Supply chains have come into sharp focus in recent times, as weather, world and geopolitical events have shown how easily even global links can be reconfigured or halted. Transparency, visibility, and resilience have never been more important. Natalya Makarochkina , Senior Vice President, Secure Power Division, International Operations, Schneider Electric discussed those characteristics and how they are increasingly provided through a combination of technologies, namely, Industrial Internet of Things (IIoT) and Edge Computing.
Sensors and data
The development of Internet of things (IoT) devices, mainly monitors and sensors that have been adapted for all kinds of industrial settings in IIoT, has allowed ever greater levels of visibility and monitoring. Entire processes, from ore to product have been instrumented to provide control at every step. However, with that level of monitoring comes ever increasing levels of data.
That deluge of data is in danger of becoming stale if it is only funnelled back to a central data lake, where it might wait for hours or even days before it is processed to produce intelligence by which informed decisions could be made. The data lake approach is still effective for the enterprise-wide view, but for evolving supply chains in particular, a lower latency, localised capability is advantageous.
Data in such circumstances is understood said to exhibit gravity, residency, and latency characteristics.
Data gravity is the theory that any aggregation of data will tend to attract applications, services and more data to it, often with the unintended consequence of becoming more unwieldy and difficult to manage. Data residency is acknowledging issues around jurisdiction and usage beyond the borders or geographies where it was captured. Data latency is time between capture and processing of data. All of these characteristics can conspire to delay turning data into actionable insights.
In areas such as food production, these issues could render the data effectively useless if not processed into intelligence in a timely manner. There is an example from the dairy industry, where raw milk collections are analysed in the truck for parameters such as fat content, and edge-based applications residing in local sites provide analysis to determine the best processing facility to receive the batch, all without the driver stopping. While the aggregate data of all shipments over time can be processed to give the high analysis, this local, immediate determination at the Edge ensures the best use of the raw material on an individual basis.
Edge alternative
Edge Computing offers the opportunity for data processing and analysis to happen close to where the data is generated, allowing abstracts and insights to be centralised where they can be more easily processed, saving time and energy in transmission and storage.
The sensor on the milk truck determines key characteristics of the load. It signals via a mobile network back to a local base where an application applies a set of policies to determine the best processing option for the raw material. Live production data then informs selection of the right facility with the capacity to handle the load, before a mobile signal directs the driver in real time.
Edge Computing thus provides the opportunity for lower latency in deriving intelligence from data that can be acted upon immediately as part of a greater effort, immediately offering solutions to issues around data gravity, data residency, and data latency.
IDC has predicted double digit growth for investments in Edge Computing, listing some 150 use cases for the technologies, reflecting the level of interest and utility for this addition to enterprise data architecture in tackling these new data issues from new methodologies and usages.
Supply chains
A good example of this is in supply chains generally, and more specifically cold chain logistics.
Many organisations have been working to fully instrument supply chains to better manage and operate these often complex systems.
According to ABI Research: “Edge Computing helps with supply chain efficiency because it frees up resources, lessens the reliance on human management, and vastly increases the latency for time-sensitive processes. As companies invest more in IoT devices and demand faster results for warehousing and transportation processes, Edge Computing is positioned as a lucrative option for companies looking for logistics management solutions.”
While many supply chains are applying Edge Computing decision making today, Gartner says that the focus over the next three years will be to identify more ‘use cases’ where connected automated and autonomous networks of edge decisions can be further enabled.
By 2027, Machine Learning (ML) in the form of Deep Learning (DL) will be included in more than 65% of edge use cases, up from less than 10% in 2021, according to Gartner.
AI and ML have been critical in areas such as warehouse automation, agriculture automation, agrifood, remote mining and automated port operations. Increasingly, retail too is developing customer experience applications, enabled by edge deployments, with examples such as Augmented Reality and smart mirrors.
Building blocks and cold chains
Other emerging technologies are also being combined to extend the power of Edge Computing in supply chains. Recently, companies such as IBM have been combining IIoT with Blockchain technologies in supply chain solution architecture. This adds to the resiliency of the architecture the immutability and transparency of Blockchains, improving security. In such applications, Blockchains operate using the proof of stake methodologies that are as much as 99% less energy intensive than with the likes of cryptocurrency examples.
One area of global concern currently is food distribution. Within this, the complexities of refrigeration present additional challenges. Cold chain logistics apply primarily to food distribution, although it also has healthcare applications. It is basically the logistics of materials that require refrigeration. This discipline is benefiting from these new solution architectures.
Cold chain logistics have been the subject of scrutiny, and much criticism, for inefficient energy use and waste. This was such an issue that the European Commission set up a project specifically to tackle the issue entitled “Improving Cold Chain Energy Efficiency”.
A combination of IIoT, Blockchain, and Edge Computing gives the perfect architecture to achieve far greater efficiency in cold chain logistics, minimising waste and inefficiency through a combination of greater visibility, real time insights and operational strategy improvements.
However, the lessons learned provide a basis for all supply chain operations to learn and improve.
Architectural ideal
According to a global trade authority, Edge Computing is the ideal architecture to enable the gathering of sensor and monitor data to be brought into a Blockchain-enabled system, whereby a distributed ledger node can run in an Edge Computing environment, participating in a larger system that is tamper resistant and resilient, while providing greater visibility and traceability to reduce losses and improve effectiveness.
There are also other benefits, in equipment health monitoring to facilitate predictive maintenance, improved audit monitoring, GPS and RFID tracking, and more effective temperature and condition monitoring.
In many verticals and sectors, the specific combination of IIoT, Edge Computing, and other emerging technologies, is providing greater insights and therefore greater opportunities to refine and improve operations.
In supply chains generally, and manufacturing and cold chains specifically, these technology combinations, underpinned by Edge Computing architectures, are enabling efficiencies and resilience that will help them to meet the new challenges of reconfigured global trade.
