Conclusion of the forecast model

The purpose of the forecast model is to obtain reasonably accurate forecasts given a limited input of a user. The model delivers insights about how accurate the sectors and warehouse operations can be predicted, in addition to that various forecasting methods and an improvement for those methods are evaluated. The accuracy of these methods are compared to a benchmark method, this naive forecast consist of an average of the past three observations.

The input of the model is chosen based on a trade-off between a minimum of 24 observations and a minimum of 18 observations as proposed by Hyndman et al. [2]. A minimum of 24 observations, 2 years of data, is preferred if enough time series data remain left. Since this is the case, 177 monthly time series data sets from different clients and warehouses with a minimum length of 24 observations are selected. Three forecasting methods are selected based on their performance at two international forecasting competitions. Over there, around 248 forecasting methods are evaluated based on the average performance on 100.000 time series data. Surprisingly, the outcome of the competitions is that the statistical forecasting methods perform better than the machine learning forecasting methods. It is likely that forecasting without using external variables such as weather conditions, product promotions, region and economic development does not use the full potential of a machine learning model. In addition to that, when performing a forecast on time series data, the last observations are always the most important one. A machine learning model does not add more value to the last couple of observations as a statistical forecast model does. So in case of a response variable with multiple predictors, a machine learning model can be the best way to predict demand. In case of time series data, a statistical forecasting methods can be the best way to predict demand.

The selected statistical forecasting methods are ETS, ARIMA and Theta. In addition to these single methods, a combination of these methods (the Comb forecast method) should also generate good forecasts. Furthermore, an own method (ExtrP) is proposed that should help to improve the forecast accuracy in a situation where demand decreases rapidly, this occurs at some clients at the end of a season. In general, a forecast method smooths out rapid changes of demand. The ExtrP method removes this smoothing element only when a forecast generated by the ETS, ARIMA or Theta indicated that demand decreases. The ExtrP method is a multiplicative parameter that must be used together with a single forecast of the ETS, ARIMA or Theta methods or with a combination of those methods.

The results of the model are focused on a three months ahead forecast. First the robustness of the methods is evaluated, this includes a test on overfitting and a sensitivity test to multiple lengths of the forecast horizon. A rolling origin, ranging between the current origin to 5 months back, is applied as

test on overfitting. A forecast method is overfitted when the errors fluctuate when less training data is used. Figure 5.5 indicates that seven out of the fourteen forecast methods are likely to overfit the data. Regarding the sensitivity test, the same training data is used for multiple test sets. It is expected that the errors of not sensitive methods increases when the test set expands in length, but does not fluctuate. Figure 5.6 identified that seven out of the fourteen forecasts methods are likely to be sensitive. The final result is that five of the fourteen methods are robust enough to use, their results are listed in Figure 5.8. A summation with brief conclusions is given below per topic.

Warehouse activity

• The activities where demand is aggregated can be better predicted. • The activities with the lowest error thus are best suited to forecast on are:

– Picking orderlines

– Picking pallets

– Shipping orderlines

– Shipping orders

– Outbound trucks

• Range of activities with low errors ranges between 10% and 14% • Range of activities with high errors ranges between 21% and 23%.

• The activities outbound trucks and returned items have one of the highest error rates. This is not really a problem for the outbound trucks since a site knows that much better in a couple of weeks in advance and this activity does not tell much about the needed workforce. The returned item can be better predicted on a less aggregated level.

Sector

• The forecast error with an forecast horizon of three months ranges between 8% and 16%.

• The forecast error with a one month ahead horizon ranges between 6% and 16%, as indicated in Table B.1.

• The technology (8%) and retail (9%) sectors are the best predictable sectors, it is likely that the are good to predict since they have a clear seasonality. A high demand season for the technology sector occurs around the end of the year and the high demand seasons of the retail sector move along with the winter and summer clothing seasons.

• The clients within the healthcare and industrial sector have a forecast error of respectively 14% and 16%.

• The forecast model improves the result of the naive forecast the most at the retail and technology sector.

Method

• The most used forecasting methods are Theta(35%)and ARIMA(25%)

• A total of five methods came out to be robust enough to use for this dataset: Theta, ARIMA, Theta-ExtrP, ETS-Theta and ETS-ARIMA-ExtrP

• As mentioned in the literature, it is useful to combine different methods into a forecast since the Theta-ExtrP, ETS-Theta and ETS-ARIMA-ExtrP outformed the single method forecasts together in 38% of the times.

• Even dough, the Comb method (ETS-ARIMA-Theta) performed well in the forecasting competition. Tough, the Comb method was not selected very often as best method and did not succeed the test on overfitting and sensitivity to multiple forecast horizons. So to conclude, in general the Comb method may perform well, but when multiple methods are used as benchmark, it is the case that one of those single methods outperform the Comb method. Only in case a forecasting model wants to include one method, the Comb method might an option.

• The addition of the ExtrP method is rewarding since it improved the result of the Theta model 25% of the time. However, the ExtrP variable in combination with 5 of the 7 other methods results in a model that overfits the data and is too sensitive for multiple forecast horizons.

5.2

Outflow rate model

This sections answers research question 2f: If the relation between the size of the pool of TEMPs and the outflow rate is known, how much could potentially be saved in the past when anticipating on this relationship?

There is a high rate in which TEMPs leave the company. One of the causes could be that TEMPs leave due to receiving less work than desired. Surveys that were held during the outbound process do not provide the support of a certain assumption about the chance of leaving due to receiving less work than expected. By analyzing data about the behaviour of TEMPs, it is expected that some relation can be found between offering less hours and the outflow rate. The steps towards this analysis are written in chapter 4.2 in which also the psuedo-code (5) is given, in this part the results are listed. The first thing to present are the settings of the model, followed by the raw outcomes of the model. Next, the results are analyzed and the way to interpret these results is given. Furthermore a section is dedicated to sensitivity analysis and a validation of the results. Finally the impact of the outflow rate is determined for existing operations in terms of how many TEMPs left due to receiving less work than wanted. The amount of money that is lost due to this outflow rate is determine in section 5.2.6, over there the possible savings are determines when the resource planning is improved.

5.2.1

Input data

The data that is used as input for the model consists of all the TEMPs that worked at the Dutch warehouses of the company. This includes also the TEMPs that worked at different sites than mentioned in chapter 2. These different sites are relatively small, but the trustworthiness of this analysis increases when more TEMPs are used as observations. The behavior of 12.273 TEMPs in total are analyzed over a period between the year 2015 and 2019. As mentioned at the description of the model in chapter 4.2, the outflow rate of a TEMP can only be analyzed when that TEMP worked for more than 20 shifts at the company. Otherwise it is unclear how many hours a TEMP usually work and how many hours a TEMP did work during the period right before leaving the company. The pool of TEMPs is reduced from 12.273 TEMPs to 7.491 TEMPs, but part of this group is still active during the time of the analysis. In order to determine the behaviour of a TEMP during their last couple of shifts, a TEMPs cannot be active anymore. When applying this filter to the TEMPs, a pool of 6.286 TEMPs is left to analyze.