Supply chain managers are gradually gaining a better understanding of their daily operations, but they may be missing out on a key aspect. While it is commendable to know what is happening today and how your supply chain compares with industry leaders and standards, it is even better to see what will happen tomorrow – before it happens. Benchmarking and key performance indicators (KPIs) are both important parts of supply chain management. They can be used as a platform to develop predictive indicators that guide you in adjustments to your supply chain now for protection against or leverage of situations likely to arise in the future.
The Benchmarking and KPI Platform
A brief recap on these items will set the scene for the paragraphs to follow. Supply chain benchmarking is the process of measuring processes, products and services to see how they compare with others. You can make a comparison with industry standards or averages, or with the top performers in your sector or in others. Thus, you might choose to record your organisation’s DIFOT performance (Delivered In-Full, On-Time) to see how well it compares to industry expectations. Benefits of benchmarking include being able to set realistic targets and adopt best practices. By extending to compare with other non-competing companies or industries, you may uncover significant differences and new supply chain methods that your own industry is not yet using.
Key performance indicators then help you to monitor progress towards the results you want. As the word “key” suggests, KPIs should be for a small set of the most important measurements to be made. They provide a quick, direct way of seeing if your organisation is meeting a target or if there is divergence from the standard it is trying to achieve. They can also be a handy way of communicating priorities to supply chain staff and an enterprise in general. While one KPI may also be broken down into component KPIs to be sure the right bases are being covered, long lists of KPIs are to be avoided. Nine or ten is often a reasonable maximum. Some organisations also get excellent mileage from focusing on even fewer.
How do Enterprises Use Benchmarking?
One of the first questions a company or organisation needs to answer is what to benchmark. For supply chains, the Supply Chain Council offers the SCOR (Supply Chain Operations Reference) framework. SCOR has three main components: process modelling, performance measurements and best practices. In the performance measurements part, there are over 150 key indicators that measure supply chain performance. While SCOR is designed to be useful for any kind of supply chain operation, it is also to be used per individual supply chain, rather than a group of supply chains. This is an important point, because one enterprise can in reality have several separate supply chains, each one defined by the customers served and the products and services provided. In this case, benchmarking is to be done on each internal supply chain operation, separately.
An enterprise then selects the core set of indicators that is the most valuable to it. The selection can be determined by business goals, KPIs that other enterprises are using, relevant industry standards and the possibility of making meaningful, comparative measurements. The principles may seem simple, but in practice, organisations must be wary of quagmire debates about what to measure, how to measure it and how to interpret the results afterwards. A pragmatic or “suck it and see” approach that starts off with a few likely candidates and refines them or adds to them (while keeping the total number small) may be the best route to improvement.
The “Perfect Order” Example
Typically, an enterprise wants to deliver its products or services to the right customer at the right time, in the right place, in the right condition, and with the right invoice. Assuming that this process is also profitable to the enterprise, it then provides a foundation for customer satisfaction and repeat business. A “Perfect Order” indicator based on the combination of the five indicators above can be defined to track overall how close the enterprise is coming to this happy state of affairs. This indicator is of particular interest because it corresponds to customer expectations: customers want deliveries to be perfect.
If standards for the five separate indicators are being achieved 100 percent, then the overall Perfect Order indicator will also show 100% (100% x 100% x 100% x 100% x 100% equals 100%). But what happens if all five factors are being achieved at only 99%? Then the Perfect Order indicator will become 99% x 99% x 99% x 99% x 99%, which equals about 95%. Of course, the Perfect Order indicator could show 95% because just one factor is underperforming at 95%, even though the others are achieving 100%, and so on for other possible combinations. In any case, if the Perfect Order indicator shows less than 100%, it is possibly to immediately drill down to check each contributing indicator to see which of those five lower-level indicators is causing the problem.
Should such a Perfect Order indicator always show 100%? In an ideal world, yes, it should. However, if your benchmarking activities show you that the industry standard is 98%, while your organisation is already achieving 99%, there may be justification for sticking with excellence as a competitive differentiator, rather than pursuing absolute perfection.
KPIs for Yesterday, KPIs and KPPs for Tomorrow
Based on past data, a key performance indicator is necessarily reactive. In other words, it shows you that a problem has occurred. As in the Perfect Order example above, you can examine contributing factors to find any culprits for poor overall performance, and take the right remedial action. However, damage may already have been done. Dissatisfied customers may voice their complaints to others. In general they are more likely to criticise an enterprise than satisfied customers are to praise it. By the time your KPI has shown you there is something wrong, you may have lost valuable customer goodwill together with future revenues and profits.
It would clearly be preferable to have indicators that give you information about what is likely to happen in the future, and not only what has happened in the past. Depending on who is talking about them, you may hear such indicators being called KPPs (key performance predictors) or KPIs, where this now stands for key predictive indicator.
