Supply & Demand Chain Executive recently publish an article by Kari Dwyer titled Paving The Way For Continual Performance Improvement that stated that through the availability of actionable data, supply chain visibility solutions become an invaluable asset in providing continual performance improvement.
The article, which pointed out that actionable data are the precursor for effective change, since isolating the root causes for specific performance measurements and providing a tactical approach to resolving them is the fastest, most effective way to gain performance improvements, also submitted that one effective and powerful way to receive actionable data is made possible through supply chain visibility solutions. This is because visibility solutions have the infrastructure in place to prominently display data that need attention, whether through alerts, dashboards, reports, e-mails, hand-held devices or text pages, and direct the information to the right people.
The author then states that visibility solutions allow the presentation of higher-level metrics with the ability to drill into the supporting detail, often through multiple layers, to get to the detail that drives action and that robust visibility solutions build metrics from the bottom up, using the most granular level of detail available to build a solid foundation as a basis for all higher-level metrics. Up to this point, I agree wholeheartedly!
The issue that I have is that the author appears to be implying that a visibility system is enough to turn data into actionable data. A visibility system is absolutely necessary, but it may not always be sufficient. Just because a dashboard turns red does not mean you have enough information to fix the problem! The author correctly notes that:
- for the majority of supply chains, corporate-level metrics and performance numbers are often comprised of results from different systems,
- information [only] becomes actionable once the data can be analyzed in such a way that a decision can be made to effect a desired outcome, and
- knowledge of pertinent information is essential to effecting change that will lead to cost savings
However, the author then implies that a visibility solution alone will meet all of these requirements. Let’s analyze the example provided which states that knowing that vendor compliance averaged only 98% last quarter is not actionable in its truest form. If you take out the two worst performing vendors, it could be that compliance was 99%, with the two worst vendors performing at 95% and 93%, respectively. In this situation, it would be wrong to chastise all of your vendors and say they had to do better – as the correct solution would be to, if they were willing, sit down and jointly work out the reasons for their poor performance along with the required resolutions, and if they were not willing, to terminate the relationship. However, just being able to drill down into the performance metric and find out you have two vendors with poor rankings is not enough. It does not tell you why the rankings were poor.
What if the rankings were poor because the supplier was consistently one day late with their shipments – would the visibility system tell you this? Presumably you would be able to drill down on the supplier’s overall performance metric and determine it was it’s delivery metric leading to its poor performance, with quality and reliability and other rankings A+. If the visibility is based on a reporting system – it might only contain this scorecard data and you might not be able to figure out that the supplier was only one day late when it was consistently late. Although the visibility system has identified the source of the problem, the data is still not actionable. Unless you know how late the shipments are on average, and the reason for the lateness, you cannot act upon the data to effect a desired outcome. What could be happening is that the truck could be showing up at 10 am when it’s supposed to be there at 8 am. Because the warehouse inventory system only tracks lateness in terms of days, each time the truck shows up at 10 am, it is recorded as being one day late in the metrics column being sucked in by the visibility system – which ignores the arrival time column, which shows it arriving on the correct day, but two hours late. Furthermore, this could have all been because of a simple miscommunication by a junior member of the procurement team who told the supplier that the warehouse needed the truck there by 10 am to have it unloaded on time, when in reality the warehouse needed the truck there by 8 am to have it unloaded on time. And as far as the supplier was concerned, it was compliant on delivery terms at least 99% of the time.
The fact of the matter is that most visibility solutions today are simply reporting solutions on top of traditional data warehouses, which suck data into a static cube and run roll-up reports on that cube to produce metrics and KPIs. Although this is often a great solution for identifying where you have a known problem, it doesn’t always give you enough information to allow you to figure out why you have the problem and what you need to do, at a detailed level, to correct it. Chances are you’ll need to augment it with a sophisticated analytics (or business intelligence) tool that can not only do a deep dive into the data in the solution and the data in the systems the data was amalgamated from, but one that can also build cubes on the spot and slice and dice them in dozens of different ways until you find the data you need to identify the source of the problem.
Furthermore, visibility tools can only tell you when you have a known problem type, they can’t tell you about unknown problem types. For example, let’s consider small package freight. A visibility solution would only generate an alert or turn a dashboard red if you were not being charged at contracted rates. It wouldn’t detect that 90% of your packages were going out as 5 pounds when, in fact, 80% of these packages should be going out as 2 pounds or less because most are simply short documents and contracts! And it definitely wouldn’t detect when you are sending federal express packages between neighboring buildings or, even worse, between two floors in the same building! (It happens!) Thus, a visibility solution on its own will never be enough – you need to constantly be applying analytics to find missed exception cases which translate into new rules that need to be added to the visibility system.