Demand Forecasting-using Simulation for SCM Environment
Venkata Reddy. S1, V. Sai Rakesh2, M. Varun3, S. Prakash4, D. Naveen5
1Venkata Reddy. S*, Department of Mechanical Engineering, Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Guntur, India.
2V. Sai Rakesh, Department of Mechanical Engineering, Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Guntur, India.
3M. Varun, Department of Mechanical Engineering, Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Guntur, India.
4S. Prakash, Department of Mechanical Engineering, Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Guntur, India.
5D. Naveen, Department of Mechanical Engineering, Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Guntur, India.
Manuscript received on May 05, 2020. | Revised Manuscript received on May 15, 2020. | Manuscript published on June 10, 2020. | PP: 285-289 | Volume-9 Issue-8, June 2020. | Retrieval Number: 100.1/ijitee.H6280069820 | DOI: 10.35940/ijitee.H6280.069820
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Abstract: Supply chain management (SCM) is an emerging field that has commanded attention and support from the industrial community. Forecasting activities are widely performed in different categories of supply chains for predicting important supply chain management (SCM) comparis ons such as demand volume in order management, product quality in manufacturing processes, capacity usage in production management, traffic costs in transportation management and so on. Demand forecast taking inventory into consideration is an critical issue in SCM. The demand is forecasted using SIMULATION and compared with various forecasting models. The paper describes an application of discrete event simulation for forecasting the demand for next few periods, where the previous demand pattern show a purely random variation and increasing trend with random variation. The main objective of the study was to determine the demand of the product for future periods based on past data using simulation technique and compare its efficiency with conventional techniques for the SCM environment. By simulation we can forecast the demand either with the same accuracy or with more accuracy by increasing number of iterations. Mean absolute deviation (MAD) is used as measure of accuracy of various techniques. In this paper, this technique is verified by considering a case study which deals with the demand of tyres over past three years(2002,2003,2004) and forecasting the demand in the present year(2005) and successful results are obtained.
Keywords: Supply chain management (SCM), Forecasting, Simulation, Random number, Mean Absolute Deviation.
Scope of the Article: Software Engineering Tools and Environments