Implementation of Fuzzy Technique in the Prediction of Sample Demands for Industrial Lubricants
Rajkumar Sharma1, Piyush Singhal2

1Dr. Piyush Singhal, Professor & Head, Department of Mechanical Engineering, GLA University, Mathura (U.P), India.
2Rajkumar Sharma, Assistant Professor, Department of Mechanical Engineering, GLA University, Mathura (U.P), India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 368-373 | Volume-8 Issue-5, March 2019 | Retrieval Number: E2937038519/19©BEIESP
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Abstract: In this paper, a case of prediction of sample demands for industrial lubricants has been presented. We have observed that the demand for most of the industrial lubricants depends on three main factors i.e. quality, cost, and delivery time. These factors are studied and compared with other competitors dealing in similar nature of products. The quality is mapped with three fuzzy parameters viz. inferior, alike, and superior. The cost is linked with three linguistic variables viz. low, identical & high. Similarly, delivery time is also associated with three sub-parameters viz. short, equal and long. First, the raw data of demand for 12 number of random samples are collected from supply chain executives of an automotive and industrial lubricant manufacturing company. Thereafter, the membership functions for the causal factors and demand are built on the basis of comparative analysis and collected data. At last, a fuzzy- inference demand model with rule base is constructed. Finally, the demands are predicted by the skilled fuzzy model. Predicted data is compared with the raw data and absolute errors are being calculated. The result shows predictions made by the fuzzy-inference demand model are in tune with the actual demands of industrial lubricants. Thus, the built fuzzy model can be utilized and generalized for effective demand forecasting for industrial products.
Keyword: Demand, Forecasting, Fuzzy logic, Membership function, Prediction.
Scope of the Article: Knowledge Engineering Tools and Techniques