Analysis and improvement of the setup time reduction effect, the order strategies and the operating curves of manufacturing operations

Outline

Introduction

Task 1 Setup time reduction

Task 3 Order stategies

Description of the order strategies

Batch size calculation

Comparison of the order quantities

Impacts on given production system

Task 4 Operation curves

Conclusions and guidelines

Appendix

Appendix 1 Results of run number 10 “Base Case”

Appendix 2 DOE 32 combinations

Appendix 3 Batch size calculation

Appendix 4 Value added time- and FTE calculation

Introduction

The following report is intended to give you a good overview how our project group worked on the tasks of the Picsim-Simulation Project.

We start with explaining our point of departure for the tasks in phase two. Moreover, this paper describes the results of the tasks no. 1, 3 and 4.

Firstly, which values are those of our starting point for phase two? We used the results from our run no. 10 from the first phase of the experiments. This case is the base case for all following results we got. How did we reach these results of run no. 10? First, we worked with try and error. That means that in the beginning we just chose some values by chance and looked which results we got. After that we started using the method of Cycle Planning (CP). After using CP and improving our results we reached the results of run no. 10 which you can find in the appendix 1.

To be able to explain our results better and more detailed we decided to explain all the figures from the SIPOC chart. What means SIPOC and what contains this figure? SIPOC is a term used in the Six-Sigma methodology. Its name results from the parts of the Top-Level-Process Design: Suppliers (S), Inputs (I), Process (P), Outputs (O), and Customers (C). SIPOC is a tool used by a team to identify all relevant elements of a process improvement. Moreover, it makes sure that all members of a team have the same understanding of the process which is to improve. The results from the SIPOC analysis are made visible in a very clear form by the use of a SIPOC diagram. In our case it contains the Flow time efficiency and the Added value time ratio.

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Figure 0.1

Now we have to make sure what is meant by Flow time efficiency and Added value time ratio.

Firstly, the answer to Flow time efficiency. Flow time efficiency is an indication we get by comparing the average flow time with the theoretical value, it is the ratio between those two factors. The relationship between them is as follows:

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Secondly, the Added value time ratio is the coefficient which you get if you correlate the Added value time and the Non-added value time. The formula to this relationship is:

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The Added value time ratio contains the percentage of workers or machine utilization because this coefficient gives you the percentage of effective work. Non-added value time contains waiting time, setup time and transportation. These different times are mostly necessary but they do not directly belong to the production process itself. To get a result which is as good as possible you should try to reduce them as much as possible. A sample calculation can be found in appendix 4.

In the picture above you can see the relation between Flow time efficiency and Added value time ratio. During the course of the second phase we always proved the results of our calculations by improving these figures. So it becomes obvious that these two indicators are the most important ones to explain our results from the tasks.

These methods we mentioned above were the basis for developing our strategy for optimization. Our team-internal requirements to say which run was a good one were an acceptance of ≥ 98 % service level. Moreover our focus was on the total costs which we wanted to reduce as much as possible.

To find a good way to combine these two factors we introduced our Project performance indicator (PPI) which we calculated the following:

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We decided for this PPI because it shows the relation between these two most important factors. That higher this coefficient is that better is our process because in this case we would have a high service level and as low costs as possible which is our final aim in this project.

What was our approach in the experiments to reach any results? It was a kind of circle that we repeated several times. The first step of this cyclic process was to give a data proposal and to look how their results fitted into our work and our expectations.

After this first proposal the fine setting started. The first step of this adjusting process was to consider the actual lead time. Second, we adjusted the safety stock level in that way that it was better related to the other values. To finish the setting process we were creating a new variation.

Of course, at the end, after all the adjustments we saved our solution.

Task 1 Setup time reduction

In this task, the main objective is to determine the setup time reduction effect on the system. For this purpose, we will measure this effects based on costs and service level achieved when the reduction is made.

The first step is to determine which work center can provide better results when setup time reduction is made. For this, we designed an experiment making 32 possible combinations for reducing setup times in each work center. This table with its result is presented on appendix 2. It is important to mention, that the only parameters changed for these simulations were the setup time and lead time. Order quantities and safety stocks were not affected during the simulations made on this task.

For this table, “+” represents a reduction down to 25 % of the original value of the setup time given on the case.

With these results we run MiniTab to study the effects on the costs when setup time reduction is made in each work center. These results are shown on Fig. 1.1, 1.2 and 1.3.

Figure 1.1

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Figure 1.2

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Figure 1.3

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As we can see on the previous figures, setup time reduction causes more effect on work center 4 and on work center 1.

By making an analysis of the total operation time and setup time of each work center (Fig. 1.4), we can support the results of our experiment by proving that work centers 1 and 4 are the two constraints of the capacity of the process.

Figure 1.4

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As a result of these analyses, our approach was to make a reduction on work center 4 as a first step and as a second step on work center 1. Our purpose with these reductions is to balance the system to have less queues, work in process and inventory. These would help to have a more synchronized process and the process flow would be much more efficient.

Figure 1.5

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Figure 1.6

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After reducing setup time on work centers 4 and 1, we find the following results:

These results show a reduction of 38,000 SEK on costs and a service level accepted according to our parameters.

Other factors as added value time and flow time efficiency are also improved as shown on figure 1.7.

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Figure 1.7

Another goal that was achieved by reducing these setup times was to make the system more flexible, by reducing lead times and order quantities.

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