Study of Bullwhip Effect in Small and Medium Entreprises Through Industrial Engineering Methods
CH.Bhanu Sri1, P.Aknadh2, Ronanki Ravi Kumar3
1Assistant Professor, Dept. of Mechanical, Sanketika Vidya Parishad Engineering College, Vizag, A.P, India
2Assistant Professor, Dept. of Mechanical, Sanketika Vidya Parishad Engineering College, Vizag, A.P, India
3M.TECH (final year),Dept. of Mechanical, Sanketika Vidya Parishad Engineering College, Vizag, A.P, India
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Abstract –
Effective supply chain management is critical for mitigating the Bullwhip Effect (BWE) in SME’s. Thus, this study examines how order batching, lead time, rationing, demand forecasting errors, information sharing and sale promotions affect the Bullwhip effect. Primary data was collected through questionnaires from 180 respondents belonging to business organizations operating in Andhra Pradesh, INDIA. From the literature, the constructs namely: Demand forecasting, Order batching, Rationing, Lead-time, sales promotions and information sharing are considered to know the influence on Bullwhip effect. It is proposed to test the hypothesis that a relationship between observed variables and their underlying latent constructs exists and to postulates the relationship pattern and then tests the hypothesis statistically. Correlation analysis is proposed to conduct to measure distinctiveness and uniqueness of the adopted construct and to check the issue of multi-co-linearity. Multiple regression analysis is also proposed to conduct to examine the relationship between the independent variables.Tabu Search’s capabilities illustrate its scalability and replicability. A quadratic multi-regression analysis interprets the input parameters (iterations, neighbors, and tabu list size) association with total supply chain cost and run time. The analysis shows iterations and neighbors to minimize total supply chain cost, while the interaction between iterations x neighbours increases the run time exponentially. Therefore, increasing the number of iterations and neighbours will increase run time but provide a more optimal result for total supply chain cost.
