The last few years have proven how difficult it can be to predict what consumers will want to buy. The truth is retailers are having a hard time interpreting and meeting shifting customer demand. With massive shifts between categories, and even between goods and services, there are more factors than ever that impact consumer behavior.
In some cases we are observing movement along well established demand curves, and in some cases we are seeing shifts of those curves, and all of the corresponding elasticities that go along with them. Coupled with the supply chain issues that have plagued retail for the past several years running, and prediction can be a mess. For example, the emergence of a more fickle and sustainable shopping mindset among customers worldwide has forced retailers to adjust expectations for whole classes of products.
Not to mention, retail staffing shortages are yet another self-inflicted challenge retailers have created, with some shoppers losing patience to the point that they actually are cutting ties with their favorite products and brands.
So, what is the solution to this incredibly complex environment? It turns out that advanced predictive analytics and data science offer a rich set of solutions to these issues when deployed in a smart way.
In fact, to break through the problems of unprecedented supply chains and the challenges of new demand patterns, many leading retailers have already begun using ML and AI to improve forecast accuracy.
Traditional demand planning, often managed by several different teams across a retail organization, may not only be ineffective but now is getting even more complicated to manage from the way customers purchase to what they are buying has diverged from historical patterns. ML-powered demand planning helps retailers predict changes in sales, trends, and seasonality quicker than ever.
Especially when the right inputs are included in the evaluation data these models work with. While machine learning is constantly evaluating data to find these patterns, AI uses this data to optimize it into actionable insights with a greater impact.
A day in the life of a traditional retailer
Let’s consider a day in the life of a traditional retail decision-maker who monitors a huge range of products across hundreds of channels. Imagine the complexity they are facing with all of the shifting consumer behaviors, supply chain issues, and consumers’ need for instant gratification. So, how can the retail planner predict what customers want and where and cater to their needs while managing their inventory seamlessly? Well, AI and ML-powered demand planning is the answer.
With an intelligent demand planning solution, such as IA’s ForecastSmart, a single retail planner can now manage a range of categories of inventory –starting from fast-moving small or seasonal items like clothes to slow-moving, high-priced items like furniture, everything can be allocated in the right place at the right time without the fear of overstocking/understocking or markdowns.
What is Demand Planning?
Demand planning is the process of predicting the demand for a product so it can be assorted and delivered more efficiently to meet the needs of consumers. Demand planning is considered a critical step in supply chain planning.
Today’s customer requirements and preferences are more complex than ever. Purchasing channels and fulfillment mechanisms have multiplied, and consumer segments are more dimensional and unique from classical models. That makes it increasingly difficult for retailers to make accurate and reliable forecasts to get the right inventory to the right location and keep the overall merchandising mix where the retailer strategically wants to deliver.
For many retailers, whether they are omnichannel giants or small brick-and-mortar stores, forecasting is their inevitable secret weapon. Demand Forecasting is the linchpin to delivering more granular merchandising and better decision-making and implementation.
So, what are the leaders in this space in retail doing, and how are they creating more effective forecasts? Let’s find out.
In today’s retail environment, retailers need richer and more granular forecasts.
– To be able to position the right amount of inventory at the right store to meet localized demands
– To optimize allocation of products and pricing decisions
– For financial planning, planogramming, retail floor planning
– To minimize markdowns and maximize total revenue
By leveraging ML algorithms and deploying a robust forecasting system that tracks the change in the demand shifts and aligns the supply side to make up for the changing customer preferences and randomness in demand, retailers can improve their inventory and merchandising efficiency. And this requires:
- Delivering models with close to zero bias at various stages of aggregation (e.g. at the supply-chain level, or class or category level)
- Having accurate directional response to external variables (promos intensity, economic trends, inflation, weather, local events)
- Proactively managing error (inaccuracy) in their models ensuring the right level of inventory positioning
Maximize the value of your data
Effective demand planning solutions will allow retailers to take data-driven steps in evaluating demand transference as ML collects and analyzes consumer choices and adapts these insights to inform the assortment, availability of stock, and lifecycle pricing of the products. If a shopper’s need-to-have item is out of stock, AI can quickly and effectively predict a customer’s next choice 90% of the time, allowing retailers to understand which products they should be purchasing more of to accommodate for the current supply chain and manufacturing issues.
Streamlining inventory management is a significant factor in maximizing space and profits. A renowned jewelry retailer faced inventory allocation and demand forecasting issues while launching new products, but when using IA’s retail demand planning solution, the retailer noticed:
- Significant improvement in forecast accuracy
- 50% drop in weeks of supply, due to optimal stocking across grades
- Top 100 SKUs allocated accurately within 5 minutes
Forecast-driven inventory begins with a data-driven demand prediction, which serves as the foundation of inventory planning and management. With accurate demand planning, inventory can be optimized for the right location at the right time, based on predicted consumer demand. This allows the retailer to optimize the product life cycle, prevent overstocking and maximize revenue. Finally, the retailer’s time-phased inventory planning should be directly connected to how consumers opt for fulfillment services – and motivated by optimized profit.
Considering how much of a company’s time and resources each day is currently spent reevaluating forecasts and coordinating with decision-makers of other departments, an optimized, intuitive demand planning forecast offers a huge change, enabling planners to manage by exception, and freeing up time for other important tasks.
Why Do Retailers Need Granular-Level Demand Forecasting?
Forecasting demand accurately is crucial to successful retailing, where consumer demands keep changing, competitors are fierce, and supply chains are challenging. To make effective buying decisions and accelerate manufacturing, allocation, and replenishment capabilities, retailers need to forecast demand at every level of granularity that considers unique time frames.
And forecasts at different stages of granularity – daily, weekly, and monthly – can be highly beneficial for businesses that want to meet customer demand effectively, gain a competitive edge over the industry, and increase revenue.
It allows demand planners and supply chain decision-makers to elevate inventory decision-making and better understand, forecast, and plan their inventories. Generating granular-level forecasts also allows retailers to minimize lost sales and prevent customer churn.
How can IA help?
Impact Analytics is a cutting-edge retail AI-powered platform for integrated enterprise planning and data-driven decision-making. Whether it is forecasting demand, allocating inventory, financial planning, or optimizing pricing and promotions, any retail process can be enhanced with IA’s intelligent merchandising suite. Combining technology innovations—such as big data analytics and ML-based algorithms, IA aims to automate commercial initiatives for its customers.
Demand Forecasting – Our advanced predictive analytics helps businesses clearly understand the impact of the different variables on demand and adjust these levers based on real-time exceptions for better and more agile retail demand planning.