Bullwhip effect in supply chains, caused by demand variability, inventory planning rules, and sourcing policies, is a phenomenon that is challenging to measure. In order to minimize its negative consequences, you and your partners have to understand how your supply chain operates. To quantify bullwhip effect, you have to build a simulation model of your supply chain that would include business processes and operations inside the network, as well as your customers and suppliers of multiple tiers.
With the anyLogistix (ALX) supply chain design and simulation tool you will be able to estimate bullwhip effect in the network, identify its causes, and test different scenarios to find out the best mitigation policies. Use ALX to:
- Gain visibility into your supply chain operations and interrelations between network elements
- Improve inventory and sourcing policies
- Cut inventory costs by reducing safety stock
- Align production plans with point of sale demand
Include important factors in your model:
- Demand variability, including random peaks occurring with probabilities and seasonality
- Multi-echelon inventory policies with actual values, operating over time
- Transportation policies and lead times, taking into account uncertainties and in-transit inventory
- Manufacturing and supplier lead time variability, taking into account production operations inside four walls and supplier reliability
Bullwhip effect is about uncertainties and random events. To quantify demand variability, you need to represent your supply chain in a dynamic simulation model, see the inside interactions in motion, and measure their results. Traditional analytical supply chain design tools do not allow for that.
ALX is the only supply chain planning software tool that includes a range of simulation modeling capabilities, including agent-based modeling. With ALX, you will be able to:
- Simulate your supply chain dynamics
- Take into account risks, uncertainties, and random events
- Measure your supply chain operations with actual values instead of averages