Tag: smart logistics data
Nowadays, everything is getting ever more complex and volatile. Today’s characteristic all comes down to “disruption is the new normal”. But the key is to defy all odds in the best possible way. To stay agile and resilient. Be it a traffic jam, a shipwreck, a container shortage, a strike, severe weather, a volcano eruption or a pandemic that throws everything into disarray. No matter the cause, companies need to ensure smoothly functioning supply chains. Read more
The current coronavirus pandemic poses enormous, unprecedented challenges for companies. Efficient risk management plays a central role in meeting these challenges. It is not so much a question of whether the current situation could have been anticipated and planned for. Rather, the focus is on how companies can now best protect their core processes and increase the resilience of their operations. Read more
It’ s nothing new that considerations of optimization focus on excessive inventories and overly high planning or disposition costs. This is all the more true in tougher economic times.
This is where the new opportunities of digitization come in: the continuous provision of data from supplier production to transport logistics and even in-house intralogistics is no longer a vision of the future. Even minimizing process costs is now a reality thanks to automation. Read more
Transport damages to goods, delays in delivery, poor delivery reliability: These are three typical use cases in which Smart Logistics Data helps to identify and counter risks well in advance. Read more
Missing supplies, i.e. delayed, deficient, and completely absent deliveries, are a serious and recurring problem for companies. But what can you do? And how can you ensure that ordered goods actually arrive on time?
Smart Logistics Data – that is, the intelligent collection, enrichment, and usage of data relevant to the logistics process – promises relief.
A supply chain has to be agile, robust and resilient. Capable of anticipating potential risks and responding in advance, detecting problems early on and flexibly circumventing them.
All this requires the intelligent use of data. But how can we really make data “smart”? Read more