The Supply Chain Simternship provides students with hands-on educational experiences with important supply chain practices as a supply chain analyst at Buhi Supply Co.
Each Round will have you view and set the Forecast Demand and Create Purchase Orders. Each Round will have the instructional video on how to use the forecast tool if you ever need a refresher.
When trying to predict future orders, you'll want to pay close attention to the Quaters in your previous Purchase Orders located in the Resources as well as the Forecasted Data in the Review Forecast.
Round 1 will have you aim to keep the actual cost of the backpack at or below the standard cost target of $29.75.
Before starting the order, you'll want to keep an eye out for the Forecasting Model you choose to use. Since the tool changes based on which forecast model is selected, the first thing to do is to use the drop-down menu to choose one of the forecasting models.
- Naive:
The naïve model simply assumes the forecast for a period will be exactly like some specified period in the past. We apply the prior quarter’s actual demand as the forecast for the next quarter, which may be a bit different from how other companies typically use the model. - Modified Moving Average:
With a Modified Moving Average (MMA), Buhi uses the average demand of the weeks from the previous quarter. Each unit of demand is treated equally regardless of where it falls within the quarter. MMA is best used on products that have relatively stable demand as it smooths “noise.” The larger the set time frame in an MMA model the larger the lag. However, using too small of a time frame can make the model too nervous. A nervous forecast is one that jumps up and down and looks jittery. It is useful to create an MMA model with a few different lengths and compare them for lag and nervousness. - Exponential Smoothing:
Exponential smoothing treats the most recent data from previous quarters with the largest weight or importance and decreases the weight of older data. Exponential smoothing is useful for products that have variable demand as the model takes all the variables into account. Exponential smoothing also allows us to effectively capture seasonal demand because it makes seasonal adjustments to forecasted time periods based on past seasonality of similar time periods. - Holt-Winter:
The Holt-Winter Model includes every variable and is able to de-season, de-average, and de-trend — essentially removing all of the impacts to the demand. It is the most complicated of the time series models but is useful in situations where demand is highly variable and impacted by many factors. Once again, this model is useful to see the very base demand that is required. This is helpful when we don’t yet know what a product’s demand will be. Knowledge of the base demand allows for the planning of raw materials, staff, transportation, and sales targets. This content is adapted from the “Principles of Supply Chain Management and Operations” courseware by Danaka Porter, M.Eng.
A few things you'll want to keep an extra eye out for when reviewing your supplier data is the quantities, Costs, and Lead Times for your inventory. The example below in the Review Supplier Data(Blue):
When creating Purchase Orders, you can see the total quantities below in the Review Forecast. Keep in mind the Lead times for items when viewing the forecasts. Below in Green:
Once you have completed all the Purchase Orders, you'll then select Generate POs. If you would like to edit your Generated POs, you'll just select Modify POs to make adjustments and then re-submit the POs.
Then below you will review all orders and select Save All POs to confirm the orders. Or you can select Start Over if you would like a new clean start at the POs.
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