Demand forecasting and planning are examples of predictions that use indicators in order to reach a conclusion. Those indicators may include sales pipeline information about with deals to be closed this month, deals in progress and the number of new leads generated per month. They may also include information about the economy and about exchange rate trends, if goods and services are being supplied in different countries.
How Do You Define Metrics for the Future?
The future usually contains more unknowns than the past. Whereas many standard industry models exist for the “rear-view” or diagnostic KPIs we were discussing before, the prognostic KPI (or KPP) that looks ahead will often need to be customised. However, there are a number of general rules that apply to both rear-view and predictive KPIs:
- They must be sufficient to cover the critical areas of supply chain operations
- At the same time, they must not be so numerous that they cause confusion or oversights
- They should be linked to priority business goals, rather than less meaningful activities
- They will need to be updated as market and enterprise needs evolve.
- They must be based on reliable data
- They must have clearly defined owners to avoid indicators operating in a vacuum with nobody checking their relevance or taking the action required.
Let’s return once again to the Perfect Order KPI example above. Predictive indicators such as those for demand planning or for transport availability and fluidity can be used to take preparatory actions to ensure that indicators such as “delivery at the right time” and “delivery in the right condition” stay at 100%. In turn, this helps the overall Perfect Order indicator to stay as high as possible.
It is also worth mentioning that KPIs based on historical data (the rear-view, diagnostic KPIs) may also have predictive capabilities. For instance, average customer satisfaction tells you about what your customers think of you after the different deliveries you have made and other factors from the past. However, the same indicator can also point to likely customer retention rates, new customers acquired through referrals, and thence to changes in S & OP costs and ultimately to future supply chain profitability.
Trend analysis from historical data may also give a glimpse of the future. Looking at past results for KPIs can reveal deeper underlying changes or seasonal effects, both of which can be used to enhance the predictive power of the KPI concerned.
KPIs, Supply Chain Complexity and Compromise
While we continually move towards a better understanding of supply chains and how they work, they remain complex. Just as equations in mathematics with more variables are harder to solve, supply chains that draw on input from multiple departments, suppliers and distributors are harder to model and tune than, say, production line scheduling on its own.
In addition, supply chains are complex enough for certain factors to be in opposition with one another. The classic example is the duo of customer satisfaction and supply chain costs. An organisation can try to push supply chain costs down by carrying less stock and being less reactive to customer demand. There is then a negative impact on customer satisfaction. If on the other hand customer satisfaction becomes the overriding criterion, costs rise.
Supply chain managers are typically aware of this. KPI pundits on the other hand may insist on having KPIs that do not work against each other, meaning that efforts to improve one KPI do not automatically worsen another. While in principle this sounds sensible, supply chains remain balancing acts between different goals and priorities. KPIs that work antagonistically against one another are all part of the territory.
The practical solution is to manage so that the KPIs concerned remain at overall optimal levels, with for example “enough” customer satisfaction and “enough” profitability. Determining what “enough” is in each case is done by experience and judgment, trial and error, relevant industry benchmarks, or a mix of these. Predictive KPIs or KPPs then help an enterprise to keep the conventional KPIs at the levels or within the ranges required.
Driving Along the Road to Better Supply Chain Performance
Supply chains are not alone in facing the challenges of understanding what has happened or what to do about it. Other functional departments and organisations use business intelligence (BI) techniques, often in the form of software programs, to generate KPIs and to better understand past and future performance. The advantage of such computer-based approaches is that they allow graphical displays to be easily generated in the form of dashboards with performance “speedometers” or “rev counters”, and displayed on a PC or smartphone screen.
The analogy of automobiles is a popular one for describing “descriptive”, “decision” and “predictive” business intelligence accordingly. Descriptive BI or analytics correspond to what a car driver sees when looking in the rear-view mirror at the road behind. We have already referred to KPIs using historical data as “rear-view” KPIs in the same way. Decision BI or decision analytics inform the driver about immediate actions to be taken (turn left, brake, speed up, etc.). Predictive BI is then similar to the GPS that the driver uses to see where the road is going afterwards, future traffic conditions, and so on.
In the same way that supply chains often have much to gain from benchmarking input from other industries, the ways in which business intelligence is being used to create, manage and react to KPIs are also potentially beneficial.
Benchmarking and key performance indicators are already a solid combination used by numerous supply chains to improve management and performance. Broadening horizons by looking at benchmark data from different industries can open up new thought processes and possibilities for an enterprise to steal a supply chain march on its competitors. Now it is also time to bring predictive KPIs or KPPs into the mix. These predictive KPIs are likely to be more specific to the enterprise concerned than the historical KPIs used so far. On the other hand, looking at how other sectors are using predictive business intelligence is likely to yield additional inspiration for improving supply chain performance too.