In our previous exploration of demand planning, we’ve unraveled the impact of statistical models on the demand planner’s role. The dynamic interplay of human intuition and technological prowess is paving the way for a more strategic focus, liberating demand planners from routine tasks. As we journey forward, our focus shifts to addressing the challenges posed by declining portfolio forecastability and the innovative technologies poised to redefine the demand planner’s role.
In the vibrant landscape of demand planning, portfolio forecastability faces turbulence due to various factors. Market shifts driven by geopolitical events, technological advancements, and unforeseen global crises introduce an element of unpredictability that challenges traditional forecasting methods. Evolving consumer preferences, shaped by trends, cultural shifts, and societal changes, add layers of complexity that demand planners must skillfully navigate. External factors, such as economic fluctuations and supply chain disruptions, amplify the challenge, making accurate demand anticipation increasingly elusive.
The XYZ analysis, a renowned method of categorizing products based on demand volatility, vividly illustrates increased variability and reduced predictability of sales across nearly all industries in the last 12 months. For a growing part of company portfolios statistical approach is no longer sufficient. More and more items need meticulous manual planning or the adoption of more agile and adaptive inventory strategies.
In turbulent times, the limitations of relying solely on statistical forecasts become strikingly apparent. While statistical models excel in capturing historical patterns, they may struggle to swiftly adapt to sudden market changes. The assumptions underpinning these models—based on historical data and relatively stable conditions—can falter in the face of rapid changes.
So, how can demand planners respond effectively to the challenge of declining forecastability in turbulent times? It demands a strategic shift towards agile and adaptive forecasting approaches, incorporating not only historical data but also real-time insights, forward-looking statistical methods, and the ability to swiftly adjust to changing circumstances. An increasing number of companies are dipping their toes into driver-based forecasting, a strategic approach identifying and leveraging key drivers influencing demand.
This method employs machine learning techniques and artificial intelligence to enhance predictive accuracy. Driver-based approach builds on the statistical model foundation, recognizing that not all products or services are influenced by the same factors. By understanding the specific drivers affecting each portfolio component, demand planners can tailor their forecasting strategies accordingly.
Driver-based forecasting relies on a detailed analysis of the various factors impacting demand for each product or service. These influencing factors can be categorized into both internal and external aspects. In specific industries, external factors such as weather conditions, economic indicators, and market trends play a significant role. Conversely, in different sectors, internal indicators like contract positions and order books carry more relevance. Incorporating these drivers into the forecasting model enhances the accuracy and relevance of predictions.
The adoption of driver-based forecasting comes with inherent challenges. Organizations are required to invest substantially, leveraging advanced analytics tools that align with this methodology. Equipping demand planners with the requisite skills and establishing a resilient feedback loop for continuous enhancement are integral components of this implementation. Many supply chain managers grapple with the evaluation of the necessity for such an investment, recognizing the complexities involved in this decision-making process. It’s a decision that involves not only financial considerations but also a comprehensive understanding of the specifics and volatility of the demand dynamics, what statistical forecast still brings and where additional drivers would be required.
In order to help companies to solve this puzzle, EyeOn came with the Fast Forecast Scan. It is a quick tool to provide rapid insights into demand characteristics and forecastability. The Fast Forecast Scan expedites the identification of improvement opportunities, guiding strategic decision-making on proper forecasting methods, be it statistical or driver-based. The scan unveils the highest possible statistical forecast accuracy for each product in your portfolio, identifying key improvement opportunities in your current forecast setup. Our experienced specialists identify items challenging to forecast with statistical methods that can have the potential for further driver-based analysis.
The Fast Scan quantifies your forecasting improvement potential, by industry benchmarking your forecast and providing insights into the maximum forecast accuracy that can be reached. All in just a few days. Watch the on-demand demo of the Fast Scan here.See the Fast Scan in action