SAP Business AI takes steps towards a sustainable self-healing supply chain
SAP Business AI takes steps towards a sustainable self-healing supply chain
John McNiff, Global Centre of Excellence, explains the focus on strategic go-tomarket topics and what this entails.
Disruption is the new norm, especially for supply chains that have to function with greater efficiencies, for a more demanding customer base, while adhering to sustainability regulations. The result is the biggest technology acceleration ever seen hitting the industry, through the explosion of generative AI. John McNiff talks about SAP’s influence on companies, enabling smoother data flows across supply chain networks with more connected and intuitive technologies, for ultimate risk resiliency, agility, and in support of the green agenda. Here, he explains this complex landscape, and how far we are away from an autonomous, self-healing supply chain.
Hi John. You have been with SAP for more than 15 years now in various roles, leading to your current position as part of the global Centre of Excellence. Firstly and briefly, what does this entail?
Basically, we run a team focused on strategic go-tomarket topics. For example, right now we are working with customers to ensure we can articulate the business value of a supply chain that doesn’t run in silos. In general, for us, the digital supply chain covers quite a broad gamut.
Everything from PLM, product innovation, supply chain planning, logistics, manufacturing, the shop floor, but also operations and service.
We have a lot in the portfolio and already, with the speed
of innovation and rise of generative AI, there is a lot to look forward to and get stuck into.
From this multi-focus perspective then, you seem perfectly placed to pinpoint where supply chain challenges and disruptions are being felt at present?
Yeah, I’d say it’s good that we were talking now, because I think even in the past few months the talk track is changing.
First, we’ve had ‘VUCA’ (volatility, uncertainty, complexity, ambiguity) that people want to talk about, highlighted of course by the pandemic, Suez Canal, etc, etc. But that view is now evolving to be on a sort of rolling stream of disruption
Basically, we run a team focused on strategic goto-market topics. For example, right now we are working with customers to ensure we can articulate the business value of a supply chain that doesn’t run in silos.
rather than battling one particular ‘black swan’ event at a time.
To that end, the focus among companies is to be less reactive, and putting strategies in place to be more risk resilient and agile.
When there are so many potential disruptions, how does SAP plan and prepare to ensure you’re enabling companies with that risk resilience and agility?
It’s actually a case of looking at what the next potential gamechangers will be, and there are four main areas on our horizon. Yes, number one is that we are in the business of helping with risk aversion and business agility, but we can provide this by accounting for future
Issues around sustainability are growing and people aren’t ready for that yet from a technology perspective. They need to start thinking about how they embed traceability and provenance around CO2 and ESG into the supply chain.
– or escalating – challenges we see coming up; of which there are three more specific examples.
First, issues around sustainability are growing and people aren’t ready for that yet from a technology perspective. They need to start thinking about how they embed traceability and provenance around CO2 and ESG into the supply chain, when a lot of this data and information is outside of companies’ own four walls.
For example, with CO2, 90% of the CO2 footprint of most big manufacturing companies typically sits in operations and the supply chain, and more that 60% on average is in scope 3 (beyond the internal enterprise). Regulations around the world are now calling for reporting of this carbon footprint in a much clearer and transparent way, so there’s a need to connect this data from across manufacturing, operations, and the wider supply chain ecosystem to give accurate measures and not just averages.
The next area of focus is helping companies not to just report, but also using
this connected thread of data to make decisions around how operations and the supply chain are run. You work backwards along the digital thread to see where shortfalls or inefficiencies exist, and to then take steps to become more resilient or efficient. This not only effects regulation compliance, but also helps to adhere to a younger customer contingent who prize sustainability higher than before, while also making yourself more attractive to investors. So, we also need to make that link between having better connected information, and then acting upon it.
And then, there is the technology itself. There has been an explosion in generative AI in particular, which has refocused everyone’s mindsets, but that doesn’t mean they necessarily understand it or know how best to use it. Helping with this understanding and to take full advantage of AI’s potential is another remit for us, alongside these aspects of sustainability, and more insight and actionable data generated from connected sources.
