Operative production planning utilising quantitative forecasting and Monte Carlo simulations

Abstract Demand forecasting is very often used in production planning, especially, when a manufacturer needs in a longer production cycle to respond flexibly to market demands. Production based on longer-term forecasts means bearing the risk of forecast unreliability in the form of finished product inventory deficit or excess. The use of computer simulation allows us to improve the planning process and optimise the plan for the intended goal. This paper presents the use of quantitative forecasting and computer simulations to create the production plan. Two approaches to production plan creation are demonstrated in a model case study. Products are characterized by varying demand and are produced on a single production line in continuous operation. The first approach uses ARIMA(2,0,2) (Auto-Regressive Integrated Moving Average) prognostic method selected as the most reliable method based on MAPE (Mean Absolute Percent Error). The second method applies Monte Carlo simulations and optimisation. The aim of the plan optimisation is minimisation the total costs connected with line rebuilding and storage of products. The comparison of the two approaches shows that planning using computer simulations and optimisation leads to lower total costs.

[1]  Rudolf Kampf,et al.  The application of simulation model of a milk run to identify the occurrence of failures , 2018 .

[2]  Reha Uzsoy,et al.  Optimization Models of Production Planning Problems , 2011 .

[3]  Zahra Hajirahimi,et al.  A comparative study of series arima/mlp hybrid models for stock price forecasting , 2019, Commun. Stat. Simul. Comput..

[4]  W. Wei,et al.  Optimal production planning with capacity reservation and convex capacity costs , 2018 .

[5]  Dušan Sabadka,et al.  Optimization of production processes using the Yamazumi method , 2017 .

[6]  Nicolau Santos,et al.  Performance of state space and ARIMA models for consumer retail sales forecasting , 2015 .

[7]  Richard F. Hartl,et al.  Simulation-based optimization methods for setting production planning parameters , 2014 .

[8]  Gitae Kim,et al.  A survey of simulation modeling techniques in production planning and control (PPC) , 2016 .

[9]  Gabriel Fedorko,et al.  The Use of Computer Simulation Methods to Reach Data for Economic Analysis of Automated Logistic Systems , 2016 .

[10]  Mirko Ficko,et al.  Multi-criteria selection of manufacturing processes in the conceptual process planning , 2017 .

[11]  Hadi Shirouyehzad,et al.  Performance evaluation of production companies using data envelopment analysis and Monte Carlo simulation: a case study , 2014 .

[12]  Carl E. Betterton,et al.  Production rate of synchronous transfer lines using Monte Carlo simulation , 2012 .

[13]  Svetlana Nikolicic,et al.  Development of S-ARIMA Model for Forecasting Demand in a Beverage Supply Chain , 2016 .

[14]  Fernando Luiz Cyrino Oliveira,et al.  Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods , 2018 .

[15]  Fahmi,et al.  Forecasting of raw material needed for plastic products based in income data using ARIMA method , 2017, 2017 5th International Conference on Electrical, Electronics and Information Engineering (ICEEIE).

[16]  Minqi Li,et al.  A metamodel-based Monte Carlo simulation approach for responsive production planning of manufacturing systems , 2016 .

[17]  Juan Frausto-Solís,et al.  Comparative Study of ARIMA Methods for Forecasting Time Series of the Mexican Stock Exchange , 2018 .

[18]  H. Pedro,et al.  Assessment of forecasting techniques for solar power production with no exogenous inputs , 2012 .

[19]  Hamed Fazlollahtabar,et al.  A Monte Carlo simulation to estimate TAGV production time in a stochastic flexible automated manufacturing system: a case study , 2012 .

[20]  Hyesung Seok,et al.  Evaluation of forecasting methods in aggregate production planning: A Cumulative Absolute Forecast Error (CAFE) , 2018, Comput. Ind. Eng..

[21]  Martin Straka,et al.  Application of EXTENDSIM for Improvement of Production Logistics' Efficiency , 2017 .

[22]  Juan Frausto Solís,et al.  Comparative Study of ARIMA Methods for Forecasting Time Series of the Mexican Stock Exchange , 2018, Fuzzy Logic Augmentation of Neural and Optimization Algorithms.