How a parameter-driven value chain drives transparency, flow and planning quality
In the previous article, we have introduced the benefits of a parameter-driven value chain. In the third part of the series, we will elaborate on the benefits using four deep-dive sections. In particular, we will show how a parameter-driven value chain enables transparency, how it creates flow, how it eases the bullwhip effect, and how it helps to reduce planning effort while improving planning quality.
Deep-dive: how parameter-driven planning works
1. Improving transparency: use parameters to link the value chain and foster traceability and E2E view
A parameter-driven approach drives standardization which enables continuous improvement
A parameter-driven approach is a crucial prerequisite to allow a real E2E perspective and E2E planning. Parameters linking the different layers make changes and their effects traceable, even within a complex network. Without parameters, replicating processes across different business units or geographies is barely possible. The definitions and rules make up the standards of parametrization, which are crucial to driving continuous improvement. This focus on standardization as a prerequisite for structural improvement is entirely in line with the insights from Lean and Total Quality Management (TQM).
A parameter-driven approach drives the quantification of value chain mechanics, which advances decision making
For an E2E perspective of the value chain, quantifiable relationships throughout the stages are elementary. These relationships and the models on top of it constitute the basis for communication between value chain stages. Information and signals from other levels, e.g., on real-time demand updates or changes in lot sizes, are the trigger to adapt the value chain, optimally in an autonomous and agile way. Already the awareness of these relationships is valuable and leads to less self-inflicted variability. Quantification and traceability amplify the positive effect. For example, if one objective is to keep inventory low, but this results in costly expedite deliveries, then the E2E perspective might reveal inferior performance. Another example is identifying the weak links in the chain and improving the reaction to demand and supply shocks in the COVID-19 situation. A good data foundation and a thorough understanding of the cause-effect relationships in the value chain empower better decisions and sensible trade-offs.
2. Creating flow: reduce complexity to increase stability
The decoupling strategy in DDMRP shows how reduced complexity directly pays off. Establishing linking parameters does not only allow easier management but also permits decoupling, i.e., separating the chain into smaller segments – without letting the chain fall apart. Most notably, decoupling results in shorter decoupled lead times, more reliable lead times, and a less severe bullwhip effect. Hence, the chain becomes less prone to delay and amplification as a result of the decoupled segments. Also, parameter-driven buffers between the value chain stages eliminate the infamous system nervousness found in MRP environments. The reason for this stabilization: the amplification of variability and nervousness stops at the borders of decoupled chain segments.
Similarly, also Lean production and Theory of Constraints (ToC) define and determine buffers so that lead times remain stable. As a result, parameter-driven planning enables a value chain to overcome self-created complexity and substitute it with more consistent, predictable, and reliable planning.
3. Mastering inventory and reducing the bullwhip effect: reduce the dependency on forecasts and set inventory in an integrated way
A parameter-driven approach reduces the dependency on forecasts which can reduce the bullwhip effect
In a parameter-driven value chain, parameters have the function to link time horizons like planning and execution. Therefore, you do not have to use plans to link time horizons anymore. Instead, you can use forecasts to set up your value chain with parameters and execute against actual demand. DDMRP treats forecasts and actual demand in precisely this way. Hence, the impact of error-prone forecasts is limited. Of course, this requires some baked-in flexibility to be able to buffer against fluctuations. One way to create these buffers is to establish strategically placed decoupling stock.
A parameter-driven approach enables the E2E control of inventory
To master inventory, one has to consider the whole end-to-end value chain, not only individual stock points. Nowadays, everyone and everything in planning claims to be E2E. Whereas we cannot dispute the right intentions, reality often looks different. Except for the consideration of E2E processes, a framework and methods for linking time horizons and value chain stages are mostly missing.
Even though Kanban is a parametrized pull approach, the E2E aspect is mostly absent. A systematic rule to decide how many Kanban cards to deploy across the chain and how to react to demand changes remains a challenge. Therefore, Kanban is typically limited to specific segments of the value chain, which often limits its application to single production centers or to linking individual ones.
The DDMRP concept goes further than sizing buffers. More than that, the value chain is regarded as a whole, decoupled but linked by parameters. If demand requires production prioritizations, then buffer management needs a direct link to production priorities. For this propose, a buffer status indicates which products have the highest priority for replenishment. This information is sent directly to production planning. Thus, the buffer status and production priorities relate to each other without the need for manual reconciliation and lengthy clarification meetings.
In ToC, buffer and capacity planning are also closely linked. The production plan at the bottleneck follows the inventory level and the priorities of the finished goods – according to ToC based on contribution margin per bottleneck minute.
4. Streamlining work: standardize and specialize to achieve higher quality with less effort
A parameter-driven approach leads to more standardized procedures, which drive automation and continuous improvement
One key advantage of a parameter-driven approach is its potential to facilitate standardization, as already discussed above, concerning parametrization. Traditional planning involves the orchestration of many individual plans across value chain stages, time horizons, and heterogeneous stakeholders. A parameter-driven approach is a shift to a clear, fact-based parameterization of value chain mechanics, which is the foundation to standardize planning and related execution activities.
This way, ad-hoc decision-making, based on tribal knowledge and methods, is replaced by standard procedures. For example, standardized buffer calculation procedures can be automated, as in DDMRP, Lean/Kanban, and re-order point systems.
A parameter-driven approach reduces complexity and enables specialization, which reduces planning and execution effort
Standardization also implies a simplification as parameters follow the same rules throughout the chain, which tends to reduce alignment effort. Depending on the level of sophistication and maturity, parametrization choices can be dramatically limited using thresholds and clusters. Thus, there is no need to parametrize every single product/location individually. This formalization results in the reduction of dimensions (i.e., governed by the decision parameters) when discussing alternatives. It may, therefore, seem to limit choices and reduce flexibility as the fine-tuning of complex and low-level plans is not possible anymore. Our experience suggests otherwise. It shows that the reduction in planning complexity outweighs this disadvantage. This reduction in planning complexity makes the value chain easier to manage, which translates into less planning effort, including more straightforward cross-functional review meetings and executive alignments. Besides, the reduced complexity enables to separate planning and execution as the inherent complexity that requires bundling of these activities in one head is not a factor anymore. Instead, the separation of planning and execution is possible with all advantages that job specialization offers. Moreover, the reduced complexity and the specialization lead to a shorter time required to train people on the job, which contributes to the structural flexibility of the planning department.
Conclusion
In this article, we have elaborated on how a parameter-driven value chain works and how it drives significant advantages. In particular, we have described how it improves transparency, how it enables flow, how it helps to master inventory and to reduce the bullwhip effect, and how it supports reducing planning and execution effort.
In the next article, which concludes our series, we will give some advice on how to start a journey to reap these advantages.
More insights:
The Parameter-Driven Value Chain, Part I
The Parameter-Driven Value Chain, Part II
The Parameter-Driven Value Chain, Part IV
We would like to thank Sebastian Hild for his valuable contribution to this article.
