Discussion

This section discusses the limitations of the research.

This research evaluated among others the task division and line balance of the operations on the TrendLine A production line. The operation times used for this analysis were taken from an MTM study performed in 2016. The data was updated and corrected for real times where large deviations were observed. However, it was found impossible and unnecessary to measure all operating times given the time restrictions of the research. Only small deviations remained which were determined to not have significant impact on the results. The TEF department should be aware that the quality of the input data for the line balancing application is of great relevance for the ability to achieve the results. Therefore, I recommend the employees to make use of the best data available. After the implementation of the Manufacturing Execution System, data is gathered on the real operating times of individual operations. Averages of the data over a large time period are reliable estimates to use as input for the line balancing application. Until then, the employees of the TEF department can work with MTM-measurements or time measurements that are made in the production line.

Another point of discussion is not taking into account how operation times can change when performed on another workstation or when the sequence is changed. As the effect of these changes was assumed to be limited, this variation has not been taken into account. Future research could focus on how to include this variation in the assembly line balancing problem.

Finally, the current situation was found to be a limitation for the implementation of robots. This research concluded that the implementation of robots is yet a bridge too far given the present circumstances. There are a couple of things that should change before reconsidering the use of robots. In summary, it is recommended to 1) reduce the length of operating sequences, 2) reduce the number

64 of different parts used for screwing and nutting and 3) fix the location of sub-assemblies. The most important setbacks are the low production volume and high variety of operations on the workstations. It was found that it is extremely difficult to robotize long sequences of assembly operations with a lot of different parts. As the operations on the workstations involve a lot of different screws and bolts and nuts, it requires a significant robot investment that is highly specific to the operations within Bosch. The more specific the implementation, the higher the cost. Moreover, robots also need a fixed operating location as they are very sensitive to location changes. As the dollies move around without a fixed location, robots cannot be used for work in dollies just yet.

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Bibliography

Arntz, M., Gregory, T., & Zierahn, U. (2016). The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis. OECD Social, Employment and Migration Working Papers, 189. Autor, D. H., Levy, F., & Murnane, R. J. (2003). The Skill Content of Recent Technological Change: An

Empirical Exploration. Quarterly Journal of Economics, 1279-1332.

Becker, C., & Scholl, A. (2006). A Survey on Problems and Methods in Generalized Assembly Line Balancing. European Journal of Operations Research, 168, 694-715.

Bendavid, Y., & Boeck, H. (2010). Redesigning the Replenishment Processs of Medical Supplies in Hospitals with RFID. Business Process Management, 16(6), 991-1013.

Bosch Thermotechnology. (2018). Bosch Thermotechnology Ltd. Retrieved from Bosch Technik fürs Leben: https://www.bosch-

thermotechnology.com/corporate/de/unternehmen/fertigungsstandorte/rechtshinweise- uk.html

de Vries, J., de Koster, R., & Stam, D. (2016). Exploring the Role of Picker Personality in Predicting Picking Performance with pick by voice, pick to light adn RF-terminal picking. International Journal of production Research, 54(8), 2260-2274.

Design Tool Inc. (2016, 5 9). Multiple Automatic Screw Dispensers for Automotive Industry. Retrieved from https://www.youtube.com/watch?v=Hs2_TIcuLdQ

Frey, C. B., & Osborne, M. A. (2013). The Future of Employment: How susceptible are Jobs to Computerisation? Technological Forecasting and Social Change, 1-72.

Fried, H. O., Knox Lovell, C. A., & Schmidt, S. S. (2008). The Measurement of Productive Efficiency and Productivity Growth. New York: Oxford University Press.

Frohm, J. (2008). Levels of Automation in Production Systems. Göteborg, Sweden: Department of Product and production Development - Chalmers University of Technology.

Frohm, J., Lindström, V., & Bellgran, M. (2005). A model for parallel levels of automation within manufacturing. 18th International Conference on Production Research, (pp. 1-6). Salerno, Italy.

Gonçalves, M. T., & Salonitis, K. (2017). Lean Assessment Tool for Workstation Design of Assembly Lines. Procedia CIRP, 60, 386-391.

Goolsbee, A., Levitt, S., & Syverson, C. (2016). Microeconomics. New York: Worth Publishers. Gurevsky, E., Battaïa, O., & Dolgui, A. (2013). Stability measure for a generalized assembly line

balancing problem. Discrete Applied Mathematics, 377-394.

Haq, A. N., Jayaprakash, J., & Rengarajan, K. (2006). A Hybrid Genetic Algorithm Approach to Mixed- Model Assembly Line Balancing. International Jourval of Advanced Manufacturing

Technology, 28, 337-341.

Hillier, D., Clacher, I., Ross, S., Westerfield, R., & Jordan, B. (2011). Fundamentals of Corporate Finance. Maidenhead: McGraw Hill.

66 Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimiazation by Simulated Annealing. Science,

220(4598), 671-680.

Krafcik, J. F. (1998). Triumph of the Lean Production System. SLOAN Management Review, 30(1), 41- 52.

Miltenburg, J. (2002). Balancing and Scheduling Mixed-Model U-Shaped Production LInes. The International Journal of Flexible Manufacturing Systems, 119-151.

Mountz. (2018). Automatic Screw Dispenser. Retrieved from Mountz - the torque tool specialists: https://www.mountztorque.com/blog/tag/automatic-screw-dispenser/

Nefit. (2017). Organisatie. Retrieved from Nefit: een merk van Bosch: https://www.nefit.nl/consument/over_nefit/organisatie/organisatie Olmia Robotics. (2018). Upgrade kits. Retrieved from https://www.olmia-

robotics.nl/shop/overig/upgrade-kits

Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A Model for Types and Levels of Human Interaction with Automation. IEEE Transcations on Systems, Man, and Cyberetics - Part A: Systems and Humans(30(3)), 286-297.

Pienkowski, M. (2014). Waste Measurement Techniques for Lean Companies. International Journal of Lean Thinking, 5(1), pp. 1-16.

RCT Gelderland. (2018). Robot, cobot... Collega's met een chip. RCT Gelderland, pp. 12-15.

Rutenbar, R. A. (1989). Simulated Annealing Alogirthms: An Overview. IEEE Circuits and Devices, 19- 26.

Scholl, A., & Becker, C. (2004). State-of-the-Art Exact and Heuristic Solution Procedures for Simple Assembly Line Balancing. European Journal of Operational Research, 666-693.

Simaria, A. S., & Vilarinho, P. M. (2004). A Genetic Algorithm based Approach to the Mixed-Model Assembly Line Balancing Problem of Type II. Computers & Industrial Engineering, 47, 391- 407.

Theisens, H. C. (2016). Lean Six Sigma Green Belt. Amstelveen: Lean Six Sigma Academy.

Uneto-Vni. (2018). CV-ketels: veelgestelde vragen naar aanleiding van het manifest. Retrieved from Uneto-Vni: https://www.uneto-vni.nl/acties/open/cv-ketels-veelgestelde-vragen-naar- aanleiding-van-het-manifest

Womack, J. P., & Jones, D. T. (1996). Beyond Toyota: How to Root Out Waste and Pursue Perfection.

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