What’s Now?
Inventory, Foundation, Learning, and AI

Given the ongoing challenges with the economy, inflation, interest rates, and other disruptions, our clients continue to be hyper-focused on all things inventory related. This inventory glut, resulting from over-buying in-demand items, significantly over-buying items not in demand, and inventory in wrong locations is pervasive across the industry. The inventory issue is further compromised by many retailers’ inability to provide a real-time, global view of inventory availability.

We continue to address client challenges of how much inventory to buy, how to strategically flow and place inventory, and even how broad and deep our clients’ product assortment should be. We recently reviewed elements of purchase order (PO) creation options, pack sizes, allocation and replenishment configuration, and demand forecasts. Across our client projects, we consider what can be changed easily in an existing system or process, not necessarily replaced.

Some of the questions we are helping our clients solve around inventory management include:

• How can we ensure inventory is deployed to limit ‘out of region’ fulfillment needs?
• Where do I deploy my inventory to minimize movement and maximize sell-through?
• What rules should be considered to segregate, reserve, or utilize inventory across geographies, brands, banners, and divisions?
• How can we use artificial intelligence (AI) and analytics to be smarter about allocation and replenishment decisions?
• How do we design and use a demand forecast for all fulfillment and generate better forecasts for our suppliers?

Many of our clients want and need more modern tools in various optimization areas, but they also need to address their foundational environment. We are currently helping several clients thoroughly assess their existing processes to identify non-value-added business activities and unnecessary variations in business processes across brands or divisions. Through identifying ways to reduce inefficiencies and create consistencies across business processes, we often also uncover unclear roles and responsibilities. In many cases, this dichotomy inadvertently causes finger-pointing, overreach, and occasionally neglected duties, resulting in missed customer expectations. To mitigate these issues, we are working to redesign client organizations to achieve more transparent, collaborative, and effective working relationships.

Regarding foundational systems, our current client work includes several core merchandising and ERP modernization efforts, with a focus on clean data and building stronger capabilities. Our clients understand that a solid core across item management, merchandise planning, buying, inventory management, etc., is critical for the more value-adding solutions to be successful. As such, our team is working closely with the business users on process definition, system requirements, system selection, and deployment planning. We recently had several full-day system demos of potential technology options (in person!) across multiple functional areas to fully show our clients what modern solutions can do.

Further, we are helping clients solve persistent data issues such as missing data, inconsistent data attribution, and the use of special characters. We are also establishing data governance organizations and processes to ensure proper and consistent data management is in place to take advantage of new technologies now and in the future. By the way, in a few weeks, we are hosting another Talk Retail to Me Transformation Happy Hour focused on master data, so be sure to join if you are interested in hearing our experts’ insights and would like to ask questions in a casual online venue.

Across process, organization, data, and systems, we are working closely with our client teams in answering questions such as:

• Is business process consistency more important than optimized processes? Which comes first? Should we attempt to be best-in-class in most areas?
• In a matrixed organization, should shared service groups be accountable for key activities or should the brand, banner, or division in which the activity is supporting be accountable?
• What data governance rules and procedures will enable us to make strides toward addressing the root causes of our bad or missing data?

Growing retailers and brands continue to be challenged in ensuring their workforce is as effective and motivated as possible. Especially in key areas of merchandising and planning, with a mix of veteran and younger associates, we have found issues with clarity of roles, knowledge gaps in processes and tools, and a lack of alignment of vision and strategy. We are helping several clients define and build new and custom learning platforms that will allow them to address these issues and ensure they have engaged, enthusiastic, and productive teams. Some of these efforts are related to specific implementation efforts, but others are purely organization and/or process-related. We are currently designing and building an interactive learning environment for a client merchandising team that will be rolled out globally in the coming months.

We have also worked with several clients in developing or enhancing their internal organizational change management and transformational leadership proficiencies. We see an intensifying desire to build these competencies, so our clients are positioned to address team and leadership recruitment and retention issues, as well as strengthen their overall teams’ resilience and growth potential.

On the analytics front, in addition to preparing for our Enterprise Intelligence user interface (UI) upgrade reveal (later this summer), two of our recent retail price optimization projects focused on warranties and private label products.

We worked with a durable goods retailer to revamp their warranty pricing. This client’s warranty program is offered to further stand behind its products and build customer loyalty, bolstering customer perception as well as profitability. The AI-driven result delivered almost a 10% increase in projected total enterprise profit, adding an impressive $70M in annual bottom-line profit to the organization.

Another client project focused on optimizing private label product price points within a category. Our approach optimized pricing against both the local retail competition and the national brand with sensitivity to key value Items. The initial results show an equally impressive impact on the client’s bottom line.

Further, across the broader spectrum of analytics, including artificial intelligence (AI), machine learning (ML), and reinforcement learning (RL), we are helping our clients get smarter about:

• How much inventory do we need to purchase and how should this inventory be allocated?
• What locations are most optimal to ship from, considering excess inventory, costs to ship, and costs of future markdowns?
• How do we set the right retail price for the right products in the right stores?
• What products should we promote in what locations during what time periods?
• How should we handle markdowns, specifically what locations and discount cadence?