Predictive analytics in supply chain

This blog introduces the broad field of supply chain predictive analytics as well as its connection with simulation and anyLogistix.

Why use predictive analytics?

Predictive analytics uses information from the past and present to produce insights about the future. Analysts apply statistical algorithms and machine learning techniques to historical data to produce probabilities and forecasts for a supply chain.

Widespread data collection in supply chains means predictive analytics can be an effective tool for their optimization and risk management — helping reduce logistics costs and improve customer service levels. The 2020 MHI Annual Industry Survey reports that 30% of supply chain managers use predictive analytics and that of the companies not using it, 57% plan to begin in the next five years.

Companies are using supply chain predictive analytics for:

• Supply chain design — for performance analysis and decision support when planning and optimizing a supply chain network
• Master planning — to help determine and deliver tactical and operational objectives
• Sourcing optimization — to evaluate external suppliers, determine policies, and test contingencies
• Inventory management — to reduce carrying costs, conduct Bullwhip analysis, and improve service levels
• Transportation planning — for strategic and tactical planning, fleet utilization and capacity planning, and policy selection
• Risk management and planning — to develop supply chain resilience by estimating operational risks and accounting for disruptive risks

Supply Chain forecasting software trends

Predictive analytics in logistics and supply chain is increasingly common thanks to two trends in computing:

• Rapidly growing data sets — Supply chains have many natural data capture points and the amount of information companies collect is growing quickly because storage costs are low, and activities are increasingly online. Due to advances in data management, increasingly large and complex data sets are not a limiting factor.
• Ease-of-use — Falling costs for data storage and processing are helping increase access to quick and complex analytics. User interfaces are improving and making predictive analytics tools more accessible and understandable. Additionally, machine learning techniques are becoming easier to use for complex data analysis.

With the adoption of supply chain predictive analytics software increasing, the value of captured data is also increasing. Companies are realizing the value of their data and using it to their advantage. As Industry 4.0 gathers pace, supply chain predictive analytics is an important trend to consider.

Simulation and Supply Chain Predictive Analytics

anyLogistix offers a powerful optimization solver — the industry-standard IBM CPLEX optimizer, to help with challenges such as finding the optimal location for facilities (Network Optimization). Such methods are already in widespread use in supply chains but, recently, simulation is playing a useful role in solving challenges.

Supply chain predictive analytics often means finding or configuring a mathematical model that performs well when tested with historical data. However, it can be hard to find a way that represents what you are studying at a level of detail that satisfactorily represents reality. In these cases, software that offers simulation can help.

Simulation helps when systems are not easy to describe mathematically or if data is not available for analysis. Instead of representing a complete system as an expression, simulations form dynamic models by describing the components of a system and their relationships. Running a simulation model then estimates the system’s behavior and, if it is verified to match the real world, simulation models can make accurate predictions.

Learn more about supply chain simulation and optimization in our white paper.

Prescriptive Analytics — responding to predictions

Prescriptive analytics goes beyond predictions by telling us the actions necessary to achieve our goals and what the wider effects of achieving them may be. After replicating your supply chain for predictive analytics, you can ask ‘what-if’ questions to help determine policies and plans. For example, what if sanctions affect a key-market, or what if storms disrupt manufacturing?

Analyzing operational and disruptive events using a supply chain model is an agile method of response. Simulations that consider the whole supply chain help determine an optimal configuration that prescribes the setup supply chain managers need to implement. From the simulation model, it is clear which steps are required to reach meet targets and what impact they will have.

Use anyLogistix for Supply Chain Predictive Analytics

For companies looking to optimize logistics processes and manage risk in their supply chain, predictive analytics is a powerful tool, and anyLogistix makes it easy to get started. With anyLogistix, it is possible to test your supply chain, develop and analyze ideas, and create plans based on the insights. anyLogistix lets you foresee the impact of changes and determine the courses of action necessary to meet your goals.

Companies around the world use anyLogistix for predicting how scenarios will develop and to create responses that ensure optimal supply chain operations. anyLogistix can connect to your databases and has a visual user interface, making it easy to integrate with your workflows. With statistics and visualizations, and experiments to predict outcomes and test ideas, anyLogistix is a powerful and flexible tool for supply chain predictive analytics.

To find out more about anyLogistix, download anyLogistix PLE, or read more about how companies use anyLogistix in our case studies.