Effects of work injury cost to overall production cost with linear programming approach

Production planning is an important activity in manufacturing industries. The main goal of production planning is to minimize the cost under the condition that the customer requirement in terms of quality, quantity, and time is satisfied. An important player (human) is with little attention in traditional production planning. This thesis studied production planning with consideration of human factor, especially human work injuries as a result of performing a repetitive operation for a certain period of time in production systems. Production planning in this thesis only takes the minimization of total production cost as its goal. A linear programming technique was employed to incorporate the cost of work injury into the total production cost model. The LINDOTM software was used to solve the linear production planning model and to analyze the solution. Finally, the benefits of the production planning, which considers work injury, were discussed. Several conclusions can be drawn from this study: (1) the traditional production planning model, which only takes the material costs and labor costs into account, cannot deal with the cost related to work injury; (2) the work injury cost could be significant in those manual-intensive assembly systems, especially with high production rates; (3) the careful design of the worker’s postures can significantly reduce the work injury cost and thus the total cost of production. The significant contributions of this thesis are: (1) the development of a mathematical model for the total production cost including the work injury cost and (2) the finding that the work injury cost may be a significant portion in the total cost of production in the assembly system that has intensive manual works.

Approach to identify product and process state drivers in manufacturing systems using supervised machine learning

The developed concept allows identifying relevant state drivers of complex, multi-stage manufacturing systems holistically. It is able to utilize complex, diverse and high-dimensional data sets which often occur in manufacturing applications and integrate the important process intra- and inter-relations. The evaluation was conducted by using three different scenarios from distinctive manufacturing domains (aviation, chemical and semiconductor). The evaluation confirmed that it is possible to incorporate implicit process intra- and inter-relations on process as well as programme level through applying SVM based feature ranking. The analysis outcome presents a direct benefit for practitioners in form of the most important process parameters and state characteristics, so-called state drivers, of a manufacturing system. Given the increasing availability of data and information, this selection support can be directly utilized in, e.g., quality monitoring and advanced process control