Master data – the hot potato in logistics
09.12.2024 – Since the beginning of my professional career, I have heard time and again how much companies suffer from poor master data quality in logistics and how urgently this issue needs to be addressed. In particular, the master data for loading and unloading requests is a pain for everyone involved – shippers, forwarders and consignees.
But concrete measures and tangible improvements? I see little of that. Nothing has changed in the last 20 years, especially in the area of loading and unloading requirements. Shippers, logistics providers and consignees all work in their own silos and don’t communicate with each other. How can they when no standards have been defined and communication has been the same for decades?
Collaboration is the key
Collaboration across the supply chain network is essential to achieve sustainable master data optimisation in logistics. Suppliers with their customers, shippers with their freight forwarders, warehouse service providers, industrial parks with industrial companies – everyone has to pull together. However, I see far too few of these initiatives in Europe, and hardly any in the area of data exchange. There may be good approaches on paper, and millions of euros have been invested in guidelines and concepts, hundreds of pages have been written. But so far there have been no concrete implementation projects.
Five arguments, or "common excuses", for not tackling the problem of unstructured and outdated master data for loading and unloading requirements in logistics
Instead, I often hear very similar, supposed arguments from companies as to why they are unable to tackle the master data problem. An almost universal pattern emerges. The topic of master data optimization is tossed around like a hot potato until it finally drops. Here is my personal top 5 list with my comments and tips.
- “We have an economic crisis.”
The phrase alone has a paralyzing effect and is universally applied to anything that sounds like action and change. In critical times, the pressure for cost efficiency and optimization increases. Master data is a “low hanging fruit” that can be implemented quickly and requires few IT resources. Especially when employees are spending a lot of time and effort on emails and phone calls to pass on relevant delivery information or to explain why another truck had to be turned away. Especially in times of economic stress, it is important for companies to reduce spending on non-value-added activities. This includes, for example, the manual and time-consuming processing of logistics enquiries for freight tenders. Crises are always opportunities: temporary downturns always free up labour resources. You just have to know how to use them effectively!
- “We are implementing SAP S/4HANA”
Anyone who thinks that SAP S/4HANA doesn’t need structured master data for logistics requirements is sadly mistaken. S/4HANA has the same free text fields for logistics requirements that R/3 had. If you don’t have this master data on your radar when implementing S/4HANA, you will have no information base for relevant processes in TMS or Yard Management. Just like before. So this wouldn’t be an improvement – it would just be a transfer of the (bad) status quo to a new environment.
- “We don’t need any data, our freight forwarders know everything.”
There are indeed carriers who know all the loading and unloading requirements and do not need any information from their customers. Especially not structured information. If you have such a carrier in your organization, you should cherish him because he’s priceless. However, you are likely to find that the cost is high. Because of the dependencies involved, you should not try to put your transport out to tender in this case: a change of carrier could be fatal to your transport reliability, and customer complaints would be inevitable. (And if you must, there’s probably an Excel spreadsheet or PDF document somewhere you can consult). What could go wrong?
- “We all have neither complaints about missing data nor special logistical requirements.”
Really? Every company has specific loading and unloading requirements. No company would say to the carrier: come when and how you like. At the very least, there are opening and closing times, loading point sites, registration procedures and often safety requirements. This minimum set of requirements applies to every carrier or consignee. And the more complex the industry, the more there are. Sometimes trucks are rejected because the carrier or driver didn’t know something they should have known. Demurrage charges are often incurred, schedules are disrupted and the goods have to be delivered a second time.
- “No time for master data. Let the AI do it.”
Digital projects that ignore the basics risk failure. Everyone knows that a bad analogue process becomes a bad digital process. And then AI is supposed to come to the rescue. But what exactly is it supposed to do with the data? AI solutions need structured data – they don’t make data structured. So to those who are hoping that AI will solve the decades-old problem of poor master data quality, I can only say: Hope dies last.
It’s time to roll up our sleeves. Improving master data quality in logistics is a challenge that can only be met by working together. For supply chain companies, collaboration means taking responsibility for accurate information and making it available digitally. It is time to put aside false arguments and take action. This is the only way to improve efficiency, transparency and resilience in supply logistics. Anyone dreaming of AI-assisted logistics optimisation must first clear out the graveyard of unstructured master data. Or in the words of Sherlock Holmes: “It is a great mistake to theorise before you have data. Inevitably you begin to twist the facts to fit the theories, instead of fitting the theories to the facts.
