Fuelled by the rapidly changing environment, the traditional reliance on individual knowledge and expertise is giving way to a new era where analytics and human skills work hand in hand. This is particularly true when it comes to demand forecasting. At the heart of this transformation is the understanding that demand forecasting is not a solitary task, but rather a symphony that requires a harmonious alignment of people, processes and tooling. Achieving maturity means moving beyond the rudimentary methods of the past, embracing sophisticated statistical models, and empowering people with an analytics and collaboration skillset, leveraged by the power of technology. 

Collaborative excellence in demand forecasting 

Cross-functional collaboration between different functions in the company emerges as a key catalyst in this journey. The days of siloed departments operating in isolation are over. Demand forecasting requires a united front, where marketing, sales, and operations converge to share insights and align strategies. Merging the various angles and contributions to enrich a solid baseline forecast, generated by a powerful forecast engine, fosters plan acceptance and plan quality. Adhering to a clear planning drumbeat throughout the organization, to gather and consolidate and forecast inputs, is essential to strengthen and truly embed the right planning behavior. Especially when a strong feedback loop is in place for in-depth review of the added value of each step in the forecast process. 

Transform demand forecasting in supply chain through intuitive tooling 

The shift towards easy-to-use, intuitive tools for capturing market intelligence is pivotal. The era of complex, unwieldy systems is fading, making room for platforms that empower users at every level of expertise. Tools should be configured in such a way that adding the right information can be done at the levels that fit the business specifics. For one case, for example, the customer-product detail level could be needed to capture key account promotional forecasts, while for other events enriching on country-product group level is required. 

Furthermore, tools providing clear demand insights drive effective forecasting. It’s not merely about collecting data; it’s about distilling it into actionable intelligence. The data-savvy organizations of tomorrow are those that can translate raw information into strategic foresight, enabling agile decision-making in a dynamic market. 

Grow and nurture forecasting and planning capabilities  

Reflecting on the people dimension, the future of demand forecasting should not depend on individual knowledge. Having the right mix in place of forecasting and planning knowledge, analytical skills, and collaborative behavior is crucial. Lasting success is about creating an interconnected ecosystem that outlives individual expertise, fostering a culture of continuous improvement and shared learning.  

Companies that keep a good eye on all four pillars can truly reach forecasting excellence: solid data hygiene, clear processes, fit-for-purpose tools, and getting the best out of people. The EyeOn Fast Scan provides organizations with a clear stick in the ground of current forecast maturity and the potential that can be reached. This can be used as a great starting point for taking the right improvement steps on each of these four pillars. 

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Building reliable futures: the vital role of data hygiene

Imagine trying to build a house without a solid foundation – it would be destined for failure. Similarly, forecasting without proper data hygiene is like building on shaky ground. One of the widely used metaphors is ‘shit-in = shit-out’, which quite literally points at the fact that entering bad quality data as an input to your model, will result in bad results.  

Data hygiene refers to the process of ensuring that data is accurate, consistent, and up to date. It involves cleaning, organizing, and validating data to eliminate errors and inconsistencies. Without this foundation, your forecasts are susceptible to inaccuracy, leading to misguided decisions and missed opportunities. The consequences of poor data hygiene are not to be underestimated. Inaccurate data can lead to flawed forecasts, resulting in suboptimal business strategies, overestimation or underestimation of demand, inefficient resource allocation, and possibly frustrated employees (for example sales colleagues might be unhappy with underestimations and therefore lower inventory). Ultimately, this can lead to financial losses, customer dissatisfaction, and damage to your brand’s reputation. 

How data hygiene enhances forecasting accuracy: 

1. Improved Decision-Making: Clean and reliable data allows decision-makers to trust the insights derived from forecasting models. This, in turn, empowers them to make more informed and strategic decisions that align with the organization’s goals.
2. Enhanced Operational Efficiency: Better data quality results in more accurate forecasts, which in turn enable better resource planning, optimizing inventory levels, and reducing the likelihood of stockouts or overstock situations. This efficiency extends to various business processes, contributing to a more streamlined and cost-effective operation.
3. Customer Satisfaction: Understanding customer behavior is at the core of successful forecasting. Clean data ensures a more accurate understanding of customer preferences and trends, allowing businesses to tailor safety stock levels in such a way that customer service level improves drastically.
4. Adaptability to Market Changes: The business landscape is ever-evolving. With accurate data, forecasting models can better adapt to changes in market conditions, consumer behavior, and external factors. This adaptability is crucial for staying ahead of the competition and seizing emerging opportunities.

