This last chapter finalizes the research by drawing the conclusions, giving the recommendations and discussing the results. Section 7.1 draws the conclusions and answers the main research question. Section 7.2 continuous with the recommendations to LNS. Section 7.3 finishes with the discussion.
7.1
Conclusions
We started this research with our main research question, which was:
“How can information about the raw material supply be used to improve the capacity planning within the processing facilities of LNS to reduce under- and overcapacity of labor and optimize the use of the
raw material”
We split this research question in 5 sub questions, which we answered during the research.
We saw that the production processes at LNS have a production-oriented layout, and that all products require the same production steps, with the exception when a product needs to be frozen. Also, we can consider the processing times to be deterministic. This helped us modeling the processing plant using an exact method. Literature shows us that Stochastic Programming is used before to solve comparable problems with uncertain supply. We conclude further that planning the capacity accurately is important for LNS, as too much capacity results in lost money due to extra salary, and too little capacity results in the delay of production of raw material, deteriorating the raw material and making it less profitable to produce. Hence, the profit generated by the raw material reflects the quality of the capacity planning. Since the processes are deterministic, we expect the number of kilograms processed fillet per cutting hour to be relatively stable. However, we saw big fluctuations in this number, from which we conclude that the current capacity planning is not optimal and has room for improvement.
Out of the three possibilities we derived from the literature to reduce the uncertainty in supply, we chose, based on three criteria we used to measure the quality of the possibility, to predict the fish catch per vessel based on causal models. We created 1 regression model and 3 machine learning models and conclude that the M5P Tree gives the smallest error for our dataset (S is 0.5). Hence, this method is best qualified to predict the supply of a vessel.
We used information of the forecasting model in the Stochastic Programming Model, since this needs the expected catch as input. We conclude that the model performs as expected, since the output of the different methods we created are logical. To analyze the output, we created three methods; a method that uses the average catch of a vessel as forecast, one method that only uses the forecasting model, and one method where we eliminate the uncertainty in supply. We expect the last method to give the highest profits, as there is no uncertainty in this case. We expect the first method to give the lowest profits, as the supply forecasts are the worst in this method. Hence, we expect the performance of the method with the forecasting model to fall within the other two methods. This is indeed the case, justifying the conclusion that the model performs as expected.
We also conclude that the raw material supply is significantly different in some months, making it not possible to base the analysis on a single month, as the raw material supply influences the production planning. Therefore, we create as few clusters as possible, combining months where the raw material supply is not significantly different. We conclude that the capacity in three out of the four clusters is
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insufficient to produce all the fish LNS gets supplied into fillet products. As a result, LNS must sell whole fish, or delay the production, resulting in loss in profits. Further we conclude that producing in overtime is easier, since too little planned capacity can be solved using overtime hours. Overtime production increases the productivity in the cutting department significantly, compared with using only the forecasting model and producing in regular time.
Using overtime production in combination with the forecasting model we created generates an increase in profit of 43.4 million NOK. Also, the productivity (kilogram processed filet per cutting hour) in the cutting department increased significantly compared with the current situation at LNS, which we determined using actual data from the processing plant in Båtsfjord. We conclude that reducing the uncertainty in supply, using the forecasting model, is valuable for LNS. We also conclude that using the forecasting model is most profitable in situations where the processing plant is not used close to full capacity, as the forecasting model yields the best improvements in the first cluster, both for the profit as the productivity. This is because when there is more capacity needed than available, it is not necessary anymore to accurately plan the supply. LNS can simply plan the capacity to its maximum level.
7.2
Recommendations
We saw in the conclusions that the forecasting model improves the current situation when we compare the results with the base scenario we created. Both the productivity in the cutting station as the profit per kilogram raw material increased significantly. Therefore, we recommend using this forecasting model in the capacity planning of the processing stations.
The case when using an extra filleting line compared with using overtime hours yields the highest profits. However, LNS needs to invest in an extra filleting line. We calculated the Net Present Value of this investment, over the expected lifetime of the filleting machine, which we determined on five years. Although the expected value of the NPV is positive, we do not recommend investing in the extra filleting line. The expected NPV is approximately 1 million NOK over five years. However, there is a probability of around 40% of losing money on the investment. Since the differences in profit follow a normal
distribution, the probability of losing a given amount of money, is the same as saving the same amount of money. The volatility of the NPV is therefore high, as Figure 6.3 shows. Therefore, we do not
recommend investing money in the extra filleting capacity. We recommend using the forecasting method we developed in Chapter 4 in combination with the use of overtime against a cost increase of 1.5 times the cost level in regular production time. We showed that this setup increases the productivity both in our created base model and in the real situation at LNS.
When the overtime costs increase with more than 6.25 NOK per hour, compared with the costs of using regular time, the current solution is no longer optimal (see Table 6.5). If this is the case, we advise LNS to stop using overtime, and produce only in regular time using the forecast model we provided in Chapter 4.
7.3
Discussion
In this research we showed the value of reducing supply uncertainty for LNS. This conclusion matches with the information the literature provides us. We showed also that, with our dataset, it is possible to forecast the supply of fishing vessels using weather information and ship characteristics. Although literature states that it is possible to forecast catch sizes with weather information, not all the predictors mentioned in the literature are significant in our model. The moon phase, which the literature considers
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a significant predictor, is insignificant in our model. The exact values of the predictors can differ when applied on a different dataset. However, we have no reasons to doubt the forecast errors of our models, as our dataset covered a whole year and there were enough data points to apply a regression model. We created the forecasting model based on catch and weather information in Troms. We used the forecasting model in the region Finnmark. We did not create or test our forecasting model in the Finnmark region. Therefore, we cannot conclude that the forecasting model in the Finnmark region is as accurate as in the Troms region. Although the forecasting model could be slightly different in the region Finnmark, the goal of our research was to show that it is possible to forecast the catch size and use this information to improve the capacity planning. Also, we only forecasted the Cod catch, and assumed the forecasts for Haddock and Pollock are as accurate as for Cod. This does not have to be true, as it could be possible that Haddock and Pollock are more difficult to predict. We give the same argument as before, stating that we showed the value of forecasting the catch sizes for LNS was the goal of our research.
We did not obtain accurate data of LNS considering the capacity planning in the past. Therefore, it was not possible for us to compare the outcomes of our model accurately with the current situation at LNS. We therefore created an own base scenario, in which we do not use our forecasting model, to compare the different setups with. This base scenario uses the average catch per fishing vessel as forecast for the raw material supply. Although this creates a scenario where LNS plans the capacity without having access to the forecasting model, we expect LNS to use a more effective and accurate way of planning in the current situation. We observe an increase in the profit of 10% to 73% using overtime compared with the base scenario. Since we do not have accurate information of the profit of LNS we cannot conclude that LNS has this potential, but we showed the value of using the forecasting information compared with using the average catch size per vessel.
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