Implementing artificial intelligence-powered predictive maintenance and inventory forecasting in retail supply chains

To compete in the digital economy, retail industry firms must supply the right product, in the right quantity, at the right time, to the right place, and at the right price. Supply chains must be customer-centric and align with customer requirements. They are challenged to shorten cycle lead times as customer demand shifts to the just-in-time ordering, while increasing the inventory to meet service objectives in the face of increased demand volatility and unpredictability. Supply chain activities, especially warehousing and inventory management, are crucial to reduce total business costs, and technology-enabled decision tools are required to optimize these activities. Online channels are the most disruptive factor in today’s retail environment, and consumers are increasingly using these channels to select their products while wanting to avoid delays in order fulfillment. This requires additional demand for fulfillment services from operation of distribution and retail branches with complex inventory policies (Choi et al., 2018; Duan et al., 2019; Ghosh et al., 2021). The disruptions have needed collaborative arrangements to reconceptualize the nature of core business, which involves the reconsideration of the product and service mix, the appropriate distribution channel, and the collaborative resources and capabilities required to support the service and product development process over the long term. Retail supply chains are now designed and managed by networks that represent more than just the flow of logistics in the form of distribution channels and suppliers, and new paradigms in performance assessment and resource planning are needed. Profit recovery is achieved through accurate prediction replacement part demand, particularly in demand spikes. Traditional demand forecasting methods use past demand history, but sales of many products exhibit seasonal, cyclical, or trend-stabilizing patterns, which are difficult to model. Time series, autoregressive statistical methods have been unable to provide accurate forecast results in terms of MSE or MAPD.