Demand and supply planning are often seen as the core activities for a retail organization. Simply put – it is all about placing the right product at the right place at the right time.
The planning that goes behind ensuring it is very complex and mostly manual. Most organizations rely on their category management and demand planning teams to make the right decisions such as what to put, in what quantities, at what time and where – consistently over time.
According to Mckinsey, applying AI-driven forecasting for retail planning can reduce errors by between 20 and 50%. That translates to a reduction in lost sales and product unavailability by up to 65%.
That is a significant value that retailers stand to miss out if they don’t adopt AI-driven technology in retail planning. While several retail leaders have wholeheartedly accepted AI as a strategic enabler for retail planning, many still believe that they are not ready.
In this piece, we ask our expert Sankha Muthu Poruthotage to cut the clutter and tell us how AI translates to value on ground and who stands to benefit from it.
Sankha has decades of extensive experience in data science and ML engineering. He has spent the last several years of his career creating intelligent retail products by embedding ML algorithms to retail functions. At Algonomy, he plays the dual role of a product management leader and a consultant to clients.
What are some of the challenges that modern retailers face today?
Well, if you look at it broadly, the challenges can be categorized into two. Retailers obviously want to have more customers buying from them, and they want them to spend more money. It is all about increasing the market share and wallet share. So the challenges most retailers face revolve around customer acquisition, customer retention, upselling, and cross selling.
On the other side of the spectrum, you have the merchandise and supply chain challenges. While getting customers to the store is the first challenge that retailers face, being able to serve them is a much more complex problem to solve simply because there are too many moving parts not under complete control.
The recent pandemic has exacerbated the situation as consumer behavior and preferences have irrevocably changed. Retailers today face complexities in several dimensions such as omnichannel retailing, value-oriented and convenience obsessed customers, new and fresh product launches, and volatile demand patterns.
As a result, retailers today are struggling to achieve optimal assortment, floor plans, replenishment plans, and inventory plans with the pre-pandemic methods.
What are the biggest pain points that the industry faces when it comes to retail planning?
It is estimated that globally around $500 billion is lost due to wastage in retail. Wastage happens due to excess stock. On the other end, you have Out of Stock (OOS) which results in revenue losses and customer dissatisfaction. This is estimated to be even higher than the wastage at around $1 trillion annually in direct loss of sales.
There is an immediate impact on the P&L if OOS and wastages can be minimized. It can be as high as 10% increment on the operational profit. What most retailers realize is that these two are the biggest challenges in retail planning, and whoever aces this juggling act between the two extremes will eventually win the race.
What are the areas where AI has proven effective in dealing with these challenges?
AI is usually associated with cognitive abilities such as vision and voice. However, the underlying algorithms such as artificial neural networks and machine learning models can be used for many other use cases such as time series forecasting. Infact AI/ML models are proven to be very effective in areas such as demand forecasting.
The other main advantage of AI/ML models is that they can bring complex associations into light. I’m talking about pricing, promotions, events, weather, and even macro factors such as unemployment or consumer spending.
Once you have a good grasp of demand and how it reacts to these factors, it can lead to better optimal discount, promotion, and replenishment strategies. Of course it needs a layer of optimization on top of forecasting which is very important to make things operationalized.
I’ll provide a simple example. A retailer and a supplier typically have a contractual agreement on the minimum order quantity. Hence, to make things operationalized, this parameter needs to be considered in the optimization layer. It is important to bring in the business parameters to automate these critical business functions.
What retail industries can benefit from use of AI in demand planning and replenishment?
I think most retailers with medium to large operations stand to benefit. However, in general, retailers who deal with perishable items will see greater benefits due to obvious reasons – they need to be more agile and accurate than others.
How long does it take to realize ROI from such an investment?
In my experience with clients, the ROI for demand planning solutions is very tangible. Our customers have seen almost instant improvement in metrics such as availability and wastage by using the solution.
As I mentioned earlier, combined impact on the P&L can be as much as 10%. And the investments are typically a fraction of it. So the return starts within a matter of a few months.
Demand Forecasting And Replenishment That Is Accurate, Robust, And Adaptive
The last two years have exposed many gaps in businesses, and this was especially true of demand and supply chain planning. Many retailers remain unprepared to address challenges such as frequent out of stocks, increasing inventory costs and wastage that come with fresher newer products, omnichannel retail, and shifting consumer behavior.
Algonomy’s Forecast Right and Order Right have helped major retailers leapfrog to an intelligent, adaptive, and agile demand forecasting and replenishment framework that simply works, every time.
Forecast Right is an easy-to-use intelligent demand forecasting solution created specifically for grocery retail demand and supply chain planners. Its robust and AI/ML powered framework helps planners go granular and capture channel-store-category nuances in their forecasts, avoiding the trap of “one-size-fits-all” associated with some of the existing solutions in the market.
Order Right is an intelligent replenishment optimization solution that helps category managers generate accurate SKU-level order plans every time. It consumes accurate demand forecasts from Forecast Right and, unlike many existing solutions in the market, optimizes order plans for supply chain constraints and parameters such as MOQ, lead time, replenishment frequency, etc. using advanced AI/ML techniques. It also powers users with advanced features such as future stock predictions, day zero predictive alerts, and risk-based order planning.