In an article for SupplyChainBrain last year, managing editor Robert Bowman listed "quantitative analysis for risk management" as one of the "Seven Best Practices for Supply Chains in 2025," noting that "banks today can model the rarest of catastrophic events — the so-called Black Swans." Bowman further advised that "supply chain managers need to be able to do the same."
However, progress made in 2014 suggests that 2015 maybe the year we’ll see the emergence of predictive risk quantification in supply chain management. Furthermore, the tools that facilitate this emergence will derive from the insurance industry, and not from banking.
While the insurance industry and supply chain managers have had limited direct interaction until now, meaningful supply chain innovation will likely arise from this evolving partnership. Combining supply chain best practices with predictive analytics and risk data — the life blood of the insurance industry — can deliver value far beyond the traditional activities of pricing and transfer of risk to an insurer.
The 2014 report by the University of Tennessee and UPS Capital, Managing Risk in the Global Supply Chain, correctly notes that “insurance providers offer solutions to circumvent, protect against, or ultimately help companies financially recover from many of these risks.” The next two sentences in the report are key: “Insurance companies possess a preponderance of readily available data on supply chain risk. Such data can be invaluable in assessing and managing supply chain risk.” That very data and the tools already in use to turn the data into actionable knowledge are the seeds from which predictive supply chain risk quantification is growing.
Why Is the Risk-Adjusted Supply Chain Inevitable?
Business dogma espouses the belief that you can’t manage what you don’t measure. Supply chains are a great example: Supply chain managers and companies served by supply chains often find it challenging to invest (a predetermined sum) in risk mitigation when the cost of a risk and the degree by which that risk may be reduced have not been quantified.
But that’s just the beginning. The importance of risk quantification goes further than understanding the return on investment from risk mitigation. The science of risk quantification provides visibility into supply chain risk in a way that allows for a deliberate choice to accept more or less risk and an understanding of the trade-offs with critical business drivers, such as inventory levels and working capital. This is known as “risk-adjusted optimization.”
Why Will It Happen in 2015?
To understand why now is the time for the risk-adjusted supply chain to emerge requires taking a look at how the insurance industry has evolved its use of data since the days when captains sought news of shipping risks at Lloyds Coffee House in the 17th Century. The first catastrophe models were developed nearly 40 years ago and are now used by hundreds of insurers, reinsurers, banks and governments to make risk-based decisions about trillions of dollars of assets around the globe exposed to property damage from catastrophic events. Many of those assets are components of supply chains, which insurers and reinsurers insure. More recently, insurers have been developing products to insure against any type of supply chain interruption. With input from retailers and manufacturers, these tools are being adapted to provide operational decision-making support for supply chain managers and are now at a point where supply chain managers can start requesting and testing different outputs. The addition of data and indices that forecast political, economic, workforce and societal risks further extends the tools beyond “black swan” events to those that have a daily impact on supply chains.
Simultaneously, shippers are focusing on the uses of data to drive enhanced supply chain visibility as a potential source of competitive advantage. Many well-known manufacturers have presented compelling cases for the ROI from enhanced supply chain visibility. Furthermore, greater visibility and agility are critical to support omnichannel retail strategies. That provides other incentives for companies to seek and obtain better supply chain data. Additionally, regulatory compliance and sustainability programs are also frequently demanding more data capture.
The fact that risk managers and supply chain managers are starting to share their expertise and tools also reflects the change that we are seeing. Risk managers are learning more about supply chain management, and supply chain managers are learning more about risk management.
Beyond Supply Chain Risk Management
Supply chain managers who see benefits beyond risk mitigation will probably take the lead and shape the use cases for predictive risk modeling.
The ability to predict and quantify risk in the same way that the other components of supply chain costs are computed will be transformational in supply chain management. This was summed up well by Kevin O’Marah, chief content officer at SCM World, in an interview last year in AvNet Supply Chain Velocity when he was asked, “What topic/issue do you think is the most overrated in discourse on the modern supply chain?” O’Marah’s response: “I think the concept of lean is most overrated. Lean works great if you are replenishing a process that is reasonably stable or repeatable, but the problem is, today processes are less repeatable and less stable than they have ever been.” He went on to say, “If you ask me, supply chain risk is a banner reason to not be too lean.”
If supply chain risk were quantified, the balance between “lean” and “resilient” (think “cost” and “service”) would be more balanced, or at least any imbalance would be the result of a deliberate strategy. As the tools and analytics to quantify and predict risk become better understood and embedded in supply chain decision-making processes, they’ll be used to support any decision with complex variables and uncertainty — that is, all decisions.
Another example of ways a supply chain can potentially leverage the data and tools traditionally used by insurers is in addressing demand risk. Although insurers and most supply chain risk managers focus on upstream supply disruptions, it’s often the demand fluctuations that ripple through the supply chain that cause the greatest economic risk for manufacturers. The tools that insurers use to understand the impact of weather and environmental change on their business can also be used by supply chain managers to better understand weather-driven demand fluctuations and even certain commodity risks. More extreme weather events that affect downstream logistics and “last mile” delivery can also be better managed with those tools.
Over time, the data and analytics that power such models will likely become a key component of supply chain planning and execution. The value of better data will become more obvious, and the models will get better as more data is captured — creating a virtuous cycle.
What Companies Can Do in the Next 12 Months
The emergence of predictive risk quantification in supply chain management is important to the sometimes-distant relationship between risk managers and supply chain managers. The more useful the models become to the planning and execution of supply chain operations, the more risk managers will have a seat at the supply chain table.
The temptation for risk managers is to point to catastrophic events as a reason to invest in risk management, but that approach may be met by the “it won’t happen to us” response. Supply chain managers have found a seat at the executive table because supply chain has become a competitive differentiator in most businesses — those with data insights have a voice as well as a seat. The risk managers who know where and how to source these models will be right next to them in 2015. Those first companies to adopt these models will probably gain a competitive advantage that may be heightened by their ability to influence how the models evolve. That advantage can be extended because the finish line is a distant one, if there is one. Supply chain managers should seek out their risk managers and ask about the predictive models their insurers use.
As 2015 progresses, predictive risk quantification will emerge from the shadows of the insurance industry to bring value to supply chain managers. Future posts will chart the progress of this initiative: the challenges, the successes, and particularly, the lessons.
Source: Verisk Analytics
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