How often do you feel like you’re drowning in data? It’s a familiar feeling for many supply chain professionals in today’s data-rich working environment.
Or maybe you are unaware that the volume of data you’re processing may be in excess of what you actually need. It’s a common pitfall at a time when the number of data sources and data points seem to be growing exponentially.
Either way, a surfeit of data can gum up the decision-making process and make it more difficult to pinpoint the root causes of problems that are undermining the efficiency of your freight network. In effect, data overload blunts your ability to take advantage of the tremendous analytical power offered by advanced transportation management system (TMS) solutions.
There are many causes of data proliferation, but here are five that, in our experience, are especially relevant in the logistics space.
Functional misalignment. The data is not aligned with the specific needs of the functional group. For example, the shipping department needs to monitor metrics such as on time delivery and cycle times, but procurement relies on a different mix of measures. It’s easy to get bogged down in streams of data generated by inappropriate metrics. The same is true for secondary metrics that support the primary set of performance measures. Look to create role-based metrics that help to create clarity and alignment with the overall strategy.
Off-target targets. Setting unrealistic or unsuitable performance targets can easily put you on the road to analysis paralysis. An on time delivery performance goal of, say, 98% is perfectly fine as long as the figure has been arrived at after careful consideration of the market as well as your capabilities. Simply pulling a figure out of the air will almost certainly generate supporting data that is not needed. Think about creating a cross-functional team to align customer expectations and your priorities with your performance targets.
Too many variations on the theme. These days, the number of ways in which data can be sliced and diced seems limitless. On the one hand, this provides huge analytical benefits; on the other hand, being spoiled for choice can tempt the user into ordering reports that are largely redundant or duplicative. Align stakeholders with one version of the truth, as well as the methods used to calculate metrics. For example, on time delivery can be calculated in several ways, including to the minute, to a 15 minute or a one hour window, to the day, or based on a “one day early is late” criteria.
Organizational fragmentation. When implementing a TMS it is routine for the system provider to sit down with the shipper and work out what types of data are needed by the enterprise. However, this becomes challenging when numerous sites across multiple countries will be plugged into the TMS. Establishing the data needs of each outpost takes more time, but the TMS might go live before this process is complete. Plan to create standardized scorecards across sites and leverage role-based metrics to create clarity.
Lack of understanding. TMS business intelligence (BI) tools have grown immensely in both scope and sophistication, providing users with analytical capabilities that did not exist five years ago. But it can be a struggle to keep up with these developments, and some tools might not be used properly or not to their full potential. For example, users might take on more of the analytical heavy lifting than they need to because they’re not utilizing all the capabilities of a BI tool. Start simple and work towards integrating more complexity over the long haul.
This list is by no means exhaustive, but tackling all or some of these issues will bring a much sharper focus to the data that is the lifeblood of effective decision making.
If you currently lack data on freight network performance and want to fill the gap, being aware of these issues will help you to avoid becoming overloaded with facts and figures that you don’t actually need. Maybe you’ll choose to outsource much of the data management burden to a third party provider as part of a managed TMS arrangement.
The pitfalls that lead to analysis paralysis are encapsulated in one critical piece of advice: Understand what your logistics strategy is, as well as the organization’s capabilities in terms of its technology and people, and ensure that your metrics are aligned with all three.
Click here to learn more about how supply chain consulting within a managed services model can help you analyze your transportation data to identify and apply cost-efficient solutions.
Editor’s note: This post originally ran earlier this year. Since it’s a relevant topic, we wanted to share it with you again.