Beer Game Analysis

BEER GAME SIMULATION 

a write up by

Bhawana Agarwal | Pulakit Gupta | Anurag Singh | Prateek Tarani | Rajat Jain | Laxmi Damesya
Mehul Gupta | Rose Mariya | Raj Shekhar Solanki | Mohammed Rashad KP

The four groups in the supply chain – retailer, wholesaler, factory depot and the factory were interviewed after a simulation beer game that created a realistic scenario of a flow of goods and orders downstream and upstream respectively through the supply chain. A gist of their experience and strategy is presented below with an analysis of the same later.

Retailer:
After the first order of four beer cases from the customer, we expected an increase in the order quantity and hence placed an order of 8 cans to the wholesaler. But since the demand from customer remained constant at 4, we decreased our order size to wholesaler to 4. But there was a huge delay in receiving the order from the wholesaler and the wholesaler was not able to deliver our orders for few weeks. Hence we were not able to meet the customer demand for three weeks from week 4 to 6. Customer demand also increased from 4 to 8 during this time. So we increased our order sizes to 12, 12 and 8 expecting further delay in order delivery from the wholesaler. Even though the orders placed were of higher batch sizes, the quantity delivered by wholesaler was much less than the order placed, however we were able to avoid backlogs due to smaller orders of 8 from our customers. Hence we didn’t place order for that week and reduced the size of further orders placed to wholesaler to either 4 or 0 cases. But the orders placed before were delivered by wholesaler and the inventory piled up as high as 16 for some weeks whereas the demand still remained constant at 8.

Wholesaler:
We tried to follow a smooth model while placing our orders to avoid unnecessary inventory pile ups. We analysed the previous orders we had received and the deviations over weeks and ordered accordingly. However, the order size from retailer increased suddenly in week 6 and continued till 7-8 weeks more which led to backlogs at our end. Due to the ever increasing pace of orders from our customers, it was difficult to keep track of what we had ordered earlier. Also, our suppliers were delivering orders with a lot of delay and were hence facing backlogs themselves. To maintain a cooperative model we did not shoot up our order size all of a sudden but gradually. However, this led to quite high backlog costs for us in those few weeks.

Factory Depot:
The ripple effect that generates with changing customer demand at the retail end causes maximum fluctuation at the very end, that is, the factory. A change in demand by 5-8 units at retail resulted in maximum inventory pile up of about 40 at our end and backlog of about 7-8 units. Thus our inventory holding cost and backlog cost increased substantially. This happened because of uncertainty in demand and supply and improper forecasting. Further coordination amongst stakeholders became tougher with passing weeks.