The most stubborn, expensive issues retailers face are due to poor demand forecasting.
Approximated demand disappoints customers and bleeds profits. Tackling the challenges in demand forecasting, however, is not easy.
Why is demand forecasting important?
The forecast of future sales demand informs almost all of the decisions a retailer makes throughout the product journey:
An accurate forecast enables you to order the most in-demand assortment mix, down to size, color, and version of each product. So, you can meet demand without being left with overstock.
Demand forecasting at store/SKU levels leads to better allocation of inventory across the entire business from the start.
Accounts for price elasticity of demand, meaning the effect that a set price will have on demand, is an important consideration when setting prices, running promotions, or markdowns.
Order fulfillment becomes more efficient; meeting consumer expectations by enabling lower shipping costs, faster fulfillment, in-stock inventory, and a greater variety of fulfillment options.
Major demand forecasting challenges?
1. Retail is Dynamic
There are a host of factors that influence demand, and these factors are in constant flux. Basing a sales forecast on what happened in the year prior yields inaccurate results. Consider just a few things that change from year to year:
- Changes in product mix
- Channels and locations open, close, or move.
- Changes made by competitors
- Changes in geodemographics
- Changes in promotional calendars and strategies
- Changes in seasonality
- Changes in vendor pricing
A shift in any of these, or other relevant factors has a domino-like effect on the business, from supply chain to SKU-level demand.
2. Identifying/accounting for all factors
A successful forecast must consider every relevant factor for each SKU at each unique location to identify how their interplay will affect demand.
This is why demand forecasting is more effective than sales forecasting.
Of all the challenges faced in demand forecasting, this one is very difficult to confront for two key reasons.
A. It is very difficult to identify all of the variables influencing demand.
B. Each additional variable makes computing demand exponentially more complicated.
In fact, an omnichannel retailer with 100s of thousands of SKUs across multiple channels is facing millions of data points.
Traditional spreadsheet-based forecasting relies on analysts to manually sift, organize, and compute all that data. Time-consuming, expensive, and prone to errors, this approach is difficult to scale.
3. Fragmented approach
Multi-channel business often leads to a ‘divide-and-conquer mentality, creating siloed channels and departments. A fragmented structure yields inefficiency:
- Time-consuming operations
- Miscommunication and lack of visibility
- Human error and missed opportunities
- Constant manual intervention, making it hard to scale
- Category-level planning
- Inability to account for all necessary variables
A unified approach across the entire product life-cycle enables retailers to get the most accurate demand forecast.
Benefits of AI-driven Demand Forecasting
Unified AI-driven solutions easily handle the factors impacting demand. McKinsey & Company found that advanced analytics let retailers outperform their competitors by 68%.
Benefits of The System:
1. Improved Visibility - You make better decisions with a clear picture of demand and inventory across your business. Ordering the right quantities for each store’s demand cuts off most inventory problems. If issues do happen, AI-powered analytics notice them faster so you can respond more effectively.
2. Reduced inventory cost - Hedging your forecasts with safety stock is expensive. It ties up inventory dollars, undermines inventory KPIs, and leads to overstocks. With an optimized forecast, logistics costs fall, open to buy grows, and inventory KPIs improve.
3. Reduced markdowns - With more accurate forecasts, you're not forced into drastic last-minute markdowns to sell off overstock. You’re able to proactively plan price drops and set optimal discount prices to maximize ROI.
4. Increased sales - Stores have the product your customers want when they are ready to buy. Moreover, prices are optimized to increase demand by accounting for the price elasticity of demand.
5. Increased inventory turnover - Stores get just enough product to meet demand without risking empty shelves. Forecasting demand at store/SKU level accounts for variables like geodemographics, which means retailers are not stuck with fringe sizes, styles, or colors.
6. Improved collaboration - A unified analytics system gives your teams a shared view of each department, channel, and store location. With everyone on the same page, communications improve, and your teams work together more effectively.
7. Reduced manual labor - The heavy lifting is done in the background to produce faster and more accurate forecasts of what your customers want to buy. The agile nature of the technology lets retailers run scenarios and make quick changes without real-world risk.
8. Easier scalability - These cloud-based platforms process millions of data points in seconds, which allows them to scale with your business on-demand. Add more stores, add new channels, use more variables. Advanced retail analytics seamlessly integrate every aspect of your growing business.
Leverage The Retail Demand Forecasting System
The Retail Demand Forecasting System helps you:
- Squeeze costs and inefficiencies from your planning processes.
- Increase revenue, profits, and market share.
- Unify your sales channels.
- Improve collaboration and communication.
For a more in-depth understanding of retail analytics definitions, types, examples, and tips, discover the Ultimate Guide to Retail Analytics.