How Emerging Technologies are Shaping the Future of Supply Chain and its impact in addressing the Supply Chain Challenges.
The supply chain industry has witnessed a paradigm shift, especially in recent times due to the disruption caused by the Pandemic. The industry is now considering an intrinsic part of the overall business strategy, thanks to the Covid-19, which has made the adoption of Digital Transformation a need of the hour in every industry. Besides, supply chain networks face enormous pressure caused by rising and shifting customer expectations for various products and services. The pre-covid era faced challenges relating to additional customer service requirements or fulfilment needs. The businesses responded by ramping up hiring and also throwing more people to address the problems. However, this strategy was unsustainable, primarily due to the constant tightening of the labour market. By then, a growing number of leaders in the supply chain hit the tipping point and started adapting technology solutions and tech-based business practices to reduce the pressures.
This opened doors for several emerging technologies such as artificial intelligence (especially machine learning), blockchain, augmented and virtual reality, the internet of things, autonomous vehicles and robots, 3D and additive manufacturing, cloud-based solutions, asset-sharing systems, big data, and predictive analytics being pitched to the supply chain market had already proliferated seamlessly. However, which of these technologies are truly ready to be rolled out broadly and still in the developmental stage, best left to the early adopters? In this article, I will be discussing how the emerging technologies that are being implemented to address the supply chain challenges faced by the businesses and others that, while generating huge interest, aren’t yet ready for business-wide adoption.
What does ‘Emerging’ mean?
What constitutes an ‘Emerging’ Technology? Like every other analyst firm, large consulting organizations or multinational corporations seem to have come up with a list of top emerging technologies based on different parameters. However, World Economic Forum (WEF) has come up with one of the best definitions – the “Top Ten Emerging Technologies” report: Emerging Technologies are those that are not in wide use, emerging technologies will provide significant benefits to societies and economies within the next three to five years. Ideally, there should be more than one organization developing the technology. According to the WEC report, emerging technologies will alter or disrupt industries and ‘established ways of doing things, especially the traditional approach.
Few technologies indeed are new in capabilities or concept. Instead, many emerging technologies are replacement tools or add-ons providing incremental changes as part of the age-old continuous process improvement, known as ‘Incremental Innovation.’ When they are new, these technologies often ride up and down the hype curve, sometimes not making the final rise or doing in a different form. However, which of these emerging technologies can ride the hype curve and meet the supply chain challenges in the next five year period.
1. Artificial Intelligence (AI): Just as automated material handling equipment and robots can augment human actions, similarly, Artificial Intelligence (AI) and Machine Learning (ML) can augment human decision making. The job is divided between humans and machines, while each performs the tasks they do best. The machines can accurately and tirelessly churn vast amounts of data and reports or connections it has found through a variety of patterns-matching, neural-network, or other techniques. Then a human expert can determine whether that observation makes sense or action needs to be taken and if that new learning should be automatically applied in the future. Besides, AI should be treated as a highly efficient data scanner that brings situations to a human’s attention rather than an autonomous decision-maker. This is because humans haven’t yet defined the world well enough for AI to understand the situational context and when should the rules be applied or shouldn’t be applied. As a result, AI doesn’t currently have the wisdom to act on its own except in the most well-defined circumstances where few exceptions exist, which could cause significant problems. Hence it is crucial to focus on AI as an enhancer, not a replacement for people.
Successful deployment of AI and ML is not easy and requires considerable skills and capabilities. The business cannot just buy AI solutions, plug it in like a utility, and expect it to churn out brilliant solutions. Instead, they will need highly skilled human ‘experts’ to guide and evaluate the machines and determine how to use the predictions and insights produced by the technology. Unfortunately, it has become highly challenging to find skilled people who understand how to structure and sort data and configure AI. As a result, people with the right skills are currently in very high demand. Therefore, most organizations’ short-term option is to contract with a service provider at a higher cost until it becomes financially viable and realistic to acquire dedicated internal resources. Besides, the job market is quite challenging as the world is heading into a gig-economy which is the future.
