Today’s manufacturing environment is characterized by rising production costs. Automotive companies are faced with a challenge of meeting the ever-changing customer needs at the minimum possible cost. Reducing the production costs is of paramount importance for a company to remain competitive in a turbulent environment. How can companies in the automotive industry increase cost savings? This paper focuses on continuous improvement for cost savings in the automotive industry. This was conducted at an automotive production line using the work-study technique. Time and method studies were conducted on the production line of one of the products. This was done under these objectives: to identify excess raw material input and non-value adding activities in the product line, to determine current factory capacity, to determine factory capacity utilization, and to implement improvements on the product line so as to increase productivity. The daily target at the company was eight complete units per shift, and the research explored the potentials of increasing that daily target with little to no increase in the number of operators and workstations. The results demonstrate an increase in productivity by 50% by adding one more workstation and one more operator at the surface finishing station. This was successfully implemented and the company is now producing 12 units per shift.
This paper is based on bottling process optimisation through continuous improvement. A case study was done at XYZ company. The Six Sigma Define, Measure, Analyze, Improve, and Control (DMAIC) methodology revealed that the bottling and capping processes were producing defects at 3 Sigma level. The 5 Whys, Pareto chart, fish bone diagram, and Suppliers, Inputs, Process, Outputs, Customers (SIPOC) model showed that loose-capped bottles (31.6%), under-fills (29.2%), and empty bottles (28.9%) caused the highest cost through poor quality. The monitoring system was designed to monitor the applied torque value, the capping head status, and the beverage temperature upon leaving the heat exchanger. The cooling system on the mix processor was designed using the closed loop control strategy. If the beverage temperature is not within 1 or 2 degrees Celsius, it is directed to secondary cooling; otherwise, it proceeds. The glycol inlet valve is actuated such that the flow of the coolant is adjusted to ensure that the primary cooling is efficient. The results show that it is possible to operate production within the Six Sigma level.
Mechanical industries use rotating mechanical equipment in their day to day operations. The equipment suffers from wear and tear, and is usually discarded as scrap. But is there a way to recover some of this equipment and reuse it? This paper uses machine learning to capture and analyse the wearing damage of bearings and gears to determine whether they can be redeemed. Finite element analysis is conducted on worn-out spur gears and pillow bearings in order to facilitate feature extraction in image processing algorithms. This converts the actual gears, bearings, and seals into CAD files. The decision-making system is designed, and it uses these CAD files to decide on the optimum manufacturing process to restore redeemable components. The mechanical components of the system are designed using SOLIDWORKS. MATLAB, Proteus software, and the Arduino micro-controller are used for the system application design and simulation. The results from tests conducted on a worn-out gear and bearing show that the gear is 4% non-redeemable, while the bearing is 60.2% non-redeemable. The decision taken by the system is to redeem the gear and to discard the bearing.
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