DoC levels of minimal batch sizes
Benefits 5. 2.1 Inventory aggregation
5.2.2 ME improvements
5.2.3 Reduction of required FTE 5.2.4 Incremental volume 4-packs 5.2.5 Reduction of repack activities 5.2.6 Reduction of required maintenance 5.2.7 Qualitative benefits
Costs 5.3.1 Investment costs
5.3.2 Warehouse handling costs 5.3.3 Production costs
5.1.
Input for Comparison
In order to estimate the expected costs and benefits we require general input data. In this section we discuss the forecasting of the volumes during the lifetime of the new production lines, the hourly rate per alternative and other input variables such as the ME of the production line.
Assumptions about new production line
The new production line has characteristics that are currently unknown. However, these are required in order to determine the costs of operating this new production line. We make several assumptions about relevant characteristics of the new production line. These assumptions apply to each
alternative. We make these assumptions in consultation with both packaging experts and production experts at Grolsch.
Assumption 1 – Machine efficiency new production line
We assume that the machine efficiency (ME) of the new production line is 80%. From the layout in Figure 4.2 we see that there are either 5 or 6 machines in series. From the manufacturers we know that a new machine has an expected uptime of 98%. The expected availability of the machines that are placed in series is then 0.985 = 90% or 0.986 = 89%. We assume the true ME is lower because of
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This also happened before at Grolsch, which is why Grolsch is hesitant to accept these numbers. Besides, there are other factors that can influence the ME as well, as mentioned in Section 2.4. Based on the ME of the other machines at Grolsch and in consultation with experts at the packaging
department of Grolsch we determine that the actual ME should be lower. We therefore assume the ME of the new production line in each alternative to be 80%.
Assumption 2 – Start-up and shutdown times new production line
Start-up and shutdown occur each running week. From the current start-up and shutdown times of the other production lines at Grolsch we assume that the start-up of the new line is 0.5 hours and the shutdown is 1 hour.
Assumption 3 – Service stops new production line
The time loss due to service stops is expected to be 2%. As mentioned in Section 2.4, service stops are time losses due to factors outside of the production control, such as breakdowns or small stoppages. This new production line is fairly simple with less machines than most of the other production lines Grolsch has. Grolsch expects 3% service stops for the keg line, Production Line 1. Grolsch expects 2% for Line 4 and Line 7. We assume that the service stops of the new production line should be equal to the service stops of Lines 4 and 7 and thus be 2%.
Assumption 4 – Number of operational weeks new production line
We expect the new production line to require periodic maintenance for 2 weeks per year. This corresponds with the current maintenance schedule of other production lines at Grolsch with similar machines. This means that the new production line will be operational for at most 50 weeks per year. Assumption 5 – Lifetime new production line
We assume the new production line to have a lifetime of 15 years. This is the lifetime that the suppliers indicate and is also the lifetime that Grolsch uses for the other production lines. Assumption 6 – Production start new production line
We assume that production with the new production line can start in 2020. Assumption 7 – Weekly M&C hours new production line
We assume that the Maintenance & Cleaning (M&C) time is 12 hours per running week. Grolsch has a similar pack machine to the one that would be used in this new production line. It currently takes Grolsch about 8 hours to maintain and clean this machine every running week, this is the longest time of all the machines in this production line. Besides, Grolsch holds planned periodic maintenance (PPM) every running week, which takes about 4 hours. We therefore assume that M&C in each alternative takes 12 hours per week.
Table 5.2 gives an overview of the assumptions made for the five input variables. Table 5.2: Assumption of input variables
Input variable Assumption
ME 80%
Start-up 0.5 hours
Shutdown 1 hour
Service stops 2%
Number of operational weeks 50 weeks
Start of production new production line 2020
Lifetime of new production line 15 years
52 | P a g e Forecasting Production Volume
The new production line has an expected lifetime of 15 years. Grolsch currently has a production volume forecast for the years 2019 to 2022. We therefore need to forecast in order to determine the benefits and costs over the complete lifetime of the new production line.
Grolsch has historical data on the production volume for the years 2016 to 2018. We use this as the basis for the forecasting model. As we believe that three data points are not enough to create a good estimate for the level and trend, we decide to use the forecasts of 2019 to 2022 to create better estimates. For each SKU we determine the years 2016 to 2018 to be the first three datapoints. We use Equation 5.1 to apply linear regression between demand and time to obtain an initial estimate of the level and trend.
𝐷𝑡 = 𝑎𝑡 + 𝑏 (5.1)
Here, Dt = Demand for period t;
b = Intercept; a = Slope; t = Time period.
The slope that we find using Equation 5.1 shows the initial trend, the intercept shows the initial level. Table 5.3 shows the initial level and trend estimates based on the actual production volumes for the years 2016 to 2018 and the forecasted production volumes for the years 2019 to 2022 for SKU ID 92122.
Table 5.3: Initial estimate Level and Trend of SKU ID 92122
Actual Production Volume (HL) Forecasted Production Volume (HL) Initial Estimate 2016 2017 2018 2019 2020 2021 2022 Level Trend 27,430 27,331 30,478 28,242 27,648 27,648 27,648 28,281 -55 With these estimates, we apply Equations 3.10 and 3.11 to determine the forecast of each SKU. We compare the created forecasts for the years 2016 to 2018 with the actual production volumes and for the years 2019 to 2022 with the forecasts created by Grolsch. That way, we are able to determine the smoothing factors to minimise the Mean Absolute Deviation (MAD) compared to both the actual volumes and the forecasts created by Grolsch. Using the Solver in Excel, we find the optimal value for the smoothing factors of both the level and the trend for each SKU.
Figure 5.1 shows a comparison between different values of the smoothing factors for level and trend. We see that the forecast created with the smoothing factors found using the Solver in Excel is the most similar to the actual production volumes and forecasts of Grolsch. For SKU ID 92122 we find
that the optimal smoothing factor of level α is 0.98. The optimal smoothing factor of the trend β is
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Figure 5.1: Comparison smoothing factors
Table 5.4 shows the MAD of several combinations of the two smoothing factors. We see that the solver offers a solid reduction of the MAD compared to other values.
Table 5.4: Mean Absolute Deviation different smoothing factors SKU ID 92122
α β Mean Absolute Deviation
0 0 727
0.5 0.5 518
0.98 0.05 158
1 1 1,103
Now that we have the level and trend estimates of the year 2022, we use these to create the forecast for the years 2023 to 2034. Table 5.5 shows the created forecast of SKU ID 92122 using this method.
Table 5.5: Forecast using Holt’s model of SKU ID 92122
Year Actual Production Volume (HL) Forecast of Grolsch (HL) Forecast (HL)
2016 27,430 28,226 2017 27,331 27,248 2018 30,478 30,478 2019 28,242 28,237 2020 27,648 27,579 2021 27,648 27,570 2022 27,648 27,574 2023 27,501 2024 27,428 2025 27,356 2026 27,283 2027 27,210 2028 27,137 2029 27,065 2030 26,992 2031 26,919 2032 26,847 2033 26,774 2034 26,701 25000 27000 29000 31000 33000 35000 2016 2017 2018 2019 2020 2021 2022 Pro d u ctio n Volu m e (HL )