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How AI solves the challenges of retail inventory management

Maintaining accurate inventory continues to be a real challenge for retailers.

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It’s tempting to stock up on inventory to ensure consumer demand is always met, but excess inventory languishes in warehouses or distribution centres. This can be expensive and time-consuming, often creating more problems than it solves.

On the other hand, stock shortages cut into margins and hurt brand image – especially if you can’t meet consumer expectations on time and in full.

The solution, however, has remained elusive — and not for lack of effort or investment. Retailers invest heavily in an alphabet soup of software to manage inventory and other links in the supply chain, from enterprise resource planning (ERP), point-of-sale (POS) and customer relationship management (CRM) to planning and logistics tools.

It’s a losing battle. Retailers are confronted by massive quantities of data that multiply by the minute, across every function that affects inventory management — demand planning, procurement and fulfillment.

Those siloed functions make inventory visibility nearly impossible to achieve with standard applications and Excel. When a problem arises, it could take days or weeks to pinpoint the root cause — that one weak link that disrupts a fragile supply chain.

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Hitting the ideal balance of right product, right place, right time can be more a matter of luck than it is of effective inventory management.

But what if retailers could apply the power of cognitive automation, artificial intelligence (AI) and machine learning to solve the previously unsolvable riddles of retail inventory management? In fact, they can.

Applying AI cognition to inventory challenges

Many retailers are seizing the opportunity offered through cognitive inventory management. This refers to the “thinking” capacity of AI to understand the multitude of real-time dynamics that affect inventory levels, such as sales history, location, weather, promotions and trends. This helps forecast inventory levels more accurately.

Cognitive inventory management is differentiated from traditional tools in its ability to predict scenarios, recommend and take actions, either with human approval or autonomously. This provides contextual, actionable information to improve inventory management decision-making.

Recommendations can be made that address questions, such as:

  • How can a retailer can reduce working capital in excess stock?
  • What’s the best way to get product from the distribution centre to the shop front?
  • How can a retailer best manage expiring inventory?
  • What are the optimal minimum and maximum safety buffers?
  • Why are retailers always overstocked at one distribution centre?

Presented with those recommendations, a retailer can accept, reject or revise a recommended action. Once they gain confidence in cognitive inventory management, they can then allow the system to function autonomously, freeing up time from routine tasks to focus on more strategic decision-making.

How does it work?

Cognitive inventory management harnesses data from disparate software systems, tracking stock in, stock out and stock remaining, and accounts for such factors as order volatility, demand volatility, available stock and more.

Using AI, cognitive inventory management can then recommend optimal stock levels for hundreds or thousands of SKUs at any given distribution centre or at the store level. That’s traditionally been the role of human planners, but data volumes and complexity make it virtually impossible for humans to truly optimise inventory management.

Let’s face it: Hiring more inventory planners and supply chain managers won’t solve the problem. Neither will out-dated software that relies on statistics. AI offers a tremendously exciting opportunity to at last gain full control over inventory.

By identifying gaps, anticipating demand, avoiding out-of-stock situations and minimising returns, retailers can optimise their inventories. They can also reduce the very real financial risks associated with excess stock buildups and underperforming products.

Getting started

Here are three important tips to get started:

  1. Be bold with next-gen tech. Innovative retailers that embrace technologies like AI, internet of things (IoT) and others can make a quantum leap ahead of rivals stuck in the status quo of 1990s style software and brute force processes. We’re seeing that first-hand as global retailers begin reaping the rewards of cognitive inventory management.
  2. Don’t wait, the future is now. It’s easy to rationalise putting off an AI project until you upgrade your ERP or deploy a new planning tool. That’s an opportunity lost. The beauty of an AI solution is that it layers on top of your existing legacy or cloud systems. AI non-invasively complements your infrastructure with groundbreaking capabilities that can forever reshape how you manage inventory.
  3. Take a holistic approach. It’s sound practice to first focus AI on a particular issue, such as balancing stock across distribution centres or retail stores, but also take a holistic approach that captures data from multiple systems. Since stock levels are influenced by dynamics both downstream and upstream, AI should be applied across all aspects of the supply chain that affect stock levels.

AI-based cognitive automation is still young, but already it’s rewriting the rules of inventory management for retailers. In a matter of years, the waste and inefficiency of conventional inventory management will no longer be written off as a cost of doing business.

By Rajeev Mitroo, Asia Pacific managing director of Aera Technology