Capgemini has taken another major step in enhancing its digital transformation services by acquiring Syniti, a global leader in enterprise data management, particularly in SAP data migrations. This move aims to bolster Capgemini’s capabilities in supporting complex data transformation projects for clients transitioning to SAP S/4HANA and other SAP solutions. Syniti has been a key […]
Since 1928, spectators at Macy’s Thanksgiving Day Parade have marveled at the iconic, giant balloons that drift through the streets of mid-town Manhattan. No party-store helium canister will do for these towering balloons, which often measure several stories high. Those gargantuan renditions of our favorite characters are all sent flying by Messer, a leader in the safe and reliable production and delivery of industrial and medical gases for more than 120 years.
Those gases also include oxygen, hydrogen, nitrogen, carbon dioxide, argon, neon, xenon, and krypton—all critical to supporting processes and products in electronics, food and beverage, metals, biopharmaceuticals, and other industries.
Business demands and market realities in the dynamic gas sector are constantly in flux, so Messer relies on real-time data intelligence to stay agile and flexible in their decision-making. Increasingly hampered by siloed data and a patchwork of third-party visualization tools, Messer embarked on a digital transformation to create a next-generation data management solution worthy of the company’s high-tech, high-flying operations.
Data with a Purpose
Messer knows critical and often lifesaving business decisions require accurate, real-time intelligence and insights. CIO David Johnston explained: “The stakes in many of the sectors that we provide gases for are really very high.”
Better Together: Customer Conversations with Messer
From steel plants to the semiconductor industry to intensive care units, “it is critical that our distribution processes are operating at a level that ensures our customers are getting what they need when they need it.”
Messer was able to rise to the moment and meet the urgent needs of its customers when the COVID-19 pandemic delivered unprecedented demand for oxygen coupled with massive logistical challenges. That global crisis revealed that Messer’s legacy IT system needed an overhaul.
Messer’s siloed data was in several different databases, which meant it was time-consuming to access, and a single source of the truth was elusive. Various visualization tools also required different data formats, which slowed reporting time and bogged the IT department as it tried to manage a complex, disparate landscape, creating, as Johnston explained, “a huge amount of complexity and a huge amount of inefficiency.”
Messer CIO David Johnston sought a solution that would provide a unified source of truth and the powerful analytics tools Messer needed to give an elevated purpose to the company’s data—a new, cloud-based data management and analytics foundation for an actual data-driven enterprise. SAP Business Technology Platform (SAP BTP) was the company’s choice.
The Modern Data Landscape Delivers
SAP BTP, with SAP Data Warehouse Cloud, SAP Analytics Cloud, and the next-generation SAP Datasphere, provided a modern data architecture to establish a single, trusted source of truth, combining 12 data sources in one solution for mission-critical business insights.
Messer has been reaping benefits across the board, from IT and supply chain cost savings to improved customer service, inventory management, and data security. “We started with five specific use cases that we went after,” Johnston shared. “As I sit here today, we’re at well over 200.”
Messer’s new, simplified IT landscape has empowered business users, who benefit from intuitive, self-service dashboards and quick access to relevant, actionable, real-time data, as well as IT personnel, to now focus on higher-value, strategic tasks. This amounts to accelerated time-to-insight and more energy and resources available to focus on future innovation.
“With a data fabric that allows us to see our business in a consistent, fast, reliable, and highly trustworthy way,” Johnston said, “what we can do is really almost unlimited.”
Next for Messer and Wise Advice with an Eye on AI
With Messer’s new, streamlined data management foundation, the sky is no longer the limit.
“Our objective isn’t just to deliver a suite of great dashboards and great analytics stories,” Johnston explained. “Our goal is to build out a strong, coherent, strategic data fabric that will allow us to unlock the power of AI and drive the next level of business transformation.”
For other organizations hoping to build the data foundation that will send them soaring into the future, ready to capitalize on artificial intelligence technologies and whatever else awaits, Johnston’s advice is to frame your technology transformation as a business transformation, and a business imperative.
“Bring the business community together around data,” he said. “All the key stakeholders, the influencers, and the owners of data from across the organization: bring them to the table.”
Thought Leadership Podcast: Johnston sat down with Thulium’s CEO Tamara McCleary to discuss why end-to-end data-driven insights are necessary, what technology solutions were required for the world’s largest privately owned industrial gases company to transform, and why Messer leveraged SAP BTP to drive a complete business transformation.
Practitioners’ Video: Johnston and I discuss why and how Messer, one of the biggest suppliers of medical oxygen and other industrial mission-critical gases, built a new, cloud-based data management and analytics foundation because the future belongs to data-driven organizations, and thriving in the dynamic industrial gases sector means responding quickly to changing market conditions.
