The EyeOn Forecasting framework

By Erik de Vos

In today’s world, it is hard to find anyone who would deny the importance of demand planning and forecasting. Focus on high inventory volumes is no longer an effective solution. Costs should go down while service should go up.  We need to handle more last-minute requests for more complex portfolios. And we need to do it all while managing the entire supply chain in a sustainable way. Without demand planning & forecasting, we would be stuck in a never-ending loop of firefighting.   

Despite the abundant evidence that demand planning & forecasting is beneficial to any organization, many planning teams are still stuck in the labor-intensive full-touch forecasting phase. What is stopping us from realizing the true value of demand planning and forecasting?  

In this blog, we will touch on the right vision of forecasting, which we call smart-touch forecasting. As any development should be linked to your context, we will offer some key questions to consider before you start, and how the answers can guide you towards a stronger approach to forecasting development. 

 

Let us focus on smart-touch planning 

As we explain in our e-book “Navigating the era of smart-touch forecasting”, smart-touch planning and forecasting is any planning set-up that balances human expertise with the available machine intelligence within the realm of demand planning. In this set-up we distinguish 3 key elements that make it smart-touch: 

  • Automate the obvious 
  • Recommend the probable 
  • Flag where human intervention is needed. 

In smart-touch we assume that both the human and the machine add value to your forecasting set-up. Automating, recommending, and flagging are skills you can expect from the machine, while interpreting and deciding is where you would look to the human. It is important to emphasize that smart-touch planning and forecasting goes beyond mere forecasting algorithms. It means implementing planning processes that derive decisions from forecasts, considering all available and relevant data. It involves redesigning roles and responsibilities across the organization, with planners focusing on key forecasting decisions and business functions providing valuable input to the machine. And it means thinking carefully about what data can add value to your forecast and how to unlock that potential.   

Obviously, data and techniques are important pre-requisites in set-ups that are aim for smart-touch and a level of machine automation that is linked to it. In the more advanced set-ups, machine learning will be used to make more accurate predictions. But smart-touch can go further than just forecasting. For example, you can think about the following:  

Knowing where to start 

Understanding smart-touch is one thing, but how do you get started? We will touch on 4 challenges you will face if you want to use machines in forecasting. As you read the 4 challenges, try to see if you can recognize them in your own organization. The first one you recognize is your starting point.  


Challenge 1: Motivation for more forecasting automation 

Let’s think about the last time you were motivated to change something fundamentally. What triggered the motivation to actually adopt something new?   

  • Were you inspired by someone who shared something new ?    
  • Did you have a positive new experience?  
  • Did a negative experience trigger you to change?  
  • Were you challenged to adopt a change? 

Whatever your trigger was, the same motivation hurdle exists for demand planning & forecasting. Moving towards more automation and smart-touch in forecasting requires motivation to change and start trusting machines. Motivation to accept and understand what machines are good at and to act as a complementary planner where machines struggle.  

Challenge 2: Figuring out which data will allow machines to add value? 

As mentioned earlier, data will be central to any smart-touch predictive system. Machines need data to do their job. Obviously, sales data is a key source for any forecasting setup, but there is so much more. By breaking down your business into the key elements that drive your demand, you will see which data you can leverage. E.g. if I work in an ice-cream business, it is likely that season, weather, portfolio, … will affect my demand. Once you know which elements influence your demand, you can start thinking about how to organize the data around it. Different types of data will require different efforts to unlock and will add different value to your forecasting. Stay tuned for our next blog on data types for forecasting.   

Challenge 3: Having an integrated approach to develop more advanced forecasting setups.  

Advanced setups like smart-touch planning, will require thinking further than putting in place some tools. We cannot repeat it enough: a tool alone will not fix your forecast problems. A tool with more advanced forecasting capabilities and smart-touch setups will help. But a tool with poorly organized data will be useless. So, start thinking about your data structures and data flows. Some of this will also require reflection on planning processes and roles, as some data only exists because people put in the right effort.  Connect the dots between process, roles, data and tools, and develop a plan to put your smart-touch forecasting set-up in place. 

Challenge 4:  Having a smart-touch forecasting personal development approach  

Finally, once you have developed towards a more smart-touch set-up, it is a matter of also connecting to a more personal development approach. Using smart-touch forecasting is a combination of processes, data and tools, but also of people acting their smart-touch roles. Think about making smart-touch roles tangible enough so they can become part of personal development plans. Have I stopped my old ways of working? Am I using the exception-based alerts to my advantage? Am I focusing on value-added enrichment rather than overall forecasting?  

The challenges as your guide  

A solid approach to smart-touch forecasting would require you to cover your angles on the 4 challenges and start with the first challenge you encounter.  

Bring in inspiration, experience and training whenever you see that people are not yet open to moving towards more automation in forecasting.  Organize data discovery to get your organization aligned on what data has value in forecasting and excited about the potential it holds. Begin tool development only after you have taken an E2E view of understanding your forecasting processes, the roles involved in forecasting, and the data structures required. And top it off with an ongoing personal perspective that encourages continuous development and opens the door to more smart-touch thinking.   

At EyeOn, we encounter these challenges on a daily basis, and we have developed our approaches to cover the 4 angles. Connect with our expert team or already start your discover with our forecasting self-assessment.  

Get your copy of the e-book here.

 

Search for