The strategy I used was a three-phased approach. First phase was to increase Inventory to support and expected Increase In demand during the first couple of weeks of the ad campaign. The second part of the strategy was to observe for a decrease (or leverage) in the amount of orders as the initial, increased interest in the product would settle and stabilize within the target group. I targeted this second phase to begin around week number eight. The third phase would begin on week number nine and would concentrate in observing a steady ordering trend with small fluctuations.
This would give me an Idea of what an average weekly order would look like. Being able to predict an average weekly order would allow me to avoid excess Inventory, EBBED backlog and to Meltzer costs. Chart 1. EBBED orders Results: The three-phased strategy that was designed for this 25-week period didn’t necessarily worked out as planned and drastic adjustments had to be performed along the process to counteract drastic shifts In consumer demands. Week 2 reflected an unexplained drop (from 100 to 701 which contradicted the assumption that creating a safe level would meet increased consumer demand.
The reasons for this remain unexplained since there was no feedback provided by the retailer. I believe consumer demand dropped leaving excess inventory at the retail location. Not sure of the exact root cause, since orders picked picked up again until week 6. Weeks 3, 4, and 6 reflected an expected Increase In orders from what I believe was a successful ad campaign. Week 7 presented another unexplained drop with no orders for that week. This caused the EBBED backlog to increase to 194.
No explanation from the retailer was recorded. Weeks 1 thru 8 represented strategy phases I and II, (increase inventory and observe for a steady flow of average number of orders). In order to maintain some type of forecast control, the orders I placed to EBBED were based on the difference needed to match EBBED backlog to the backlog of fluctuations of incoming weekly orders were the main causes for bullwhip, and not human over-estimation. Inventory levels during this time period were at zero balance, which favored our bottom line.
Weeks 9 thru 25 depicted phase Ill of my strategy, to expect a steady flow of orders, make the ordering more predictable (always expecting minimal fluctuations). Again, it didn’t quite worked out as planned. Orders fluctuated every two weeks and towards the end it seemed that a pattern loud be set at 138. The ordering strategy for this third phase was based on keeping track of the bi-weekly fluctuation by trying to order along those numbers trying to keep up with the “flow’. During this third phase there were five peaks in retail backlog: wake- 129; wake-125. 5; wake-132. 5; woo-132, and wake-119. . Highest EBBED backlogs during this period were: wok-144; wok 1-132; wok 5-156; wake-124; and wake-141. Inventory stayed at zero during this period. Conclusions: The CHUB campaign was successful, even though I still need to further look into weeks 2 and 7 and gather data. This will help me determine if the drastic downward fluctuation in orders was due to human error, unexplained event in consumer behavior, retailer infrastructure problem, or Just mere excess inventory at the retail location. Recommend visiting the retailer and establish viable communications flow for better two-way feedback.
There were a few EBBED backlogs I consider significant (depicted in previous paragraph). These backlogs were the result of orders I placed based on forecasts that weren’t met due to a significant downward shift. These patterns became more obvious after week 12. I believe a erred longer than 25 weeks would be optimal for better data mining. I conclude by saying that our friends at the CHUB did a great Job shipping the product to us. Their support was reliable and timely and our Inventory costs were minimal throughout the entire period. All problems that I saw originated downstream. Part 2.
To: Tom Javelins Subject: Lessons Learned from Bull-whip Effect * The Bull-whip Effect occurs when changes in consumer causes the companies in the supply chain to order more goods to meet new demand. Most companies order more than they can sell to create a safety buffer. This extra inventory increases and decreases during normal market fluctuations. * When demand falls, the front-end of the supply chain will decrease inventory, at the same time, this action will amplify the extra inventory on each company up the supply chain. * There are behavioral causes (management behavior, I. . , ordering too much) as well as operational causes (individual demand forecasts from each company) for this. * Lack of communication up and down the chain can also cause this effect. * The Bull-whip Effect occurs when changes in consumer causes the companies in the supply chain to order more odds to meet new demand. Most companies order more than they can sell to create fluctuations. * When demand falls, the front-end of the supply chain will decrease inventory, at the same time, this action will amplify the extra inventory on each company up the supply chain. There are behavioral causes (management behavior, I. E. , ordering too much) as well as operational causes (individual demand forecasts from each company) for this. * Lack of communication up and down the chain can also cause this effect. My ordering strategy – CHUB There are additional factors that can contribute to the Bull whip effect such as 1- poised demand, 2-price volatility, 3-variation, 4-order quantity, 5-short game, 6- imbalance of traditional inventory, 7-lack of collaboration, and 8- insufficient lead time.
Chart Comparison My main mission was to minimize my backlog and my retail customer’s backlog, as well as keeping my inventories at a minimum. Comparing my ordering historical chart to the cumulative chart, we can see that my ordering history was overly cautious due to the uncertainty caused by drastic drops in demand. All and all I believe that for the most part, my orders tracked well responding to retail orders. There were a few instances in which a significant bull whip spike caused by a marked decrease in demand that forced me to order on “perceived” needs.