Integrate this critical consumer demand factor to improve retail planning and replenishment.
Retailers and other consumer-focused businesses need to consider many factors when forecasting consumer demand. Everything from broader macroeconomic conditions (like employment levels and inflation) to buying trends (is a product’s sales growth accelerating or turning negative) to the type of product (is it a staple item or discretionary purchase) play a role in determining a core demand profile.
While the above-mentioned items are important factors behind demand variations, they tend to change gradually (usually months or years) over time. Other variables, such as pricing and promotions, will drive more immediate demand responses adding an additional level of complexity to forecasts.
Like pricing and promotions, the ever-present external variable of weather will also generate rapid and significant demand responses. One major difference – the weather is not a demand lever a retailer can control. However, with the right analytics, companies can proactively manage the weather’s impacts.
A Forbes article entitled Retailers Have Always Faced Unpredictable Changes Impacting Supply And Demand – Weather Shouldn’t Be One suggested “that rapidly changing environmental conditions, like weather, need to be factored into their supply and demand forecasts.” The article went on to reference a study by UBC Sauder School of Business that concluded “that retailers take into account weather changes on a seasonal basis, but are leaving a lot of money on the table by not factoring in the daily weather in the places were consumers live.”
Remember video games that had cheat codes that gave a player extra powers or the ability to advance to the next level in the game? In the world of retail, “WDD” turns out there is a consumer demand cheat code businesses can use to stay a step ahead of weather-based sales volatility. Getting to the next level of sales and profitability means reducing error in demand forecasts, minimizing out-of-stocks, and lowering inventory costs. WDD, or Weather-Driven Demand, helps businesses quickly address these objectives.
No other external variable shifts consumer buying as frequently, immediately, or directly as the weather. Planalytics’ Weather-Driven Demand (WDD) precisely calculates when, where, and how much demand for specific products increases or decreases due to changes in the weather. Incorporating WDD helps retailers improve both pre-season planning and in-season inventory management.
How does one apply the WDD cheat code and begin capturing the benefits? Integration into ERPs, planning and demand forecasting software, and other solutions is critical to scale WDD analytics (unit- or percent-based adjustments) to thousands or tens of thousands of product, location, and time intersections.
Planalytics’ partnership with Groupsoft enables retailers and other businesses to systematically address weather volatility in solutions such as SAP Ariba, SAP S/4HANA, and other platforms. Groupsoft’s expertise in integrating and implementing these solutions ensures that businesses can fully leverage the power of Weather-Driven Demand (WDD) to enhance their demand forecasting and inventory management processes.
By combining Groupsoft’s technical know-how with Planalytics’ WDD analytics, businesses can gain the following benefits:
Improved demand forecasting accuracy: Integrating WDD analytics into demand forecasting software allows retailers to more accurately predict sales and inventory requirements, taking into account the impact of weather on consumer behavior.
Better inventory management: With more precise demand forecasts, retailers can optimize their inventory levels, minimizing stockouts and reducing excess inventory costs.
Enhanced pricing and promotions: Incorporating WDD analytics enables retailers to more effectively plan pricing strategies and promotions in response to weather-driven changes in consumer demand.
More informed decision-making: Retailers can use WDD insights to make data-driven decisions in areas such as product assortment, store layout, and marketing campaigns, all tailored to the specific needs of their target consumers.
Increased agility: By anticipating and reacting to weather-driven fluctuations in demand, retailers can adapt more quickly to changing market conditions and stay ahead of their competitors.
Once WDD metrics are integrated, the weather-informed demand adjustments help retailers improve planning and replenishment processes.
- Proactively address the built-in, weather bias error in plans. When planning the season or year ahead, businesses all too often fail to factor in the impact of weather on sales. When left unaddressed, retailers end up unknowingly “chasing last year’s weather” immediately adding error to plans since the weather’s impact on sales rarely repeats itself from one year to the next. WDD analytics are used by retailers to deweatherize sales history or remove the weather bias embedded in performance results. From a total business perspective, plan accuracy improvements are typically around 5% with 20% or higher gains common for specific products, locations, and time periods.
- Weather “smart” store-level replenishment. For in-season activities like replenishment, WDD adjustments based on near-term weather forecasts offer significant benefits. Instead of basing replenishment volumes on historical averages or recent sales trends, WDD analytics keep retailers a step ahead of those abrupt weather-based demand swings that are all too common. A store that saw sales spike on warm and dry conditions the last two weeks can end up with too much inventory when the weather pivots and becomes unfavorable for sales. Alternatively, retailers can miss sales due to out-of-stocks when the opposite scenario arises.
A Retail Dive article estimated that out-of-stocks could be costing retailers a trillion dollars annually. On the other hand, overstocking ties up capital and can drive up related costs, including waste and markdowns. Using weather analytics in plans and forecasts can help retailers better align inventories with consumer demand on a localized basis throughout the year.