Exposure to supply chain risk has increased substantially in recent years and effective risk management is becomming a growing challenge. One reason for increased risks is that supply chains are becoming more vulnerable: globalisation has made supply chains highly complex, just-in-time inventory means less tolerance of disruption, and news of ethical and sustainability failures spreads instantly via social media. A second reason for increased supply chain risk is that risk events are increasingly frequent and severe: for example, Iceland’s volcanic eruption, Japan’s tsunami and Thailand’s floods occurred in quick succession, affecting many global supply chains. And at the level of individual companies, risk events affecting the supply chain range from the ethical – such as exposure stemming from poor working conditions in factories – to safety issues such as product recalls for car and food manufacturers.
Supply chains often involve hundreds of direct suppliers, who in turn are reliant upon many thousands of lower-tier firms. This scale makes identifying and assessing supply chain risks a challenging task, yet many large businesses still use overly simple approaches that can give a false sense of security. For example, it’s dangerous to rely on ‘traffic light’ indicators of risk levels in the supply chain: an amber indicator can be viewed as indicating medium risk, but may actually conceal missing information and hence major risks. Similarly, the popular technique of conducting risk assessments only on the top 100 suppliers in the supply chain by spend can mean that risky areas are overlooked. Simple models that just add and multiply values aren’t equipped to handle incomplete information – such as lack of knowledge of the supplier’s own supply chain – and this is something that’s vital for a full understanding of risk and effective risk management.
To help your organisation improve supply chain risk management, we have developed an innovative approach that can model the probability of risk events at any level in the supply chain. This approach to risk management enables you to work with incomplete information while gradually making it more complete. We can also help embed advanced supply chain risk management techniques into your business.
Model the probability of risk events in the supply chain
Events originating in the lower tiers of supply chains can have enormous implications for effective risk management. Examples include the closure of Merck's Xirallic plant at Onahama following the Japanese tsunami, and the explosion at the Evonik plant in Marl, Germany. PA has therefore developed an advanced risk management tool, PerimetA, which models the probability of a risk in any tier of the supply chain on higher tiers, including your own business. The model takes into account both established facts and incomplete information.
Improve information on supply chain risk
We can help you improve your information through an iterative process. Our model evaluates (and can place a monetary value on) the impact of incomplete information, so you can prioritise the gathering of information towards the areas that will most improve understanding of your supply chain risks and support better risk management. We provide practical help in addressing any gaps in your risk data, adding (for example) analysis of social intelligence, or research by our Knowledge Processing Centre which can analyse country-specific risks, key company events, and many other sources of risk in the supply chain.
Integrate advanced risk management into the business
With backgrounds in a wide range of industries, our consultants understand how your business operates and can help you integrate new information and techniques into your operations. We’ll work with your own people to implement any improvements in risk management suggested by the risk model, building their skills as we do so. Areas where we can help include:
Assessment and improvement of sourcing procedures
Identification and management of strategic suppliers
Supply chain and logistics diagnostics and improvement
Alignment of commercial incentives with intended outcomes
Collaboration with vendors to deliver higher levels of innovation