Productos y servicios financieros

Product Type C is the standardised version of Product Group 1 and is sold by customer Z to multiple end-consumers in varying order quantities. We analyse the sales planning process of Product Type C more thoroughly as this products’ demand is more frequent and not only project based. To construct a sales planning for product type C we use the sales planning model from Figure 5.1. This model suggests including historical data, customer input and employee input to maximise the accuracy of the sales planning. We start with analysing the historical data, this teaches us that the coefficient of variation is 1.86 compared to the average weekly demand. As this coefficient is relatively high we analyse the order quantities more deeply by comparing the frequency of ordering with specific order quantities. The results of this analysis are presented in Table 5.1. This table shows us the frequencies of ordering in 2012 compared to the specific order quantity of the order in the order quantity row.

From this table we conclude that 88% of the historical orders concerned smaller order quantities than four items. These small orders are generally unpredictable and unknown in advance of production. Additional research teaches us that, in contrast to the small orders, large order demand concerns expected project demand which is generally known before the 38 weeks lead time of

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Nedap UV. The contrast between the small and large demand behaviour requires a separation between the orders based on their order quantity as is introduced in Chapter 4. ‘Large orders’ include orders with a higher order quantity than three items, and its sales planning will be based on actual orders. ‘Small order’ concerns the orders which require a maximum amount of three items, this demand is uncertain and unexpected and therefore unpredictable. To make sure components are available for these unpredictable orders as well we construct a sales planning for the small order demand. We start analysing the historical demand by using a mathematical model for forecasting upcoming demand. For Product Type C only the level and trend components are added to the sales planning as these components can be

identified by analysing the trend lines in Figure 5.2. A seasonal component cannot be identified as the historical data is limited to only seven periods of historical data. A set up discussion with the Nedap UV account managers and the customer made us conclude to exclude the existence of a seasonal trend. For the demand situation in which only a level and trend component are considered, a regression analysis is initiated as demand is expected to be a linear in time.

There are only seven periods of historic sales data available for small quantity demand as the orders started in Period 4 of 2012. We forecast the upcoming demand in periods as is the standard time frame for demand forecasting at Nedap. In seven historic periods only 38 orders were placed. Analysing the data provides more stability when we consider these 38 orders over 7 periods instead of over 31 weeks, for that reason we use periods as a forecasting time frame. The regression analysis provided a ̂ value of 0.863… and an ̂ value of 5.119… by the calculations presented below:

̂ ∑ ( ) ∑ ( ) ⁄ ( ) (4.2) ̂ ∑ ̂( ) _(4.3)

The values found form the basis of the regression analysis results presented in the ‘Periodic Forecast 2013’-row of Table 5.2. The forecast of the first period of 2013 (in the table indicated as Period 8) will for example be calculated by: ( )

The two remaining rows of Table 5.2 will be getting back to later on in this section. The final sales planning should also concern the customer input and employee input when following the sales planning model of Figure 5.1. As mentioned before, the customer input results in the sales planning

Table 5.2 Sales Planning Table for 2013 of Small Orders of Product Type C

Figure 5.2: Trend lines of the Historical Sales of Product Group 1 over 2011-2012

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of the large type of orders. The small order demand is interpreted as unpredictable by the customer and is for that reason not forecasted. For that reason we have to exclude customer input from the small items’ sales planning. To check the mathematical model of the small orders we subject the mathematical forecast to the employee input. The product type is assumed by the employees to remain in an early stage of its life cycle. This early life cycle stage in combination with some special confidential circumstances, which are introduced in Appendix G, makes a growth in demand probable. Altogether, the account managers expect a growth of demand of

50% for the complete product group over 2013. This growth expectation is in line with the visualisation of the trend presented in Figure 5.2. The demand of Product Types A and B is expected to remain stable and for that reason all demand growth has to originate from Product Type C. This results in a demand increase for Product Type C of approximately 117%. The growth of this demand is assumed to be equally divided between the small and large quantity orders of this product type. This means the growth of demand for both of these order quantity categories is expected to be 117%. For the large orders this demand growth will automatically flow through the sales planning process as products are make-to-order driven. For the small quantity orders this growth should be implemented in the sales planning. For historical sales 54% of the items sold are quantified as small orders, this percentage represents a total demand of 60 items. The growth of 117% of this total quantity will result in a small order expected sales amount of 130 items in 2013. This sales expectation should now be aligned to the sales expectations from the regression analysis according to Chapter 4. The forecasted demand for 2013 by the regression analysis is 159 sold items. Analysing this gap of 29 items between the mathematical forecast and the employee forecast shows that the first two sales periods were far below the average sales rate over the analysed seven periods. This indicates an introduction period in the demand trend which is explained before in Subsection 3.2.2. We assume the regression analysis to have overestimated the trend growth of demand because of the included introduction period. So, in this situation the employee input overrules the regression analysis as we consider the employee input to contain less forecast errors.

To align the mathematical forecast to the employee input we calculate a new trend for the regression analysis which compensates for 29 items over Periods 8 to 17. The sum of the series of periods from 8 to 17 equals 125, to recalculate the trend we have to subtract ( ) from the trend value. The new trend value equals: . The results of these calculations are presented in the ‘Recalculated Forecast’ –row in Table 5.2. The ‘Periodic Forecast’-row of this table presents the periodic expected demand rounded to complete products. To translate the periodic forecast into a weekly product sales planning we present Table 5.3. In this table we divided the specific periodic forecast over the amount of weeks like is done for Period 1. In case a week planning does not end in full items we plan the remaining items in the most central weeks of the period, such as for Period 2. This

Table 5.3: Sales Planning 2013 for Small Quantity Demand of Product Type C

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method is chosen to optimise the ability for hedging variances combined with avoiding excessive stocks. Table 5.3 presents the final outcomes for the weekly expected demand for small quantity demand of Product Type C. The total expected demand for small orders for 2013 will finally come down to 130 items in a growing trend throughout the year. The sales planning of the large quantity demand needs to be added to complete this sales planning for Inventi. This is done based on the input of actual orders and customer forecasts for the large quantity demand. These customer orders and forecasts are available, but excluded from this research for confidentiality reasons. Although the mathematical historical analysis was not essential as it was overruled by the employee input, it provided the basis of the forecasts. This total overrule will not occur regularly as in this case the introduction period of the specific product was the special cause.

5.2.2 Aligning the Service and Inventory Management Process