Digital Transformation » How AI enables the cognitive supply chain

Imagine a scenario where you have a critical client meeting and your flight is delayed due to aircraft maintenance and a lack of spare parts. Imagine another scenario where a manufacturing company cannot commit to an incremental business opportunity due to inflexible production lines. Or a promotion that results in a significantly lower uplift than estimated, leading to supply chain losses from excess inventory. Supply chain-operations are far too slow to respond to changes in demand.

All of these scenarios impact market growth and cost. They drive the need to make faster and more effective decisions, form seamless collaboration with supply chain partners, enhance real-time visibility of risks and opportunities and create closed-loop adaptive planning. For this reason, supply chains must be highly responsive, agile and flexible in order to meet the CXO’s growth agenda.

The enterprise of the future

Technological advancements, such as the internet of things (IoT), cloud computing, big data, and artificial intelligence (AI), are able to dramatically empower supply chains. With the influx of data and access to digital technologies, supply chain management is at a new tipping point. It is now possible to capture, store, process and share data in real-time to make faster and more effective decisions.

To survive and thrive in a world where change is constant, enterprises have to evolve in a different way. They have to approach issues within the sphere of the organisation, instinctively.

The future of business is the instinctive enterprise, with AI at its core, which bridges the interrelation between people, processes and domain knowledge. The instinctive enterprise uses digital technologies with internal and external data to spot patterns, create predictive insights and act before others see what’s coming. It breaks down silos and embraces partnerships, even with competitors, to become agile and improve customer experiences. By empowering employees with technology and creating fluid, purpose-driven career paths, its people make decisions faster.

The instinctive enterprise has a supply chain that anticipates rather than reacts to changes in demand and it collaborates with suppliers across the supply chain, supported by an agile workforce. We call this the cognitive supply chain.

The cognitive supply chain

Unlike transactional supply chains which react to exogenous shocks and operate more sequentially, a cognitive supply chain can anticipate and predict unexpected impacts and react before calamity strikes. It is able to harness the power of an ecosystem to adapt and effectively operate.

Future supply chains will leverage advanced technologies, like machine learning (ML) and other AI, to predict risks and opportunities. They will be digitally led, yet process-centric – as opposed to being merely digitally enabled.

For example, a global consumer products goods company was plagued by hits to on-time delivery due to an inability to anticipate various events negatively impacting its supply chain. By applying ML to a host of datasets, the company was able to better predict when and where events may adversely affect on-time delivery and adjust accordingly, which would, in turn, dramatically drive up on-time delivery rates.

Global supply chains are connected ecosystems that have become more complex and are affected by many variables. They are made up of various stakeholders and capabilities necessary to meet a customers’ needs, including suppliers, transportation companies, internal/external manufacturing, warehousing and technology providers. This ecosystem will morph over time as the needs of the company, customers and products change, and may even contain partners who are competitors in another channel or geography.

Enterprises also need an ecosystem of connected technologies to propel the transition to a cognitive supply chain. AI and other digital technologies enable the transition to a cognitive supply chain by augmenting and automating processes. They:

  • Integrate unstructured data and new data sources, using ML with unstructured data to drive superior performance
  • Perform tasks faster and more reliably, such as driving higher case fill rates by using ML to accelerate root cause analysis
  • Enable smarter and faster decisions – for example, an AI-based dynamic routing system can analyse real time status of shipments, delays, and traffic conditions to suggest routing changes
  • Recommend actions based on impactful insights – for instance, an AI-based intelligent alert system can drive real-time actions enhancing on-time, in-full, thus reducing retailer penalties
  • Provide remote monitoring and diagnostics, such as using ML when interpreting signals from airplane systems to alert maintenance crews in advance of any issues.

Technology adoption is a foundational element of a cognitive supply chain that supports enterprise growth.

New operating models demand an adaptive workforce

Enterprises need new operating models with centres of excellence that cultivate talent and provide scale to drive cost, service and working capital management. To leverage the power of a cognitive supply chain, enterprises must have an adaptive workforce.

Talent working with cognitive supply chains will develop greater domain, data science, and digital expertise. Planners will no longer be limited to simply interpreting outputs from planning applications or  exception management. They will interact more across several functions, such as finance, sales, manufacturing, warehousing, transportation, etc.

As roles in supply chains start to merge, planners must equip themselves with end-to-end supply chain knowledge. An adaptive workforce will look at problems more holistically and strategically, solving problems that are no longer myopic in nature but span across the supply chain value network.

The move to an autonomous supply chain

With more enterprises embracing AI and the expeditious evolution of digital solutions, we can envision components of a completely autonomous supply chain. Two current examples include transformation driven by autonomous vehicles and blockchain technology:

  1. Autonomous fleets and material handling
  • Vision-guided fully autonomous mobile robots are optimising and eliminating manual movement of material from one location to another. These robots are approximately four times more productive and faster than human counterparts.
  • Semi-autonomous/autonomous trucks can coordinate their movements to move as a platoon closely together over long stretches of highway. As autonomous vehicles become more widespread, these platoons are projected to cut fuel costs by close to 20%.
  • Autonomous “ghost ships,” ships without crews, will be in operation in the next few years and will initially ferry products short distances. For example, Rolls-Royce has already announced plans to launch autonomous cargo ships by 2030.
  • Drones, robots, and autonomous cars will transform last-mile delivery. Ecommerce giants are very excited with the prospects of drones making deliveries and food retailers already trialling robots and autonomous cars are delivering fresh hot orders to consumers.
  1. Safe, smart and efficient exchange through blockchain: Enterprises expect blockchain to have the widest reach and impact on material, financial, and transaction visibility, as well as substantial impact on customer experience. For example, a large global manufacturer is using blockchain to track details from the genesis of a customer order to its purchase orders to tier two and three suppliers of raw materials. The result is never before seen real-time visibility of material and data, with access by all associated parties.

Blockchain will also allow consumers and food safety regulators to track the history of the product placed on the shelf at retail stores – right from the source of produce, with automated handoffs record-keeping across the supply chain, lessening errors and providing visibility and value additions made at each step, even how well (or badly) the product was handled during shipment.

The significance of a cognitive supply chain is clear. With its ability to foresee issues associated with supply and demand, it enables employees to deal with such problems in a much more strategic manner. This empowers an instinctive enterprise to stay ahead of the competition. Furthermore, through management of a connected ecosystem and nurturing of an agile workforce, this not only supports business growth, but it also encourages improved ways for technology and insights to work together.