Explore more success stories of customers leveraging SAP BTP to drive transformation that overhauls legacy IT systems:
DuluxGroup: How can an innovative cloud-based integration shape a modern and hybrid IT landscape?
Lufthansa Group: Co-creating a data fabric infused with context and governance for accurate business-wide data-driven decision-making
For the full episode and the on-demand Better Together: Customer Conversations series, visit sap.com/btp. To share input on the topics and technologies you want us to cover, or if you are interested in being a guest on the show, email us.
Timo Elliott is vice president and global innovation advocate for SAP BTP at SAP.
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This year at SAP Sapphire, SAP unveiled its vision for AI: to transform supply chains from digital to adaptive and ultimately toward autonomous, characterized by improved massive data-driven intelligence, system-driven recommendations, predictive insights, and contextualized decisions.
According to the IDC 2024 Supply Chain Survey, 63% of respondents “have an AI strategy linked to business objectives” to improve operational efficiency, business resilience, and increase employee productivity.
Our objective in this digital, adaptive, autonomous supply chain journey is to utilize the breadth of AI technologies to assist customers in achieving their business goals and drive significant value regardless of their current maturity level in business practices and technology.
Tap into AI and optimize your risk-resilient, sustainable supply chain to predict, manage, and deliver at your best
Business leaders see significant challenges in integrating AI into business applications and processes, especially when trying to make sense of vast amounts of unstructured data. These challenges specifically stem from:
Data quality: Data is inconsistent or not reliable nor current.
Organizational Readiness: Organizations continue to rely on on-premise systems that pose challenges around stale data, integration, and scalability.
Volatility: Global AI regulations are constantly evolving, for example the EU AI Act. Technologies such as large language models (LLMs) are also rapidly changing in performance and price with new models and providers popping up almost weekly.
These challenges underscore the need for a thoughtful approach to integrating AI into supply chain processes. SAP Business AI delivers a comprehensive set of relevant, reliable, and responsible AI solutions that effectively tackle many of these hurdles and ensure seamless AI adoption throughout your supply chain processes.
For decades, SAP has led the way in supply chains, empowering its customers to streamline their complex end-to-end supply chain processes for enhanced efficiency, agility, and resilience. The SAP Supply Chain portfolio introduces innovation through various technologies, including AI, to transition your supply chain from digital to adaptive, with the ultimate vision of instituting an autonomous supply chain.
There are three possible maturity levels where you could stand in your business transformation. It is also important to acknowledge that different lines of business are on different maturity levels in an organization – while supply chain planning may already be somewhat adaptive, manufacturing operations may still be at the start of a digital transformation.
Digital: Digitalization is the starting point. The SAP Supply Chain portfolio automates manual processes, enabling a seamless, digital end-to-end business process and digitalizing paper-based systems. This results in better access to data, greater visibility, and control over the entire supply chain, setting the foundation. Most importantly, SAP provides the tools and systems to get data ready for more advanced stages.
Adaptive: This step is marked by an integration of cutting-edge technologies into business processes, including the use of predictive analytics and simulation for decision-making and Big Data, as well as the introduction of Joule, SAP’s copilot for every supply chain cloud application. These technologies empower supply chain professionals with intelligent insights and recommendations, enhancing decision-making for greater agility and resilience.
Autonomous: Next, we envision a path characterized by technological, procedural, and data enhancements that will propel the supply chain into an autonomous era. This transition will happen gradually but ultimately enable supply chains to operate autonomously with minimal human intervention, resulting in even greater efficiency, adaptability, and responsiveness. It empowers supply chain experts to focus on truly value generating activities and spending precious time on only the most critical disruptions and business opportunities.
Technologies that Lead Toward Autonomous Supply Chains
SAP has been developing innovative AI capabilities that are fully integrated across digital supply chain applications, catering to diverse customer technology readiness levels. For example:
Optimization models, rule-based predictive analytics, and heuristics improve, for instance, transportation plans, production scheduling, supply plans and spare part fulfillment. This allows a powerful way to automate decision-making in balancing conflicts of interests, such as customer service levels versus supply chain cost.
SAP-owned machine learning is applied across the SAP Supply Chain portfolio; for example, in gradient boosting algorithms for demand forecasting, intelligent lead time predictions and failure curve analysis. SAP also offers customers a “bring-your-own-model” approach to enable extensibility for specific domains such as visual inspection and anomaly detection.
Generative AI, when embedded in digital supply chain applications and combined with Joule, will allow users to conduct complex business transactions in simple conversational ways. We will augment tasks like conducting what-if scenarios for supply chain planning, assisting in new product ideation, analyzing manufacturing issues to accelerate the onboarding of new equipment, and assessing advanced failure modes of assets with generative AI.
With these technologies, our goal is to support customers wherever they stand in their journey and help them move from a focus on mere digital transformation to highly adaptive processes — and ultimately toward autonomous supply chain systems.