A word that springs to mind here is ‘visibility’, enabling companies to get a better picture of what is possible, but also where their current bottlenecks exist?
It is absolutely a key word, however when you say ‘visibility’ to people, they still have an image of a control tower and just having that visibility in a first instance –tech giving a representation of the status quo. When asked whether they have complete visibility though, and whether they can make decisions from that picture including outside of their business’s four walls pertaining to the supply chain, very few can say they have that capability.
Where we focus our conversation around visibility is on the ability to act on the information being presented. The ability to connect pieces of information across functions and departments. The ability to process the information and respond in an optimal way. The ability to leverage new technologies to provide realtime, continuously updating insight so that companies can remain agile and risk resilient.
To SAP, visibility now isn’t just a picture. It’s the ability to connect external data, alerts and events with business data and transactional information, such as sales orders, production orders, transport orders, and all other elements that drift in and out of the immediate four walls of a business. Not only that, but it’s also then about understanding the impact of information you’re receiving on each strand of the operation. Effectively creating the digital twin of the ‘network’ at present, despite the promise of technology, this complete notion of visibility is unachievable for many.
You mentioned the term ‘digital threads’ earlier. Presumably, it is the formulation of these digital threads that you’re talking about when you describe this improved connectivity and flow of data?
Absolutely, but again it’s a bit of a misunderstood phrase in the same breath as ‘end-to-end visibility’. In some industries or contexts, it refers to an engineering change, for instance, that could then be applied across the board seamlessly thanks to all systems updating that information.
What we mean by digital
thread, however, is all the data, records, orders, communications and information that needs to be connected. But more than that backbone, it’s about having analytical capabilities and decision-making support on top of that information. Then, from a supply chain perspective, it’s also about bringing in external data from other systems and leveraging the complete ecosystem. This might implicate partners down the chain, but also systems around weather data for example, or transport complications in real time.
Everything that relates to a company’s ability to deliver on a promise to customers,
needs to be weaved into this thread, analysed automatically in real time, and lend to more efficient and informed decision making.
From a practical standpoint, where does SAP fit into this equation? In a perfect world, I’m sure you’d advocate a complete suite of SAP solutions for optimised synchronisation, but that won’t be feasible for all?
The first thing to do is take as much advantage as you can of the backbone and of the system of record, there is a wealth of hidden knowledge
Having consistent master data in a system of record is very powerful thing. But you’re right – transformation is big, intimidating and expensive, so there is much more of a shift to becoming as composable as possible. This means taking smaller pieces of augmentation at a time, and we help to make sure that everything is integrated and synchronised across the value chain.
Architecturally, there is also a shift from the rigid monolith setup versus best-of-breed alternative, to something in-between, which leverages
consistent platform. This helps from an IT perspective, where you’re sharing as much of that digital backbone master data as possible and embedding the bits you need to embed. For example, with the green agenda, you can connect material master data with emissions factors which is something that seems quite boring in the backend, but in the context of the users in the supply chain, if they can see the impacts of picking this material from this supplier, or routing it via this transportation provider, they can measure the emissions factor changing across each
Automation can drive efficient change but it needs to be connected and have a clear vision to realise its potential for boosting reputation and shareholder value, says Fred Akuffo…
A week doesn’t go by these days without a new supply chain challenge hitting the headlines. Whether it’s geopolitical issues, workforce shortages, new regulations or extreme weather events, supply chains seem to be coming under increasing pressure. While understandably, business leaders are concerned about the future viability of their organisations, at least according to PwC’s Annual UK CEO Survey, they are also fully aware of the need to transform. Technology and more specifically, AI, is seen as the essential differentiator.
Everyone is talking about generative AI (GenAI) and with good reason. AI will have the greatest impact on the supply chain industry over the next few years and GenAI will be a key player. From forecasting and managing risk through to optimising processes and sourcing new suppliers, GenAI will play an increasingly pivotal role.
