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Posted 22 January 2021 by
Trevor Miles
Thought Leader

Resilient supply chain planning: It’s all about the variability

Few mention planning as a means to enhance supply chain resilience other than the article written by Tim Payne of Gartner titled, “Mastering Uncertainty: The Rise of Resilient Supply Chain Planning.” I take the perspective that planning is a key enabler of Yossi Sheffi’s categories of flexibility and culture. Planning, done properly, detects changes quickly and provides rapid (re-)planning across a value-stream—balancing demand, inventory and supply—providing great flexibility. However, this also requires a change in culture.

Gartner’s Payne has several suggestions/prescriptions for increasing supply chain resilience through planning. (A clue of which is his use of “uncertainty” in the title of his article.) At the heart of uncertainty is variability. We are uncertain because things around us are changing and we can’t predict the changes.

Payne’s suggestions for supply chain technology leaders who are responsible for supply chain solutions can be summarized as follows:

  • Change the supply chain planning (SCP) paradigm from deterministic to resilient by leveraging the combination of new technologies and a mindset change on how planning is perceived.
  • Make faster and multiple predictions by leveraging cloud platforms for hyper-scalability.
  • Model and simulate the physical supply chain and align decision making across the supply chain by using a digital supply chain twin.
  • Ensure the different planning decisions required by resilient planning are supported by deploying planning analytics in line with the CORE model.
  • Drive from unknown uncertainty toward known variability by utilizing artificial intelligence (AI) and machine learning (ML) for better predictions.

For this blog, I want to focus on the first and last bullet points.

Deterministic to resilient

All planning solutions on the market today are deterministic, meaning that they can utilize only one number for demand, capacity, throughput, quality, etc., despite knowing these parameters are variable or, to use the true antonym of deterministic, probabilistic. As Payne writes, “This problem is caused primarily by uncertainty—uncertainty of demand, uncertainty of supply, uncertainty of delivery, etc. Supply chain plans typically take little or no account of uncertainty.” Because traditional planning tools are deterministic. Ergo, we cannot achieve resilience using traditional planning tools.

Unknown uncertainty toward known variability

The reason we have “unknown uncertainty” is that all traditional planning tools use master data extracted from ERP systems and other data stores. Not only are these master data values often incorrect, ERP systems are also deterministic, meaning they contain only one value for any number of parameters used for planning. Despite knowing that all these parameter values change depending on operating conditions. However, as neither ERP nor planning systems capture the variability of these parameters, we end up with unknown uncertainty. So, we cannot look toward traditional planning tools to solve this problem.

Enter smart parameters

Axon can analyze vast quantities of historical data to surface the demonstrated performance of your supply chain, especially of the parameters used by planning systems. Not only do we provide the most likely value for these parameters, very importantly Axon also surfaces the variability.

In other words, the analysis performed in Axon provides a better value to feed to deterministic planning tools, and it also allows organizations to make risk-based decisions by providing a complete analysis of the variability. The obvious value to use in the deterministic planning systems is the mode because it is the most frequent value. However, choosing the maximum for supplier lead time, for example, can ensure that the materials are never delivered late. However, this is at the cost of higher inventories because most of the time the supplier will deliver earlier than the expected delivery date.


Axon customers report getting much more realistic plans out of the planning systems, which provide greater tangible results, such as reduced inventory. Another intangible result is the increased confidence in the results generated by the deterministic planning system, which translates into greater adoption.

Equally important is the tracking of these variables through constant analysis to detect any trends.

The moment a significant deviation or trend is detected, both the business process improvement and the planning team can be notified. If detected early enough, the trend can be reversed; if the trend effect is negative, or accelerated, the trend effect is positive.

Above all else, Axon provides a much better understanding of where variability is experienced. Because Axon also provides Value Stream Analysis (VSA), you can identify opportunities for improvement immediately, as well as understand the benefits and limitations of the improvements.

By helping you move from a state of “unknown uncertainty” to “known variability”—and feeding smart parameters to traditional deterministic tools—Axon provides the first step in achieving resilient supply chain planning.

Much has been written about resilient supply chain planning recently, particularly by Gartner, and by many of the management consulting companies. A few examples are below:

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