AM  Vol.9 No.12 , December 2018
Development of a Tool Cost Optimization Model for Stochastic Demand of Machined Products
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.
Cite this paper: Pantoja, F. , Songmene, V. , Kenné, J. , Olufayo, O. and Ayomoh, M. (2018) Development of a Tool Cost Optimization Model for Stochastic Demand of Machined Products. Applied Mathematics, 9, 1395-1423. doi: 10.4236/am.2018.912091.

[1]   Tomotani, J.V. and de Mesquita, M.A. (2018) Lot Sizing and Scheduling: A Survey of Practices in Brazilian Companies. Production Planning & Control, 29, 236-246.

[2]   Gershwin, S.B. (2018) The Future of Manufacturing Systems Engineering. International Journal of Production Research, 56, 224-237.

[3]   Conceicao, S.V., et al. (2015) A Demand Classification Scheme for Spare Part Inventory Model Subject to Stochastic Demand and Lead Time. Production Planning & Control, 26, 1318-1331.

[4]   Dimla Snr, D.E. (2000) Sensor Signals for Tool-Wear Monitoring in Metal Cutting Operations—A Review of Methods. International Journal of Machine Tools and Manufacture, 40, 1073-1098.

[5]   Teti, R., et al. (2010) Advanced Monitoring of Machining Operations. CIRP Annals-Manufacturing Technology, 59, 717-739.

[6]   Arrazola, P.J., et al. (2013) Recent Advances in Modelling of Metal Machining Processes. CIRP Annals-Manufacturing Technology, 62, 695-718.

[7]   Byrne, G., et al. (1995) Tool Condition Monitoring (TCM)—The Status of Research and Industrial Application. CIRP Annals-Manufacturing Technology, 44, 541-567.

[8]   Kouedeu, A.F., et al. (2014) Stochastic Optimal Control of Manufacturing Systems under Production-Dependent Failure Rates. International Journal of Production Economics, 150, 174-187.

[9]   Li, C.R., Sarker, B.R. and Yi, H.Z. (2014) An Optimal Stocking Policy for Machining Tools with Stochastically Distributed Lifespan and Demand. International Journal of Production Research, 52, 6175-6191.

[10]   Li, C.R. and Cheng, J.D. (2015) An Optimal Inventory Policy under Certainty Distributed Demand for Cutting Tools with Stochastically Distributed Lifespan. Cogent Engineering, 2.

[11]   Parent, L., Songmene, V. and Kenné, J.P. (2007) A Generalised Model for Optimising an End Milling Operation. Production Planning & Control, 18, 319-337.

[12]   Conradie, P., Dimitrov, D. and Oosthuizen, G. (2016) A Cost Modelling Approach for Milling Titanium Alloys. 7th Hpc 2016-Cirp Conference on High Performance Cutting, 46, 412-415.

[13]   Braziotis, C., Tannock, J.D.T. and Bourlakis, M. (2017) Strategic and Operational Considerations for the Extended Enterprise: Insights from the Aerospace Industry. Production Planning & Control, 28, 267-280.

[14]   Neugebauer, R., et al. (2012) Resource and Energy Efficiency in Machining Using High-Performance and Hybrid Processes. Fifth Cirp Conference on High Performance Cutting, 1, 3-16.

[15]   Andrew-Munot, M. and Ibrahim, R.N. (2013) Development and Analysis of Mathematical and Simulation Models of Decision-Making Tools for Remanufacturing. Production Planning & Control, 24, 1081-1100.

[16]   Syntetos, A.A., Boylan, J.E. and Disney, S.M. (2009) Forecasting for Inventory Planning: A 50-Year Review. Journal of the Operational Research Society, 60, S149-S160.

[17]   Shang, J., et al. (2008) A Decision Support System for Managing Inventory at GlaxoSmithKline. Decision Support Systems, 46, 1-13.

[18]   Wang, Y.-C., et al. (2018) Optimization of Machining Economics and Energy Consumption in Face Milling Operations. The International Journal of Advanced Manufacturing Technology, 99, 2093-2100.

[19]   Wang, J., et al. (2002) Optimization of Cutting Conditions for Single Pass Turning Operations Using a Deterministic Approach. International Journal of Machine Tools and Manufacture, 42, 1023-1033.

[20]   Mascle, C. and Gosse, J. (2014) Inventory Management Maximization Based on Sales Forecast: Case Study. Production Planning & Control, 25, 1039-1057.

[21]   Taylor, J.W. (2003) Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing. Journal of the Operational Research Society, 54, 799-805.

[22]   Ferbar, L. and Vehove, A. (2013) The Improvement of the Holt-Winters Method for Intermittent Demand: A Case of Overnight Stays of Turists for Some Community in Republic of Slovenia. 45-52.

[23]   Cunningham, C.R., et al. (2017) Cost Modelling and Sensitivity Analysis of Wire and Arc Additive Manufacturing. Procedia Manufacturing, 11, 650-657.