Supply Chain Data Analytics Need to Prescribe Actions
Supply chain management is an integral part of businesses and plays a key role in making or breaking an organization in today’s hyperconnected world. On the one hand, where supply chain management helps in reducing the operating costs and improving financial position, on the other, it boosts what is known as ‘customer experience.’
While historically a lot of corporate time and money have been spent on Business Intelligence and rendering of supply chain data to the users and decision-makers, the industry is still at a nascent stage when it comes to ‘prescribing’ actions to create competitiveness. For quite a while analytics, in its ‘current-state,’ providing only the historical perspective, served quite well. But in this age of hyper-connectivity and heightened customer expectations, the need for high-level of maturity to foresee the future, and anticipate demands is quite imminent. In other words, businesses need to be at the top of this game to serve their customers better.
Even today, most companies are still hung up on the historical supply chain data and put that in some form to create a visual dashboard that tells only about the current state. This data could be about the current inventory, sales, lead time, etc. These reports, whether graphical or tabular, show what the current state, target, gap is, and so on in the business. However, this is nothing but rendering of supply chain data to the users and decision-makers as is. It lacks both predictiveness and prescriptiveness. It does not mandate any action that an organization needs to take to fix a particular problem. This kind of data is just good enough to tell you where you are, without prescribing a solution. It misses the vital element of advanced analytics, which can work by analyzing real-time data, predicting future scenarios and prescribing complex, profitable decisions on the spot.
What we require today from the data is not simply highlighting of the problems but also building a machine learning-based algorithm which gives effective solutions. For example, if an inventory outage is predicted at a store, the retailer, depending on the time available for a response, can assess available options. The actions prescribed could involve a change in the mode of transportation, re-sequencing of manufacturing, changing pricing of a product for the rest of the month or expediting a set of purchase orders.
If organizations build predictive algorithms based on the real-time data and productionize those using machine learning to produce actionable insights, the managers on the floor can make use of them to solve a lot of business challenges. The predictive models incorporate supply chain data from external and internal sources to determine the likelihood of an inability to satisfy demand at a particular time. Once an impending supply chain issue is detected, a prescriptive action can be launched to either mitigate it or solve it.
Today’s analytics is just good to make us situationally aware. However, advanced predictive analytics has to kick in to make a difference. The next-gen analytics, based on machine learning, is a great asset to design predictive models, which can sense supply chain issues in time to execute prescriptive measures and actionable insights. As a manager on the floor, what is that s/he can do to ensure that there is an action taken to address a situation/problem? This will help the organization in not losing revenue or incur more cost or even save it from any embarrassing customer experience.