November 2019 | Commentary | The Lean Supply Chain

Drilling Deeper Into Your Supply Chain

Tags: Supply Chain Management, Technology , Big Data, Supply Chain

Paul A. Myerson, instructor, management and decision sciences at Monmouth University and author of books on Lean and the Supply Chain for McGraw-Hill, Pearson, and Productivity Press, 732-571-7523

Data analytics is the science of examining raw data to help draw conclusions about information. When applied to the supply chain, it allows companies to drive insight, make better business decisions, and verify or disprove existing models or theories.

Data analytics breaks out into the following categories:

Descriptive analytics. In supply chain, descriptive analytics helps companies better understand historical demand patterns, how product flows through the supply chain, and when a shipment might be late.

Diagnostic analytics. Once supply chain problems occur, you need to analyze the source. Often this can involve analyzing data in the systems to see why a company was missing certain components or what went wrong that caused the problem.

Predictive analytics. In the supply chain, predictive analytics could be used to forecast future demand or the price of a product.

Prescriptive analytics. In the supply chain, you might use prescriptive analytics to determine the optimal number and location of distribution centers, set inventory levels, or schedule production.

Cognitive analytics (a potential subset of any of the above). This helps an organization answer complex questions in natural language in the way a person or team might respond to a question. It helps companies think through a complex problem or issue such as, "How might we improve or optimize x?"

As a result, supply chain analytics are also the foundation for applying cognitive technologies, such as artificial intelligence, to the supply chain process. Cognitive technologies understand, reason, learn, and interact like humans, but at enormous capacity and speed.

Using these categories of analytics can give you a leg up on the competition as traditional measures tend to be based on historical data and not focused on the future. They don't relate to strategic, non-financial performance goals such as customer service and product quality or directly tie to operational effectiveness and efficiency.

Other useful applications for analytic techniques include:

Evaluating disaster risk. Supply chain disruptions can come in many forms. As a way to evaluate the risk to a supply chain, you can classify events that cause disruptions into two types: super events (disrupting all suppliers simultaneously) and unique events (disrupting only one supplier).

Managing the Bullwhip Effect. This phenomenon describes the tendency for larger order size fluctuations as orders are relayed up the supply chain (toward suppliers).

Supplier selection analysis. Suppliers are often evaluated on far more than simply the price offered.

Transportation mode analysis. A faster shipping method is usually more expensive, but saves pipeline inventory costs. This is the core trade-off in transportation mode analysis. Other important considerations could include on-time as opposed to fast delivery, coordinating shipments to maintain a schedule, and keeping a customer happy.

Warehouse storage. Placement decisions within huge warehouses with dozens of trucking docks and thousands of items can be complicated.

With the power of analytics, companies can fine-tune their supply chains in ways that weren't possible in the past. If your supply chain management models are based only on past demand, supply, and business cycles, you could be missing opportunities to use analytics to achieve a competitive advantage.






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