Our Guest Post today comes from Charles H. Render. He is Owner and Lead Consultant at Render Analytics, and also Co-Founder and COO of AnalyticsBox
Operations management and supply chain practices are increasingly becoming optimized and even automated by data analytics, the topic of Module G in your text. The days of old-school human calculations are over. Decisions are being made based on machine learning algorithms, and processes are being improved through complex AI-generated statistical analyses. Let’s look at three operations management practices that are now being entirely managed through data:
Predictive Inventory Management
Large corporations no longer use unsophisticated inventory management practices that involve human decision-making. Companies like Amazon can determine what you are going to order before you do. Amazon’s “Anticipatory Shipping” program uses a predictive analytics model created using customer data such as prior purchases, order frequency, cart contents, and search history. The algorithm ensures the relevant products are shipped to the closest hub in anticipation of the next customer order. The result: Customers enjoy the benefits of faster shipping times at a lower cost, increasing their brand loyalty and providing Amazon with an increase in sales.
Forecasting
Forecasting models are a common OM tool to predict future outcomes based on historical information. But creating the optimal model that does the best possible job in projecting future outcomes correctly is not a simple exercise. Most data-driven organizations now use machine learning to create these models. AnalyticsBox, a tech start-up that helps businesses grow their online presence faster and more efficiently, uses the Holt-Winters seasonal methodology (which is a time series model that incorporates exponential smoothing–see Chapter 4) to provide users with a high-accuracy prediction of their future web traffic. Their model is constantly improving based on new data, and the algorithm’s constant enhancements are entirely automated requiring no human intervention.
Location Strategy
Choosing where to open a new retail store, factory, distribution center, or corporate office is a critical decision for businesses. Location strategy is a complex problem as there are so many factors that go into the optimal decision. as seen in Chapter 8. For a new retail location, for example, a business should consider the traits of their customers, local traffic trends, population density, labor costs, and much more. For an international giant like Starbucks, the scale of their growth simply makes it too complex to rely on traditional location-determining methodologies. Instead, they use an in-house mapping and business intelligence platform to evaluate nearby neighborhood demographics, retail centers, and public transportation access points.
It is nearly impossible nowadays to work in OM without relying on analytics in some capacity. We are processing and converting data into actionable, profit-driving insights at an ever-accelerating rate, and soon enough, no decisions will be made without the use of data science.
This was a fascinating piece. Thank you for posting. I am going to try to incorporate Module G in my syllabus next semester.