Effective collaboration between retailers and consumer brands is pivotal in today’s competitive retail landscape. A crucial aspect of this collaboration is forecasting purchase orders from the retailer to the brand, a process that has evolved from simple estimations to sophisticated, data-driven strategies. This article delves into the stages of this evolution, highlighting the growing importance of point-of-sale (POS) data in shaping demand forecasts.
The Initial Approach: Simple Percent Increases or Decreases to the Wholesale Forecast
Traditionally, brands did not use retailer point-of-sale data to construct their wholesale forecasts. Instead, they relied heavily on the retailer’s guidance, often using straightforward methods like applying percent increases or decreases to their demand forecasts. This approach, while simple, needed more precision and insights for optimal inventory management.
Incorporating Point-of-Sale Data for Enhanced Forecasting
Initial Store Sets to Help with Forecasts
Let’s walk through an example:
Step 1: Determining the Number of Stores
The retailer decides to place the new product in 150 of its stores. These are the “doors” where the product will be available.
Step 2: Estimating Sales Velocity Per Store
Based on historical data of similar products, the brand estimates that each store will sell approximately five units of the new product per week.
Step 3: Projecting the Sales Period
The initial sales period for this new product is projected to be 20 weeks.
Step 4: Calculating Total Demand Forecast – Bringing it all Together
To calculate the total demand forecast for the 20-week period, the brand multiplies the number of stores by the estimated sales per store per week and then multiplies this by the number of weeks.
Calculation:
- Number of Stores (Doors): 150
- Estimated Sales per Store per Week: 5 units
- Sales Period: 20 weeks
So, the total demand forecast is: 150 stores × 5 units/store/week × 20 weeks = 15,000 units
Based on this calculation, the brand forecasts a total demand of 15,000 units for the new product over the 20-week period. This forecast will guide the brand in production planning, inventory management, and logistics to ensure adequate stock is available across the 150 stores to meet customer demand without overstocking.
Exception Reporting – Store Distribution Accountability to Ensure Forecasts are “True”
This process entails meticulously verifying store counts against the figures initially agreed upon during sell-in discussions. For instance, retail buyers may indicate to the brand that a new item will be featured in 75 stores. However, as point-of-sale data begins to accumulate, the brand has the opportunity to compile “distinct counts” reflecting the actual number of stores carrying the inventory. It’s not uncommon to find that the actual number of stores participating in the new product rollout differs from the number initially proposed during the sell-in phase. This discrepancy highlights the importance of ongoing monitoring and adaptation in retail distribution strategies, ensuring alignment between planned and actual product placement.
Predictive Analysis
When retailers supply forward-looking, predictive point-of-sale (POS) data segmented by individual items, it empowers brands with the capability to conduct predictive analysis for weeks of stock (WOS) analysis. This approach plays a pivotal role in accurately identifying overstocked or understocked SKUs. By leveraging this data, brands can achieve a more optimal balance in their inventory management. This streamlines the supply chain and ensures that product availability is finely tuned to meet consumer demand, minimizing the risks associated with excess inventory or stock shortages.
Purchase Order Plans Provided by the Retailer
The Role of Machine Learning
1. Creating P.O.S. Predictive Models:
2. Analyzing the Relationship between POS Predictive Models and Actual Purchase Orders
Conclusion
The transition from fundamental forecasting methods to advanced, data-centric strategies signifies a major leap forward in the synergy between retailers and brands. In this evolving landscape, effectively utilizing point-of-sale data and integrating technologies like machine learning enables brands to generate more accurate forecasts, minimize inventory imbalances, and improve overall business performance. To facilitate this level of sophisticated analytics, brands can form strategic partnerships with SaaS companies such as Krunchbox. These collaborations are instrumental in establishing the necessary data pipelines, ensuring that brands can access the cutting-edge tools and insights needed to thrive. As the retail sector continues its progression, data-driven methodologies become increasingly crucial, laying the groundwork for more streamlined, profitable collaborations between retailers and consumer brands.