GRAFICO N.º 3.1: “MODELO SOBRE LA ORGANIZACIÓN EMPRESARIAL”
7. Desarrollo del objeto de estudio:
7.1 La Pequeña y Mediana Empresa (Pyme)
In this section, we mention critical remarks on X-Firm current situation regarding the calculation of the service level, the calculation of the total distribution costs, the formula of the forecast error, the
implementation of the item classification, the DOI, and the target service level in inventory control, and the approach for developing the current capacity plan.
The calculation of the service level
X-Firm calculates the service level in money values.
We note that the service level that is used for inventory control should be in unit cartons and not in money value. If we calculate the service level per SKU, the usage of unit cartons and money value will give the same results, since the price will be canceled out during the calculation. This is not the case if we calculate the aggregate service level, the usage of unit cartons and money value will give different results, since the price of each SKU may be different. The aggregate service level in money value tends to give higher results than the aggregate service level in unit cartons if you prioritize the demand fulfillment of the expensive SKUs (see Appendix B).
The calculation of total operational costs of distribution warehouses
X-Firm only uses fixed holding costs and omits the product value in the total holding costs calculation.
It means that the costs to store a high value SKU is relatively cheaper than to store a low value SKU and vice versa, since the carrying rate is the same. It is not common to calculate it that way. The calculation of the total inventory holding costs should also consider the product value. X-Firm uses the average number of pallets to calculate the total inventory holding costs.
The use of the average number of pallets from the capacity plan is not equal to the required number of pallets to be stored in the warehouse, since it does not consider the demand fluctuation. We should use the expected or the maximum on-hand stock in pallets, since it takes into account the forecast errors.
The formula of the forecast error
The denominator in the forecast error formula is the rolling forecast (see Equation 2.2-1). This practice underestimates the actual value of the forecast error, especially if the real sales is smaller than the sales forecast (i.e., the actual sales achievement is lower that the rolling forecast). Since the value of the rolling forecast, i.e., the denominator, is relatively larger than the value of the actual demand, X-Firm gets smaller forecast errors from that formula which is desirable by the sales department. Theoretically, the denominator should be the actual demand, since we want to measure an error of the forecast. Appendix C provides on example of different results if we calculate the forecast errors using X-Firm’s approach and the theoretical approach. We use a different approach to calculate the forecast errors for our solution.
The implementation of item classification, DOI, and the target service level in inventory
control
X-Firm does not explicitly do an item classification for its inventory control.
the service level, X-Firm has implicitly applied the ABC classification which is very useful for inventory control. Nevertheless, it is necessary to make a well-defined item classification to know how important an SKU is for X-Firm, so they can control the inventory according to the item class.
X-Firm uses the same DOI as a reorder point for all SKUs.
It is better to set a different DOI for a group of SKUs with a long production lead time (e.g., RTD products), because the longer the lead time, the higher inventory level or DOI it should hold. The DOI calculation is only based on the average demand.
X-Firm has to consider the forecast errors to calculate the safety stock that influences the reorder point. X-Firm underestimates the reorder point by omitting the forecast errors. X-Firm also does not include the target service level to the safety stock calculation.
The target service level determines the safety factor that is used to calculate the safety stock. By excluding the target service level, X-Firm also underestimates the buffer or safety stock that they should have to deal with demand uncertainty.
The approach of the current capacity plan
The logistics manager did not consider the seasonal effect in capacity planning
X-Firm has its seasonal period for brand category A1 from May until July 2012. The same seasonal period also holds for the year 2013. During the seasonal period, the demand (i.e., in money value) can increase between 35% - 260% depending on the SKU. In the current capacity plan, the logistics manager determined the required capacity for the distribution warehouse only based on the simple average of the 15 months historical demand without considering the seasonal period. Using this approach, we are certain that the needed capacity will be higher than the current capacity during the seasonal period even though it is only for 3 months. The capacity plan should consider the seasonal effect so the management of X-Firm can foresee and anticipate the risk of not having enough capacity during the seasonal period.
The logistics manager used the historical demand of the last 15 months in capacity planning The logistics manager intended to use the historical demand from January 2012 until March 2013 because he wanted to include the demand of brand category A2 that were very high in the first quarter of 2013. It is not common to use the historical data of the last 15 months as an input for a capacity plan. Normally, people use the historical data of the last 12 months or one year period (i.e., from January until December).
The
assumption of “the more data is better” is not always correct, especially if the data contains seasonality. In the historical data of 15 months, X- Firm includes 12 months of regular demands and 3 months of seasonal demands. While using the 12 months historical data, X-Firm uses 9 months rather than 12 months of regular demands. Note that the regular demand has a lower value than the seasonal demand. As a result, the simple average demand of the historical demand of 15 months is lower than the simple average demand of the historical demand of 12 months. Another advantage of using the historical demand of 12 months is that it represents the natural demands of a complete period of sales in one year. The logistics manager used the DVP for distributor allocation.
We argue the benefit of using the DVP, since the customer demands are considered in cartons or in money value. Since the volume and the quantity of customer demand is proportional, we can directly use the total demand of a distributor in cartons for distributor allocation.
The logistics manager did not take into account the multi drop list in distributor allocation. As we understand from the customer allocation process, the multi drop list is very critical for the distributor’s order delivery. By omitting the multi drop list in capacity planning, the probability that the logistics manager did not assign the distributor in the multi drop list to the correct site is very high. The results of incorrect distributor allocation results in a wrong capacity plan for each distribution warehouse.
The logistics manager used “lack DOI” of 1.5 to adjust the total number of pallets in the current capacity plan.
It means he upgraded the total number of pallets up to 50% of his original calculation which is very high for an adjustment. His approach to calculate the “lack DOI” based on one random point of warehouse utilization in the low season (i.e., as an empirical data) is also not correct, since the warehouse utilization might fluctuate daily.