A phenomenon is known as the “bullwhip effect” illustrates the instability and erratic nature of product and supplier orders at various points in the supply chain. In other words, a company’s inventory is immediately impacted by changing or increasing client demand. Businesses frequently try to forecast demand, gathering what they think is the ideal quantity of raw materials and resources required to meet client demand effectively and on schedule. Variations can frequently be increased as one moves up the supply chain from raw material suppliers to consumer demand, which causes problems with time, cost, and inventory in supply chain management. The ‘Bullwhip Effect’, also known as the Whiplash Effect within forecasting is a way of describing the effect of large swings within the supply chain.
Jay Forrester of MIT is credited with coining the term “bullwhip effect,” in 1961 which it first used to describe supply chain demand variations.
The end users are in control of the supply chain in this scenario, and even a slight change in demand has a major cascading effect throughout the supply chain. As the ripple spreads away from the customer, it gets bigger and bigger, and the action at the other end of the whip is more pronounced.
Reasons for the Bullwhip Effect
This is due to the simple fact that it is unknown what drives disruptions or what the actual demand is. Instead of the end consumer, manufacturers rely on their inventory planning on the orders they receive from their direct customers.
The bullwhip effect may be caused, among other things, by lead-time challenges, a lack of communication, inaccurate demand assessment and forecasting, inadequate supply chain visibility, and unstable supply chains.
How to Manage the bullwhip effect
For businesses, understanding the bullwhip effect, its sources, and how it affects overall expenses is the first step. Supply chain management relies heavily on demand forecasting, which is best accomplished by firms through the timely synthesis of information.
Real-time visibility can greatly benefit from technology-driven solutions. Let’s examine artificial intelligence as a potential tool for pinpointing and ultimately eliminating the main causes of the supply chain bullwhip effect.
Artificial intelligence is one of the most effective and promising ways to decrease the bullwhip effect. It is acknowledged for improving the processing of demand signals and minimizing supply chain delays, which ultimately breaks the bullwhip effect. This is accomplished by drawing inferences from data and recognizing trends.
Inventory optimization in today’s supply chains is powered by AI technologies, and warehouse and stock managers are kept up to date on the status of parts, components, and finished goods in real-time. The AI system generates inventory recommendations based on supplier deliveries and previously purchased data as machine learning becomes older.
Additionally, it can automatically produce detailed projections that assist the business in making decisions, as well as find issues that a human could overlook internally or within a supply chain. This improved analysis will increase revenues and sales while also saving businesses a significant amount of manual labor time. Better AI projections, according to McKinsey, can result in a 65% decrease in revenues that are lost due to out-of-stock goods. Warehouse costs can be decreased by 10 to 40% by not keeping items that aren’t selling well. Supply chain resilience can be ensured by AI implementation in stages.
Several consulting firms are advertising their abilities to integrate artificial intelligence (AI) into businesses’ demand planning procedures. Tech giants like Amazon and Microsoft have introduced Artificial Intelligence (AI) tools for enhancing demand planning. In fact, according to a recent study by the Institute of Business Forecasting (IBF), AI will have the most influence on demand planning over the next seven years.
AI is one of the best technologies for making demand forecasting or prediction accurate and quick, which promotes business growth and makes it simple to compete with rivals.
Conclusion
The bullwhip effect can be effectively managed by employing artificial intelligence to forecast demand. Demand forecasting, which is a type of predictive analytics, traditionally analyses the process of estimating client demand using historical data. Machine learning algorithms can be used by businesses to predict changes as accurately in consumer demand as feasible. These algorithms are capable of automatically recognizing patterns, locating intricate links in big datasets, and picking up indications for changing demand. Understanding demand patterns throughout all stages of the supply chain by sharing data, using AI in analyzing it and collaborating with other managers will become critical for taming the bull and bringing in visibility in Supply chain.
Also read: Metaverse and its role in Supply Chain Management
