How does a parameter-driven value chain work and what are the benefits?
In our first article, we argued that a parameter-driven value chain is the route forward that works much better than a traditional value chain centered around hierarchical planning – be it with or without the latest technology enhancements like AI or ML. We said that the reason for our conviction is that linking the value chain across stages and time horizons based on parameters has many advantages compared to mastering an abundance of individual plans.
In this article, we will give more insights into why and how a parameter-driven value chain works and what type of benefits you can expect.
An alignment of a lower-level decision with the upper level is only required if the lower-level decision seeks to leave the allowed boundaries. The result: the parts of the value chain can operate on their own, like self-regulating control loops within the precise confines of the parametrization. Apart from exceptional situations, the stages and horizons are in sync without the need for constant adjustments to the plans to keep them under control.
In the following section, we will illustrate the critical aspects of parameter-driven planning and its benefits.
Parameter-driven planning creates transparency
In traditional planning, no one else than the experienced planner could tell whether an order would make it in time or not. No one else has the experience to interpret the plan in a meaningful way. With parameter-driven planning, the picture gets much clearer. Instead of chasing orders across the value chain and anticipating any impacts of the next planning run, only the integrity of suitable buffers (time, capacity, inventory) matters. As long as the buffers are intact, lead times between the stages are stable, and target performance is ensured. Also, the impact of changing parameters like lead times and buffer levels is immediately clear – instead of wondering how postponing one order impacts the others.
Go with the flow: how flow makes sure that the sum of parts adds up
Among other things, Lean production, DDMRP, and Theory of Constraints (ToC) have something particular in common: they put a strong focus on the continuous flow of material. But how does flow help with planning? And how does parameter-driven planning come into play?
Lean: match supply and demand by creating a one-piece flow, aligning production, and implementing pull
Lean or what is sometimes called Just in Time (JIT) is centered around the following elements to create a smoothly flowing supply process that is matching supply with demand. The key aspects of Lean are producing at the rate of customer demand and implementing a pull system.
Producing at the rate of the customer translates customer demand rates to takt-times across the value chain. Besides work organization and product design challenges, the need to maintain these takt-times, and thus flow, requires that buffers are located and sized accordingly. Otherwise, the supply chain may not be able to respond to customer demand and its fluctuations. However, it is one downside of Lean that it lacks the rigor in locating and sizing inventory systematically. Therefore, it is not easy to resize and adjust buffers to demand fluctuations that occur due to reasons like seasonality, promotions, peaks, or product lifecycle. Production smoothing, which is at the heart of Lean, can be very challenging in situations with fluctuating demand.
Finally, implementing a pull system is critical to make sure that supply and demand are in balance. Setting up Kanban or a Make-to-Order system are options for implementing pull. Kanban relies on sizing buffers and card counts, as referenced in the previous paragraph.
Demand-Driven MRP: decoupling throughout the value chain makes sure that goods flow and plans are feasible
In Demand-Driven MRP (DDMRP), the value chain decouples at critical points, e.g., smaller sections define the chain with a stock buffer separating the parts and linking them at the same time. Decoupling happens for different reasons. What they have in common is that they all support goods’ flow: stabilizing lead times, reducing lead times between stages, protecting bottlenecks of starvation or blocking, and synchronizing of goods’ flow. Clear rules guide the parametrization, i.e., the calculation of the inventory buffers, considering demand and supply characteristics, like variability and lead time.
As the complex chain of steps breaks down into decoupled segments, lead times are becoming more stable, the outcome gets more predictable, and the plan and reality are much more likely to converge.
To get a more comprehensive overview of DDMRP, make sure to check Markus Kuhl’s article “Why now is the time to transform supply chain planning”.
Theory of Constraints: a structured approach to de-bottleneck the value chain and to facilitate flow
The core of ToC is about identifying bottlenecks of a system, aligning operations to the bottleneck, and eliminating the bottleneck constraints. In a production environment, synchronization follows the so-called drum-buffer-rope (DBR) principle. The bottleneck and its throughput define the drumbeat. Identifying the bottleneck, monitoring the utilization, and setting up the buffers together follow a parameter-driven approach.
In contrast to purely parameter-driven approaches, the subsequent subordination of other processes does not follow a schema. However, flow is improved, which makes sure that planning is meaningful as execution and planning are much more likely to converge.
Make better use of your inventory dollars: tame the bullwhip finally and make outcomes plannable
The bullwhip effect is a well-known term for the amplification of variability across the upstream stages of the value chain, even though downstream customer demand is relatively stable. One approach to reducing this effect is to decouple value chain stages. Inventory, extra capacity, or flexibility act as a shock absorber and make the overall value chain more manageable. As a result, the overall bullwhip effect reduces significantly in most cases.
Decoupling is an integral part of Lean Kanban, ToC, and DDMRP. The changed role of the forecast in DDMRP is also adding to the dampening of the bullwhip effect: forecast changes are not directly impacting the propagated plans anymore, but only indirectly via buffer resizing. Usually, this resizing is less prone to adding to variability because buffer sizes are typically not immediately adjusted.
Inventory and flexibility buffers can finally serve their original purposes: to mitigate customer demand variability, instead of the variability that you have created yourself.
Moreover, a well-defined, fact-based buffer sizing mechanism itself makes buffers more robust and efficient. An improved buffer sizing mechanism makes your value chain more efficient in terms of service level and inventory. With its parameter- and rule-based buffer calculation, DDMRP is more advanced than Lean production in this regard, even though both pull the same levers. Also, DDMRP, ToC, and Lean Kanban share the focus of protecting the bottleneck by reducing variability. However, DDMRP offers a more comprehensive approach to determine precisely how to decouple.
Separate the configuration and planning/execution layers and reduce firefighting
Parameter-driven planning lays the foundation of streamlined operating models. The reason: it clearly distinguishes between parameter configuration and executing against the parametrized rules, e.g., buffer replenishment. This distinction enables the E2E synchronization as it is much easier to align the tactical configuration layer than to align a collection of plans on different levels. The execution stays within the limits of the configured control ranges, and firefighting reduces to handling the exceptions from execution within the configured parameters, ranges, and rules.
Also, the separation of responsibilities between configuration and execution will almost naturally lead to differentiated planning roles and appropriate organizational models, e.g., a distinction of central planning and decentral planning, and the implementation of planning centers of excellence.
Overall, the separation of configuration and execution activities brings about the opportunity to reduce effort through specialization and automation, and to significantly diminish firefighting.
Conclusion
The parameter-driven value chain differs significantly from traditional planning. Parameters link the value chain, which leads to E2E visibility and transparency. Also, there is no need for an abundance of intricate, detailed plans anymore. The focus is on creating flow, making better use of bottleneck capacity, and reducing the bullwhip effect. As a result, enterprises can reap significant benefits from making this transition.
In the next article, we will go into more detail on how the parameter-driven value chain generates the benefits that we have presented in this article.
More insights:
The Parameter-Driven Value Chain, Part I
The Parameter-Driven Value Chain, Part III
We would like to thank Sebastian Hild for his valuable contribution to this article.
