Resilient Supply Chain Planning: it’s all about the variability
Much has been written about resilient supply chains recently, particularly by Gartner, and by many of the management consulting companies, a few examples of which are below:
- Gartner: 6 Strategies for a More Resilient Supply Chain
- McKinsey: Building Supply Chain Resilience
- BCG: Designing Resilience into Global Supply Chains
- Yossi Sheffi in HBR: Building a Resilient Supply Chain
- EY: How to build resilient supply chains in times of crisis
But let us first explore the meaning of resilience:
As stated by EY, we had architected our supply chains for efficiency, leaving them fragile:
“The COVID-19 crisis exposed the vulnerabilities of supply chains engineered primarily for cost and speed. These supply chain models were not flexible enough to detect and quickly respond to volatile changes in supply and demand, thus leaving decision-makers flatfooted and unable to adapt as conditions changed daily.”
BCG takes a common approach to increasing resilience through increasing capacity and inventory buffers:
“They are exploring options for diversifying and regionalizing their manufacturing and supply networks, adding backup production and distribution capacity, and reoptimizing inventory. Companies are also seeking to improve their supply chain flexibility, risk-monitoring capabilities, and capacity to respond rapidly to new shocks.”
A contrasting perspective is taken by Yossi Sheffi who writes that
“Companies can develop resilience in three main ways: increasing redundancy, building flexibility, and changing the corporate culture. The first has limited utility; the others are essential.”
The rise of resilient supply chain planning
Interestingly 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, also requiring a change in culture.
Interestingly Tim Payne has several suggestions/prescriptions for increasing supply chain resilience through planning, a clue of which is his use of “uncertainty” in the title. At the heart of uncertainty is variability. We are uncertain because things around us are changing, and we can’t predict the changes.
Tim Payne’s suggestions for Supply Chain technology leaders, 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. I will focus on other bullet points in later blogs.
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 Tim 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
LOP.ai 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 LOP.ai also surfaces the variability.
In other words, the analysis performed in LOP.ai provides a better value to feed to deterministic planning tools, and it also allows organization 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.
LOP.ai customers report getting much more realistic plans out of the planning systems, which provides 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 LOP.ai provides a much better understanding of where variability is experienced. Because LOP.ai 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, LOP.ai provides the first step in achieving resilient supply chain planning.