When retailers in Australia discuss Artificial Intelligence, the conversation quickly turns to its impact on demand and supply planning. After all, the goal of planning is to mitigate working capital requirements against stock availability aspirations, and AI is ideally suited to this problem.

As a demand planning system, Quantiful has proven to impact both demand and supply planning positively. It improves cash efficiency, revenue, and margin. However, the benefits of bottom-up SKU location planning (which underpins Quantiful’s approach to demand planning) go beyond improving the basic financial metrics. 

This paper describes five less-expected outcomes that Quantiful customers benefit from when deploying Quantiful at scale inside their operations. These are far from exhaustive, but they should demonstrate the breadth and depth of AI's impact on retail.

• Optimise assortments at scale

The days of ABCXYZ planning for inventory and assortment are fast becoming outdated. This rather crude method of store categorisation has been widely used to determine the assortment depth and breadth. Still, it fails to consider the ever-growing store-level nuances that retailers deal with.

The most successful retailers have store assortments that precisely meet the needs of the consumers that shop there. For this to happen, we need to let the consumers' demand define what product and how much of that product is available in particular stores. 

With AI, retailers can build demand models that optimise store space (GMROF) with the products we know will sell the most. They run dynamic stock levels based on SKU Location forecasts. For many, this is a significant shift. Still, it is also essential, as it allows them to dynamically set the assortment by catchment and remove the need to cluster or categorise based on legacy methodologies. The result is significant improvements in customer experience and bottom-line profitability.

By understanding the needs of a store’s specific demand profile, Quantiful can dynamically support the assortment planning for each retail location. This typically results in a 65% reduction in lost sales, a 10% increase in in-store revenue, an 18% decrease in the store SKU count, or, more importantly, dynamically setting assortments to meet the catchment demands of the store.

• Track business performance at individual store level

Given the limitations of traditional reporting systems, most retailers have little choice but to look at a ‘whole of business’ view of their operations. Without the ability to look at granular store-level performance, they are missing out on many store-level opportunities to make incremental improvements to assortment, stock and discounting.

In the same way that AI allows SKU location forecasting, it can also pinpoint areas of inefficiency at a store level. With SKU location data, retailers can, for the first time, benchmark individual stores using metrics previously reserved for whole-of-business analysis, such as CCC, GMROI, GMROF, availability, and turn. Similar to identifying poor-performing products, cash-use efficiency from the bottom up can be used to identify poor-performing stores.

With bottom-up data, Quantiful customers can benchmark individual stores using common supply chain metrics and build store-level business improvement plans that drive more sales, better margins, and improved stock turn.

• Run limitless ‘What if?’ Scenarios

Very few retailers have the luxury of running relatively complex ‘what if’ analyses. Without a robust analytics engine, a mountain of data, and some super-smart people, retailers too often rely on their gut to make crucial decisions—and their decisions are usually sub-optimal. 

The benefit of good SaaS products that leverage AI is scenario planning, which will execute the entire portfolio forecast independently on all products. This means understanding the cannibalisation, substitutional or even the halo effect of one decision on another product line, and all executed virtually in real-time.

For example, AI has the horsepower to understand:

• The relative variable influencers of demand on SKUs could be discount sensitivities, weather, date, etc. This means a retailer can accurately predict the effect a price change on a particular SKU in a specific store will have on other products in that store or other stores, such as e-commerce.
• Being able to scenario plan regionally allows maximising contribution margin based on the demand or behaviour of the catchment area the store is serving.

These two capabilities  allow businesses to do powerful scenario planning - answering questions like

“If I wanted to win market share in the appliance sector in New South Wales, what price point would I need to meet, and what impact would it have on the sale of vacuum cleaners?”

“If I needed to mitigate obsolescence in a particular product, where in Australia should I send it to sell it at the highest margin and mitigate the sale of other products?”

Even with a high SKU Location count, Quantiful allows planners to scenario plan at any level in the product hierarchy or distribution network with confidence that the SKU Location execution will be 75% accurate, resulting in fast, agreed-upon trade marketing executions.

• Change-up store work

As a retailer, you want your in-store teams to focus on selling, engaging with customers, merchandising, and ensuring the store looks appealing—all the things that make the till ring. You don’t want your store team to do admin and fight with stock issues (too little and/or too much).

The pain associated with poor short-term planning can be seen on the shop floor. To keep shelves stocked, managers need to execute manual orders, ad hoc receipting and assortment planning, which all take valuable time, time that should be spent selling! 

With Quantiful driving the planning process, store managers create 75% fewer manual orders and out-of-cycle receipting, which frees them up to focus on customer and revenue-related activities.

• Get planners back to planning.

Introducing a centralised planning system inevitably impacts the day-to-day work that demand planners do, particularly if they come from a world of spreadsheets.

The complexities of today's portfolios and distribution networks make Excel onerous to manage. It's slow, unfit for purpose, and often sits on an individual's laptop. Too many planners spend too much time reconciling top-down forecasts into tactical, operational outcomes.

With more accurate planning at the front end, the number of forecasts that have to be manually adjusted drops precipitously. Quantiful customers have found they only have to manually adjust 2% of forecasts at store assortment level (compared to over 60% previously) when 98% of forecasts are running unsupervised actions. With more time to think, planners can return to what they are good at: supporting sales generation across an agreed plan.

Quantiful is generating over 50 million unsupervised replenishment actions for retail customers across AUS/NZ, resulting in an average of 35% working capital requirement and 55% improvement in SKU Location stock turn (by revenue)