Complex market conditions create an environment in which agility, responsiveness, and supply chain robustness are key. In transforming supply chain planning capabilities, it is therefore essential for companies to invest in a strong data-driven foundation. A solid master data foundation with high-quality planning parameters and strong integration with digital planning systems are no longer a commodity but a necessity.
Business challenges in supply chain planning
Looking at the state of business and current capability levels in the area of planning parameter management, we clearly see that companies with a competitive edge are able to overcome the following business challenges:
- The lack of data quality awareness: Even though master data management is on a rise we still see that companies poorly maintain master data and constantly postpone its assessment. As a result, there is no visibility and awareness of underlying master data quality and the impact it has on overall planning performance.
- The lack of ownership: Planning parameters are not a simple siloed matter; value creation lies in connecting the different functional contributors to achieve cross-functional alignment regarding a representative planning parameter value. To govern this, it is key to have a clear governance and ownership structure in place.
- The lack of a standardized review process: Defining a parameter at initial creation is one thing. At the same time, it is essential to have a well-structured and standardized review process in place. Ensuring that the supply chain planning parameters used day-to-day reflect reality, are robust, and well aligned.
- The lack of automation: The biggest struggle in planning parameter management is that it requires time and manual effort invested to align different data sources, calculate review, approve, and enter master data into different planning systems.
Impact on bottom-line performance
What is the impact when a transportation lead time between two production locations is actually 4 days instead of the 7 days currently registered in your ERP system? In this case, you are most likely holding excessive inventory. Goods will therefore stay in stock for a longer period than necessary. At our customers, we continuously come across real-life examples where unreliable supply chain planning parameters have a negative impact on bottom-line performance. Discrepancies between actual performance and maintained planning standards mean that projected plans and inventory projections are inaccurate, leading to supply chain impacts such as overstocks or underutilization of production assets.
How to build a solid data foundation
To overcome the key challenges in supply chain planning parameter management and to build a solid data foundation, we are strong advocates of taking a step-by-step approach while at the same time keeping the end goal in mind. So, foundational capabilities first, followed by smart automation. Now, what do we exactly mean by that? Our approach for building foundational capabilities consists of the following blocks:
- Focuses initially on defining clear business rules and parameter definitions. Ensuring that there is cross-functional alignment.
- Relies on automatically retrieving all necessary transactional and master data, consolidating it, and applying calculations based on predefined business rules.
- Entails meaningful dashboards enabling the user to easily derive insights into the data.
- Creates alerts to directly inform users about major discrepancies.
- Outlines the review process per parameter: who is responsible for doing what, by when, and how.
- Deals with change management. Operationalizing the process is what matters most.
Want to know more?
Is your company experiencing significant deviations between plan and execution? Are you under the impression that you are keeping inefficient stock levels (either over or under-stocking)? Your planning parameters probably need review.
Once you have a solid supply chain planning parameter framework in place, you can start thinking about smart automation to improve efficiency in the data workflows and eliminate human bias in the process. For more on smart automation please stay tuned for our next blog post. Follow us on LinkedIn so you don’t miss it.