Supply chain practises understand this – they are no strangers to automation having frequently adopted machine learning to monitor and manage resources and predict product demands, for example. However, much of this automation is in isolation. And we are also seeing this with GenAI projects, springing-up within departments as knee-jerk reactions to technology trends. For organisations to really see the benefit of AI-driven transformation in their supply chains, there needs to be a re-conne ction, a strategy, a removal of siloed operations and a unification of data to enable intelligent decision making.
To that end, we have compiled a list of the five moves organisations need to make in FY25, to lay solid foundations for AI automation, while managing costs, improving resilience, and easing sustainability pressures. These steps will springboard organisations into a more innovating FY26.
Visibility is crucial for effective supply chain management, enabling operational decisions and mitigating external risks. Digitisation of assets is the process of creating a cloud-based digital model of a supply chain, for example in an advanced planning system (APS). This allows for demand and supply forecasting and scenario testing.
Data generated from physical assets (eg. machines and lorries) helps businesses make real-time decisions. Technologies such as RFIDs, chips or integrated circuits (ICs) and satellite tracking enable organisations to understand fully the movement and production of goods. By creating a digital record with real-time asset data, organisations can monitor the movement of goods, identify potential delays and risks, while also facilitating emissions reporting.
Watch out for new partnerships between supply chain illumination tools and ERP and APS vendors. These partnerships enable the integration of external market events into a planning process, enhancing decisionmaking capabilities. Collaboration platforms can also help organisations collect data from suppliers and logistics providers.
Data readiness is crucial for effective AI implementation. Understanding the sources and value of data allows businesses to create a data value map, focusing on high-value datasets, while considering the overall data estate. This also presents an opportunity to consolidate AI projects, to ensure end-to-end supply chain impacts are considered. Even if a tech transformation is not currently on the roadmap, preparing data for the future establishes a strong foundation for implementing new technologies effectively when the time comes.
We find that businesses who embed data value maps in their supply chain strategy are proactive in responding to supply chain instability. They are also better at integrating sustainability at the core of their supply chains, and leveraging advanced technologies within industry 4.0 and AI, in a more cohesive manner.
To address data challenges, focus on three key areas:
> Data quality: Establish a master data management process to ensure clean and accurate master data.
> Data availability: Make sure data is easily queryable and accessible to other systems for seamless integration and utilisation.
> Data consistency: Consolidate data from multiple systems into a single location through data replication or real-time queries to ensure consistency and coherence.
Implementing a data lake is a common approach to address these challenges. This provides a cloudbased solution that can connect to analytics and decision intelligence platforms in real-time. It is crucial to integrate data from ERP and APS systems to establish a connection between the supply chain and the broader business.
With growing ESG reporting requirements and pressure to demonstrate Net Zero strategy, companies must have visibility of the carbon implications of their products and services. However, capturing consistent data to support this process can be challenging. Establishing data sources and responsibilities for carbon data capture is time-consuming but essential. Once in place, a baseline can be established, followed by target setting.
A centralised carbon ledger, integrated with the accounting system, supports decision-making and transparency. This ledger, similar to financial accounting systems, enables understanding, reporting, and compliance with regulatory requirements, such as CSRD.
By incorporating cost and carbon information, companies can make informed decisions and run scenarios to progress towards sustainability goals. Software vendors are supporting these requirements by launching tools which assist in data gathering from suppliers, measuring the carbon footprint of products, and creating a digital ledger of carbon accounting data. These tools can be used to track and manage a company’s carbon footprint, driving sustainability and meeting regulatory obligations.
Most supply chain professionals complex datasets can require visibility often leads to decisions
Data chatbots offer a quick designed to handle numerical their data and receive answers
Having instant access to data maker can quickly identify reports to be generated, they Keep an eye out for integrated your data into a 3rd party chatbot.
Fred Akuffo Supply Chain Operations Transformation Partner PwC UK
Tom Alesbrook Supply Chain Operations Client Relationships PwC UK
Authors:
Fred Akuffo, Kassidy Roussel, Niall Briggs
Think about the journey to multiple rungs on a ladder. the first step, with autonomous goal, the final step.
