Appendix A: VeloLine: Management report

As of right now, the production of our bikes is not very efficient, therefore only a few products can be sold and it takes a long time until the customer receives his/her bike. Given this fact, an external consultant has been hired to analyze the production process of the company and try to improve its efficiency. It was found that customers tend to buy components that are given in the catalogue and only customizing the design rather than having the components custom-fitted to their dimensions. The consultant used this data to come up with a new model for a new assembly line that, as the expert stated, should “guarantee a production of at least 130 bicycles per day” . After introducing the new assembly line, we alreade experienced a huge success, since our number of customers greatly increased and is expected to increase further. This creates a problem for us, because the current line can neither meet the demand of the expected 160 bikes/day after 6 months nor the even higher numbers expected after a year. The following will analyze this problem and look for a solution to adapt the assembly line in a way that the company is able to cope with the expected rise in demand after one year.

If we analyze the current assembly line developed by the consultant, we can find three workstations that limit the daily maximum capacity of the production and prevent it from reaching 190 products a day. The bottleneck of the current process can be found in workstation 5, since it takes the longest to finish one product with the process time being 3.25 minutes. With an operation time of 7.5 hours, or 450 minutes daily, this process and therefore the whole production has a design capacity of 138 (138,46) products a day, therefore missing 52 products to fulfill the expected demand, while as the current demand of approximately 120 products/day can be fulfilled. Other workstations that do not reach the needed capacity are workstation 1 (capacity of 160,72 products/day) and workstation 2 (capacity of 188,81 products/day).

To give information about the assembly line’s current efficiency, it was first needed to calculate the cycle time. With an operation time of 27000 seconds (=7.5 hours) and a current demand of 120 products a day, the cycle time can be determined with 225 by dividing the operation time with the demand. Next, it is needed to take the number of workstations, being 6, and the throughput time of the system (836 seconds) into consideration. With this information, an efficiency of 61,93% can be calculated by dividing the throughput time with the cycle time times the number of workstations. An additional information that can be taken with this information is the minimum number of stations needed to reach the demand, which is 4 workstations (by dividing the throughput time of 836 seconds with the cycle time of 225). If we calculate this with the expected demand of 190 products, the time per workstation (cycle time) may not exceed 142.11 seconds. Furthermore the current number of 6 workstations is the minimum that is needed to reach this capacity (see calculation scheme above).

One attempt for reaching the capacity of 190 products a day would be to use the reserve-pool employee and create a seventh workstation. That way, the processes could be dispersed on more employees and therefore more products could be processed. The model I came up with looks as follows:

Workstations Processes Process time (s)

1 1-4 121

2 5-8 97

3 9-11 128

4 12-15 105

5 16-19 130

6 20-21 120

7 22-24 135

Using this model, no workstation would exceed the cycle time of 142.11 seconds. The design capacity of this assembly line would be determined by the last workstation, that is being the bottleneck here.

The system would have a design capacity of 200 products per day (27000/135) and would therefore exceed the forecast of 190 asked products. Using the same calculation as explained above while changing the number of workstations to seven and the cycle time for the forecast demand, there would also be a rise in efficiency to 84,04%.

One problem this might bring is the issue of missing the spare employee. That said, the failure of an employee, for example due to illness, would shut down the whole production, which would make the production fall behind its schedule, since there is no one that could work as a replacement of the missing employee. Another problem can be seen in the need to re-educate the employees since the tasks each workstation had before shifted. This is associated with costs and time effort as well as a fall in efficiency in the time of adapting the new model.

To conclude, it can be said that the current model could not cope with such a high demand as it is expected to be in a year. Furthermore the efficiency of the current system is not as high as it could be. The model that was explained earlier presents a possible solution to the issue of design capacity but it should not be forgotten that it includes risks as well. The model would include a great increase of efficiency from 61,93% to 84,04% and a capacity of 200 products a day, that can meet the expected demands in one year.