Challenge IoT - The digital logistics chain
Technologies such as IoT and advanced image recognition systems hold a multitude of favorable optimization opportunities, especially for logistics. So far, however, the topic is still in its infancy in many companies. The question of how to finance new technological solutions is particularly complex because logistics is an interplay between a wide variety of partners, each with their own unique starting points.
For example, an industrial company may have the motivation to automate its yard logistics, but the logistics service provider does not have the resources to adapt to a variety of different solutions at its customers.
"The biggest challenge in adopting new technologies is incompatibility with existing systems": This is what the study "Opportunities of digital transformation" of the German Logistics Association (BVL), for which around 1,350 participants were surveyed. Although 73 percent of the companies surveyed rate the opportunities of digital transformation for their company as high to very high. However, more than half of the companies want to wait for tried-and-tested practical solutions.
"This attitude can be detrimental to business, because in the digital age, a high speed of innovation is more important than ever"
warn the authors of the study.
Data-based logistics
The drop in the price of sensors, RFID chips and cameras makes it possible: Today, an announced transport can be verified at the factory gate using a cognitive service with OCR license plate recognition, and the system automatically opens the barrier for it and assigns it a place.
More and more logistical objects such as containers, swap bodies, rail cars, pallets, bins, trucks, tanks and ships are equipped with corresponding technology, generate data and make it available.
Technologies for location determination, real-time tracking and real-time visualization eliminate the massive problems that arise in practice today because you don't know exactly where a transport, equipment or product is located, for example.
Sensors for temperature monitoring or level measurement trigger automated process steps, drones help with inventory, apps on mobile devices replace manual steps and record data directly at the location of the process event.
New data coming
Probably the biggest change to digital logistics is the handling of numerous hardware data from sensors and other edge devices: Very few companies are currently prepared for this.
On the one hand, IT infrastructures and architectures must be specifically geared to handling a wide variety of data formats. On the other hand, the architectures of the process-controlling SAP systems, which deliberately seal themselves off from the outside world, make it difficult to integrate non-SAP real-time data.
Companies face numerous challenges in this regard: Not only do the systems have to be opened up intelligently, but it is also necessary to identify which data can specifically help at which point in the process.
This means that in the digital supply chain, the systems at the respective point in the process must be able to smartly integrate data from different sources and orchestrate the process.
The example of a company from the food industry shows how data can contribute to process automation: Here, the trailers must have a certain cooling temperature before transport.
Previously, this process took place purely manually, with an employee tracking information on cooling temperatures on the screen and then triggering appropriate processes in the system.
Today, a sensor in the trailer reports when the temperature is reached, and the trailer is automatically released in the system for loading. Connectivity between end devices and the SAP system is a particular challenge, especially for large corporations whose systems are divided into Internet and intranet zones: The discussion of security aspects often takes longer here than the actual implementation.
Flexible intermediate layer organizes data
In addition to a transparent data and process model, the digital supply chain also requires an integration layer on which data flows together and a set of rules defines what happens with which data and which process steps are influenced in what way: no small challenge.
In the future, intelligent algorithms for self-learning systems could also help to create and maintain these sets of rules. An integration layer is also important because logistics and industrial companies are coming into contact with more and more (IoT) platforms - in other words, with more and more data hubs for connecting external partners.
Generic integration services for flexible interaction with new data sources are therefore crucial. A typical example is the classic inventory process in which something is counted in the warehouse using a scanner.
Such a service "counting order" should therefore be programmed in such a way that it can be supported with any technologies, but as a result a counting result for the respective inventory documents is recorded and processed by the system.
Shipping companies need to analyze and decide which providers they work with and what the layers between external partners and their own systems look like. Instead of developing hundreds of interfaces and responding individually to ever new solutions that are currently mushrooming, only a generic approach can work.
Sensors and other hardware in the electronics sector are also subject to increasingly short product life cycles: they should therefore be able to be replaced quickly with new ones. The Leogistics Digital Supply Chain software solution forms the starting point for this flexible approach.
Digitalization in the automotive industry
The automotive industry is a good example of how significant optimization potential can be leveraged in the area of equipment movements. The material stock is often stored in containers on trailers in the trailer yard and must be brought to the loading gate at the production site just in time or just in sequence.
Because many movements take place every day, approaches such as big data analytics are worthwhile. The data analysis then clearly shows under which constellation which employment order took how long. This information can be used to develop sustainable, learning site logistics.
Only when the movement requirements are transparent does it become clear how they can be covered by intelligent plant transfers. For example, a German premium manufacturer at its largest US production site in Spartanburg was able to use Leogistics to optimize its inbound supply chain management.
450,000 cars roll off the assembly line here every year: The site is a hub for traffic and material flows on a monumental scale. Every day, 1,100 trucks arrive here alone, apart from 50 shipping containers of imported material transported by rail from the port.
In the connection of SAP ERP and telematics data, a central supply chain control tower today ensures a consistently transparent visualization of the supply control across all transport routes. Weather and traffic data are included, and geofences make it possible to plan for delivery delays at an early stage.
Bottleneck loading ramp
According to the BVL study, 61 percent of data needs on material flow disruptions are still unmet today, for example. A particularly critical bottleneck in site logistics is the loading and unloading of vehicles at the production site.
Since there is only limited space at the ramp, there are so-called time windows (slots). In current slot solutions, slot management initially involves only the binding allocation of time slots. If a delivery is late, the loading locations have to call the freight forwarder, who in turn often has to call their subcontractors.
Valuable time is wasted, and it is often too late to reschedule. Only an advanced solution such as the supply chain control tower approach implemented by Leogistics takes real-time dynamics into account. It shows how valid the planned slots still are at any given moment. If necessary, a truck planned for later that is already in the vicinity is brought forward.
Policy of small steps
The implementation of digitization is a comprehensive task that is often not tackled due to a lack of capacity or tight budgets. Nevertheless, it is essential in order to keep pace with the emerging dynamics of business models.
Small steps often help to get things moving. In many projects, it has been possible to develop the first manageable steps together with user companies on the basis of design thinking approaches and proof-of-concept projects, which bring very concrete benefits.
A Leogistics user from the chemical industry, for example, solves the problem of a not very large loading ramp used to load drums with temperature-controlled contents with an automated real-time solution.
If the truck does not arrive on time, the drums used to have to be reheated and staged again. Today, thanks to digital and real-time-based inflow monitoring of incoming shipments, the system can reliably say that a truck is on time as soon as it passes through a geo-fence outlined around the plant and will thus reliably appear at the plant gate 20 minutes later. This saves considerable effort.
