Predictions: The Supply Chain in 2022

The Coronavirus pandemic, driver shortages and, more recently, additional red tape following Brexit, have caused massive supply chain disruptions in the UK. This has focused minds on the obvious need to shift operational strategies and entire business models in order to remain competitive and fulfil new customer demands. One of the trends resulting from the crisis is the increased adoption of certain technologies, particularly by market leaders, for whom they are an opportunity to differentiate themselves from competitors.

One motivation for implementing new technologies is a change in customers’ demands. Similarly to the B2C sector, B2B customers now expect a wide variety of choices with regards to products, size, contents, and delivery techniques. They expect products that are tailored to their requirements, arriving in increasingly precise delivery windows.

This level of precision requires visibility of customers’ needs, of markets and competitors, of the environmental impact, demand and supply, a supply chain’s in-built risks and how its structure and policies affect its performance. As a result, there is a shift towards the connection of assets, materials and products on visibility platforms, which help to supply data to other areas of the enterprise, where they can help to facilitate decision making based on visibility information.

Read more: How predictive analytics is reshaping transportation management

Another trend is environmental, social and governance (ESG), which has to be taken into account in all decisions. Reporting is no longer focused only on how much profit a business generates, but also on the ESG impact of the business’ activities. These criteria should be built into supply chain systems and software. Supply chain managers will need all the essential capabilities provided by improved visibility and collaboration if they want to make progress in these areas while staying competitive.

Volatility in demand and supply will remain a defining factor, which is why increasing agility is another key trend – meaning the extent to which a company can respond to events such as surges or drops in demand or supply outages, all while meeting its financial, operational and ESG goals. This can be achieved by, for example, stocking more inventory, nearshoring or reshoring, or by regionalising supply chains. Agility also includes capabilities for supply chain management processes, e.g. integrated business planning, continuous sales and operations planning (S&OP), segmentation, control tower, synchronisation and collaboration, vertical integration probabilistic planning and scenario management.

Supply chain management software of the future

Companies are moving away from one-size-fits-all supply chains towards more tailored supply chains with specific characteristics. As a result, the one-size-fits-all database approach has been replaced by use-case-driven databases, and, in turn, event-centric architectures that are better able to deal with individual customer demands and disruptions. What are the technologies that can help to face the future challenge of synchronising long-term planning with short-term execution?

Supply chain software systems have traditionally been specialised and fragmented, with their own technologies, architectures, and data models. In future, we will be seeking convergence – the merging of separate technologies into a unified whole. Instead of having separate business teams and processes for supply, demand, inventory and distribution planning, market leaders have one team, with one process, both coordinated by one technology platform.

A digital twin is essentially a model of the supply chain that’s mirrored in software. Software models have always been part of packaged supply chain software. Today these models are incredibly fast and much better able to represent the world. They’re based on digital signals via edge computing, and they have far less latency (the time span it takes for data to pass from one point of a network to another).

Artificial intelligence (AI) and machine learning allow software to learn from data, rather than being completely driven by rules configured by humans. AI analyses the data, tells us about our previous decisions, how they were made, and which of them had the best outcome. All of this can inform future decisions under comparable circumstances. Future supply chain management (SCM) software will be characterised by its ability to adapt and make decisions by itself, without the need for human intervention.

Streaming architecture helps companies to shift towards real-time updates of large amounts of data, allowing them to move towards an event-centric architecture as opposed to a historical database-centric architecture. As this is a critical means for keeping the digital twin up to date and avoid latencies this will be progressively more important for SCM software because it supports the precise synchronisation of supply chains across time and function.

Combining the digital twin with an extensible data model offers a way of delivering much more precision when synchronising operations across, e.g., distribution, retail, and manufacturing. In future, SCM software will have general-purpose extensible data models that can be extended across functional domains, meaning the data model can precisely represent multiple relationships (e.g. manufacturing, warehousing and distribution) and use cases across a variety of environments.

Read more: “Europe has not been smart”: IE meets Prof. Marco Taisch

Closing gaps is a big part of supply chain management. When inventory or sales are too low or too high, or when deliveries are delayed, supply chain operators will try to close the gaps. With Control Engineering Logic, monitoring provides visibility of the whole process and how it is performing. When a deviation is discovered, action is taken to close the gap. This is a fundamental aspect of the supply chain management process.

Workflow describes the sequence of screens, clicks and other interactions a user executes. In most cases, these steps will be rigid and predefined; making changes requires time-consuming configuration or perhaps even changes to the software code. A flexible workflow engine is a vital component of any modern software application with respect to providing adaptability to the changing nature of work. Prescriptive analytics offers insights into which action should be taken when there are supply chain disruptions, taking into consideration (and learning from) previous decisions.

None of the above technologies by itself can be a silver bullet. They should be used as and where it makes sense, wherever they can assist companies to profitably deliver precision, sustainability and resilience and in the face of volatility and variability. Thankfully, today’s technologies are more powerful and flexible than ever and will continue to evolve toward providing greater levels of precision and helping fulfil the changing needs of customers.

- The authors, Kelly Thomas and Anand Medepalli, are Company and Board Advisor, and Chief Product Officer respectively at Shippeo.

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