Global consumer packaged goods (CPG) with annual revenues in excess of $10bn; over 30,000 employees, exporting products to over 130 countries.
Advanced Analytics Solutions
Predict inventory levels using advanced analytics
Minimizing the effects of channel distortion has always been a challenge in forecast-driven supply chain decisions. The solution uses the Bullwhip algorithm to enable more precise prediction on required inventory level therefore reduces the cost of overstocking.
What is the Bullwhip effect?
Bullwhip effect, also known as Forrester effect, is an observed phenomenon in forecast-driven distribution channels.
Looking at business further back in the supply chain, inventory swings in larger and larger “waves” in response to customer demand, with the largest “wave” of the whip hitting manufacturer.
What causes Bullwhip in supply chain?
- Changing forecasts → changing safety stocks
Suppliers not only react on changed demand, they adapt the level of safety stock. Thus variability increases.
- Procurement in batches adds variability
- Variability of prices (especially: promotions) has an effect on variability of demand.
- Facing shortage of supply customers tend to order more than their actual demand. After the shortage is over, cancellations occur.
Negative Consequences, Risks:
- Overstocking: greater safety stocks
- Inefficient production
- Low utilization of distribution channel
- Hazzard of stock-outs → poor customers service and lost sales
- Contractual penalties
- Inducing of various costs for manufacturer
The implemented mathematical models alleviate the Bullwhip effect in demand forecasting, minimize human distortions, thus optimize inventory level and improve supply chain decisions.
- Reduced safety stock level (0-30%)
- Improved demand forecasting (10-50%)
- Eliminated stock outs (0-25%)
- Improved service level (0-10%)
- Channel utilization (0-30%)