Optimisation through automation : Implications and opportunities of bin picking in manufacturing

Sammanfattning: Background Manufacturers have to adopt modern technologies to compete at the top of their field. However, adopting new technologies can be expensive and difficult to validate prior to implementation. One technology that has difficulties receiving a wider application is bin picking. Bin picking uses vision technology to communicate with an industrial robot. Consequently, the technology enables robots to pick randomly sorted objects. Research finds that the difficulty in assessing its performance can explain the lacklustre application of bin picking. In addition, research on bin picking is primarily focused on its technical difficulties and neglects information that can be valuable for potential adopters.  Objectives This thesis aims to aid decision-makers in assessing the implications and opportunities of bin picking. Furthermore, the thesis desire to inspire potential adopters by analysing the viability of bin picking through feasibility and the tangible and intangible benefits in a real-world setting. Methods A utility function is developed and assigned categories based on interviews with suppliers and adopters of the technology and literature review. The utility function highlights the feasibility and the intangible benefits of a bin picking solution and enables ranking among alternatives. The highest scoring article is used to conduct a feasibility study in collaboration with suppliers of bin picking technology. Based on the feasibility studies, a DES is created to highlight the implications that may arise in a real manufacturing environment. Finally, financial calculations through NPV, IRR, PP and DPP are created to evaluate the solution. Results All NPV calculations (excluding a 12.5 \% discount rate) are positive with enough years. The IRR is positive when the time span exceeds 11 years. The PP is 10-11 years while the DPP is 12-13, 14-15, 19-20, 32-33 years at a discount rate of 2.5 \%, 5 \%, 7.5 \% respectively 10 \%. However, the investment is never recoupable at a discount rate of 12.5 \%. The categories of the utility function have a clear impact on the feasibility and intangible benefits of the technology in a real-world setting. Bin picking relieves MMH tasks for operators, frees up facility space and reduces the collision risk. However, there are several risks with a bin picking solution. Conclusions Bin picking can become financially viable through automating MMH processes. However, how much capital is released depends on the man-hours spent in the previous process. The feasibility of bin picking implementation is dependent on the geometric complexity of the article, the sorting method inside the bin, the surrounding environment and the time margin. Decision-makers need to account for these factors prior to implementation. The intangible benefits can incentivise decision-makers to implement bin picking, even if the financial calculations show a net loss on the investment.