5 min read

The reinvention of demand forecasting: Planning for the future when you can’t look to the past

Evan Quasney

VP of Global Supply Chain Solutions

Volatility, uncertainty, complexity and ambiguity aren’t new dynamics for supply chains. The alarm bells have been ringing steadily in recent years, as evolving consumer behavior, new competition, and channel proliferation have made demand management more challenging than ever. Today, the alarm bells are deafening and impossible to ignore, as the outbreak of the novel coronavirus has upended life and business as we know it. Here’s the good news: most companies now have an undeniable call to action to transform their supply chains and move past the traditional demand planning and forecasting approach. Focusing on integrated demand management, which blends sales and finance functions with improvements in supply chain, creates a path forward for companies to achieve new levels of transparency, collaboration, and ultimately business performance, enabling them to face market challenges with new levels of agility and resilience.

The best predictor of future events isn’t necessarily past events

Historic performance has long been the cornerstone of traditional demand planning and forecasting. Looking at year-over-year or quarter-over-quarter sales volumes and assuming some growth in statistical models serves as the primary forecasting approach for many companies.

The COVID-19 outbreak has rendered that approach useless, sending shockwaves throughout worldwide supply chains. The efficacy of traditional demand planning in our modern, fast-changing business environment had been waning for some time as advances in collaboration, analytics, and process management have given life to broader forecast inputs. But the pandemic lays bare the limitations of traditional demand planning based on historical statistical modeling and patterns and has left many users of traditional models struggling to make sense of their path forward.

The diminishing value of traditional demand planning and history-based statistical modeling underscores the need for a new approach for predicting the future, which requires planning that integrates market intelligence, advanced algorithms, and internal and external collaboration across multiple processes.

Bringing together external and internal signals

As we look at where we’re going now, it’s critical for companies to assess not only their forecasting process, but also the external market forces that drive demand. Combining the process, and internal and external data, in a modeling platform gives the insights necessary to better understand where the market is going and how to respond with maximum profitability and service. While historical signals will always maintain some level of utility, market-based forecasting minimizes historical data as the overriding signal and instead incorporates many input signals to create a robust, multi-faceted picture to sense demand over multiple horizons—short-term through long-term.

So, what are the input signals that companies should focus on? Answering this question starts with understanding both the independent and dependent drivers of demand. External data like weather, seasonal travel patterns, foot traffic, or pollen count can be incorporated into a forecasting platform to understand how external factors impact consumer behavior, which, in turn, affects demand. Trends from social media platforms like Facebook and Instagram deliver a real-time pulse on the market based on topics discussed and trending hashtags, creating an opportunity to drive sentiment data into a forecast. In the current environment, public health data, overlaid with these inputs, becomes a critical driver for organizations of all types to best assess the product mix and volume that should be offered.

额外的“拥有”输入关键的利益相关者s in the supply chain, such as manufacturer incentives, balance of trade, competitor products and pricing, or end-distributor promotions, serve as additional valuable data points to incorporate into an integrated revenue management solution to understand the impact on product quantity, as well as net price paid. Combining these internal and external data sets in a common demand signal repository and then applying advanced algorithms—such as artificial intelligence or machine learning—to evaluate which demand drivers will influence volume and price adds further richness to the forecast picture and enables more prescriptive decision making.

Layer forecast process atop demand signals

More often than not, external factors and indicators become powerful signals—but how are those signals consumed by the organization to arrive at a final plan? This is where the forecast process itself becomes critical. Oftentimes, forecast process is misconstrued as the “statistics” part before consensus demand and adjustment. Integrated demand management takes a different approach. It starts with looking at the performance and impact of each substep in forecasting (e.g. segmentation, naïve forecast, unconstrained algorithmic forecast, over-ride, promotion lift, etc.) and methods like forecast value added (FVA), to measure incremental performance of specific steps of the forecast process for that product and location combination over time.

Using insights from FVA, integrated demand management then assesses performance of each step, identifies areas of waste, and re-orders some of these activities to increase productivity of commercial and volume planning teams, giving confidence to business decision makers based on using a single source of truth. As a result, planners spend less time processing data and can pivot to become more focused on external drivers, insights, and market intelligence. In this role, they can drive strategic, commercial direction into decisions and demand in the volume and business forecasting process.

The future of demand forecasting starts now

Successful demand forecasting requires a platform that enables companies to act with agility, recalibrate rapidly and most importantly, incorporate different scenarios as needed to ensure increased forecast accuracy delivers the ultimate business outcomes. This relates back to the business’ financial and revenue plan because improving forecast accuracy consistently over time drives the kind of profitability that instills confidence in board members and business leaders. Leveraging a platform that supports strategic decisions gives financial leaders the predictability and visibility they need to navigate uncertainty and position their business to accelerate out of the curve in the next phase.