Predictive Analytics: Future of Supply Chain

Before discussing predictive analysis in the supply chain environment, end-to-end visibility must be guaranteed. Procurement, ticket logistics, warehouse, inventory, manufacturing, order fulfillment, transportation, distribution …

Each process is connected with the previous and subsequent one and in each phase, different components are involved. It is what is known as an extended supply chain, a continuous flow of materials and information in which a delay or failure at any point, propagates through the system preventing efficient execution. When you get a good synchronization of all the data and all the elements of this network system you can talk about a mature supply chain.

Upon reaching this level, organizations should consider introducing advanced predictive analytics. Making this decision will allow the organisation to see the first fruit very soon such as:

  • Long-term forecasts that allow evaluating whether customers will continue to need the product offered.
  • Knowledge about the stability of suppliers, which allows decisions to be made to ensure that the necessary critical resources are available.

Of course, the secret of a good forecast is the continuous update, since every minute, every transaction, every operation or every process cycle leaves new data that contains tons of critical information for the future of business.

Anticipating problems related to supply, making financial investments in certain plants and products and getting a very close idea of ​​the fluctuations to which the real production will be subjected to, are all possible, thanks to data!

At the same time, the short-term oriented predictive analytics must take into account a series of variables in greater detail at each stage of the process that will facilitate direct control of the supply chain, even in the most complex circumstances.

What are the effects of predictive analytics on the supply chain?

There are many activities that can be improved with the contribution of predictive analytics within the scope of the supply chain. Specifically, the following five applications should be highlighted:

  1. Analysis for replenishment planning: It improves product availability at points of sale and boosts customer satisfaction levels. Some examples:
  • Integrated planning with the retailer, distributor and channel level.
  • Optimization of compliance logistics, especially in relation to warehouse functions.

2. Analysis of demand: It facilitates the monitoring of the forecast with the actual sales. It improves the accuracy of forecasts, the availability of products in store and prevents the loss of sales. For example-

  • Detailed demand forecast at the point of sale level.
  • Analysis of the deviation from the real forecast against the SKU level.
  • Integration forecast with promotional events and days to refine the forecast.

3. Inventory optimization of finished products: It allows you to make better decisions regarding stock, reducing costs and inventory while improving customer service. It’s based on-

  • Inventory budget optimization.
  • Safety stock level recommendations.
  • Inventory segmentation to facilitate the application of customized strategies by type of customer.

4. Predictive transport analysis: It deals with the optimization of loads and transport routes, improving equipment utilization, contract supervision and containing total freight costs.

  • Route optimization.
  • Legislative and contractual compliance.

5. Network planning and optimization: It makes possible to verify the suitability of the manufacturing and storage facilities. Its positive effects are noted in the reduction of fixed and variable operating costs.

  • Evaluation of the number of physical plants for manufacturing and storage and their performance.
  • Optimization of material flows to meet the demand of different customer segments in cost-saving conditions.

Improvements in the supply chain must be introduced both from the bottom up and from the top down. Capturing the value in the short term is as important as maintaining the strategic vision. Predictive Analytics is an enabler that helps us preparing for external changes, planning how to protect profitability at the product level in case of failure from a key provider, unmask opportunities to increase revenue consistently or designing ways to protect margins when demand falls.