Hybrid Model building for forecasting New Product Demand in Retail Chains
Saroj Kanta Biswal1, Biswajita Das2, Soumya Mishra3
1Dr. Saroj Kanta Biswal, Associate Professor, Faculty of Management Sciences, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India.
2Mrs. Biswajita Das, Research Scholar, Faculty of Management Sciences, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India.
3Dr. Soumya Mishra, Assistant Professor, Faculty of Management Sciences, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India.
Manuscript received on 04 August 2019. | Revised Manuscript received on 09 August 2019. | Manuscript published on 30 September 2019. | PP: 8101-8103 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6440098319/2019©BEIESP | DOI: 10.35940/ijrte.C6440.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Predicting the demand of new products is extremely crucial as it has a strong bearing on manufacturing decisions, marketing efforts, sales strategies, financial planning and profitability. Predicting new product growth poses the challenge of having limited historical data points to model. In addition, they have highly uncertain future demand patterns and are heavily influenced by a host of external factors that are not completely known in the initial stages of the product life cycle. This solution adopts a hybrid methodology by analyzing the influence of new product adoption, its replacement cycle, behaviour of innovators and imitators purchasing this product and the impact of leading macroeconomic indicators on its demand.
Keywords: New Product, Innovation, Imitation, Adoption, Replacement, Macro Economy.
Scope of the Article: Social Sciences