2. Robotic Automations: Robotics and Automation have already augmented human actions and mentals tasks rather than mimic people or replace them. The growing interest in robotics is driven by the fact that labour has become more expensive and difficult to acquire and, importantly, retain. As a result, the general managers of manufacturing and logistics are now focusing on channelling humans to do more of what they are good at while reducing their time on tasks that they aren’t good at.
Robots and other devices are already available that perform far more accurate jobs than any humans at well-defined, specific, and repetitive tasks, like reading strings of numbers, bar codes and letters; palletizing like-sized boxes; conducting high-speed visual inspections; and transporting bins, pallets, and cases from point A to B. Besides, they can work tirelessly with the highest efficiency. On the other hand, people are not good at performing extremely monotonous tasks. They will get bored and lose attention, particularly when driven at high speeds for an extended period. Furthermore, humans are not good at recording or copying information. Similarly, if people spend their time walking around (e.g., moving from one pick-up location to another in a distribution centre), then the company is wasting a valuable resource.
Robots are easier and faster to retrain. Retraining can happen in mass with just a software update, even while working on their current tasks. Despite these advantages, there are still some jobs in the manufacturing and logistics industry that are cost-effective when performed by humans. Humans are still the best investment for picking a wide range of shapes and sizes of products combined in a container and placing them in another container or getting them ready for shipment. Humans are good at also seeing patterner unanticipated and making connections between observations. In short, organizations should explore opportunities for Robots and devices to assist humans with the data recording tasks, moving, and inspecting, while allowing picking and decision making to be performed by humans.
3. The Industrial Internet of Things (IIoT): The emerging technology that holds great promise is the Industrial Internet of Things (IIoT) and Industry 5.0; the complementary concept is gaining momentum for a broad range of more timely data using process sensors (for temperature, pressure, humidity, locations, etc.) and automatic identification of transactions (using voice-based or vision systems such as RFID, bar codes, etc.) and processing that data to enable faster and better supply chain resource allocations. These kinds of data retrieval technologies are not new. What is emerging are the tools like edge computing, control towers for filtering, channelling, and analyzing the enormous flow of data to provide visibility of asset and demand status.
Industrial IoT has shown great promise to provide the real-time data analysis required for better decision-making if the business can ensure or meet the demand as agreed. And how to deploy the resources efficiently to meet the demand. Besides, the IIoT enables the adaption of Digital Transformation for better-distributed decision-making. However, the challenge lies in integrating the data using data models consistent for all tools used in the production and distribution hand-over amongst the operations, suppliers, partners, and customers.
Therefore, before implementing IIoT, the business must invest in many prioritized pilot projects within its operations. Because building an all-encompassing centralized infrastructure and framework before implementation is a strategic mistake. Today, companies are also trying out of quick prototyping an approach of smaller and fast failed experiments, which might produce unseen big wins that central planning cannot build or envision. The fastest way of finding a solution is when the small teams closer to the problems could quickly define and implement a prototype solution that can be integrated into the centralized plan and adapted to the organization’s supply chains.
Besides the above emerging technologies, some of the following technologies hold great promise. However, they are still nascent, and the industry is experimenting and testing their readiness for widespread adoption:
1. Blockchain: Blockchain technology has an enormous potential to enable secure, fast, and visible transactions among the partners in the supply chain network. The significant value blockchain provides the ability of trusted systems to share and trigger approvals and payments of transactions automatically based on pre-defined conditions. In addition, electronically enabling transactions that automatically initiate contractual actions or payments can increase efficiencies. Despite all its potential., it currently tends to rely on one or two large, influential trading partners dictating the use of blockchain within a limited, predefined chain of partners. Its goal, however, is to connect together a much more complicated ecosystem (made up of a collection of businesses, roles, and industries) than has been linked together before. Signing up to participate in such a system requires a high level of trust in a set of previously unlinked trading partners. Creating that level of trust requires significant effort. It can be challenging to get two departments in the same company to share information or two companies to agree on data transfer and sharing policies. However, the feasibility of blockchain technology is currently being tested across industry verticals for implementation.