Investing in data hygiene for precision demand forecasting

A proven method to implement effective data hygiene practices is to ensure that your employees receive proper training, and everyone has an incentive to adhere to standardized validation steps. Also, major steps can be made when investing in a tool that is capable of automating repetitive data extraction and manipulation steps. 

In the era of data-driven decision-making, solid data hygiene is the key to unlocking the next level of forecasting success and a crucial step to implement both basic statistical models and driver-based forecasting. Businesses that prioritize the cleanliness and accuracy of their data are better equipped to navigate uncertainties, adapt to market changes, and make strategic decisions that drive long-term success. As you embark on your forecasting journey, remember that the quality of your predictions is only as good as the quality of your data. Invest in data hygiene today to secure a more prosperous tomorrow.  

Curious about the status of your data hygiene and the potential it offers for forecast optimization? At EyeOn, we’ve developed the Fast Forecast Scan: a quick tool which provides you with rapid insights into the demand characteristics and forecastability of your business. As a first step we perform a thorough deep dive in your data and provide actionable data quality insights. With improved data quality, the Fast Forecast Scan provides you, within a few days, with data-backed insights on the highest possible forecast accuracy that can be reached and identifies the main opportunities for improvement. Witness the transformative power of the Fast Scan through our on-demand demo.

The post The next level of demand forecasting: why solid data hygiene is the key to success appeared first on EyeOn.

Navigating the challenges: declining portfolio forecastability 

In the vibrant landscape of forecast and 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.

The path forward: agile and adaptive forecasting approaches 

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.

Difficulties in adopting driver-based forecasting 

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 and that can have potential for further driver-based analysis. Witness the transformative power of the Fast Scan through our on-demand demo.

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Empowering demand planners in the digital age 

Let’s face it: the job of a demand planner isn’t always the most glamorous or appealing to the younger workforce. For some the typical perception of a demand planner conjures images of someone endlessly crunching spreadsheet numbers in a dimly lit office, removed from the excitement of the market and customer interactions. In this age of rapid technological advancements, how can we free up vital time for the demand planner to interact with various stakeholders? 

One promising approach is to harness the wonders of modern technology and analytics to reduce the tedious aspects of the job. Instead of demand planners getting bogged down in manual number crunching, what if they could spend more time engaging in meaningful conversations with account managers, marketing, and customers? This is where the importance of best-in-class statistical forecasting models comes into play. By leveraging cutting-edge statistical approaches, demand planners can free themselves from the shackles of routine data manipulation, and instead focus their efforts on strategic thinking and dynamic decision-making.

Read our related blog: Declining forecastability: Can your portfolio still be forecasted?

Elevating demand planning through statistical models 

By automating the routine, repetitive tasks of demand planning, we create room for demand planners to add their valuable insights and creativity to the mix. They can build stronger relationships with account managers, gain a deeper understanding of customer needs, and refine the forecast when necessary. The demand planner of the future should be a strategic thinker, a collaborator, and a communicator, rather than just a number cruncher. 

Statistical forecasting isn’t about replacing human judgement; it’s about enhancing it. With the right tools and technology, demand planners can make informed decisions based on robust data-driven insights. Statistical models, equipped with the power of big data, adding machine learning techniques where needed, can provide a solid foundation for forecasts that demand planners can then fine-tune according to their unique domain knowledge, market insights, and experience into the process.

Reshaping the role of the demand planner

The art of demand planning is at a crossroads. It can remain a monotonous job focused on manual data crunching or it can transform into a dynamic, exciting role at the intersection of technology and human ingenuity. The future of demand planning is a blend of human effort and statistical effectiveness, and it’s an exciting path to be on. 

Are you ready to discover how statistics can build a solid foundation for your demand planning as well as bring more value to the role of your demand planners? The Fast Forecast Scan provides you with rapid insights into the main demand characteristics and forecastability of your business. It reveals the highest possible statistical forecast accuracy that can be reached and identifies the main opportunities for improvement. All in just a few days. Watch the on-demand demo of the Fast Scan.

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