Developing leading indicators for Business Continuity risk

Karl Rosenblum, Global Head, Manufacturing & Risk Strategy, Alcon

Karl Rosenblum, Global Head, Manufacturing & Risk Strategy, Alcon

The industry is getting weary of all the supply issues continually battering us. Tired of hearing the term “supply chain” in the news, but it is the current reality. We are often shocked to discover that what was once a commodity has become a critical component that creates a risk to production or, at worst, shuts down manufacturing! We don’t know about changes that impact our manufacturing supply until it is too late, leaving little time to mitigate the crisis. We become reactive to the latest communication from the supplier.

This comes from an array of possibilities. A tier-three or tier-four supplier of a component or material cannot deliver, and it dominos through the upper tiers of the vendors. Geo-political changes impact what was previously a secure supply chain: transportation channels, regulatory changes, and financial strain on both large and mom-and-pop suppliers.

The question becomes- can we get ahead of these events, or are we in a perpetually reactive mode? We are not blind to risks. We typically have some form of business continuity within our companies, and the more advanced programs identify the most impactful risks and put projects in place to provide backup. Regular reviews occur within the organization, leveraging internal metrics and external input through supplier communications, but we end up with lagging indicators of the increased risk or impact. What was once green becomes yellow and then red. These metrics rally resources to mitigate the risk, but it ends up as a crisis management “tiger team” throwing a massive number of resources against an issue to resolve it in the most timely way. “This needs to be fixed ASAP!” Wouldn’t it be nice if we could have some form of leading indicators? The answer is “yes.” We get there by leveraging our internal and external data sources.

The Internal: Company-generated data

Let’s begin manufacturing. There are three key areas associated with single source risk: 1) those associated with the supply of raw materials and semi-finished components;2) the processes to convert that material into finished products; 3) manufacturing sites (e.g., can the product be made in a different facility?). Breaking down the first area, supply, what percentage of those items used in manufacturing have a second or third supply source? One could also leverage the vendor’s delivery performance, On time and in full. Are things steady or a little shaky? How are things trending?

Moving on to the next area, within the site, is there redundancy in equipment, facilities (e.g., air compressors, HVAC, or energy), and people? What happens when our one subject matter expert leaves the company along with the knowledge?

Lastly, do we have redundancy in manufacturing capabilities? Can we migrate production to a second site if a fire or some weather-related calamity shuts down the site?

These single sources are only a piece of our internal landscape. Thinking broader, our support functions can provide a risk input as well. Leading and lagging indicators can be found in HSE, HR, and Regulatory. Finally, don’t forget to leverage sales data. Are sales exceeding the plan? Is manufacturing running out of capacity sooner than expected? Do the materials ordering plans need to be adjusted? Has there been a shift in customer ordering patterns that necessitates a change in manufacturing?

The External: Data from outside the company

Often, news from external sources drives us to action concerning business continuity. Most likely, we learn of the event as it has happened. A hurricane, a gas explosion, and we react to the possible impacts as it filters through the organization. However, many external sources can offer information on the landscape and trends that we can leverage as leading indicators. There can be single reports based on sectors such as transportation or labor statistics, which could influence the company or its suppliers. Indeed, several companies provide commercial data, analytics, and insights on a supplier’s financial and trade compliance risks and who they supply, and who supplies to them. There are also large insurance companies that collect risk data and provide analytics either as reports or subscription services.

Putting it all together

With this landscape of internal and external data, we have numerous sources to leverage. Many offer their own trends, which can act as leading indicators, “is this supplier going to continue to underperform,” “what is the trend for the OEE of a new production line,” “am I seeing a decrease in transportation capacity?” But other data points will seem more like lagging indicators. What we need is a way to integrate all of the pieces of information. I recommend thinning down the list to what makes sense. Start and then iterate based on the performance of the analytics. It also makes sense to have a mix of internal and external data. Also, consider how often information is updated and evaluate if it will apply to the company and products. Once data sources are in place and a collection mechanism, it is time to leverage analytics. Anywhere from Excel to AI. It also makes sense to weigh the impacts to offer a balanced perspective on all influences, which could provide a risk rating. The ultimate goal is a total risk score which could also be shown as a heatmap. Once in this format, aggregate across a particular portfolio or dive into the product, process, or material granularity. If a certain vendor is identified as struggling financially, what is the impact across the supply and portfolio of products? If a certain manufacturing site is seeing an increased number of audit observations, again, how does that impact the capacity risk and portfolio of products? Does the vendor map from an external analytics company show the supplier is doing fine, but several tiers down, there is a supply disruption brewing that will eventually work its way through to the company? The advantage of integrating the various data sources is that, individually, no alarm bells may be going off, but multiple aggregate pressures put the products at risk. This is the power of analytics and offers the ability to develop leading indicators, buying us the time to develop plans and successfully respond instead of reacting.