BY HANS BERGGREN
At PipeChain, our vision is to deliver software solutions that support automated decision-making in supply chains. This vision is based on the fact that the level of maturity for increased digital collaboration within industrial supply chains is gradually rising—as it is now. It is also a vision that is becoming increasingly achievable thanks to the development of AI-powered software solutions.
When PipeChain was founded in 1999, our vision was based on roughly the same goal, but at that time we believed it was automatic inventory replenishment based on the sharing of information regarding inventory levels, gross demand, actual deliveries, and a number of critical parameters that would revolutionize the world’s supply chains. Now, however, we see an evolution unfolding before us, driven by an ever-increasing degree of digitized flows within supply chains, ever-greater access to high-quality flow data, and ever-improving conditions for building effective cloud-based decision-support applications that, with the help of AI, can guide users toward the right decisions. This will gradually drive the industry’s supply chains toward more efficient processes and smarter flows.
Identify Your Level of Maturity
A key factor in successfully increasing the level of collaboration within supply chains—with digitized workflows serving as the natural foundation for this collaboration—is understanding where a company stands in terms of its maturity level. What I mean by this is that those in charge at the company understand where the company stands and how the business can continue to develop toward an ever-higher level of maturity. Gartner has developed an excellent description of five maturity levels to serve as a guide for this understanding.
Gartner's Maturity Stages in AI Development
Phase 1: React — Silo-based, autonomous, and reactive thinking
This phase is characterized by autonomous departments or units within the company—such as sales and manufacturing—that manage logistics priorities through manual processes and various non-integrated systems. There is a lack of holistic processes, standards, and systems across functional boundaries, and consequently, a lack of coordination between them.
Phase 2: Anticipate — Functional Thinking for Scalability and Efficiency
The logistics function is being centralized to increase efficiency and productivity. Activities and performance are monitored across the entire organization and reported from a holistic perspective, which improves the ability to forecast demand and, consequently, enhances the ability to plan operations. The focus is on creating standardized processes and methods to achieve economies of scale and increased efficiency, thereby boosting profitability.
Phase 3: Integrate — Integrate your operations with your supply chain
The logistics function is now being integrated into the company’s entire supply chain. The focus is on understanding how logistics affects customer service, purchasing, and manufacturing. Productivity gains and cost reductions are achieved through a higher degree of supply chain integration (customers, suppliers, third-party logistics).
Phase 4: Collaborate — Collaborate with a focus on your Value Chain Network
In this phase, logistics is an integral part of the company’s supply chain vision, where trade-offs between profitability and customer value can be made in a planned and deliberate manner. Customers and suppliers are digitally integrated, and information sharing is extensive, with a high degree of visibility in addition to pure transaction automation.
Phase 5: Orchestrate — Act as the conductor of your network to create customer value
Logistics and supply chain management solutions now support horizontal processes across the company’s ecosystem of business partners, which—in addition to enabling efficient and profitable operations—opens up new business opportunities. The business flow through the company’s supply chain is also real-time, enabling further improvements in visibility, better-informed decisions, and opportunities for increased market share and growth.
Part of the company's digital transformation journey
Applying AI/ML to supply chain processes requires this kind of reasoning. Furthermore, before we begin our digital transformation journey with a client, most companies we come into contact with are at the equivalent of Gartner’s Phase 2 and on their way into Gartner’s Phase 3. This further implies that AI/ML in Phase 3 should focus on the company’s own understanding of what is happening in its processes, as well as the ability to use AI/ML to improve and refine decision-making. As supply chain efforts mature and evolve toward Phase 4, AI/ML support can increasingly focus on collaborative processes and improvements in joint decision-making to drive ever-greater value creation.
IBM's Maturity Framework
In the field of AI/ML, there is a similar maturity framework, in which, for example, IBM, in a 2021 report (“AI Maturity Framework for Enterprise Applications,” March 2021), highlights the following three levels, as shown in the figure above:
Silver Level: At this level, the company explores what AI is, how it can impact its operations, what tools and technologies are needed to start using AI, what data and how much of it are required, and so on. This is a stage that does not directly impact the company’s business; rather, it is intended to gain experience and generate ideas on how AI can create value for the company.
Gold Level: At this level, AI solutions deliver business value without requiring the staff involved to be data engineers in order to use the services. Data quality and the ability to create value through process automation are demonstrated at this level.
Platinum Level: The company has now reached a level of AI adoption where it is business-critical to its operations and organization. The company now also relies on AI-based services, with a key focus on anomaly detection, where AI plays a vital role. AI capabilities are sophisticated at this level, which means that the company’s AI services are in a phase where they are gradually learning more and more based on data from the real-world situations the company encounters and the feedback the organization provides to the AI services.
Starting Your AI Journey
At PipeChain, our vision is to deliver software solutions that support automated decision-making in supply chain flows—solutions that not only streamline processes, resulting in immediate value creation, but also create greater flexibility and smarter flows that deliver significant added value in the long term. However, we have realized that both Gartner’s maturity phases for efficient and lean-based supply chains and IBM’s corresponding maturity framework for the implementation of AI support in business operations imply that the AI journey we are now embarking on must start at the right level to succeed. We have therefore chosen an industry segment that is in as mature a ”Gartner phase” as possible—namely, the automotive industry—and customers in this segment who have also made significant progress in their digital transformation. Furthermore, based on IBM’s framework, we have selected ”Phase 2 Gold,” where our first AI/ ML-supported application has access to sufficient high-quality data, is based on a process that can be highly automated, and addresses an area with sufficient potential for improvement so that our customers’ users can derive significant value from the service.
A big first step for us…
We are therefore eagerly looking forward to working with our customers to complete the development of our software solution for analyzing delivery schedules in the automotive industry. The launch is scheduled for spring 2022. The software is, of course, cloud-based and features a visual component at its core—providing visibility into historical plans and how they change over time—supplemented by traditional KPIs for forecast quality, over- and under-forecasting, and delivery accuracy. In addition to this functionality, we are introducing an AI service based on ML (machine learning). The purpose of the AI service is to use it as a decision-support tool for identifying deviations that are ”worth” focusing on and taking action regarding, or for the absence of deviations—where the AI service is trained to flag a call to action because the absence of a deviation is unexpected. The starting point for our AI project has been to create a so-called MVP (Minimum Viable Product), where, as a first step, we develop a product that is as limited as possible while still creating enough value to be relevant to the market.
…but not for humanity (yet!)
Our AI journey with our customers has only just begun, but it will still be a major step for us, even if at this stage it isn’t a particularly big step for humanity. However, our vision is definitely to help make the world of supply chains better through automated decision-making!
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Hans Berggren is the CEO of PipeChain Group
