Self-learning supply chain 


Self-learning supply chain

Self-learning supply chain


Over the past few years, global supply chains have been hit by a series of unexpected events. The coronavirus, the war in Ukraine, Brexit and a container ship stuck in the Suez Canal have combined to delay shipments of everything from bicycles to pet food. Use artificial intelligence and other tools to respond more quickly to supplier issues, adjust production or monitor raw material availability. Supply chain shocks are inevitable, but you can minimize them with these cutting-edge tools. 

  • supply_chain_disruptions.png

    High cost of supply chain disruption                                                                           

    $184 million
    is the average annual revenue loss companies face due to supply chain disruptions



    of companies say visibility into their global supply chain is more important now than it was two years ago


    of companies still use manual methods to manage supply chain risks

  • prescriptive_data_analytics.png

    The Gartner agency reports that 70% of companies use only descriptive data analytics (describing the past state), 15-25% use predictive data analytics (describing the future state) and only about 1-5% use prescriptive data analytics (advising users what steps to take in follow-up to the analysis of hidden trends in the data).

  • waste.png

    Businesses without prescriptive data analytics run into problems with poor product design, inaccurate estimates, ineffective planning and waste. They are limited by descriptive analytics that look to the past, they need to move from "What happened?" the "What will happen?" and "What steps should we take?"

  • chaos_butterfly_wing_effect.png

    In a traditional supply chain, many assumptions are made based on human experience and rules, such as estimating production time in a process or machine setup time. This leads to inaccuracies that lead to unreliable plans. It starts with a small deviation - a technological operation takes longer than planned - and soon, thanks to the chaotic effect of the flapping of the butterfly wing and the accumulation of inaccuracies, the whole plan is invalid.

  • main.png

    In contrast, a self-learning supply chain learns about business processes by continuously analyzing historical data and generates predictions of key performance indicators and recommendations for improving process planning. For example, it captures the difference between a planned and an ongoing task, analyzes the cause of this difference, and uses this knowledge in future planning to reduce this variation.

Three steps of the self-learning supply chain process



Step 1: data acquisition
Step 2: acquiring knowledge
Step 3: planning and optimization
An example of using a self-learning supply chain in practice
Step 1: data acquisition

Obtaining updated real-time data from the supply chain, e.g. using the MES (Manufacturing Execution System) adaptive control system 


  • Characteristics of the order

  • Data from production terminals

    • Adjustment times
      times Waiting times
      Data from sensors on machines
      Production cycle time

Step 2: acquiring knowledge
Step 3: planning and optimization
An example of using a self-learning supply chain in practice