We all want better information for our risk analysis (well, at least everything reading articles like this does) and DCDR Research is building a data feed to get you those metrics. But even when you’ve got that data, there’s often one other problem.
How to use it?
Not to worry. Here’s a guide on how to use these reports specifically and if you want some more general background on using metrics in your risk assessments, take a look at these blog posts.
(And if you really want to go deep, take my risk assessment course using code ILOVEDATA to get $50 off.)
How to Use the Metrics
The set of metrics used by DCDR Research is based on the premise that certain factors have an outsized influence on events. By tracking these factors, we can assess how an organization is likely to be affected and react accordingly. (Read the white paper for a full explanation here.) Importantly, the limited data set cuts down on ‘noise’ in the assessment to make things faster and more efficient.
So DCDR Research provides you with updates on the key metrics but how do you put these to work? Here’s the process
First, identify which factors affect your organization.Second, write cause and effect statements for each factor describing how a change affects your operations.Third, assess the potential impact of that change.Fourth, apply the live metrics as the threat component of your risk analysis.Fifth, decide what steps to take (either in preparation or response)
This might look like a lot of work, but it’s similar to all other risk analysis processes and, importantly, steps one to three are all set up so you’re only repeating four and five regularly as the data changes. (You will need to review one, two, and three every so often so it’s not quite set it and forget it, but these aren’t things you do on a day-to-day basis.)
That said, it will be a fair amount of work the first time, especially if you’re starting from scratch, but you’ll already have some idea of how sensitive you are to certain factors so you won’t be starting with a blank sheet of paper. And even if you were, you can take a layered approach and increase the level of detail over time. This makes the workload much more manageable and also allows you to adjust and refine things are you see the real effects of changes over time.
Finally, keep in mind that this system isn’t designed to give you an exact risk valuation down to a few percentage points. Instead, it allows you to put your risks into a hierarchy of most to least severe so you can focus on what’s going to have the biggest impact on your organization and can plan or react accordingly. This means that while we want it to be accurate (as close to the truth as we can) we aren’t trying to be too precise (measuring to the smallest degree possible).
Fig 1 – Accuracy vs. Precision…
How do the Metrics Affect Your Organization?
Start by reviewing the list of metrics. A few will immediately stand out as critical – e.g. crude prices determine the cost of fuel for your fleet of vehicles or wheat is a bellwether for changes to the cost of ingredients for your restaurant. You might also have previous assessments or metrics that you track as a business to help you identify which of the factors affect your business most.
Importantly, unless you’re in a business dealing with the primary factor – in the case of crude, an oil and gas firm – you’ll always be looking for the secondary and tertiary effects of a change. Sometimes these can be complex and not immediately obvious. For example, for an aid agency, an increase in bread prices may increase demand for their services in addition to the potential for increased tension and unrest in the areas where they operate.
However, others may not be so clear right away but you’ll spot their effects as you start asking ‘what if?’ and think about the primary and secondary effects that a change may have on your business. Meanwhile, some factors may have positive and negative effects. For example, if you manufacture vehicle parts, a change in gas prices, triggered by increased crude prices, could simultaneously increase demand for economy sedan parts as people economize while also increasing your shipping costs. You’ll examine these relationships in detail in the next step so just list these generally for now.
Determine the cause-and-effect relationships
Once you’ve identified the general cause-and-effect relationships, refine these into statements along the lines of ‘if x happens, we see y, which affects our organization in z ways’. For example:
If gas prices drop, truck purchases go up which increases demand for our commercial parts.
If shipping demand increases, prices increase making our imports more expensive and subject to delays as supply chains tighten, meaning that we will see delays in getting parts to our manufacturing facility, delaying assembly.
Eventually, you’ll end up with a series of statements, beginning with the main causal factor (‘if crude prices rise…’) and branching out into multiple effects on your organization. Again, capturing all of these will take time, especially if you’re just getting started, but taking a layered approach and repeating the exercise will allow you to refine things gradually.
Some of these statements will identify positive outcomes – upside risks. We’ll look at these shortly but for now, we’ll just look at the process for assessing downside risk.
Evaluate the effects
Finally, you need to determine the magnitude of the impact. Review each cause and effect statement and look at the effect. Now, determine how much of an impact that event would have on your objectives, rating these on a simple scale, such as the example below.
Fig 2 – Example impact matrix
The result is a value that you can then use in conjunction with the threat rating to assess your risk using a risk = threat x impact formula. In this case, the threat elements will be based on our key metrics and the impact on your sensitivity to that factor. The impact part is where the metrics come in.
Anything with a zero rating can be ignored or listed for review later.
Using the metrics
Now, we can apply the metrics. Let’s start with the relative values.
In the current updates, we track key metrics over a 90-day period to help you determine if things are relatively high (expensive) or low (cheap) so you can use these relative values in your decision-making. (90-days is used as this matches most businesses’ quarterly planning cycles. Annual metrics, such as population growth, can be used in the same way for strategic planning.)
Fig 3 – Example of key metrics’ relative values
These relative terms (high, low) can be converted into simple values as follows:
Fig 4 – Threat values table
You can then calculate a risk rating by combining the relative threat value and your impact evaluation for that factor using a risk = threat x impact formula.
For example, if your business is highly sensitive to changes in shipping costs, you might assess this factor as having a significant impact on your business. If prices are very high for the period, you could assess the risk as follows:
risk = threat (very high) x impact (significant)
=> risk = 3 x 4
=> risk = 12
We end up with a result of 12 out of a possible 15 meaning we would describe that risk as critical using the table below.
