Introduction
Large quantities of disparate data are created, gathered, and stored in production environments. These data, which may be categorized as big data owing to their properties, provide a significant opportunity to discover specialized expertise and develop new data-analytic instruments that can be utilized to enhance the effectiveness of manufacturing systems considerably. Production facilities gather data via various methods, including inventory tracking, supply chain monitoring tools, and manufacturing procedures. The field of operations management concentrates on supervising the everyday operations of commercial businesses. These companies have a vast quantity of data that must be examined for the organization to operate efficiently. Identifying the valuable information hidden inside the enormous amount of data is vital. This data may be obtained using techniques of predictive analytics that foresee the hidden patterns within the data. Big data and predictive analytics empower organizations across all industries to anticipate future actions by transforming uncertainty into valuable and usable data.
Predictive Analytics
Predictive Analytics is a type of data analysis that examines data to acquire conclusions. As a result of predictive analytics, operations management can anticipate how individual operations will perform. Data collected in its unprocessed state may be leveraged to reveal hidden behaviors and patterns. One can use predictive analytics to evaluate data from any point in time. Predictive analytics uses historical and previous data techniques to forecast future outcomes (Braschler et al., 2019). Predictive analytics tries to identify relationships and patterns in data that were formerly unknown. Data from both the past and the present are required for predictive analytics.
Historical data analyses the past; for example, information from a business’s rewards program may be used to assess purchase habits and predict the kind of discounts a customer prefers. Predictive algorithms use transactional and historical data patterns to generate quantitative simulations representing tendencies (Kumar & Garg, 2018). In addition, they may give customers, patients, and others prediction ratings. The program then stretches the prediction ratings to current danger and opportunity detection data. Historical data consists of both organized and unstructured information. The word “structured data” refers to more organized and described reports and is often stored in specific areas such as databases (Braschler et al., 2019). Unstructured data is often free-form, making it incredibly difficult to manage. This information must be processed and organized before it can be utilized for predictive analytics. Real-time data consists primarily of inconsequential information that businesses have never previously considered. Real-time data include weather, air traffic, traffic, financial markets, social networking APIs, company sales and registrations, and corporate records. The present level of technology poses a threat to organizations that continue to use old technologies. Real-time predictive analytics may distinguish judgments by seconds, minutes, or even hours, based on the industry.
Moreover, scaling and using conventional analytics tools in operational decision-making is challenging. The superior performance of predictive analytics is attributable to their utilization of data to generate more precise, tailored, and targeted choices to maximize customer value. Since these customer-related choices are made at the front lines of a firm, they should be made quickly and incorporated into operational processes. Considering this, there is a challenge known as the information-to-action gap that prevents many firms from using forecast analytics to their advantage when contemplating alternatives of this kind (Braschler et al., 2019). Decision management is a tried-and-true technique that uses predictive analytics to make operational processes more scientific and shorten the time between having an idea and implementing it.
At least once every year, the decision to reactivate an outlet will be made. It impacts sales, market share, and profitability, so most companies will invest much money and time to achieve the best results. Using analytics to grasp the anticipated value, expenses, and risks of the contract will improve the strategic rationale behind the decision to replenish or not. Accordingly, every one or two years, a maintenance determination will be made for each client. Every choice determines whether or not a company will retain a specific client and how it will do so (Surendro, 2019). Throughout the firm’s life cycle, the company’s client network is one of its most valuable assets; consequently, these management plans affect how the corporation will treat these customers.
Thus, it is possible that the value of keeping one customer will not add up to much, and as a result, this option can seem far less important than the choice of whether or not to redesign an outlet. The primary purpose of decision management is to optimize the usefulness of each operational decision. It is not difficult to observe how the decisions made throughout the operation build up to a cumulative value. Therefore, reliable information is essential when making choices, whether those decisions pertain to day-to-day operations or the organization’s long-term strategy (Braschler et al., 2019). It is not practicable to do data analysis by hand and instead rely on the judgment of qualified users for making the types of high-volume, more real-time decisions that are being discussed here. Organizations need to evolve along the analytic maturity curve and use predictive analytics to deliver high levels of dependability at scale. Only then will they be able to meet customer demands.
