Current Situation
Financial and product aspects of assortments and categories take precedence, with limited consideration for microspace constraints, landed cost on shelf, life-cycle pricing, and promotions. Capital allocation is made within, not across, categories, and in-store range and investment localization are disconnected from online strategies. Assortment optimization is not standardized for SKU rationalizaiton, and most retailers face ongoing SKU expansion without curation.
Goals and Objectives
Assortments and categories are managed as portfolios of merchandise inclusive of product, price, promotion, and placement, thereby broadening the parameters affecting assortment decisions. Assortments will include consideration of attributes, customer segments, store location, channel, capacity, and localized market demand. There is continuous development of assortments, SKU rationalization, and selection of assortments rather than a single point in time or discrete decision-making time frame.
Technology Deployed
Demand forecasting tools, supervised and unsupervised machine learning, deep learning AI, robotic process automation (RPA); cognitive processing including generative AI, NLP, NLG, and scenario planning; cloud, industry cloud, prescriptive trade-off analytics, visual AI, IoT, and predictive personality insights; reasoning through market and competitive content; multicloud management; and network/autonomous network infrastructure
Use Case Summary
Transform merchandise assortment planning, buying, pricing, and allocation to a continuous optimization of merchandise and demand generation investment portfolio.