Many companies struggle to their workforce is not on board to a lack of user adoption and people-focused AI transformations the workforce, leading to sheet and profit and loss accounts.
Consider the following:
> Assess workforce exposure of familiarity and experience technologies.
> Develop comprehensive adequate training and Don’t underestimate the successful adoption of AI.
> Design AI-ready business redesign business processes
Avoid the risk of employees after training and technology
By investing in your workforce for AI, you can pave the way transformation in your supply
professionals would advocate for the use of data in decision making. However, obtaining new reports or insights from require weeks of work from analysts or data scientists making it impractical for off-the-cuff queries. This lack of decisions being based on gut feelings rather than concrete data.
quick and efficient solution to this. With a similar end user experience to ChatGPT, these tools are specifically numerical data. By integrating them with ERP and Advanced Planning Solutions, decision makers can easily query answers in real-time.
data empowers decision makers to make better-informed choices. In the event of a supply disruption, a decision affected suppliers, assess the overall impact, and prioritise materials accordingly. Instead of waiting for they can simply ask the data chatbot. integrated planning assistants embedded in your APS and ERP solutions in upcoming releases, or consider feeding chatbot.
to an intelligent supply chain as ladder. Complete end-to-end visibility is autonomous decision-making the ultimate
to get their foot on the ladder when board - 70% of transformations fail due and behavioural change. Prioritising transformations will drive efficiency across tangible benefits for the balance accounts.
exposure to AI: Understand the level experience the workforce has with AI
comprehensive training programs: Invest in change management initiatives. level of training required to ensure AI.
business processes: Thoroughly review and processes to align with AI capabilities. employees reverting to old ways of working technology investments.
workforce and considering their readiness way for a successful and impactful supply chain operations.
While 21% of UK CEOs say their organisation will not be economically viable within 10 years on current trajectory, not investing in these transformation strategies will see any organisation fall towards the back of the pack and face significant economic challenges. The visionaries and market leaders have clear transformation agendas around AI in particular. This brings competitive advantage, increasing market share and minimising operating costs. Supply chains are increasingly crucial to modern business and organisations have to take note. AI can have a big role in shaping decision making but navigating the complexity of change can be a challenge. While these five key moves help illustrate that challenge, operationally, environmentally and financially they are also an opportunity.
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Are you able to provide a couple of examples of how this composability is reflected in your evolving offering?
For a number of years we’ve been building products that integrate into different areas of business operations, but the important differentiation in recent times has been around this composability and synchronisation. So, making them composable but tightly coupling them with that master data, with the ability to add decision capabilities on top, underpinned by a common user interface as part of a bigger suite.
What’s getting really exciting is the move towards intelligent technologies, though, which a lot of people haven’t taken advantage of yet. So, moving to SAP S/4 HANA – it’s infused with analytical capabilities, with the ability to get out more data and do all this clever analysis and bring data together from different sources. Honestly, a lot of people have not really taken advantage as much as they could from that, because they’ve kind of shifted from what they had, to doing it the same way but with a different system.
Where things like AI come in now is we can start to build models that learn from the whole of the SAP ERP suite of 20,000-40,000 customer processes. Whereas at the moment, if people want to do digitisation, or add intelligence, they’re doing so on a case-by-case basis, using their own tools, and to some degree, creating a new spaghetti-soup bestof-breed landscape. What we’ve got the potential to do with the platform now, and especially with the explosion in AI capabilities, is to say, because it’s in the cloud, we can build these foundational business AI models and allow you to access them, fine tune and ground them responsibly, and apply them to your data; to become much more automated, intelligent and even generative.
This is where having all of these building blocks starts to become a real differentiating and transformative landscape. A lot are building data lakes and putting nice analytical tools on data lakes, which means they extract some information but then when they find out they’re missing some context, they have to go back and create new extracts. We have the
ability to get at any data or any information, keep its context where it exists, and bring greater analytics. And especially with AI, we can start building models that learn from previous incidents and relationships.
