sharing in supply chains. Production & Operations Management. Vol. 21 Issue 3, 444-464.

Ellinger, A., Shin, H., Northington, W.M. & Adams, F.G. (2012). The influence of supply chain management competency on customer satisfaction and shareholder value. Supply Chain Management: An International Journal, Volume 17 • Number 3, 249–262.

Fildes, R. & Goodwin, P. (2007). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces. Vol. 37 Issue 6, p570-576.

Fildes, R. & Hastings, R. (1994). The organization and Improvement of market forecasting. The Journal of the Operational Research Society. Vol. 45, Issue 1, 1-16.

Fildes, R. & Kingsman, B. (2011). Incorporating demand uncertainty and forecast error in supply chain planning models. Journal of the Operational Research Society. Vol. 62 Issue 3, 483-500.

Fildes, R., Goodwin, P. & Lawrence, M. (2006). The design features of forecasting support systems and their effectiveness. Decision Support Systems. 42(1), 351-361.

Fildes, R., Goodwin, P., Lawrence, M., & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting. Vol. 25 Issue 1, 3-23.

Fildes, R., Goodwin, P., Lawrence, Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting.

Forslund, H. & Jonsson, P. (2007). The impact of forecast information quality on supply chain performance. International Journal of Operations & Production Management. Vol. 27 Issue 1, 90-107.

Franses, P. H. & Legerstee, R. (2011). Experts adjustment to model-based SKU-level forecasts: does the forecast horizon matter? Journal of the Operational Research Society. Vol. 62 Issue 3, 537-543.

Gunasekaran, A., Patel, C., & McGaughey, R.E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics 87, 333-347.

Ho, C.J. & Ireland, T. C. (2012). Mitigating forecast errors by lot-sizing rules in ERP- controlled manufacturing systems. International Journal of Production Research. Vol. 50 Issue 11, 3080-3094.

Hsu, C. C., & Chen, C. Y. (2003). Applications of improved grey prediction model for power demand forecasting. Energy Conversion and management, 44(14), 2241-2249.

Huang, L.T., Hsieh, I.C. & Farn, C.K. (2011). On ordering adjustment policy under rolling forecast in supply chain planning. Computers & Industrial Engineering. Vol. 60 Issue 3, 397-410.

Jonsson, P. & Gustavsson, M. (2008). The impact of supply chain relationships and automatic data communication and registration on forecast information quality. International Journal of Physical Distribution and Logistics Management. Vol. 38, No. 4, 280-295.

Klatch, W. (2007). How to use supply chain design to reduce forecast friction. Journal of Business Forecasting. Vol. 26 Issue 1, 23-31.

Lau, H.C.W., Ho, G.T.S. & Zhao, Y. (2013). A demand forecast model using a combination of surrogate data analysis and optimal neural network approach. Decision Support Systems. Vol. 54 Issue 3, 1404-1416.

LeBlanc, L. J., Hill, J. A., Harder, J. & Greenwell, G. W. (2009). Modelling uncertain forecast accuracy in supply chains with postponement. Journal of Business Logistics. Vol. 30 Issue 1, 19-31.

LeBlanc, L. J., Hill, J. A., Harder, J. & Greenwell, G. W. (2009). Modelling uncertain forecast accuracy in supply chains with postponement. Journal of Business Logistics. Vol. 30 Issue 1, 19-31.

LeBlanc, L. J., Hill, J. A., Harder, J. & Greenwell, G. W. (2009). Modelling uncertain forecast accuracy in supply chains with postponement. Journal of Business Logistics. Vol. 30 Issue 1, 19-31.

Lee, H. L., & Billington, C. (1992). Managing supply chain inventory: pitfalls and opportunities. Sloan management review, 33(3), 65.

Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management science, 43(4), 546-558.

Liao, T.W. & Chang, P. C. (2010). Impacts of forecast, inventory policy, and lead time on supply chain inventory-A numerical study. International journal of Production Economics. 128, 2, 527-537.

Lockamy, A, & McCormack, K. (2004). Linking SCOR planning practices to supply chain performance An exploratory study. International Journal of Operations & Production Management, 24(12), 1192-1218.

Makridakis, S., & Wheelwright, S. C. (1977). Forecasting: issues & challenges for marketing management. The Journal of Marketing, 24-38.

McCrthy, T., & Golicic, S.L. (2001). Implementing Collaborative Forecasting to Improve Supply Chain Performance. International Journal of Physical Distribution & Logistics Management, 32(6), 431-432.

Mishra, B. K., Raghunathan, S. & Yue, X.H. (2009). Demand forecast sharing in supply chains. Production & Operations Management. Vol. 18 Issue 2, 152-166.

Mishra, B. K., Raghunathan, S. & Yue, X.H. (2009). Demand forecast sharing in supply chains. Production & Operations Management. Vol. 18 Issue 2, 152-166.

Morlidge, S. (2014). Do forecasting methods reduce avoidable error? Evidence from forecasting competitions. Foresight: The International Journal of Applied Forecasting. Issue 32, 34-39.

Nenni, M. E., Giustiniano, L., & Pirolo, L. (2013). Demand forecasting in the fashion industry: a review. International Journal of Engineering Business Management, 5, 37.

Nikolopoulos, K. & Fildes, R. (2013). Adjusting supply chain forecasts for short-term temperature estimates: a case study in a brewing company. IMA Journal of Management Mathematics. Vol. 24 Issue 1, 79-88.

Olhager, J. (2013). Evolution of operations planning and control: from production to supply chains. International Journal of Production Research. Vol. 51 Issue 23/24, 6836-6843.

Oliva, R. & Watson, N. (2009). Managing functional biases in organizational forecasts: a case study of consensus forecasting in supply chain planning. Production & Operations Management. Vol. 18 Issue 2, 138-151.

Parks, J. (2012). The forecasters capability and empowerment. Foresight: The International Journal of Applied Forecasting. Issue 27, 12-13.

Rai, A., PAtnayakuni, R., & Seth, N. (2006). Firm Performance Impacts of Digitally Enabled Supply Chain Integration Capabilities. MIS Quarterly, 30(2), 225-246.

Rotemberg, J. J., & Saloner, G. (1989). The cyclical behavior of strategic inventories. The Quarterly Journal of Economics, 104(1), 73-97.

Stadtler, H. (2005). Supply chain management and advanced planning––basics, overview and challenges. European journal of operational research, 163(3), 575-588.

Stadtler, H. (2005). Supply chain management and advanced planning––basics, overview and challenges. European journal of operational research, 163(3), 575-588.

Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8), 799-805.

Taylor, J. W., & Buizza, R. (2003). Using weather ensemble predictions in electricity demand forecasting. International Journal of Forecasting, 19(1), 57-70.

Hyndman, R.J., & Athanaspopolos, G. (2014). Forecasting: principles and practice. OTexts.