By Guy Yehiav
It can’t be denied – supply chain digitization has escalated quickly over the last 30 years as enterprises needed a better way to manage the amount of data coming through their doors. With the increased availability of analytical solutions and the adoption of machine learning capabilities, looking inside consumer packaged goods and retailers’ supply chains today you’ll see operations that go well beyond basic optimization. This is partly due to the entrance of prescriptive analytics. This new way of operating allows companies to perform an increasing array of advanced analytical tasks – taking business intelligence solutions beyond their previous capabilities.
There are a few reasons for this, but first let’s discuss the evolution of the supply chain to showcase how analytics entered the conversation and why heads of supply chains are now turning to prescriptive analytics as the solution.
Driving the Future Forward
Looking back 20 years ago, supply chain management, while complex, still served a linear function. The focus was on silo optimization to minimize per unit production costs. This meant suppliers focused on reducing costs first, before factoring in the actual demand for their product. However, this theory is only cost effective when the supply meets the overall demand.
Fast-forward five years, the supply chain model shifted and enterprises adapted what was called the “demand-driven approach.” This radical change caused the supply chain to find ways to optimize each step of the chain, bringing the importance of demand to drive logistics and collaboration management to the forefront of supply chain analysts’ minds.
Moving ahead to today, with the introduction of the Internet of Things (IoT) and smart machines interpreting data, new types of advanced optimization can be found throughout the chain. Enterprises have welcomed this modernization because it offers new types of coordination and collaboration between all channel partners and helps optimize operations, leading to a more streamlined service. So, what is still not working?
Realizing the Need
With the introduction of the IoT, supply chain managers could now pull data to provide new insight into the efficiencies of each step of the supply chain. However, even with all the new data streaming in, supply chain analysts needed an effective way to manage it. As a result, analysts found they didn’t have the tools to do so and were not collecting a lot of pertinent information from the channels they need to analyze the full picture. This problem introduced a new wave of optimization, which created the need for advanced “smart” analytical solutions.
Technologies like predictive analytics entered the picture and provided visibility over the movement of goods which helped storerooms and distribution centers (DCs), but problems still arose. Analysts realized they needed a solution that would allow them to sort the data, cluster it, identify opportunities automatically, and provide increased visibility and people accountability within all distribution channels. Enter prescriptive analytics.
Providing a Solution
Prescriptive analytics leverages existing data to provide simple, actionable tasks across the supply chain, from DCs to stores to headquarters, to eliminate negative data behaviors or encourage and duplicate positive ones. One of the biggest advantages prescriptive analytics provides is the creation of simple, plain language descriptions with corresponding actions from the data based on sophisticated statistical models run by machine learning algorithms. With a prescriptive analytics platform, supply chain analysts have the capability to different patterns from each channel. The insight then demonstrates where in the supply chain they need to improve, giving employees the ability to complete a process more proficiently. By leading supply chain analysts to specific actions that need to be addressed, prescriptive analytics cuts down the time spent pulling and sorting data from the identified patterns and instead empowers teams to focus their attention on other moving pieces of the chain. But most importantly, it provides unbiased prescriptive tasks that anyone can follow, without the personal biases and knowledge companies rely on to make supply chain tick.
Prescriptive insights also help track the current and future performance of the supply chain. For example, prescriptive analytics can identify bottlenecks before they occur, then highlight how they can be resolved, optimizing the outcome. Knowing where your product is located at any point in the supply chain, being able to predict or notify the appropriate handler of supply chain disruptions, and having contingency plans to address any of these issues have a big influence on profitability, resource planning, and customer experience by fulfilling the demand nodes wherever they are.
Leading the Charge
Prescriptive analytics has enabled supply chains to become more efficient and agile than ever before. Because of its ability to understand and act on data to help make rapid and effective business decisions, prescriptive analytics is already making an impact in industries spanning from retail, CPG, oil and gas and healthcare. Analytics are here to stay, but prescriptive analytics are just getting warmed up. The future of analytics is prescriptive since it democratizes your data across the supply chain.
Guy Yehiav is the CEO and founder of Profitect.