In a rapidly digitizing retail space, small differences have the potential to give retailers the competitive edge they need to thrive. Stock optimization enables retailers to give customers an effortless and breezy shopping experience, where all the things they need are quickly available. This creates an excellent customer experience and increases brand trust, thus improving customer relationships in the long term.
According to McKinsey, product and trend life cycles are shortening just as the viability of traditional growth pipelines—through brick and mortar expansion—decreases. This contributes to the critical value of stock optimization and its potential to give retailers the edge they need to compete for market share.
Traditional methods of stock optimization are fueled by instinct and past seasons’ sales data, which provides an incomplete picture of retail needs. That’s where artificial intelligence (AI) and machine learning come in; they can equip retail organizations with the insights and predictions they need to optimize stocking and support their customers.
However, machine learning isn’t a magic bullet. It can’t drop into an organization and immediately generate value. The real work of AI isn’t the technology, but rather the organizational and cultural changes that retailers need to implement in order to activate the possible value. For example, if an organization does not have the infrastructure in place to collect and process data about disparate stores’ sales records, even the best machine learning model will be unable to generate valuable insights.
The Top Three Challenges
In order to incorporate machine learning and other advanced technologies into workflows, each retail organization will face unique growing pains based on their structure, culture, and specific business domain. However, there are three challenges that organizations undergoing this change consistently face, which can impede their progress:
- Failure to Tie Into Business Objectives — Many AI systems can get disconnected from business priorities and the organization they are embedded within. Unless there are clear goals and established ROI metrics, retailers will be unable to focus their technological growth and thus likely slow their progress. Trust in machine learning systems is critical to encourage adoption, but if early efforts cannot demonstrate value, then stakeholders are unlikely to support investment in later improvements.
- Missing or incomplete data — Without a robust data infrastructure that can support the collection, secure storage, and consumption of data from a variety of sources, stock optimization attempts will likely fail. Setting up foundations will pay off in the long run (and likely the short term).
- Lack of agility to handle changing conditions — Trends and product needs can change rapidly, and stock optimization systems need to be agile enough to recommend redistribution accordingly. However, even a system that can recognize stocking needs isn’t agile enough to accommodate change unless the notifications trigger broader actions from logistics teams (maybe marketing and sales, too).