An article by Ieke le Blanc
“Fixing a hole where the rain gets in”
“Fixing a Hole Where the Rain Gets In “ is a song by the Beatles from 1967. Today’s supply chain managers might know this feeling all too well. Risks are everywhere, which supply chain risks to manage before something goes wrong, and the rain gets in, is the key question.
Recent supply chain disruptions caused by the COVID-19 pandemic or the current war in Ukraine prove how easily supply chains can be disrupted and indicate the inability of many businesses to react rapidly to unforeseen changes in demand or supply. The tension between China versus Taiwan / the US is another example: We should not underestimate possible implications. Next to the vulnerabilities related to the semiconductor industry, the Taiwan Strait is a key route for ships passing from China, Japan, South Korea, and Taiwan to points west; according to Bloomberg, 88% of the world’s largest ships passed through the waterway this year.
With reduced gas supply from Russia and the current labor and raw material shortages, it doesn’t look like we are going back to ‘normal’ anytime soon. Rethinking supply chains for resilience is not optional anymore. It has become a mandatory duty for the supply chain professional and business leader.
Conventional supply chain risk management assessments don’t work!
Conventional supply chain risk management assessments typically follow a three-step approach:
- Brainstorming possible risk events
- Assessing the probability of risk events
- Determining possible mitigation actions
Brainstorming might be a ‘fun’ activity, but how well have we been able to come up with the multiple crises we currently face in the world? Humans have been notoriously bad at estimating probabilities too.
Having recognized the above weakness, companies should stop thinking about risk events, and focus on the importance of the individual links in their supply chains. An approach already taken, for example, to manage supplier risks. It often starts with making an ABC analysis based on yearly spending. Using the Pareto principle: 80% of the spend is with 20% of the suppliers (A). For these most important suppliers or components, strategic buffers are created and alternative suppliers are contracted. However, the approach is prone to fail.
Even the tiniest missing chip or screw, can stop an entire factory with a knock-on effect through multiple chains. The availability of materials we have taken for granted has become scarce and disruptive.
The solution: time-to-recovery and time-to-survival analysis
Back in 2014, Simchi-Levi et al. published an article describing a different approach, by making use of mathematical modeling techniques. This approach identifies supply chain dependencies and bottlenecks by removing a ’node’ from the network and determining the impact on performance metrics compared to the baseline. A node can be a supplier, a factory, a production line, or a warehouse, but also more granular, to the level of a bill-of-material item. Key elements in the approach by Simchi-Levi are the concepts of ‘time to recovery’ (TTR) and ‘time to survival’ (TTS):
- Time to recovery (TTR): the time it takes for a node to recover to full functionality after a disruption.
- Time to survival (TTS): the maximum amount of time the system can function without performance loss if a particular node is disrupted.
The time to recovery (TTR) is an expert judgment input. The model identifies the performance loss caused by the absence of the node. If the time to recovery is longer than the time to survival and the impact (performance loss) is high, these are the suppliers or supply chain assets to protect.
In various articles the approach was proven to be successful, however it hasn’t been widely applied in the industry. This is a real pity. We see three reasons:
- The approach requires the development of a mathematical representation of the companies’ supply chain. It requires a skill that we nowadays call data science.
- The approach is quite data intensive and (master) data reliability is an issue for many companies.
- Risk resilience wasn’t a hot topic a few years ago. It wasn’t obvious to invest in something you don’t know you will ever need. Firemen are not praised for their heroic behavior in fires that didn’t occur.
The digital twin supply chain as an enabler
The digital twin, maybe one of the hype words of the last years, is in essence nothing else than a digital representation (mathematical model) of the current supply chain. It enables various what-if analyses. Powered by optimization technology and with the right capability and knowledge available, this can be the basis for the risk modeling approach. Hence it overcomes the 1st reason mentioned above.
The 2nd reason, data quality, is somehow there to stay. However, the risk of doing nothing because of poor data quality might be severe. It’s another reason why master data management is one of the most important fundamentals. In the EyeOn consultancy practice, we see more and more companies understanding this. It has become part and a necessity to the success of many digitalization initiatives. ERP and APS systems simply need to be fed with reliable and accurate information too.
The 3rd reason, sense of urgency, is well understood in the meantime. There is a saying it is best to “fix the roof when the sun is shining”, we are afraid, we have to do the repair while it is already raining sometimes.
Optimize your supply chain risk management? EyeOn is there to help!
Are you assessing and optimizing your supply chain network to become more resilient? At EyeOn we are there to help you on this journey. We have the experience and expertise to support projects and help you build in-house capability. In case you want to accelerate and structurally embed supply chain risk management, our planning services team can help with a digital twin as a service. We develop and maintain a model of your supply chain and are ready to run an assessment with live data when needed.