The Role of Big Data Analytics in Orchestrating Operations Management

Introduction

Manufacturing environments produce, collect, and store enormous amounts of diverse data, such as inventory management, supply chain applications, and production processes. Using a supply chain model to analyze operational activities allows for a quick and effective reaction. Computer simulations that take into account the entire supply chain may assist in establishing a best-case scenario that can be used as a blueprint for action by supply chain management (SCM). The forecasting model makes it easy to see what has to be done to achieve goals and the subsequent results. According to Raman et al. (2018), for organizations’ SCM to function effectively, a significant amount of data collected from the settings mentioned earlier must be evaluated, and the results used to improve operations. Obtaining this data is possible via predictive analytics tools that predict the underlying trends in the data. Although predictive analytics enables firms to forecast future behaviors by translating ambiguity into valuable data, it can yield inaccurate insights that lead to SCM problems.

Predictive Analytics

Among the most exciting developments in SCM technology is predictive analytics. Predictive analytics has existed for years, but tools have only been commonplace and sufficiently cheap for SMEs to adopt in the last few years. Prescriptive modeling allows businesses to improve their supply networks in previously impossible ways (Gupta et al., 2020). Big data and predictive analytics are increasingly being used to forecast consumer behavior and enhance supply chains as data collection and storage capabilities increase. Insights and forecasts may be made from historical data. Predictive analytics and other machine learning technologies have been incorporated into SCM to improve supply chain analytics, provide accurate demand predictions, enhance inventory management, and decrease expenses through waste reduction (Wamba et al., 2018). With big data analytics, businesses can monitor every supply chain step, from sourcing and manufacturing to transportation and retail.

Predictive Analytics Use in Supply Chain

Demand Forecasting

Predictive analytics enables businesses to forecast future client demand. This is a significant benefit offered by predictive analytics. Perhaps this is the ultimate benefit of using predictive technologies. It enables firms to take action prior to an actual rise in purchases rather than after consumers begin to protest over production delays and income possibilities. Forecasting demand may help foresee future market dynamics and supply correspondingly, assisting business resource organizations (Gunasekaran et al., 2017). For instance, the predictive model might assist businesses in estimating demand for their goods in a particular location. In turn, this will allow the businesses to either increase operations or seek affiliates with excess capacity who could deliver more units at periods when sales are predicted to surge.

Planning and scheduling manufacturing with the use of predictive models is a bonus. Organizations can ensure they have sufficient raw materials for processing within a certain time by considering all data from prior sales history, demand prediction, and more. Thus, analytics obtained from the supply chain can be utilized by businesses to organize their manufacturing schedules. Application software that helps with demand management, prediction, and optimization makes this a reality (Rahimi et al., 2021). When the findings of these simulations are integrated with other pertinent information regarding expenditures and resources, businesses can calculate the optimal level of stock to maintain at any given time of year across all locations.

Inventory Management

Predictive analytics can significantly enhance many fundamental business operations, including inventory management. Organizations utilize supply chain analytics to assess the ideal inventory level, relying on past data on client patterns of behavior and anticipated events, including such holidays, that may trigger a spike in demand for certain commodities. Inventory monitoring and avoiding stock-ups are critical to any SCM process, particularly for non-durable items such as foodstuffs and pharmaceuticals (Robertson, 2021). In such situations, predictive analytics may be immensely valuable, as the algorithm can change estimates depending on data streams from sales representatives and future demand to ensure the smooth functioning of business processes.

Pricing and Cost Optimization

Numerous commodities and services experience regular price fluctuations based on supply and demand. For instance, fuel prices are often highest on weekends and holidays when consumption is substantially high. Businesses can use predictive analytics to improve pricing models by determining ideal price levels based on past data on commodity volume sales at various costs and market variables such as changes in conversion rates (Robertson, 2021). Additionally, a predictive system may assist businesses in mitigating the risk of potential pricing errors resulting from random errors during computation or bottlenecks in acquiring factual data required to establish prices effectively.

Supply chain executives can use forecasting analytics to develop a baseline simulation that considers prior supply chain data and accurately predicts what will occur if specific variables, such as item cost, stay constant. Predictive analytics gives firms a computerized method for determining their most advantageous competitive edge (Bradlow et al., 2017). For example, if they chose to reduce their pricing or enhance their profits. By using predictive modeling, businesses understand how many aspects influence purchasing choices, like price adjustments and product promotion, enabling supply chain experts to alter pricing plans and enhance sales income.

Customer Experience

Insights into consumer behavior gained via forecasting analytics may help businesses provide a better service to their clientele. Using algorithms, it is possible to predict what consumers will purchase and determine whether and when they will revoke or return an item (Rahimi et al., 2021). Businesses may utilize the data they have collected from consumers to make product recommendations or provide customized discounts thanks to the predictive analytics technologies used in SCM.

By giving distinctive product suggestions more inclined to resonate with them than other alternatives, this analytics approach helps buyers and businesses maintain current clients while acquiring new ones. Predictive analytics may be used to recognize client groups, making it more straightforward for firms to alter supply chain systems and commodity pricing based on demand at various price ranges or to bring new items to the market if particular types of purchasers are more willing to acquire them.

