• SUJIT KUMAR SINGH
• Prof. (Dr.) Brij Bhushan
• Prof. (Dr.) S. Srinivasan

Nowadays, SCM industries are facing so much issues with rapid change in demand of customers, and it is very difficult to forecast these demands with the traditional methods. These rapid changes cause so many problems like order fulfillment, be competitive with other SCM partners, notice customers experiences, irregular inventory, making/increasing sales with an increment of holding cost. Existing nonparametric trends of data are not sufficient for handling the irregularities present in these demands. A proposed MASSCM architecture with machine learning approach of artificial intelligence and integration of Time Series and Support Vector Machine (SVM), shows significant success in forecasting demand in a rapid change in customer’s choice and taste. This work with SVMs reduces mean absolute deviation, mean absolute percent error and mean square error, provides a fast and simple methodology to get the forecasting data, and indirectly reduces the inventory cost of a system. The experimental data was taken from a well-known Mobile manufacturing company where professional’s advice has been considered accordingly.

Multi-Agent System (MAS), Service Oriented Architecture (SOA), Online Product Purchasing Scenario (OPPS), Support Vector Machine (SVM), Mean Absolute Deviation (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE).

"PROPOSED MAS-SCM-SOA ARCHITECTURE TO FORECAST CUSTOMER’S DEMANDS IN AN ONLINE PRODUCT PURCHASING SCENARIO (OPPS) USING SUPPORT VECTOR METHOD", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.332-338, April-2019, Available :http://www.jetir.org/papers/JETIR1904T46.pdf

"PROPOSED MAS-SCM-SOA ARCHITECTURE TO FORECAST CUSTOMER’S DEMANDS IN AN ONLINE PRODUCT PURCHASING SCENARIO (OPPS) USING SUPPORT VECTOR METHOD", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp332-338, April-2019, Available at : http://www.jetir.org/papers/JETIR1904T46.pdf