CHAPTER 4: DATA AND MEASUREMENT
4.2 Data
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medium manufacturers and gets discounts from electronic component suppliers to help their customers decrease their costs. In addition, W company provides Original Equipment Manufacturers (OEM) and Original Designer Manufacturers (ODM) assistance and considerable number of electronic components to produce their end products. Also, W company helps small companies to control their inventory pooling.
Furthermore, W company implements vendor management inventory (VMI), buying electronic components from electronic components suppliers and managing inventory in the plants for their customers. As a distributor, W company receives soft orders from customers, and replenishes items to plants to assure sufficient items for customers’
needs. Therefore, we collect inventory, forecast, and sales data from W company to examine if forecast sharing is essential and related to a distributor’s sales and inventory management.
4-2 Data
Our data come from one major customer of W company. For the customer, W company provides the VMI service for the 13 plants. We obtain operational data of the 13 plants from 2016/10 to 2017/10. We have three sets of operational data: sales, inventory, and forecast. The sales data set includes the purchased items and quantities, the plant stored the item, and the transaction date. The inventory data set includes stored items and quantities, the plant stored the item, and the upload date. The forecast data set includes demanded items and quantities, the plant to which the items is distributed, and the demand date and MRP date of the item, where MRP date is the time the customer provides the forecast.
To examine our research hypotheses, we integrate the three sets of operational data.
Because of the long lead time of semiconductor components, a customer has to indicate
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the demand 13 weeks ahead. Such a signal is represented as the forecast information 13 weeks prior to the actual demand date. As market conditions evolve, the customer would continuous to provide forecast 12 weeks prior, 11 weeks prior, and till the frozen window: 5 weeks prior. In other words, an actual order is associated with a series of forecasts from 13 weeks ahead to 5 weeks ahead, the frozen window to which a customer no longer can modify quantities. Thus, we follow the operational logic to match the sales data set with the forecast data set (see Figure 4-2.1). Because inventory is a periodic measure, we cannot locate specific inventories for each order. Instead, we aggregate data at the month level. We calculate the average inventory of an item in a month, and then match with the aggregated monthly demand of the item, which in turn is associated with aggregated monthly forecasts from 13 weeks to 5 weeks prior. The aggregated monthly forecasts are actually average aggregated monthly forecasts because the number of forecasts from 5 weeks to 13weeks ahead are different. For example, in a month the number of forecasts 5 weeks ahead might be four but the number of forecasts 6 weeks ahead might be two. Following the process (see Figure 4-2.2), we combine the three operational data sets, and remove missing value. To reflect forecast variations, we eliminated observations with less than four forecast signals. In addition, we eliminated any item associated with only one observation as these items have no variations in a panel setting. In total, the sample contains 7821 observations for 583 components.
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Figure 4-2.1 The integration of forecast and sales data set
Figure 4-2.2 The integration of forecast, sales, and inventory at the month level
To examine the relationship among forecast sharing, inventory and sales, we operationalize the variables in Table 4-2.1. We measure forecast sharing by calculating the average forecast of aggregated monthly forecasts from 13 weeks to 5 weeks prior.
Inventory and sales are measured by the average inventory quantity and sales quantity in the monthly integrated data set mentioned above. To examine forecast fluctuation,
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we calculate standard deviation of aggregated monthly forecasts from 13 weeks to 5 weeks prior. In addition to standard deviation, we also measure median absolute of deviation (MAD): median(|𝑋𝑋𝑖𝑖 − 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑋𝑋)|) and quartile coefficient of dispersion (QCOD): 𝑄𝑄3−𝑄𝑄1
𝑄𝑄3+𝑄𝑄1. To examine order diversification, the degree of orders spread among
items, we use entropy measure: ∑ [𝑃𝑃𝑚𝑚 × ln (1/𝑃𝑃𝑚𝑚)]𝑖𝑖 . Pi is the proportion of each item accounted for total orders at plant p in month t. The entropy measure was used to measure product diversification (Jacquemin & Berry, 1979; Palepu, 1985). Higher value indicates higher diversity of orders, which is distributed among more items with less volume of each. For example, when order diversification is 0, the total orders of the hub in the month is concentrate on one item. When order diversification is 3.697341, the total orders of the hub in a month is spread across 102 items, and 𝑃𝑃𝑚𝑚 is in the range of 0.0000675 and 0.1102. Table 4-2.2 and Table 4-2.3 provide descriptive statistics and correlation for the variables used in the analysis.
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Table 4-2.1 Description of variables
Research variable Measurements items Description
Forecast sharing Average forecast The average forecast of each item i from 5 to 13 weeks before demand date in plant p in month t.
Forecast fluctuation
1. Standard deviation (STD) 2. Median absolute of deviation (MAD):
median(|𝑋𝑋𝑖𝑖− 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑋𝑋)|) 3. Quartile coefficient of dispersion (QCOD):𝑄𝑄3−𝑄𝑄1
𝑄𝑄3+𝑄𝑄1. QCOD sometimes are null because most of the aggregated monthly forecasts from 13 weeks to 5 weeks prior are zero, in turn resulting in zero Q1 and Q3.
The variance of forecast of each item i from 5 to 13 week before demand date in plant p in month t.
Order
diversification
Entropy measure:
∑ [𝑃𝑃𝑚𝑚 × ln (1/𝑃𝑃𝑚𝑚)]𝑖𝑖 , where Pi is the proportion of each item accounted for total orders at plant p in month t.
How the total orders are distributed among various items.
Larger value means less concentrated order. In other words, total orders are distributed among various items with low volume of each item i in customer plant p in month t.
Inventory Inventory quantity The inventory of item i
managed by plant p in month t.
Sales Sales quantity The quantity of item i
purchased by the customer from plant p in month t.
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Table 4-2.2 Descriptive statistics of variables
Variable Mean Std. Dev. Min Max
ForecastSharing 32043.87 112567.4 0 2235456
ForecastFluctuation (STD) 10352.74 32617.71 0 635570.3 ForecastFluctuation (MAD) 6654.792 25446.18 0 610175.5 ForecastFluctuation (QCOD) 0.385727 0.319175 0 1
OrderDiversification 3.100769 .4988452 0 3.934536
Inventory 199792 630514.8 50 1.71E+07
Sales 73681.71 268122.3 0 5694000
N= 7821, but only N for ForecastFluctuation (QCOD) is 6950 because of null values resulted from zero Q1 and Q3.
Table 4-2.3 Correlation of variables
(1) (2) (3) (4) (5) (6) (7)
ForecastSharing (1) 1
ForecastFluctuation (STD) (2) 0.8252 1
ForecastFluctuation (MAD) (3) 0.7695 0.9273 1
ForecastFluctuation (QCOD) (4) -0.1531 -0.0278 -0.0054 1
OrderDiversification (5) -0.0342 -0.0251 -0.0298 -0.0696 1 Inventory (6) 0.6421 0.5125 0.455 -0.1469 -0.0396 1 Sales (7) 0.7553 0.5844 0.5236 -0.1367 -0.0353 0.6085 1
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