The problem of Predictive Analytics

Artificial intelligence systems cannot think in the same manner as humans. Consequently, their forecasts can be complex for humans to understand or accept logic. While concerns for human welfare are at the forefront of arguments for design transparency, there are additional considerations. Unlike humans, algorithms cannot explicitly apply newly acquired insights to immediate use (Robertson, 2021). It can only use the information it has acquired for that one application. Consistent application of such knowledge can only be successful after persistent modeling and training. Therefore, it is reasonable to conclude that AI technology, particularly predictive analytics, might not benefit SCM.

In 2016, many surveys and polls estimated that Donald Trump’s probability of becoming the U.S. president was lower than thirty percent. These forecasts, which turned out to be incorrect, shocked the world. Against this backdrop, researchers have been transparent about the limitations of predictive modeling, stating that their models progressively lose their effectiveness and require a significant amount of concentrated work to be effective. In light of this limitation of forecasting analytics, several scholars have proposed an alternate approach: prescriptive analytics.

Solution

Predictive analytics shows the potential effects of different activities, whereas prescriptive analytics advises organizations on what those actions should be. Several statistical methods are used in the latter case, most of which are borrowed from math and computer science (Bertsimas & Kallus, 2020). Prescriptive analytics is similar to descriptive and predictive statistics, but it places more value on actionable insights than passive data collection. The best way to do this is to gather data from various sources and include that data in the final process. Next, the algorithms produce and re-produce alternative decision chains that might have varying effects on the enterprise.

Prescriptive analytics is useful because it can evaluate the impacts of a decision based on several possible outcomes and then recommend the approach that is most suited to follow for a company to achieve its goals. Prescriptive analytics greatly benefits operational processes (Tempelmeier, 2020). It enables businesses to investigate the most effective course of action before committing to a strategy, allowing them to save resources while achieving the greatest results possible. Organizations that can realize the maximum capabilities of prescriptive analytics are making extensive use of the technique in various contexts. The approach makes it feasible for fintech companies to identify the proper price cut for a service to attract a larger number of customers while retaining the profit margin.

The benefits of using business intelligence in decision-making are clear, but most companies lack the resources to implement them. The field of data science is complex, and only around one-quarter of businesses today consider themselves data-driven. One of the biggest problems many businesses face is dealing with unstructured data (Tempelmeier, 2020). As a result, there is a growing need for highly trained business professionals adept at handling and making sense of large amounts of data. Therefore, to optimize profits, educating staff on big data management is vital. Businesses will not have to go for outside help with these tasks using this method.

Conclusion

Predictive analytics in SCM helps organizations estimate future growth and minimize lost market opportunities. Several businesses throughout the supply chain, encompassing production, merchandising, purchasing, logistics, distribution, sales, and marketing, have invested extensively in AI technologies to gain a competitive advantage from analytical modeling. New products might be developed using big data to gauge market interest. Using big data might speed up the development of new products in response to market demand. Further, with an accurate demand forecast, businesses could efficiently manage their supply chain. Given its usefulness in facilitating the identification of defective items, operational efficiency, and the enhancement of SCM, big data analytics has emerged as a critical tool for operations management.

Consequently, big data analytics is essential for SCM as it helps in sourcing, manufacturing, transportation, and retail. Among all the AI applications for prediction, predictive modeling stands out as the most promising. When handling a variety of data streams, it is critical for businesses to choose which business analytics strategy to use. Utilizing predictive analytics in these circumstances is not advisable. Prescriptive analytics, on the other hand, is more practical and efficient. With the advantages of big data in SCM, businesses should combine the two approaches.

References

Bertsimas, D., & Kallus, N. (2020). From predictive to prescriptive analytics. Management Science, 66(3), 1025-1044. Web.

Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The role of big data and predictive analytics in retailing. Journal of Retailing, 93(1), 79-95. Web.

Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317. Web.

Gupta, S., Drave, V. A., Dwivedi, Y. K., Baabdullah, A. M., & Ismagilova, E. (2020). Achieving superior organizational performance via big data predictive analytics: A dynamic capability view. Industrial Marketing Management, 90, 581-592. Web.

Rahimi, I., Gandomi, A., H., Fong, S., & Ülkü, M., A. (2021). Big data analytics in supply chain management: Theory and applications. CRC Press.

Raman, S., Patwa, N., Niranjan, I., Ranjan, U., Moorthy, K., & Mehta, A. (2018). Impact of big data on supply chain management. International Journal of Logistics Research and Applications, 21(6), 579-596. Web.

Robertson, P., W. (2021). Supply chain analytics: Using data to optimise supply chain processes. Routledge.

Tempelmeier, H. (2020). Inventory analytics prescriptive analytics in supply chains. BoD – Books on Demand.

Wamba, S. F., Gunasekaran, A., Papadopoulos, T., & Ngai, E. (2018). Big data analytics in logistics and supply chain management. International Journal of Logistics Management, 29(2), 478-484. Web.