Let us take the example of Blockchain adoption by the Auto Giant BMW: Automotive supply chains are highly complex ecosystems that involve numerous players at different delivery stages. Furthermore, all the stops in an automotive supply chain can also change rapidly according to the needs of various industrial parts, including the availability of raw materials, the supply of specific components from a particular factory, and the demand for automobiles in different regions. Therefore, resulting in many partners in a supply chain managing their data. This has created a ‘dark’ network in which it is difficult to track a component’s origin or visibility of the supply route.
In 2020, BMW announced the launch of its blockchain-powered supply chain management solution called “PartChain.” According to BMW, PartChain was launched in conjunction with its ten suppliers. BMW uses distributed ledger technology to provide transparency and traceability of components and raw materials used in the production of automobiles. In 2019, BMW conducted a successful pilot project for purchasing front lights. BMW initiated the PartChain project to provide seamless traceability through the entire supply chain without the need for a separate, manual tracking system by using blockchain technology to connect all the parts.
This move is designed to take the digitalization of purchasing at the BMW Group to the next level. BMW’s vision is to create an open platform that will allow data within supply chains to be exchanged and shared safely and anonymized across the industry. Besides, Blockchain technology has proven extremely useful for supply chain management. Examples include Ford Motor Co. and IBM Corp. using the technology to track cobalt for electric car batteries and Volkswagen using Minespider GmBH’s blockchain for similar raw materials tracking. In addition, other automakers have used the technology to track cars, such as Hyundai Motor Group’s partnership with Blocko Inc. to track used vehicles and the Mobility Open Blockchain Initiative, a consortium which includes BMW, that is field testing a blockchain-based international vehicle identification system.
2. Additive Manufacturing or 3D Printing: Additive manufacturing is next up on the list of technologies that are not quite ready for scale. Additive manufacturing shows excellent potential for the on-demand production of critical replacement parts or product customization. But while additive manufacturing currently works well for prototyping and visual proof of concept, it is not yet ready for higher volume production. However, that does not mean that you shouldn’t be conducting pilot projects with the technology. Your company should still investigate how additive manufacturing can be used to serve customers (both internally and externally) in new ways. But you should likely not be buying your systems until you are ready to scale up their use, as the tools and level of expertise involved are quickly evolving. Instead, work closely with companies specializing in additive manufacturing or companies with additive manufacturing capabilities that supplement their already proven capabilities in contract manufacturing.
Augmented Reality (AR): While other technologies that expand human capabilities, such as AI and Robotics, are proving to be capable of producing value today, current applications of AR have not found a great deal of traction. Despite great implementation examples in entertainment, gaming, and industrial, AR has not been widely adopted in industrial settings yet. The primary exception is in the areas of maintenance and manufacturing tasks, where AR is helping workers to learn and better perform a wide range of functions. For example, AR enables better evaluation and guidance during processes rather than relying on post-process quality control checks after mistakes are made.
Organizations that are not already exploring use-cases for AR in maintenance and manufacturing that involves replacing or enhancing the use of manuals. They should consider whether employee training could be improved by learning to do a task first in an AR or virtual reality (VR) environment, similar to flight simulators. Be aware that the future may bring a broader range of applications when AR and VR no longer require costly custom applications development.
To summarize, first, the focus should be on emerging needs and not the technologies. It is easy to get carried away by the emerging technologies and their promises. Instead, focus first on the customer or at least the end-customer. Then working backwards through the supply chain and identifying how these new technologies can be applied to the business will help you save money, time and resources. Finally, the way forward, technology solutions will need to provide shared value among a network of partners, like in a cloud-based ecosystem that offers solid capabilities for data gathering, filtering, analysis, advice, evaluation, communication, resource allocation, and measurement. The skills we learned in the elementary school of “working and palying well together” (Collaboration) need to become one of the core capabilities for every organization to succeed and take full advantage of the emerging technologies that will address the supply chain challenges.
