The consumer goods competitive landscape is changing with startling rapidity. Digitally empowered shoppers have adopted cross channel shopping behavior. As a result, retailers find themselves in hand-to-hand combat for the shopper’s dollar in every neighborhood. These retailers are looking to their manufacturer trading partners for help in localizing assortments. Manufacturers who ignore the retailers’ requests for help in tailoring assortments to local needs will lose influence and lose SKUs from the shelf. Retailers who fail to respond to local shopper needs will lose shopping trips and in the long run will lose shoppers themselves. Walmart’s negative experience with their “Project Impact’s” Draconian assortment reduction is the clearest possible evidence of what can happen to the strongest retailers when they ignore the importance of assortment.
Given the pace and scope of change, manufacturers and retailers require a transformative leap in their way they manage their inventory – both in warehouses and on store shelves. This in turn brings us to the million dollar question.
How can retailers decide the right mix of inventory for their stores?
While there could be many ways to do it – the most sustainable way is to use Machine Learning.
People excel at spotting patterns and making adjustments based on feedback, while computers excel at processing huge amounts of data quickly. Put those capabilities together and you have machine learning, a technique with the potential to help businesses dramatically improve their inventory planning. Implementing a machine learning-based approach to inventory forecasting can create a massive advantage for forward-thinking organizations.
Using inputs like customer demographics, sale prices, item promotions, competitor information and even weather – machine learning can help businesses predict and optimize demand and replenishment with far more accuracy than elementary or manual methods. This in turn lets your store stock only those items which the customers want – on the shelves where they are most likely to buy them.
Often, hitting the sweet spot for a brand gets tricky. Figuring out what products to sell and which to leave out of the assortment requires some fine tuning. While there has been significant improvement with a customer-centric approach in the age of social media marketing and instant feedback, retailers are still fighting to make headway when it comes to merchandise planning- a set of interlinked decisions made months ahead of the collection’s preview.
With Machine Learning, there’s now a way for retailers and supply chain leaders to plan a cost-effective inventory stocking process. Take a look at the top 3 use cases through which ML can help inventory optimization :
Demand based stocking
Good inventory management revolves around a single contradiction: keeping enough stock in the warehouse to ensure the business keeps moving but not enough stock to drain its limited cash reserves. This contradiction lies at the heart of the role of store manager. It’s a job where boring is best, where every need of the business is anticipated, where many urgent calls are crisis calls of someone else’s making, yet where the inventory team need to find a solution. All done while simultaneously not sinking all of the company’s cash into non-moving stock.
Machine learning solutions can accurately predict demand, taking into account product properties, marketing events and market-wide signals. Taking into account hundreds of signals, including first-party data and external signals about competitor prices and public events , these ML solutions can account for stock-outs, inventory turns and holding costs to properly balance under-buy and over-buy risks.
Assortment optimization doesn’t live in a vacuum. There is a big opportunity for retailers and manufacturers to achieve the perfect balance between macro-space optimization, strategic and local assortment, and micro-space optimization. The ability to address strategic assortment decisions further upstream in the process requires automated space planning capabilities to efficiently communicate and execute valuable category strategies.
Machine Learning seamlessly integrates with your existing macro and micro space planning solutions, enabling retailers to easily manage their most valuable asset – physical store space in every store and every shelf.
Improving Customer Experience
To be competitive today, retailers must meet the needs of their most valuable customer segments and, to do this, utilize customer metrics and preferences to drive localized assortment and planograms. But the bigger your inventory is, and the more your customers are – the harder this mapping of loyalty with purchase becomes.
Machine Learning solutions tightly link the demographic and customer loyalty data with your inventory data, enabling more granular and relevant store clustering. This helps identify financial opportunities by each cluster group and execute on each of them. The end result is that each store has items that customers needed ,giving them a worthwhile experience while keeping your costs in check.
Integration with your existing inventory management — depending on the size of your company, you may be using SAP, Xero or any other myriad of software for your inventory management. Ideally would would need to integrate the machine learning model with their APIs and build a separate dashboard for the inventory team to quickly obtain insights.
These models are data hungry. Unfortunately machine learning systems are extremely data hungry and require a lot of inventory data to build a reasonable model. Whenever clients ask us how much data we’d like, we answer ‘everything you can give us’. This can be the biggest problem for machine learning implementations at the moment, simply not enough data. The best way forward here is to build a data warehouse to combine your all your existing data so that the ML models can give our results with reasonable accuracy
Inventory items are not homogeneous. It’s also important to remember when looking at machine learning models for inventory that each item is different and needed to be treated differently. There are some items are highly predictable and regular in their movement, whereas some items are highly unpredictable but nonetheless equally vital to keep in stock. Think about undertaking significance testing prior to building any artificial intelligence implementations. This allows you to understand what external data is important for being able to predict items demand and to also see which items are predictable or not.