Three Sources of Big Data to Curb Supply Chain Risk

If you drive your costs down to zero, your revenue could be zero, too.
Jeffrey A. BurchillOctober 6, 2015

Although good CFOs never take their eyes off spending, you need to be very careful about trying to wring too much cost out of your supply chain.

This is where Big Data analytics can go awry. Many CFOs deploy Big Data analytics to drive out supply chain costs, and often they’re rewarded on that metric. Believe me, I love cost-cutting as much as the next CFO. But there’s a point of diminishing returns: If you drive your costs down to zero, your revenue could be zero, too.

Cost-cutting techniques such as using sole-source suppliers, lowest-cost providers, and plants in countries where labor is cheapest can be recipes for disaster. As your supply chain costs fall, your risk can soar. If a brittle supply chain eventually snaps – an outage at a single site can halt your product flow – costs can go through the roof as you lose revenue, market share, brand value, and shareholder value.

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Supply chain disruption often results from overreliance on a sole supplier or a key supplier that, for whatever reason, unexpectedly fails to deliver promised components. The supplier’s impairment can be physical exposures – such as a human or natural hazards – or a financial impairment such as bankruptcy.

To ensure your business resiliency, you need the right level of supplier diversification at cost-competitive prices. Big Data analytics can help you achieve this. Here are three fields of risk-related big data to analyze: the CFO’s “private access” data, property risk data, and global risk data.

CFO’s ‘Private Access’ Data

As a CFO, you possess a lot of data at your fingertips that others may not, including accounts payable, accounts receivable, manufacturing data, cost of goods sold, and assorted vendor records. You can compile this data and use analytics to uncover insights concerning both your direct supply chain and your extended one that the naked eye would miss.

Let’s say you’re a car manufacturer, and your sound system suppliers are all unwittingly depending on the same maker of knobs. One storm, fire, or bankruptcy at the knob-maker’s business could disrupt your output for weeks. Analytics can reveal this needle-in-a-haystack risk before it hurts you.

Analytics applied to proprietary CFO data can also help you quickly arrive at fair prices for thousands of components, making it much easier and more cost-effective for your company to contract with second and third suppliers to add needed redundancy to your supply chain.

Property Risk Data

If you own buildings and plants around the world, you’re insuring them. In addition to getting a policy and an invoice from your insurer, you should be getting hard data pertaining to the hazards on your properties – including fire, flood, wind, earthquake, and equipment-failure risk. You should be getting estimated costs for eliminating (or minimizing) those risks, scorecards as to how risky you are compared with your industry, and a range of options for prioritizing risk reduction according to criteria such as cost, cost/benefit, loss probability, and revenue dependency.

Let’s say your insurer’s risk engineers are collecting 1,000 data points during each visit to a client site. Let’s also say they do thousands of visits every year and have been doing so for decades. They should be using this massive data source to continually refine your risk assessments. You should be able to see the resulting insights on dashboards.

Among the potential disruptions are natural occurrences you might be tempted to overlook. Perhaps your plant is in the heartland, which sounds safe, but may be in the flood plain of the Mississippi River. It’s not a question of if a flood will occur but when. Or maybe you’re on an earthquake fault line or exposed to high winds. That should be part of your supply chain risk equation, since these conditions may make it difficult to get supplies to your company. Another factor to consider is how much your suppliers may be susceptible to natural hazards.

Global Risk Data

How can Big Data help you when you’re taking a macro look at your global supply chain? Perhaps you’re selecting among potential suppliers, deciding where in the world to locate your next facility, or simply evaluating your existing supply chain for inherent weakness.

You’ll want to know about a range of risk factors, including region-specific natural hazards, terrorism, corruption, economic health, emergency management capabilities, local supplier quality, and the integrity of telecommunications, transportation, and energy supply networks.

Data on these crucial variables is available from a range of sources, including the International Monetary Fund, World Bank, and World Economic Forum. You can combine it with your own data for a multifaceted analysis or a single risk score for a selected region. You can use analytical tools to compare and contrast different regions where you may locate a plant or where a supplier has its operations based.

All of these data types are just examples of the rapidly growing wealth of information you can compile and analyze to make your supply chain sturdy and cost-effective. Remember, cost reduction is important, but you shouldn’t be aiming for the lowest day-to-day operational costs. You want to look to contain your costs over the long term by avoiding the disruptive problems that can put you out of business.

Jeffrey A. Burchill is senior vice president of finance and chief financial officer of FM Global, one of the world’s largest commercial and industrial property insurers. Burchill has held this position since 1999.