Fig 5 – Risk description table
Finally, you take this risk rating and use it in the cause-and-effect statements you devised previously allowing you to write a risk statement like this:
From a decision-making perspective, we’ve now got a neat summary of the problem and a numerical evaluation of how severe it is. As you tackle this problem, you can refer back to the threat (iron and steel costs) and impact values to assess how effective your proposed solutions are.
That’s fine for dealing with how things are today, but what about forward planning?
That’s where the trend metrics come in.
The trend metrics show the movement of each metric over a 21-day period both as start/end values (the solid bar) and the fluctuation over that period (the arrows). In the example below, the VIX measurement of market volatility in the US was relatively flat relative to the start and end of this period but it fluctuated significantly over these three weeks. Meanwhile, ocean freight had some fluctuation, but the trend was unmistakably downwards.
Fig 6 – Example of the trends metrics
So if we add these values to our decision-making, we can not only say what the risk is today by taking the relative value, but we can have a sense of where things are going in the short term. That way, you can avoid underreacting to a metric that’s high but is probably moving higher, or overreacting to something that may have peaked and be moving downwards. Something as volatile as the VIX example above, lets you know that you’re making decisions in a period of great uncertainty.
These trends metrics shouldn’t be mistaken for predictions or forecasts but these do give you a sense of how a metric is behaving. So in addition to the relative value, you can use this trending valuation in your decision-making by combining this trend information with your original risk statement.
Prices have been fluctuating slightly but seem to be steadily increasing.
A statement like this would be a good starting point for discussion. You’ve put the data into context, briefly noted how this affects your business, and given a sense of how the situation is trending (without offering a long-term prediction).
More generally, you can use your cause-and-effect statements to determine what effect a trend might have on your organization and plan accordingly.
Risk vs Opportunity
Until now, we’ve discussed how to use the merics to assess risks – the things that can get in the way of your success. But what about the reverse? Can you also use the metrics to identify opportunities?
In short, yes: you just need to add a set of negative values.
You’ll use exactly the same process and values as above but add a set of negative values to the relative terms. Then, go back to the impact statements and clarify which direction has a negative impact, and which is positive. These will be specific to your business or organization: for example, in the example of fuel prices, a low price is good for drivers but bad for refineries.
Apply that positive and negative difference to your threat metrics so you end up with a set of values from -3 to +3.
Fig 7 – Threat values table showing positive and negative values
Now, when you assess your risk, you have a range from -15 (critical downside risk) to +15 (critical upside/opportunity) which you can describe using the terms in the risk description table in Fig 5 above.
If we go back to the iron and steel example from earlier, we can see how a reversal of prices could create an opportunity for the same firm. Expensive steel creates difficulties for them, so they use the negative values for high prices, meaning that the original risk calculations would become:
evaluation = negative (very high) x impact (significant)
=> -3 x 4
=> -12 or Critical risk
However, if prices suddenly dropped and were relatively low, the calculations would look like this:
evaluation = positive (low) x impact (significant)
=> 2 x 4
=> 8 or Significant opportunity
Now, instead of worrying that their projects might be financially challenging, new opportunities may have arisen, or they may have the chance to stock up on supplies to take advantage of the low prices, improving their margins.
Whatever the result, they’ve used the same process to identify an opportunity.
This can be a little difficult to manage initially, and you can get stuck in conversations where you have double negatives, and things get confusing fast. However, if you keep in mind which direction is good and which is bad, you’ll easily be able to apply the negative and positive values to your calculations.
What to watch for
Always keep in mind that these metrics are designed to give you an accurate but not precise view of the risk you face. But these data, especially the trend lines are not predictions and while a trend might continue for a while, there can be fluctuations within that pattern (look at the range of values in the VIX above as an example). Moreover, there can always be macro events that cause huge changes very quickly.
However, even though we’re not in the prediction business, there are things that are worth tracking as a way to get a little early warning of how things might change.
And that’s where the final part of the updates comes in.
The reports flag the things we think will change the situation by adding a ‘what to watch’ comment where possible. These aren’t the only things that can move a market or affect a metric, but these events are what we think will have an outsized impact on things: Beijing’s policies affect iron, steel, oil and shipping, while what the Chair of the Fed says will move the US markets with knock-on effects worldwide. Like these two examples, some things may seem like no-brainers but others, like tension over number plates in Kosovo, may not be on most people’s radar.
Going back to your own cause-and-effect statements, and understanding what affects your business, will let you develop some of your own ‘watch lists’ of things to track alongside our metrics. Consider this some fine-tuning you’re adding to our base model.
Then, when you see one of these flags ‘fluttering’, you can review the situation in a bit more detail, ask ‘what if..?’ and take appropriate steps.
The intent is to develop an accurate, but imprecise model, to help decision-makers by tracking some metrics that have an out-sized effect on a wide range of factors. By illustrating where these metrics fall relative to the last 90 days (e.g. prices are high vs. prices are very low) and showing which way they’re moving, we’re helping you and other decision-makers narrow the scope of your discussion while adding additional context.
That way, instead of trying to track every price fluctuation or market movement, and then do your own analysis, these metrics help you focus on what matters most. There will still be particular factors relevant to your business or industry and you must still monitor these. However, these metrics will help you contextualize the ‘everything else’, particularly factors that you might not have thought were relevant previously.
As always, remember that these aren’t predictions, and prices and values are delayed by at least 12 hours so do not use these metrics for tactical decision-making.
Changes / Feedback
I want to get this model right so I will keep iterating so expect to see tweaks along the way (the changelog is here).
I need help! Your feedback and insight are critical so please let me know what you think. Drop me a line at email@example.com with your thoughts.
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