Across industries, supply chain leaders will be replacing, upgrading, or expanding the capabilities of their advanced supply chain planning systems (APS) in the coming years to achieve their digital transformation goals and boost performance.  

Implementing advanced supply chain planning systems requires a thoughtful and systematic approach that ensures alignment with business objectives, seamless integration with planning processes, a robust and integrated functional solution across the IT landscape, and smooth adoption by the organization. 

In this blog, we dive deeper into what drives this steady increase in the implementation of advanced planning systems’ capabilities. On top of that, we also share the 5-phase digital transformation framework towards creating sustainable value from your next APS implementation.

Global disruptions as a new norm

Market dynamics and market complexity are at an all-time high. You can hardly speak of impactful supply chain events anymore, as global disruptions are no longer the exception, but the new norm. Many companies have experienced in recent years that they lack the digital planning capabilities to meet their needs for supply chain visibility, planning agility, scenario planning, and speed of decision-making.  
 
Companies are increasingly looking for ways to improve the efficiency of their planning process, automate repetitive planning tasks, and optimize network capacity and inventory. Building on a solid operating model, advanced supply chain planning tools provide the technical capabilities to achieve these goals. 
 
The pace of technological advancement and tool stack evolution (e.g., the retirement of SAP APO) is forcing companies to rethink their future digital strategy. There are many options to choose from, and companies need to decide which APS strategy best fits their longer-term needs and priorities.

Advanced supply chain planning systems implementation: focus on business and design

While the benefits of advanced planning systems’ capabilities are clear, the path to realization is not always that clearly defined.  

In our experience, companies fail to realize the full value of an implementation because they focus too much on the technical solution and not enough on business readiness, functional and applied design expertise, and a robust APS capability and support model. In addition, an advanced supply chain planning systems implementation requires focus, and investment in creating the optimal mix of process excellence, They start their advanced planning system digital transformation with a focused (re)definition of the supply chain operating model, which is then translated into a well-standardized process and organizational blueprint prior to vendor selection. 

1. Transformation plan & blueprint 

They start their advanced planning system digital transformation with a focused (re)definition of the supply chain operating model, which is then translated into a well-standardized process and organizational blueprint prior to vendor selection.  

2. Tool selection 

They ensure that user requirements are clearly defined and unambiguous, and they identify unique and high-value use cases that the tool should support as input to the vendor selection process. On top of that, they conduct a proof of concept or proof of value to assess the functional capabilities, intuitiveness, and performance of the solution(s) before making a final tool selection decision. 

3. Advanced planning systems readiness assessment 

They evaluate and make targeted investments in the supporting capabilities that will determine the system’s robustness and level of adoption. Specifically, they perform an early assessment of master data maturity and alignment of master data processes, governance, and support tools.  

Next, they make sure to assess and upgrade planner skills and enable timely change engagement. Finally, they define upfront what the future advanced planning system support model will look like and anticipate the required organizational changes. 

4. Advanced planning system design & solution development 

They ensure early vendor involvement in the functional design and solution design efforts. This is to gain a solid understanding of the functional coverage supported out-of-the-box versus the required customization (also considering the solution development roadmap). They apply an agile approach to configuration development with a good balance between core functionality, usability/user engagement, and performance.  

They make well-balanced decisions on the phasing of complexity, allowing for the adoption of baseline planning capabilities before introducing more advanced functionality. 

5. Go live, operationalization, and continuous improvement 

They allow for sufficient time during hypercare to transfer knowledge and skills from the central project team to local owners. A clear ownership and governance model for the different categories of support and continuous improvement (integration, performance, master data, functional enhancements) is in place.  

On top of that, they have dedicated specialist roles in place with clear ownership and resource allocation, because over time, an SME or key user model is not sufficient to drive structural standardization and continuous improvement of the solution. 

Ready to drive sustainable value from your next advanced supply chain planning system implementation?

Our extensive implementation experience has led to the creation of a proven framework that will help you become an advanced supply chain planning system champion and realize sustainable value. 

