A modern approach to inventory optimization: Neal Analytics Q&A
Cloud technologies and the Internet of Things (IoT) are dramatically changing how retailers and CPG organizations manage and sell inventory. IoT devices are available to install in coolers, vending machines, shelves, and more to monitor stock, conditions, and the customer experience. With IoT, CPG brands and retailers have unprecedented access to data and insights that reduce costs, improve productivity, and increase revenue.
To hear first-hand how IoT is changing inventory optimization and what retailers and CPG companies should think about when considering inventory optimization solutions, Luke Shave, Microsoft CPG & Retail Industry Marketing Lead, Microsoft Cloud & Enterprise Group spoke to David McClellan, Practice Director at Neal Analytics.
David McClellan (left); Luke Shave (right)
Luke Shave (LS): Why is inventory optimization such an important area for retailers and consumer goods companies alike?
David McClellan (DM): Inventory optimization is part of the fundamental offering that retailers and CPG companies present to their customers. When the products you’re offering aren’t the right ones, you’re wasting money and missing opportunities to satisfy customers.
LS: What keeps brands from considering a new inventory optimization solution? What makes customers realize that it is a worthwhile investment?
DM: The belief that what you have is good enough or is all that’s available. Many retailers and CPG companies we visit have never considered approaching SKU rationalization the way we do. We like to prove it’s a worthwhile investment with immediate business insights from their data.
LS: What benefits do customers see from having access to granular SKU data that they don’t necessarily expect?
DM: One of the fastest and easiest ways to realize value from our approach is the analysis of SKUs that consistently outperform others across a certain dimension, like channel or region, but have one or two markets where they really don’t perform. This is where it’s easy to identify the shortcomings of high-level or infrequent analysis. Remove those problem SKUs that’ve been sitting on shelves for months, and you can see immediate improvements at stores.
LS: What impact is the whole digital transformation narrative having on the area of inventory optimization?
DM: Inventory optimization is far from new. Today, what cloud analytics and machine learning provide is the ability to scale the work of dozens of analysts and managers, pushing those insights out of the analytics department and into the hands of operations via mobile and online dashboards. This frees the analytics department up to focus on new and more complex problems, and gives operations the ability to make better decisions every day.
LS: What are the most exciting technological developments poised to change the way inventory is managed? For example, what does IoT offer?
DM: IoT has the potential to unlock a new wave of dynamic restocking, route optimization, and inventory allocation capabilities that immediately inform the business on what’s selling, where, when, and how much. This makes it much easier to understand why, which is the key to understanding how to grow your business.
LS: What about machine learning?
DM: The why is where machine learning comes in, combining IoT with contextual and historical data. Ideally, a dynamic and responsive retailer won’t need to stock as much product in warehouses or on shelves, only carrying what customers want and need at a given moment in time. IoT and machine learning also have the potential to tip the scales back in favor of brick-and-mortar through fantastic new experiences that make going out and shopping fun again.
LS: Can you talk a little about how you’re working with Microsoft to embed new capabilities into your solution? What features are you most excited about?
DM: The Microsoft Power BI solution is a fantastic way to surface advanced analytics and machine learning results at all levels of a business. We can rapidly deploy flexible and highly configurable solutions in Azure using tools like Data Factory and ARM Templates to meet our customers’ unique requirements. We appreciate how easy it is to author high-quality machine learning algorithms inside of Azure Machine Learning since it allows us to rapidly iterate and prototype models with and without custom code.
LS: What’s unique about the Neal Analytics inventory optimization solution? What makes it stand out in the market?
DM: The main difference is our empirical approach. It’s not yet another analysis of SKU characteristics against each other to ensure there’s good coverage of features – it’s an analysis of which products stores are carrying that result in maximum store sales. Instead of focusing on what it will take to maximize the sales of a single SKU, we focus on what SKU assortments should look like in order to capture maximum demand across all SKUs.
LS: What common gaps in inventory optimization practices did Neal Analytics see when creating the inventory optimization solution? How were you able to use your data expertise to fill those gaps?
DM: The typical gaps relate to frequency and granularity. Most companies manage SKUs a few times a year, and usually at a fairly high level. The infrequency of updates causes underperforming products to sit on shelves not paying their fair share of the rent, while potentially high-selling new SKUS sit in a warehouse unable to generate any revenue.
Neal Analytics experts have seen this trend many times, so we know what to look for when we dive into client data. That knowledge makes it quick, easy, and cost-effective for business stakeholders to evaluate our inventory optimization solution, as well as the analytics power that the Microsoft Cloud provides.
LS: Any closing comments or call to action for people reading this post?
DM: Aside from thanking my team for all their hard work in building out Inventory Optimization as an app, I’d like to invite anyone interested in testing our solution in their business to give it a try. We have the solution available on Microsoft AppSource as a demo sandbox and can discuss its applicability to your business case in a quick phone call. The data requirements are straightforward ERP and CRM datasets, and it doesn’t take much to see whether it’s right for you. Thanks for reading!