When you have a tech shift dovetailing with external events, changing customer demand and the sustainability aspect all at once, this need for connected and intuitive insight must be paramount?
We see it play out in examples all the time. For instance, logistics might know that something is delayed or short of required volume, but manufacturing would usually only find out when they book it into the warehouse. They then create a three-week plan based on materials that either aren’t coming or that will be late. It’s not just a system, but rather a process change that’s needed, where the system needs to enable better cross-departmental collaboration.
The above example also brings in other areas that SAP is working on such as starting to use natural language and analytics to enable more
seamless interactions, through foundational tools such as Joule and Just Ask. Here, you could ask what late shipments to expect, from who, and what impacts they will have on the shop floor, before making recommendations on how to resolve. These questions can only be answered if there is data feeding into each other, from logistics to warehousing to distribution and manufacturing; also incorporating the external aspects (weather, transport, etc) we discussed earlier.
It’s about mapping the complete situation to give a real time overview of what’s possible, so that smarter decisions can be made ahead of time, rather than having to react to a late shipment or delayed arrival and risking the ultimate delivery of items you’ve promised.
Where do companies start, then? It’s often the first step of a transformation that proves most daunting…
This is indeed the question, and where people are struggling. There are so many strands to it and so many exciting technologies implicating things like AI
and natural language as we’ve discussed. It’s not just a transformation of tech, but of ways of working and the people impacted within that. However, on the plus side, nearly all companies are aware that they do need to transform and to align themselves with these innovations. It doesn’t necessarily mean they know what everything means though – and that’s where SAP and our partners come in, not just as tech providers but as guides of each customer’s respective roadmap.
There’s a whole world of new capability, domain expertise, process change, where we really need to work together as technology providers, implementation consultants, business process consultants, and the companies themselves. The latter link in the chain can’t be overlooked, though. I see a lot of comments from decision makers pleading that we ‘stop pushing technology for technology’s sake’ – a comment I 100% agree with. But that statement can’t be an excuse to ignore the potential of tech like AI or cloud altogether. It should be a nudge to better
understand the applications at your disposal to then see the true impacts they could have on your business.
Obviously, we’re talking about your customers’ supply chains, but your own ecosystem must be so important in order to keep ahead of the curve and to keep delivering these futureproofing technologies?
When we talk about composability, about providing a platform, there is a mass of whitespace, especially around the usage and incorporation of data, and formatting things in a particular way. We have an industry cloud strategy where people can build applications that complement solutions, using the same business technology platform, using AI in different ways.
Partners like PWC have great business process experts in the context of the supply chain so bring knowledge to us around such tools and processes. Bringing that knowledge together for customers, we don’t have enough people to tell everyone how to go about it, what needs doing, the types of data needed, the AI
tools to adopt, how to adjust processes. Partners like PwC and others bring both solutions and consultation that complements our business technology stack and also fills the gaps where needed. It’s about offering an ecosystem that helps people go through a transformation, understand business impacts, apply technologies and solve very specific problems. Having those partners within that ecosystem, is hugely important.
Looking ahead as an ecosystem, but also as a global heavyweight such as SAP, what is your focus for the coming 12 months?
If you look at it strategically, I think SAP’s focus overall is to become the business AI backbone – this decision support platform for end-toend business. In the context of the supply chain, that brings opportunities for efficiency increases, for people to become more predictive, to connect processes, and get that context across the entire supply chain. People talk about things like the autonomous supply chain or the self-healing supply chain, and nobody is there yet, but it’s something we’re striving for and making steps towards. Over the next 12 months, we want to see those next steps of more autonomy and improved decisions off
the back of connected data for our customers.
This progress will focus on areas such as sustainability where transparency, connectivity, traceability and visibility are all bottlenecks for companies, but more generally we want the next year to be one of increased adoption of solutions that will make supply chains more resilient, more agile and more accepting of ground-breaking benefits such as AI.
In doing so, our customers will be better equipped to stay ahead of that new norm where disruption is a continuous state of play, rather than a series of individual events.
