Metodología para el análisis de la plasticidad

The overall results are listed per sector, per warehouse activity and per method. The results are benchmarked with a naive forecast model, since no actual forecast values for those 177 time series data exists. The naive forecast model uses the mean value of the last three observations as forecast. The error measure that is used to evaluate the performance of both models is the MAPE (Mean Absolute Percentage Error). This measure is easy to interpret and therefore good to use from a company perspective. There are some situations where the MAPE is not a good measure. The MAPE cannot be calculated when demand is zero. If the forecast is considerable higher than the actual value, the MAPE value will be bigger than one. To overcome this last issue, a selection of forecast models with a reasonable MASE value is chosen. From that list, the forecast with the lowest MAPE is selected. If that MAPE is higher than one, that forecast will be penalized since it will unnecessary effects the overall performance. A check is included in the forecast model to discard any time series with zero demand, but this should not be necessary since it is expected that there is always demand within a warehouse for the given activities.

Forecast performance per sector

There are four sectors in which the clients of CEVA Benelux are categorized. Figure 5.1 shows the result of the forecasting model per sector for a forecasting horizon of three months. Overall, the forecast model performs better than the naive forecast. An summary of the findings of the figure is given below.

• The MAPE values of the forecast model with a forecasting horizon of three months ranges between 14% and 19%.

• The MAPE values of the naive forecast with a forecasting horizon of three month ranges between 18% and 25%.

• The model has more difficulties to predict demand for the industrial sector, since the accuracy of the naive forecast corresponds with the ones of the retail and technology sector.

• The model has the same performance for the healthcare, retail and technology sector, but the healthcare sector does not show the same rate of improvement compared the retail and technology sectors.

• Based on improvement, the model shows the best result for the retail and technology sector. • Based on MAPE, the model has a low MAPE for the healthcare, retail and technology sectors.

Figure 5.1: The result of the forecast model per sector.

Forecast performance per warehouse activity

As mentioned before, it may be useful to evaluate the performance of the forecast on the individual warehouse activities. It can be a very valuable insight if it appears that certain warehouse activities are good to predict by the model so the amount of labor needed could be predicted with a relative high accuracy according to the need of that activity. In case it appears that it is very difficult to forecast a certain activity, the recommendation should be that this specific activity should receive more attention. The forecast performance per activity is listed in Figure 5.2. Overall, the model performs better than the naive forecast. An summary of the findings of the figure is given below.

• The MAPE values of the forecast model with a forecasting horizon of three months ranges between 9% and 25%.

• The MAPE values of the naive forecast with a forecasting horizon of three month ranges between 12% and 33%.

• The model performs better when predicting aggregated demand such as orders, orderlines and pallets. Individual demand, such as single units seems harder to predict.

• The demand of outbound trucks can be predicted with the highest accuracy, but this is quite obvious since an outbound truck consists of aggregated demand and often an outbound truck has fixed pick-up times, even if the truck is fully loaded or not.

• The demand of inbound trucks can not be predicted with a high accuracy. An explanation for this, could be that the replenishment of the warehouses for an upcoming season have no fixed month. So for example a fashion retailer can ship it’s cloths from Asia to Europe during July and November to prepare for the upcoming winter season.

• The intensity of returned items are hard to predict. But the MAPE of the naive forecast is also high, therefore the stream of returned items must fluctuate a lot without a certain pattern. • Based on improvement, the shipped units and items returned show the biggest improvement

(around 10%) between the model and the naive forecast.

• Based on improvement, the number of outbound trucks and shipping total show the smallest improvement (around 2%) between the model and the naive forecast.

The activities where the forecast model performs good, depends on a low MAPE (lower than 15%) and a significant improvement compared with the naive forecast (improvement bigger than 5%). The activities that score good on both criteria are listed below:

• Shipping orderlines • Picking orderlines • Picking pallets

Figure 5.2: The result of the forecast model per warehouse activity.

The activities where the forecast model improve the benchmark method significantly (more than 10%), but does not have a low MAPE value are:

• Shipping units • Items returned

The activities where the forecast model cannot improve the benchmark method significantly (less than 3%) are listed below. The number outbound trucks do not fluctuate a lot since there are agreements about the frequency a truck leaves a warehouse, fully loaded or partial full loaded. That is the reasons forecast model cannot improve the benchmark a lot. The number of total units shipped does fluctuate a lot over time, without a distinct demand pattern. Over here, more aggregated demand should be used as input for the forecast model.

• Outbound trucks • Shipping orders • Shipping total

Forecast performance per method

The forecast model evaluates different settings for all of the 14 different forecast methods and combination of forecast methods. A group of good performing methods are chosen by having a low MASE value and the best method is selected to have the lowest MAPE value of that set of methods per time series data. In order to know which method or combination of methods are performing good given the 177 different times series data sets, Figure 5.3 is made.

According to Figure 5.3, the Theta models selected quite often and the addition of the Extra-P parameter seems to be working in most cases. Altogether, both methods are used for around 30% of all the time series data sets. Below, more insights are about the frequency of best selected methods are given:

• The abbreviations of the methods are as follow: ETS = E, ARIMA = A, Theta = T.

• All the methods are selected at least once, that indicates that the behaviour of the time series data is quite diverse.

• The literature section suggested that a combination of methods can outperform single methods. Based on this figure, it cannot be concluded that this occurs for this data set, since the MAPE values are missing. But the single methods are selected more often (70%) than a combination of methods (30%).

• The ExtrP model seems to be a good addition, since it is selected 96 out of the 177 times (55%). • The combe method (ETS, ARIMA, Theta and ExtrP) is selected for only 3%, so it is likely that

Figure 5.3: The amount of time a forecast method was the best of all the methods used in the forecast model in case a forecast horizon of 3 months is used.