By Erik de Vos
After reading our previous blogs on demand forecasting and planning techniques and important demand drivers for supply chain forecasting, we hope you’ve gotten a better grip on what smart-touch planning and forecasting entails. Now let’s explore how we can make it a reality. Achieving something starts with understanding your current position and taking the right steps to enhance your capability.
The road toward demand forecasting and planning with machine learning
At EyeOn, we believe that the best forecast results are achieved by working in a set-up that balances human and machine effort. At the core of smart-touch forecasting sits the assumption that a machine will always be better equipped to detect patterns from data, while humans will always be needed to bring intelligence that cannot be found in the available data. This assumption then leads to 3 principles: use the machine to automate the obvious, use the machine to recommend the probable, so the human can decide and use the machine to flag where intervention is needed.
However, the step up to full usage of machine learning in demand forecasting is often a bridge too far. Many companies in practice still apply a ‘full-touch’ forecasting approach, meaning that the forecast is mainly the result of a lot of manual editing. So why not bring in statistical forecasting as a first step-change? Statistical forecasting is the technique that leverages historical demand/sales data to predict future demand. Wherever you have structured historical sales data, there is the opportunity to plug in statistics to do part of the forecasting work. By getting the basics right with statistical forecasting, it can be the steppingstone towards optimal ‘smart-touch’ forecasting: balanced machine and human efforts to provide the best-possible forecast in this ever-changing environment.
While every company has its unique context, we’ll outline three typical forecast maturity stages on the road towards smart-touch forecasting that might resonate with your own. In describing these stages, we focus on how forecasting is organized as part of the demand planning set-up.
Maturity stage 1: Demand planning with full-touch forecasting
Firstly, there’s situation 1 – what we refer to as ‘full-touch forecasting’. In this scenario, planning processes, roles, data, and tools are still in the early stages of development. “Full-Touch” then indicates that there is little automation in how the forecast is created or in other words, without human effort there most likely would not even be a forecast. Here, the forecast often serves to secure stocks rather than steer business decisions. As a result, the forecast undergoes detailed editing almost daily. Excel is usually the forecast weapon of choice, not allowing to track why the forecast was edited and learn from the work that was done.
Evolving out of this stage typically requires a process-people-data-tool approach with high attention to awareness and change management. Start by experiencing the value of more organized demand forecasting and planning for your organization and build an organization that is used to thinking in demand drivers.
Maturity stage 2: Demand planning with statistical forecasting & enrichment
Next up is situation 2, characterized by an organization embracing ‘statistical forecasting with enrichment’. To streamline forecasting efforts, the company has introduced statistical forecasting models to uncover hidden insights in sales history. As this does not deliver a good enough forecast for all products, there will be processes that bring the relevant enrichment information to the surface so it can be decided how to take this into the forecast. In general, there is more awareness and understanding on how to plan & forecast, and on the value of forecasting.
However, it is also voiced that demand forecasting and planning can be labor-intensive in the early stages of this set-up, even with the objective of delivering one forecast version only. We would argue that you need to experience these challenges to become aware that a set-up of statistical forecasting with enrichment requires tuning as you go.
In the best set-ups, you will already see several smart elements appearing and we could consider those set-ups as ‘smart-touch with statistical forecasting’. For example, applying portfolio segmentation brings focus and distinction to the demand forecasting and planning approach. Organized enrichment processes that connect automatically in the forecast, reduce manual efforts, creating room to focus on specific situations. Connecting forecast enrichment to formal assumption logging makes the forecast learning process smarter. Tracking value-add for all manual enriching of the forecast, brings in awareness and supports conscious enrichment.
Maturity stage 3: Demand planning with smart-touch forecasting & machine learning
Finally, we arrive at Situation 3 – ‘smart-touch planning & forecasting with machine learning’. Here, the organization is adept at working with data and recognizes the potential of qualitative and timely data to automate obvious tasks. Trust is placed in a machine learning engine to generate an unbiased and accurate forecast based on the information provided. Decision processes are aligned to leverage the forecast as a starting point for gap-closing decisions instead of merely managing targets.
In this setup, the machine smartly covers the obvious aspects, while humans focus their smart efforts where the forecast shows potential for additional value. Roles and responsibilities are structured to emphasize how each element contributes value to the organization.
Want to dive deeper into these maturity stages and the essential aspects of a smart-touch set-up? Our smart-touch forecasting ebook, provides you with a detailed explanation of each maturity stage, helps you decide your ideal future outcome and how to plot the journey towards it.
Define the roadmap that will bring your demand forecasting and planning lightyears ahead
The above-mentioned maturity stages might give you a first idea of what stage your current forecasting set-up is in. However, there are many different factors that we should consider.
Before you start your smart-touch journey, it’s important to identify your company’s maturity level in aspects essential for a successful smart-touch setup, namely: intrinsic motivation to work with data, belief in the feasibility of automation, an organized approach to data, and a clear direction for sustainably developing smart-touch planning and forecasting.
Finding out beforehand the status of these elements and which ones require more attention in your journey will not only prevent unpleasant surprises later but also speed up your forecast transformation journey significantly.
The forecast self-assessment will help you discover:
- Your current position on the road to future-proof forecasting
- The most suitable roadmap forward based on your current position
- Practical steps to accelerate progress in your forecast maturity level
Discover your best next step to speed up your forecast optimization journey – in just 10 minutes. Get started here.