The problem of Predictive Analytics
Robotic systems do not “reason” or “understand” in the same way that people do. Similarly, their predictions may be so complicated that it may be difficult for humans to locate the rationale or adopt it. This makes it challenging for machines and people to describe how they function. Consumer safety is the primary justification for design openness, but there are other factors. Computers struggle to apply what they have learnt, in contrast to humans. That is, they have problems transferring their knowledge to new situations. The knowledge it has gained is exclusive to a single application. That is why it is safe to say artificial intelligence, especially predictive analytics, may not be effective when applied in operations management.
In 2016, many opinion polls projected that Donald Trump’s chance of winning the presidency was between 15 and 30 percent. Nevertheless, as the world eventually discovered, their predictions were wildly inaccurate. Some statisticians issued warnings about the inaccuracy of surveys, and the state-based ballot tallying process in a vast country such as the United States rendered polling much more difficult (Pietruska, 2018). The majority of data-enabled surveyors’ inability to correctly forecast the results of the 2016 United States Presidential Elections sent shockwaves across the world of data science (Reggio & Astesiano, 2020). Data scientists have been open about the shortcomings of predictive analytics, saying that their algorithms lose their efficacy gradually and need a lot of focused effort to get right. When polls fail to forecast an outcome, it is usually because voters’ self-reports do not match up with their actual behavior, a critical insight into politics that cannot be gained from other Big Data techniques. Greater importance is given to psychological behavior than to demographic characteristics. Given this problem with predictive analytics, some researchers have suggested prescriptive analytics as an alternative solution.
Solution: Prescriptive Analytics
Prescriptive analytics tells businesses the optimal course of action, while predictive analytics reveals the raw outcomes of various actions. The area of prescriptive analytics is significantly influenced by computer science and mathematics and employs many statistical methodologies (Lepenioti et al., 2020). While it is closely connected to both predictive and descriptive analytics, prescriptive analytics emphasizes implementable findings above data tracking. This is accomplished by collecting information from various predictive and descriptive sites and incorporating it into the decision-making process. The computers then generate and re-generate potential choice sequences that might affect an organization differently.
Prescriptive analytics is instrumental since it can assess the effects of a choice predicated on several future possibilities and then suggest the most appropriate strategy to pursue to meet an organization’s objectives. The use of prescriptive analytics has significant operational advantages (Lepenioti et al., 2020). It allows companies to examine the best action before adopting solutions, which helps them save time and money while obtaining the best possible outcomes. Companies that can tap into the potential of prescriptive analytics are employing it in several different capacities. For instance, it makes it possible for decision-makers in the medical industry to optimize profitability by advising the most effective treatment methodology for both patients and providers. They also make it possible for financial organizations to determine the appropriate price reduction level for a product to increase the number of clients while maintaining the same level of profit.
Future Course of Action
Notwithstanding the evident advantages of employing data analytics in decision-making, most businesses lack the capabilities required to utilize them. Data science is challenging, and less than one in four companies today identify as being data-driven. Several companies cite the need to manage unstructured data as one of their main challenges (Kulkarni, 2019). Thus, the need for skilled business experts who can manage and comprehend data is expanding at an alarming rate. Accordingly, educating personnel on big data management is necessary to maximize returns. This strategy eliminates the need for organizations to outsource this work to external experts and services.
Additionally, combining prescriptive and predictive data will allow businesses to take proactive actions that lead to better results. The solution is to integrate various analytical skills depending on the problem at hand and the difficulty of finding a workable alternative. Typically, the procedures and information required to enable actionable insights do not always exist. In such situations, businesses may begin with solutions that use current data to generate instant results while concurrently implementing the tools and procedures required to enable more complicated analytics.
Conclusion
Big data may be used to create new goods depending on consumer demand. Leveraging big data may facilitate the creation of new goods based on client preferences. Furthermore, manufacturers will effectively organize their distribution network with an exact demand prediction. Thus, big data analytics is crucial for operations management as it assists in detecting damaged goods, enhancing operational quality, and improving supply chain management. Predictive modeling is the most exemplary machine learning method for forecasting company growth. Deciding which data analytics method is important when organizations are dealing with multiple data sources. Using predictive analytics in such situations may not be ideal. Instead, using prescriptive analytics would be more effective and viable. Organizations should integrate both methods to leverage big data benefits in operations management.
References
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Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57-70. Web.
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Pietruska J. L. (2018). Looking forward. Prediction and uncertainty in modern America. University of Chicago Press.
Reggio, G., & Astesiano, E. (2020). Big-Data/analytics projects failure: A literature review. 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 246-255. Web.
Surendro, K. (2019). Predictive analytics for predicting customer behavior. 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), 230-233. IEEE. Web.