
By Chris Lauchner and Geoff Shive | Demand Planning
There is only one guarantee in today’s inflationary economic landscape: consumer goods companies that fail to plan for unconstrained demand not only leave cash on the table but also miss opportunities that will impact long-term credibility and relationships among consumers. In simplest terms, unconstrained demand represents sales unaffected by pricing, out-of-stock merchandise, delivery delays or other supply chain limitations. This true customer demand paints a clear picture of what is possible in the marketplace versus sales that are influenced by a brand’s ability to satisfy demand.
Unconstrained demand is susceptible to near limitless market disruptions including inflation, fickle customers’ ever-changing preferences, seasonality and unforeseen factors. For example, consumer goods marketers and retailers currently are navigating fallout from volatile weather patterns including Hurricanes Helene and Milton; mechanical failures like the collapse of Baltimore, Maryland’s Francis Scott Key Bridge, a strike (albeit a short one) at ports on the East and Gulf coasts and domestic and international political unrest.
This unpredictability can take a damaging toll on logistics, customer relationships and most importantly, merchandise sales. There is, however, an opportunity for change.
Companies that transition planning strategies away from historical analysis and embrace a more forward-looking understanding of demand drivers are better positioned for ongoing success. Specifically, true demand forecasts create the clearest picture of what is possible in the marketplace versus relying on sales or orders derived solely from constrained demand.
Success does require a new mindset — one that centers around improved analysis, cross-division collaboration and an enhanced technological infrastructure. The first step in this new process centers on new forecasting processes. Analysis prerequisites for forecasting unconstrained demand include understanding:
- Dynamic market conditions including economic and weather trends, among other factors,
- Customer preferences and needs, as well as competitive influencers, and
- Merchandise attributes that enable shoppers to make educated purchase decisions.
The rich data resulting from this process provides a granular understanding of true demand patterns. It is also a critical foundation required to create actionable sales plans that can be converted to daily production- and shipping-level SKU forecasts.
Specifically, improved forecasts are a key component of integrated business planning (IBP) and sales and operations planning (S&OP). Beyond steering the future direction of operational goals, these forecasts should be the basis for upcoming, long-term fiscal financial plans.
Once forecasts are established, brands and retailers can tackle the next rung in the unconstrained demand ladder: robust cross-department collaboration. Alliances between demand planning, sales, marketing and supply teams across production, procurement and logistics enables the entire organization to work together to satisfy demand and avoid missed opportunities due to manufacturing issues, stock-outs or excess inventory. The result is two-fold: a more resilient supply chain and improved customer satisfaction.
Yet, the sheer breadth of product assortment, increasing customer bases, expansive sales territories and operational siloes all dampen even the best laid forecasting plans. Thus, it is imperative for all business users to have access to the fastest, most robust tools to leverage data — making advanced demand planning solutions a must.
Successful open platforms are integrated with artificial intelligence (AI) and machine learning (ML) — systems that learn from the data they are processing — further elevating performance and results. These smart tools position users to accurately and efficiently estimate demand and automatically translate forecasts at a very detailed level.
Despite arming decision-makers with the best technology arsenal, there are still bumps in the road toward effectively forecasting unconstrained demand. Among the biggest is a lack of experience and analysis skillsets among demand planners. Why? Because many planners lack advanced, comprehensive analysis expertise.
But there is a solution. When adopting a forecasting platform, companies need to be ready to create a process that can support data sources across time, merchandise information and customer expectations. Once the process is documented, data is accessible and the platform effectively supports efficient reporting tasks, enterprise-wide analysis training is a must. The faster users are familiarized with functionality and workflow, the faster reporting processes can be aligned across marketing, sales and the supply chain.
Where the rubber hits the road, however, is when enterprise teams can work together to define long-term goals, priorities and metrics. It is these forecasts that support cross-functional strategic planning — and account for both risks and opportunities — that will enable brands and retailers to build successful forward-looking plans.
Looking ahead, advanced forecasting processes will be even more mission-critical in understanding and harnessing unconstrained demand. However, consumer goods companies have their work cut out for them. An ever-volatile marketplace will continue forcing companies to stay “in-the-know” about how much product can be sold under ideal circumstances — as well as the many factors that impact supply.
By embracing forecasting processes, cross-departmental collaboration and an enhanced technological infrastructure, brands and retailers are positioning themselves to expertly allocate the resources needed to meet future demand; maximize efficiency and improve customer satisfaction in a more stable and realistic way.
Experts at Demand Chain AI can help brands and retailers understand how best to address unconstrained demand in today’s marketplace. If interested, please click HERE and a Demand Chain AI representative will contact you.