Throughout our daily lives, we use prediction to help us make many different decisions. It happens almost instinctively, far more often than we realize: using a navigation system to predict the fastest routes on our way to work or turning to our local weather station to determine whether it will rain tomorrow or not. Understanding how to make an educated prediction can make our lives run more smoothly, so why shouldn’t we use it to optimize our supply chain? By leveraging the smart use of SAP, utilizing predictive analytics can be easier and more efficient than we might have ever suspected.
What Are Predictive Analytics?
Predictive analytics draws its power from a wide range of methods and technologies. Through the use of advanced data, statistical algorithms and machine learning techniques, the likelihood of future outcomes based on historical data can be predicted with reasonable accuracy. Through this tool, we can go beyond learning what happened to understanding how to prevent an issue from reoccurring.
From identifying something simple that has minimal impact on our supply chain to an issue that is far more critical – such as pinpointing damaging recurring patterns – predictive analytics lets us foresee potential disruptions or threats with a reasonable degree of accuracy.
When we use predictive analytics correctly, we can identify factors that result in reduced quality and production failures. And we can optimize all areas of the supply chain to keep it humming along. When armed with a single source of truth and actionable insights, we become poised to plan and take fast action when unexpected situations arise. And that turns us into a more capable and agile organization.
Where Can You Use Prediction in a Supply Chain?
Prediction and predictive analytics can be used in many areas when optimizing a supply chain. One of the key areas is demand forecasting, which is the process of planning and predicting supply to meet material demand. Through the use of qualitative and quantitative insights as well as the smart use of SAP, we can be ready to provide what our demand forecast is anticipating.
Why Does Predictive Analytics Matter?
When surveyed, CIO's say the top three benefits of implementing advanced analytics are improved quality and speed of decision-making, better planning and forecasting and process efficiency.
Predictive analytics takes this to the next step by making it possible to look into the future with more accuracy and dependability than previous tools ever allowed. Understanding what predictive analytics has to offer enables us to drive more accurate downstream demand plans and to shift quickly and confidently to reduce inventory and boost profitability.
For example, predicting the location and rate of machine failures and optimizing raw material deliveries based on projected future demands makes it possible to overcome unforeseen disruption and boost efficiency. Determining optimal inventory levels to satisfy demand while minimizing stock lets us reduce inventory costs and empowers us to satisfy customers with greater precision. These are just a couple of benefits of predictive analytics provides to take your supply chain to the next level.
How Can We Optimize Predictive Analytics to Work Harder for Us?
The evolution of predictive analytics has come a long way since the computerized modeling techniques of the last century. Yet, even with its increased sophistication, predictive analytics loses its potency without adherence to a single source of truth.
When we introduce a proliferation of spread sheets and third-party tools and when our master data doesn’t support our strategic needs, we set ourselves up for failure.
First, before anything else, we must keep in mind that our data is an asset, and we must ensure data integrity. Inaccurate data or siloed behavior can and will result in poor results. What we need above all else is a commitment to a single source of truth from a single, integrated resource.
Once that is accomplished, good things start to happen. Predictive analytics and prediction links to MRP through the use of past sales orders and forecast demands. These, in turn, are tied to the bill of materials and inventory balances (through redline graphs) to generate planned supplies in a time phase manner. This assists in balancing the demands for finished products and sub-assemblies with required supplies.
SAP gives us the ability to track current and historical data and make educated predictions based on trends and forecasts. To use but one example: when tracking inventory, red line graphs display our current and historical inventory data, enabling us to identify our own patterns. Within that data, we may find problems such as large amounts of dead stock, obsolete stock and even stock outs. Green line graphs show us how your inventory data will look in the future. If we decide to make changes to poor inventory performance, green line graphs reveal how these fixes will affect future performance.
What Does the Future of Predictive Analytics Look Like?
With S/4HANA, the sky’s the limit. We gain even more potential to implement predictive analytics, which can have a dramatic impact on our bottom line. More specifically, embedded in the SAP Analytics Cloud, predictive forecasting drives organizations to understand past data trends to make accurate predictions for the future. The algorithm can use existing information based on previous trends in data, identify and disregard outliers where needed, and explain relationships within the master data that spotlights new insights and provides a clear path forward.
Prediction and predictive analytics are just a small step in the journey of optimizing our supply chain. Using SAP to its full potential involves a continuous commitment to learning and applying new concepts. Through a clear understanding of patterns and future trends, we get the closest thing possible to a crystal ball into the future – and a clear competitive advantage to prepare for whatever that future holds.