Our guide is your essential ingredient for APS success, helping you discover: 

• The essential role of APS in your digital transformation  
• The position of APS in your tooling landscape  
• The 5 key phases of a successful APS implementation 
• A proven checklist for each phase to get started immediately 

In this guide, we provide an end-to-end APS digital transformation framework and best-practice experiences to help you increase your chances of success and realize sustainable value. Download it here.   

The post The 5 phases of an advanced supply chain planning system implementation appeared first on EyeOn.

A few weeks ago, we had the opportunity to attend the International Symposium on Forecasting (ISF) in Dijon. It was great to engage with forecasting experts from industry and academia and to gain fresh perspectives on supply chain demand forecasting. One of the things that makes ISF stand out is its holistic approach to this topic. Yes, there is a significant focus on the technical aspects, such as new model architectures. But we all know that the forecast generated by a forecast engine is seldom the final forecast that ends up being used as input to make decisions within a business. Demand planners play an important role in enriching this “baseline” forecast by (if done well) adding information that was not captured in the model. This aspect was also given considerable attention at ISF – thanks among others to ongoing research by Robert Fildes and Paul Goodwin. 

At EyeOn we are also passionate about optimizing the demand planning process as a whole; making sure the overall process is efficient, and each component is adding value. In this blog, we will present our views on some effective best practices. 

Evaluating the quality of the entire demand planning process 

Most organizations measure the quality (i.e., forecast accuracy and bias) of the end result of the demand planning process; often referred to as the “final” or “consensus” forecast. However, these metrics alone don’t capture the value added throughout the process. For instance, you might be satisfied with an 80% forecast accuracy, but if the baseline forecast accuracy was 85%, you’ve invested valuable time and resources only to diminish the forecast quality. This example highlights the importance of tracking the Forecast Value Add (FVA) of enrichment: that is, to what extent are demand planners improving the baseline forecast. 

Monitoring only the FVA, however, isn’t enough. Imagine a scenario where the FVA is 10 percentage points, indicating that demand planners are excelling at improving the baseline forecast. Does this mean the overall demand planning process is flawless? Not necessarily. It might be that your forecast engine is underperforming; leading demand planners to spend considerable effort on enrichments that a higher-quality forecast engine could have handled more efficiently. In other words, if the forecasting engine were better, the planners wouldn’t need to spend as much time on adjustments. This example emphasizes the need to also evaluate the quality of your forecast engine by comparing it to a simple benchmark (e.g., by comparing the forecast accuracy of your forecast engine to a naïve forecast). In short, to assess the effectiveness of our overall demand planning process, we must evaluate the quality of all the individual components. 

But there’s more to consider 

While having these basic insights is a good starting point, they may not necessarily offer guidance on how to improve the quality of your enrichments. For this, you need to track details of the enrichment process, such as the number and type (e.g., direction, magnitude) of enrichments. This allows you to gain perspective on: 

• Which type of enrichments have historically been associated with positive/negative value add 

• Which parts of your product portfolio benefit most from demand planner interventions 
• The time invested in the enrichment process 
• Potential biases in enrichments

From insights to action 

Insights are valuable, but they should be in service of improving the process. Data-driven insights in the performance of the baseline forecast engine and the behavior of the demand planning team should provide concrete suggestions for process improvements, ultimately leading to: 

• Improved quality of enrichments, resulting in higher forecast value add and a better demand plan 
• Enhanced efficiency of your demand planning process through the adoption of a more targeted enrichment strategy
   

How EyeOn can support 

Making the value-add in forecasting tangible is at the core of what we do. And we offer multiple ways to get experience with it or even get started in a fast way.  

• Play our Forecast Game: a business game where you compete against others to create the best possible forecast, applying best-practice principles around forecast enrichments. Measuring and learning on forecast performance is at the heart of the game set-up.  

• Use our Smart-touch dashboard: a ready-to-use dashboard that provides all the insights discussed in this blog. Let’s connect your data and unlock direct insights to improve your forecasting and demand planning process.  

• Get your copy of our Statistical Forecasting e-book: ‘Statistical Forecasting as your steppingstone towards AI’. In the e-book, we explain how to set up statistical forecasting to create an optimal foundation for machine learning-based demand planning. Download it here. 

If you’d like to learn more, feel free to reach out to Rijk van der Meulen or Erik de Vos.

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