Control the Bullwhip Effect: How Artificial Intelligence Prevents Demand and Supply Fluctuations in the Supply Chain

In its widely acclaimed 2023 Risk Report, industry expert Everstream Analytics cites five risk factors that pose increased potential threats to multi-echelon supply chains this year. These include bankruptcies, cyberattacks, ESG violations, volatile commodity prices and dependencies on China. The tricky part is that effected companies don’t often realize any threats until it’s too late. This problem is known as the bullwhip effect – a smaller motion at the beginning of the bullwhip ultimately leads to a huge disruption at the end.

While a relatively small change in demand at the beginning of a supply chain may not seem to have any significant implications at first, it could lead to increasingly larger and much more serious fluctuations in demand by the end of the line, usually caused by coordination and/or communication problems between everyone involved. These problems would be vastly improved with better transparency, planning and clear coordination along the supply chain – which is exactly where selected AI-based decision intelligence tools from paretos can help.

How to prevent the bullwhip effect

As one of the global key market players in the field of Decision Intelligence, paretos offers its customers solutions that enable complex decisions to be made in real time, thanks to data-informed insights and optimized forecasts. The following are three examples to illustrate how the bullwhip effect can be tackled with specific AI-based measures in supply chain management:

1. Real-time analysis of data
   To minimize supply chain distortions caused by the bullwhip effect it’s essential for a company to quickly and comprehensively learn of any triggers that can change demand and supply of goods. Data is collected, analyzed and processed using real-time analysis tools from various sources (including internal, external, digital, geographical and historical). Instant reliable decisions can then be made concerning orders, inventories, production capacities, transport times and market trends to ensure ongoing efficiency and profitability of the supply chain.
2. Improvement of information flow
   Most participants in a supply chain have to make their predictions and decisions based on limited information that is available to them – without a full understanding of demand at the end of the supply chain. Therefore, to avoid the bullwhip effect it’s essential to use DI to optimize the flow of every piece of real-time information available so that real-time data and alerts concerning changes in critical factors are immediately available to all suppliers, distributors and all other participants. Only when maximum transparency is created and everyone has access to the same level of knowledge can a company respond accurately to prevent triggering unnecessary orders or production.
3. Automation of orders and processes
   Implementing automated DI solutions makes it possible to minimize human errors, delays, bottlenecks or unused resources that normally act as multipliers to amplify the bullwhip effect. By replacing manual decision-making with AI-based automation of orders and other supply chain processes – with decision automation as the highest form of AI autonom – orders are processed quickly and accurately and cost overruns in production, warehousing and transportation are avoided. Because decisions are based on real-time data, it ensures that orders, for example, are never based on forecasts but always on factors such as actual demand, allowing raw materials, processed materials and inventory to be accurately predicted and optimized. This can streamline the supply chain, increase throughput and, ultimately, reduce costs.