Abstract
Cutting tool management in manufacturing firms constitutes an essential element in production cost optimization. In order to optimize the cutting tool stock level while concurrently minimizing production costs, a cost optimization model which considers machining parameters is required. This inclusive modeling consideration is a major step towards achieving effectiveness of cutting tool management policy in manufacturing systems with stochastic driven policies for tool demand. This paper presents a cost optimization model for cutting tools whose utilization level is assumed to be optimized in respect of the machining parameters. The proposed cost model in this research incorporated the effects of diversified machining costs ranging from operational through machining, shortage, holding, material and ordering costs. The machining of parts was assumed to be a single cutting operation. Holt-Winters forecasting technique was used to create a stochastic demand dataset for a test scenario in the production of a high-end automotive part. Some numerical examples used to validate the developed model were implemented to illustrate the optimal machining and tool inventory conditions. Furthermore, a sensitivity analysis was carried out to study the influence of varying production parameters such as: machine uptime, demand and cutting parameters on the overall production cost. The results showed that a desired low level of tool storage and holding costs were obtained at the optimal stock levels. The machining uptime had a significant influence on the total cost while tool life and cutting feed rate were both identified as the most influential cutting variables on the total cost. Furthermore, the cutting speed rate had a marginal effect on both costs and tool life. Other cost variables such as shortage and tool costs had significantly low effect on the overall cost. The output trend showed that the feed rate is the most significant cutting parameter in the machining operation, hence influencing the cost the most. Also, machine uptime and demand significantly influenced the total production cost.
Highlights
Research in the field of cost optimization modelling for stochastic inventory management and control has significantly intensified over the past few decades [1]
This paper develops a nonlinear cost optimization model based on an inventory policy for cutting tools at optimum machining parameters for the production of high-value mechanical parts
This study has presented an inventory control analysis premised on stochastic demand of machining tools
Summary
Research in the field of cost optimization modelling for stochastic inventory management and control has significantly intensified over the past few decades [1]. The ability to minimize the overall production cost of a machining process while adapting to the ever growing levels of stochasticity in the open market can be considered a major step towards its competitiveness. This capability must, occur without adversely affecting throughput and production quality amidst multiplicity of inherent market conditions. The cutting tool represents an essential element to the entire machining system This is so because its effect is directly linked to a multiplicity of factors such as: the quality of finished product, cycle time of operation and the cost of machining amongst others. The optimum behavior of a machining operation can be influenced by any one of these member elements
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