COVID-19. High inflation. Market uncertainty.
Consumer goods companies remain under pressure to maintain some semblance of control over their supply chain even after years of intense disruption. The manual forecasting processes many companies use to stay centered through the chaos have been pushed past their limits. Consumer demand is constantly shifting based on the latest gloomy economic projections, leaving companies reliant on forecasts based on historical order data unable to accurately predict even the immediate future.
Companies looking to get out from under the immense strain of these disruptions have increasingly turned to demand sensing to do so. Demand sensing refers to the practice of integrating internal data with retailer point-of-sale (POS) data and other external data sources to create and adjust short-term forecasts. The inclusion of external data and the use of machine learning software helps companies “sense” what’s happening regarding consumer demand and make changes accordingly. Forecasts built using demand sensing solutions achieve better accuracy and adjustment times than ones created using traditional forecasting processes.
The high accuracy and cost reduction achieved through demand sensing comes at a time when consumer goods companies sorely need both. And the benefits extend beyond uncertain economic climates. Companies seeking relief from economic woes should improve their supply chain processes with demand sensing, a solution that pays off quickly and saves businesses time and money well into the future.
Demand sensing solutions leverage data and machine learning to improve accuracy and flexibility
The argument for using a demand sensing and machine learning-driven forecasting solution over traditional (and often more manual) forecasting comes down largely to data—namely, the ability to quickly and accurately process vast amounts of it from numerous sources.
Exclusive use of internal and historical order data limits forecasting potential
Supply chain decision-makers and planners have traditionally developed forecasts based on internal and historical order data. The logic is sound to a degree. But a reliance on the manual processing of internal and historical order data has a few major shortcomings:
- Internal and historical order data can’t account for unprecedented external disruptions that fundamentally alter consumer demand year-over-year. (For example: Demand in April 2020 was certainly different than that of April 2019.)
- Internal and historical order data can’t detect shifts in consumer demand that occur within days — shifts caused by unforeseen events like social media trends or unexpected retailer promotions and pricing changes.
- Internal and historical order data can’t anticipate retailer orders that occur after initial pipeline fills for planned events like product introductions and terminations, seasonal changes, and assortment changes.
Consumer goods companies should avoid these pitfalls even in sunnier economic situations, but these issues are especially costly in the current market. Manual forecasts require the wrangling of massive amounts of data on the part of planners. And demand can change dramatically in a matter of days, creating a situation where planners constantly react to changes but never quite catch up to them. These inaccuracies in forecasting ultimately lead to waste, especially for businesses dealing with fresh products or other goods with extremely short shelf-lives.
Strengthen automatic forecasting with external and POS retail data
Demand sensing instead embraces daily forecasting to improve accuracy and reduce both cost and waste. Current, relevant data from retailers and other external sources informs these forecasts, which update automatically in response to new information. For instance, a forecast based on distribution center (DC) reorders and strengthened with granular POS retail data will reflect current demand better than one based solely on DC reorders. Reorders reflect what was happening recently, whereas POS data is closer to how consumers are acting now.
Constant data updates and automated adjustments prevent a strict adherence to dated forecasts, and the machine learning function promotes continuous performance improvements. Demand sensing software also considers factors like current trends, retailer decisions, market conditions, and even external events like inclement weather to present businesses with the most accurate forecast.
3 ways demand sensing reduces costs with better forecasts and responses
AI- and machine learning-powered demand sensing software may seem like a wish list item to businesses beholden to their spreadsheet-based supply chain systems. Yet an increasing number of companies already use such tools to optimize their supply chain planning processes.
Companies still weighing the adoption of demand sensing software should consider these three major ways demand sensing positively impacts revenue:
Detects immediate changes in demand and consumer behavior
Demand sensing solutions process streams of current external data instead of relying solely on historical trends. This allows consumer goods companies to see demand changes as they happen versus making assumptions based on previous years.
Companies combine three sources of data to build and adjust demand sensing forecasts:
- Internal company data. This category includes price changes, assortment plans, product introductions and terminations, promotions offered to retail and wholesale customers, and any other data generated from within the organization.
- Retailer data. These sources include POS data, pricing incentives, retailer promotions, assortment decisions, marketing campaigns, current and projected store and DC network inventory levels, open sales orders, and any other data created by retailers.
