Strengthening your supply chain one link at a time.
When performing a supply chain network study there are many data inputs that go into a completed analysis with a resulting recommendation. One of the main components is transportation data. This blog focuses not only on how incomplete or inaccurate data impacts a supply chain analysis but also identifies missed opportunities for transportation groups and companies.
What is incomplete transportation data?
Depending on a network study’s ultimate goal there are various types of transportation data used as input. Some studies are “high level good enough” projects where general assumptions are adequate for achieving study results. Other studies are more detailed and use precise transportation shipment data. This shipment data should include at minimum five distinct factors: origin, destination, transportation cost, transportation mode and shipment size.
Incomplete transportation data would be defined as records that do not contain one or more of these five components. For example, a company does not provide a large percentage of shipment costs for the shipment dataset. Or a company does not have a large percentage of the shipments’ shipment size. Or a company does not have actual ship from or ship to locations and instead provide corporate address locations. Or a company does not provide actual or accurate shipment modes. When these pieces of data are missing or inaccurate then assumptions are used. Assumptions get in the ball park of real costs. But actual data are the real costs.
From a macro dataset perspective, the larger percentage of records that contain incomplete data the greater use of assumptions. There is a point where so much work is devoted to cleaning data and using assumptions, that one runs into the “garbage in garbage out” potential. An example is a dataset that contains 50,000 shipment records and 50-75% of these do not have accurate costs to the point where assumptions needs to be made on cost. The study is now foreced to back into a total transportation cost (assuming there is a total transportation cost) using assumptions to the detail shipments. Using assumptions is necessary to some extent and is typically straightforward and applicable to the project. And in some cases when assumptions are overused it may lead an incorrect ballpark result that are then applied to a strategy for the company, such as warehouse location tradeoff analysis. The result is a suboptimal implementation.
What are the reasons for companies not providing accurate transportation data?
Why do companies do not provide complete transportation data? The simple answer is this data is not available. Why is it not available? Because it typically is not a core goal for the company or transportation group. And one would conclude that the benefits of having accurate shipment and carrier data are simply unknown to companies.
Transportation data via a TMS is typically bolted on to an ERP system. An ERP system is an accounting and order system that typically does not include the necessary attributes required for transportation data. Thus, there can be inconsistencies and missing data. Transportation data can also come from a third party, such as a freight audit company. In some of these cases the audit company may not have the a focus on data accuracy and completeness as a core competency and only focus on freight payment.
Without a complete TMS or a solid data warehouse storing transportation data, transportation and carrier data typically resides in different forms in different departments within a company. The format may be in Excel files and include or exclude attributes and costs, or summarized in methods that are specific to that specific department. And then there is the time used to clean, audit and fix the data on a project by project basis. And it can be time consuming.
The underlying issue is that achieving accurate transportation data for a project is typically a very manual process for companies in various formats for various specific unrelated goals and projects.
Benefits of having accurate transportation data
On demand accurate transportation can be extremely useful to companies and provide value in various ways. Accurate and timely KPIs would be the immediate benefit. Tactical analysis that typically takes days to complete would be automated and streamlined. Strategic projects that may be on a wish list are now a distinct possibility.
There are potential unrecognized lists of tactical and strategic projects that would now be available if transportation data is accurate and timely. Think of the options as it relates to transportation routing, inventory management, delivery times, extra costs attributed to inefficient routes or shipments flows, and supply chain disruptions where speed is necessary. And the timing aspects of these new projects are the key. Analysis that takes days and weeks can be streamlined to be used in a more effective manner.
How to reduce the impact of bad transportation data
First a transportation data implementation strategy that is aligned with senior management is required. This would also include a strategy aligned with the IT group. Prior experience indicates discussions with these two groups are absolutely necessary to make sure all parties are on the same page with an understanding of the goals and benefits.
The tasks for making transportation data a powerful tool are more routine. They key here is that they require dedicated resources to perform. But the benefits are real. These data cleansing tasks would include the following three areas.
Data cleansing and validation – Regularly clean and validate transportation data to ensure accuracy, including addresses, zip codes, and geographic coordinates. This task is basic and incredibly important and usually on the chopping block when it comes to budgets. But the benefits are incredible.
Data sharing practices – Establish clear data sharing protocols with partners to ensure consistency and accuracy across the supply chain. The partners could be carriers, brokers, warehouses, etc. And accuracy and the ability to automate datasets is key.
Regular data audits – Conduct periodic data audits to identify and address data quality issues proactively. Audits could be a system of scripts to identify outlying data, missing data, and inaccurate data. It could be any process used to verify the data is accurate the first time.
Conclusion
The benefits for having accurate transportation data and organized datasets are numerous. The reasons for not having accurate data and datasets are short sighted. Senior management does not want to hear how it takes a number of weeks to complete a transportation project with the disclaimer that this data “is in the ballpark”. So, the logical solution is to develop a transportation data strategy with senior management and use your transportation data as a competitive advantage.
—Tom Schaefges, St. Onge Company
