Tag: Smart Supply Chain
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.
Up until now, seamless, end-to-end good traceability has been more of a pipe dream than a reality. But there’s good news: thanks to the clustering of innovative Industry 4.0 technologies, comprehensive supply chain Track & Trace is now possible – also known as industrial traceability.
Nowadays, everyone is preaching that data is the new oil. But unlike oil, it’s not (just) the sheer output volume that matters here. What really counts is the efficient use of this data so as to draw the right conclusions in terms of the supply chain reliability. Read more
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
A materials controller needs up-to-date information at all times for production planning purposes. He needs to know whether the goods required are going to arrive at the plant in time – and whether they will do so in an intact state, a very important point when it comes to sensitive components. And all this preferably in real time, of course. But this is a lot more than what most track-and-trace solutions have to offer. Read more
A leading aerospace company optimized its global supply chain with SupplyOn through an innovative Industry 4.0 project: integration of the supplier’s MES (Manufacturing Execution System) gives both customer and supplier a virtually real-time, joint view of the supplier’s situation – in terms of demand, stock and production. Replenishment planning is checked against customer requirements, i.e. production orders against stocks. The result is transparency and trend feedback – enabling stock reduction and increased supply reliability.
There are plenty of Track & Trace solutions (T&T) available. Logistics Service Providers (LSP) have been offering it as an add-on for years and several start-ups focus on this aspect, too. So it’s nothing new, right? Or is it? Read more
A leading aerospace supplier has further optimized its inbound supply chain with SupplyOn. As part of an innovative industry 4.0 project, sensor tracking was used to implement the real-time monitoring of deliveries. This not only aims at continuously determining location, but also the early detection of quality defects during transport due to excessive temperatures or moisture. Read more
Track and trace, that is, determining the location of shipments, is definitely nothing new. Yet everyone still seems to be talking about it. How come?
Easy: We know that networked production as well as “smart factories” require reliable information on the delivery status of components. But this also translates to delivery logistics, where it’s important to know, for instance, where a spare part is located and whether it will reach its final destination on time or whether the parts will arrive at the assembly plant (CKD) as scheduled.
Sure, logistics service providers are already able to provide plenty of data regarding the location of a shipment – granted, not always in real-time, but still. Yet, how do we connect our systems with those of the logistics service providers? What do we do with the data? How can parts and status notifications be linked to each other without requiring an inordinate amount of effort and time from service providers and suppliers? How can we avoid having to enter data for different customers into individual custom portals? And how can all this data be analyzed effectively? Questions abound. Read more