- External data. Comprehensive demand sensing solutions also factor in competitor actions, weather, sporting or entertainment events, and other complex or random events that are likely to impact consumer demand.
Powerful software equipped with machine learning capabilities parses through this massive volume of data to determine how demand is shifting daily. Three major cost-saving benefits of immediate demand change detection include:
- Fewer lost sales through improved availability
- More accurate product supply compared to actual demand during step-changes in promotions
- Reduced waste and spoilage, especially for fresh products with short shelf lives
Automates forecasting adjustments
Robust demand sensing solutions minimize the amount of manual work required to create and adjust forecasts. AI- and machine learning-capable programs can detect trends and demand changes faster than any team of humans could, significantly reducing the time between trend detection and forecast adjustment. This lightning-fast reaction time helps limit costly over- and understocking.
Machine learning also trumps manual forecasting in another big way: the ability to capture and build from insights. It takes workers time and effort to perform a manual analysis, and that work must be repeated each time a forecast is created. A machine learning solution instead automatically retains the information gleaned from these analyses and learns from each event, preventing planners from continuously repeating the work.
Automation allows flesh-and-blood planners at consumer goods companies to focus on high-value tasks outside of the solution’s capabilities—namely, exception evaluation. A demand sensing solution identifies forecasting outliers and brings them to the attention of the planner, who in turn determines whether the forecast is valid or not. The machine uses external data sources to give these exceptions context and help the planner make the correct judgement regarding forecasting accuracy.
Provides a comprehensive picture of demand needs
Reliance on any one source of data hinders forecast accuracy and handcuffs business to rigid forecasts. A forecast based exclusively on POS and consumer demand drivers like promotions could lead to an inventory that doesn’t align with the actual order need of the retail customer.
This issue presents itself most particularly in the case of new items delivered in relatively large packs. The creation of a forecast based only on POS data may lead to the supplier building up a large amount of stock too quickly. Companies then house stock that won’t be needed until several weeks or even months later, creating obsolescence issues for items with shorter lifecycles or limited shelf life.
A business can instead combine retailer POS data with historical order data to quickly adjust inventory, distribution, production, purchasing, and sales plans with an up-to-date forecast. Companies must also consider inventory buffers, lead times, delivery schedules, and order batch sizes in each stage of the supply chain to determine future supply chain needs with accuracy.
This consolidation of up-to-date data from multiple sources also allows businesses to plan distribution and inventories better, especially in scenarios when the flow of goods is not stable. Consider a company that creates a baseline forecast ahead of running a promotional campaign. Even slight inaccuracies in the forecast can result in costly stockouts. But demand sensing software updates daily and tracks the realization of the forecast in real-time, allowing companies to take immediate action if units are moving quicker or slower than anticipated.
Demand sensing sheds light on other situations involving an unstable flow of goods as well, including:
- New product launches
- Product ramp-downs
- Marketing campaigns and promotions
- Seasonal goods
- Holiday-related fluctuations
Gain certainty in an uncertain market with a robust demand sensing solution
The factors fueling the current supply chain frustrations won’t last forever, but new complications are likely to arise as the intricacy of the global supply chain increases alongside consumer demand. Investment in a new or improved demand sensing solution helps companies better withstand current and future issues. Demand sensing software can decrease lost sales by increasing availability, reduce inventories, and optimize processes to produce long-term cost savings.
Consider Atria, a RELEX customer and one of the leading food suppliers in Northern Europe with €1.5 billion in net sales as of 2021. The company needed to improve its forecast accuracy for highly seasonal goods and meat products with exceptionally short shelf lives. Atria used RELEX’s demand sensing capabilities to achieve an incredible 98.1 % weekly forecast accuracy while reducing manual forecasting changes by 13%.
RELEX has helped consumer goods companies improve their forecasting and decision alignment for more than a decade. Our solution can be custom fit to solve your unique pain points and optimize even the most complex supply chains. And our long history of working with retailers worldwide means that no one in the world knows better as to what and how retailers buy from consumer goods companies.
Don’t get caught clinging to outdated manual systems during the next big disruption. Take advantage of RELEX demand sensing and reclaim control of your supply chain
This article is the second of four focused on helping consumer goods companies navigate the market shifts we’re currently seeing across the globe. The first article details the fundamentals to maintaining control of the FMCG supply chain, and the two articles to follow will provide an in-depth look at demand shaping and supply chain collaboration.