Analytics & automation changing the face of supply chains

John Matchette, Senior Supply Chain & Operations MD with Accenture, says digital twins are among the most innovative use-cases of AI in supply chains.
John Matchette, Senior Supply Chain & Operations MD with Accenture, says digital twins are among the most innovative use-cases of AI in supply chains.
AI and machine learning are helping organisations reimagine their supply chains at a time when resilience and visibility have never been more important

Today’s companies are using AI and analytics to mitigate risk and ensure continuity through global supply chain disruptions. These powerful tools enable organisations to automate tasks they previously never could, while providing deeper insight for faster, better decisions.

John Matchette, Senior Supply Chain & Operations MD with Accenture, is also the organisation’s Data & AI Solutions Lead. According to Matchette, digital twins are among the most innovative use-cases of AI in supply chains.

A digital twin is a virtual supply chain replica that represents assets, warehouses, logistics and material flows, as well as inventory positions. It can stress-test potential operational and financial risks and impacts created by major market disruptions, disasters, or other catastrophic events.  

Matchette says: ”Scenario modelling has become increasingly important. Supply chain stress tests can enable companies to not only understand how resilient their supply chain and operations are, but also to identify the weakest links and quantify the impact of those links’ failure to fulfil their role.”

He explains that digital twin-driven modelling allows companies to design a network to optimise cost and customer-service levels, “while simultaneously analysing their carbon footprints”. 

“This ensures they meet sustainability targets, while delivering the best service for their customers,” says Matchette. “For instance, a company can design a network that reduces shipping times by minimising the distances trucks drive and thus cutting fuel consumption and emissions.”

Digital-twin tech is a strong point of AI and ML 

In addition, Matchette says process re-engineering is another area digital twins tech excels in. 

“Processes have become increasingly complex due to global expansion and growing customer diversity, which means they are less efficient and more costly,” he says. “A digital twin can help businesses understand where bottlenecks and waste are bogging down work, and then model improvement interventions.”

Inventory management is a third digital twin strong point. Matchette says: “Modelling can optimise inventory in a single warehouse, as well as across the entire network, and modify inventory levels according to demand. This helps ensure products customers want are in stock, when they want them. Twins can also minimise inventory shipping distances.”

Although digital twin tech is a powerful use of AI and data, Matchette feels “we’ve only scratched the surface of what analytics and AI in supply chain networks can do”. 

He points to new research from Accenture, which reveals that just 12% of firms have advanced their AI maturity enough to achieve superior growth and business transformation. 

Matchette says: “In the next five years we’ll see companies reimagining supply chain networks to orchestrate change, simplify life and positively impact business, society and the planet.” AI and analytics will, he believes, “be critical to that effort”. 

He adds: “Companies will be able to collect data from across the supply chain network, consolidate it in the cloud and apply AI models to achieve a real-time view into the state of suppliers.” 

Data-based AI modelling allows businesses to identify supply risk 

This, he says, will allow organisations to to proactively identify risks and predict impact across the supply chain. Examples of such risk might include:

  • Identifying a supplier’s inability to source a vital raw material, before it impacts production
  • Ensuring suppliers’ carbon footprints are in line with agreed-upon levels
  • Ensuring suppliers are sourcing materials sustainably and responsibly

As well as AI and data success stories in supply chains, Matchette warns that businesses can also experience problems – particularly around data overload. 

“Data is not useful unless it is collected and used effectively, and this is where the cloud comes in,” he says, adding: “The cloud can consolidate a vast range of relevant data sources, both external and internal, and make data interoperable across business functions.” 

“Armed with data-driven predictions, supply chain leaders can then more intelligently and proactively decide how they should respond to and meet demand, including determining what are the most appropriate actions to take in production, pricing, promotions and fulfilment.”

One venture helping businesses take data-driven supply chain decisions is TealBook, which uses AI and machine learning (ML) to enrich and distribute supplier data globally. 

Automation ‘turns light on to supplier data’ - TealBook

Tealbook’s founder and CEO, Stephany LaPierre, says: “We basically turn a light on to the master data and provide enrichment of the information in a more complete, more insightful way that's continuously dynamic”. 

LaPierre says that the problem for organisations is the abundance of solutions coming to market across all supply chain requirements, particularly around ESG. 

“A business might look to implement between five and maybe 15 solutions over time, but if these all require suppliers to enter data via a portal, how can you possibly scale this?”

This, she says, is where the importance of dynamic supplier data comes in. The traditional way to collect such data was to use specialised services to clean, categorise, and supplement it. LaPierre points out that the trouble with this approach is the data tends to “decay as soon as you receive it”. 

Another way to use dynamic data is to set up a portal in which suppliers can enter and update any relevant information. “The problem is, many suppliers simply won’t do this at the speed or scale you need,” LaPierre says.

She goes on to explain that the main strength of data harvested dynamically using AI and ML is that it “enriches data in an automated way and gives you complete, quality data across 100% of your suppliers”.

She adds: “Automated dynamic data reduces the dependency on services and on humans to maintain information across software solutions, and reduces the dependency on suppliers to visit multiple portals. 

“To achieve digital procurement success, organisations absolutely need quality and complete supplier information at the speed, scale and visibility that meet these requirements. Tier one suppliers are already a challenge, but now we're talking about tiers two and three, as well as Scope 3 emissions.

“Once you turn the light onto your master data, you can see duplications, categories where too many suppliers are very similar, and where you should be driving consolidation and compliance among existing suppliers. The things you can see are almost magical.” 

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