April 18, 2023 | Supply Chain Software Blogs
Predictive analytics enables companies to gain deeper insights into business data and forecast trends using historical data, enabling them to take proactive measures rather than reactive ones.
As more and more companies deploy advanced data analytics, the predictive analytics market is projected to grow to $38 billion by 2028 from 12.49 billion as of 2022.
Predictive analytics does so using statistical techniques, data mining, data modelling and AI and machine learning. With businesses adopting this cutting-edge technology, they will be capable of optimizing supply chain processes, cutting risks and driving savings.
The first step in supply chain predictive analytics is opting for a mathematical model that can most accurately represent the trend you want to understand. This would require you to test several forecasting models. Generally, this entails testing the model with known historical data and refining it until it is capable of reliably forecasting.
The second step is to add existing data and run the model to provide trends. It is critical to have high-quality data as that would enhance the accuracy of predictions. It's important to know that the model uses probability theory to determine most scenarios; it cannot predict the future.
Lastly, the ability to visualize results easily is essential for supply chain predictive analytics modelling. For this, models must have dashboards to present findings.
Also read: Know What’s Coming: Leveraging Predictive Analytics In Procurement
The role of predictive analytics in the supply chain can be understood by the following use cases:
Predictive analytics forecasts demand by analyzing patterns found in historical datasets and other variables such as seasonality, customer demand, economic conditions and weather using suitable mathematical models.
These predictions can help businesses stay on top of their inventory levels, manufacturing and production schedules and shipping strategies. Predictive analytics enables businesses to stay proactive and cuts stock waste and losses pertaining to overstocking, stockouts and emergency shipping.
Leveraging machine learning and advanced algorithms to analyze data, predictive analytics can help determine the most efficient route for shipments. This data could be real-time traffic situations, weather forecasts and current road conditions. Predictive analytics can use this data to suggest the shortest and least congested route – saving time and cutting emissions.
Apart from optimizing inventory, predictive analytics can help improve warehouse operations. It can offer data-driven layouts – the ideal position to stock a particular item. It does so by analyzing delivery times, customer demands in a specific time, price and popularity of products. This reduces the travel time of workers to locate products, reducing efforts to move and rearrange products, optimizing warehouse space and improving overall warehouse efficiency.
Any bottleneck in the supply chain can create a massive liability – risking customer satisfaction and profits. In absence of predictive analytics, businesses have to deal with disruptions with reactive supply chain risk management practices. Predictive risk management allows the audit of all processes and components of a supply chain to spot any bottlenecks or irregularities that might destabilize operations. For instance, predictive risk management will factor in threats of weather when procuring raw materials from a specific region.
Moreover, businesses can evaluate the cost and consequences of their operations and how new potentialities/strategies would impact their profits. Though one can’t prepare for all possible scenarios, predictive analytics can help put contingencies in place to cut losses and ensure efficient supply chain management.
Predictive analytics can help with identifying the best suppliers for the business in terms of costs, delivery diligence, sustainable practices and more. It can also analyze the current supplier base and market conditions to get visibility into supply performance.
Additionally, businesses can spot best practices and improvement opportunities. This would help better manage supplier relationships by avoiding pitfalls, negotiating favorable terms and better profits.
Here are a few points to consider on using predictive analytics in supply chain management:
The first thing is to clearly define what questions need to be answered using predictive and questions that mean the most to your organization. Also, ensure that these questions are aligned with your KPIs. This will enable you to effectively leverage this advanced analytics method to get tangible results.
An obvious but important point to consider is to gather vast amounts of quality data. Predictive analytics will use this data to answer your queries. Ensure your datasets are relevant, robust and sizable enough for predictive modeling.
All bottlenecks and opportunities that predictive analytics detect for you will be of no use in absence of contingency plans and lack of stakeholder communication. Ensure proper communication channels are in place so that the predictions end up in the right persons and timely preventive measures can be taken.
Predictive analytics in supply chain management is essential for today’s businesses that want to enhance their supply chain and procurement processes. It can mitigate risks, identify opportunities and enhance supplier relationships. Getting started with predictive analytics requires identifying business needs, quality data and optimal communication plans.