7 ways prescriptive analytics can support supply chain planning

In today's global market, it has become a business imperative to leverage data analytics to increase resilience, boost growth, and manage disruptions. Data can be used to analyse operational histories and industry conditions; uncover the causes of growth, stagnation, or decline; predict future disruptions or developments, and implement new processes to remain competitive and overcome future challenges.

While descriptive and diagnostic analytics help us address the questions ‘What happened’ and ‘Why’, predictive analytics forecasts what may happen in the future. Based on the answers to all these questions, prescriptive analytics suggests what our next steps should be.

Through machine learning and artificial intelligence (AI), businesses can gain access to relevant data to support their future growth and help prepare them to take on disruptions. This data can help bridge the gap between long-term planning and day-to-day operations, increase flexibility, and improve customer centricity.

How can prescriptive data analytics be applied to supply chain operations?

Prescriptive analytics in supply chain operations works by combining data analysis, machine learning, and optimization techniques to provide actionable recommendations that improve efficiency, reduce costs, and enhance service levels.

Various sources such as GPS tracking systems, RFID tags, transport management systems, and enterprise resource planning (ERP) systems, alongside external data like geopolitical news, weather forecasts, and traffic information contribute to the formation of a dataset. This real-time and historical data is then integrated into a centralized digital system to provide prescriptions or instructions on what steps must be taken.

Below are 7 ways prescriptive analytics can help improve supply chains:

1. Demand forecasting
   While predictive analytics uses historical data, market trends, and other variables to predict future demand, prescriptive recommendations rely on these predictions to suggest optimal inventory levels, transportation resources, and schedules to meet demand efficiently.
2. Resource allocation
   Deciding on resources based on cargo size and destinations can also be supported with prescriptive analytics. Programs can examine available resources such as vehicles, drivers, and warehouses and develop models to allocate resources effectively, balancing workload and minimising idle times. They can provide schedules and resource allocation plans that ensure timely deliveries and optimal utilization of assets.
3. Route optimization
   The same goes for route optimization, which is based on data on current routes, delivery times, fuel consumption, and traffic patterns. Algorithms like linear programming, genetic algorithms, and machine learning models are then adopted to identify the most efficient routes for cargo transportation that minimize delivery times and costs.
4. Load optimization
   With software to analyze data on shipment sizes, weights, and delivery destinations, machine learning can apply optimization techniques to determine the best way to load trucks or containers, maximize space utilization, and minimize the number of trips required.
5. Predictive maintenance
   Descriptive and diagnostic analytics of transport data help monitor equipment, vehicle, and vessel use. This data helps identify signs of wear and tear, while predictive analysis indicates the expected future need for maintenance or predicts potential failures based on historical data. Machine learning models can then be used to recommend maintenance schedules and proactive measures to prevent breakdowns and reduce downtime.
6. Risk management
   Whether based on data regarding geopolitical events, weather issues or other disruptions, machine learning and AI can predict potential risks and create multi-scenario analyses to evaluate the impact and suggest contingency plans and alternative routes.
7. Performance monitoring
   When companies input their logistics goals (reducing costs, faster delivery times, improved resource utilization), AI can monitor current performance data and create models and strategies to ensure ongoing improvement.

Prescriptive analytics has the scope to impact supply chains from origin to destination by providing recommendations that influence production scale, labour requirements, equipment use and maintenance, route selection, and pricing.

Accurate data inputs are key to the success of these recommendations and can help secure businesses by avoiding risks and reducing costs. It can support businesses’ future plans to reduce the environmental impact of their supply chain operations and ensure compliance with regulations and standards.