Examples of AI Innovations
Let’s explore specific innovations delivered in the SAP Supply Chain portfolio: These cases are classified according to their primary focus on digital, adaptive, and autonomous as a continuous journey. These innovations will be enriched over time and every business may find themselves at a different stage of maturity in different parts of their supply chain processes.
Digital
Intelligent Lead-Time Prediction for More Accurate Integrated Business Planning This machine learning extension enables the extraction of historical lead times from goods movement data in SAP S/4HANA and analyses outliers and key influential factors for lead time changes to predict future lead times. The results are then uploaded as input for planning runs within SAP Integrated Business Planning, thus facilitating better decisions, and enhances plan adherence by considering the dynamic nature of actual lead times and future trends.
Everstream Powered Risk-Aware Supply Chains for SAP Integrated Business Planning Critical data for decision-making can sit outside of the four walls of a company. Using external network data is imperative for better decisions based on supply chain risks in a digital transformation. This capability integrates external incident data — for example from Everstream Analytics — into SAP Integrated Business Planning and enables companies to proactively anticipate and manage external risks in their supply chain planning process. This integration allows supply chain planners to perform risk-aware planning by actively reflecting external risks affecting their supply chain, such as geopolitical shifts, material shortages, global regulations, extreme weather events, and more. Figure 1 illustrates this innovation:
Fig. 1: SAP Integrated Business Planning – Planner Workspace
SAP Product Lifecycle Management (SAP PLM): Generative AI for Recipe Formulation In the process industry, technology plays a crucial role in recipe creation and formulation to meet consumer demands. A fast time-to-market of launching a new recipe, in compliance with local regulation and sustainability targets, can be paramount to the competitiveness of a company. Generative AI integrated into SAP PLM assists in developing, enhancing, and customizing recipes, thus expediting product innovations, enhancing productivity, and supporting product compliance and sustainability for manufacturers of formulated products. Figure 2 illustrates this innovation:
Fig. 2: SAP Product Lifecycle Management – Generative AI for Recipe Formulation
SAP S/4HANA Transportation Management: Intelligent Goods Receipt Analysis In high-volume logistics industries, the manual tasks involved in inbound goods receipt can be time-consuming and error-prone. By leveraging AI-based document extraction, this use case automates data extraction from freight documents, seamlessly integrating them into SAP S/4HANA, enhancing efficiency, accuracy, and time savings.
SAP S/4HANA Enterprise Asset Management: Intelligent Maintenance Order Recommendation LLMs optimize maintenance planning, promote agility, and improve asset performance and reliability in SAP S/4HANA enterprise asset management. In this innovation, generative AI supports users in the assessment of maintenance requests by recommending appropriate tasks and spare parts, thus enhancing operational efficiency. Figure 3 illustrates this innovation:
Fig. 3: SAP S/4HANA Enterprise Asset Management – Intelligent Maintenance Order Recommendation
SAP Integrated Business Planning: Interactive Planning Assistant Demand, supply, and inventory planners often use complex machine learning algorithms. By using generative AI and conversing with Joule in natural language, planners can gain insights into the models’ variables, constraints, and decision processes, leading to much more informed decisions and proactive planning. SAP helps customers boost explainability but also enable what-if scenario simulations for complex planning decisions. Figure 4 illustrates this innovation:
Fig. 4: SAP Integrated Business Planning – Demand Planning
SAP Digital Manufacturing: AI-Driven Visual Inspection In industrial manufacturing, human-led visual inspection can lead to potential quality issues and is synonymous with a higher degree of manual labor. This capability applies computer vision AI into the production process to automatically identify defects and allocate non-conformance for production decisions, thus supporting a consistent and objective quality analysis that contributes to cost savings associated with reworks and recalls. Figure 5 illustrates this innovation:
Fig. 5: SAP Digital Manufacturing – Visual Inspection
Join the evolution and embrace AI, whether you are in a digital transformation or already driving toward adaptability or are taking your first steps toward autonomous supply chains or functions.
More Details
Ready to revolutionize your supply chain using artificial intelligence? Here’s how to get started:
Register in the Early Adopter Care Program to explore Joule in SAP Integrated Business Planning for conversational search
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The promise of generative AI is in its ability to support individual businesses with individual needs; not just automating work, but identifying what work can or should be automated, as well as the best place to start.
With the release of new process AI capabilities for SAP Signavio — process recommender and performance indicators recommender — SAP is taking a step closer to realizing this promise.
Realize holistic, data-driven business transformation across your organization – unlocking faster time to insight and adaption
Embedded into SAP Signavio Process Transformation Suite, these novel capabilities deliver instant, tailored process and metrics recommendations that will help process owners and process analysts accelerate process model design, more readily define their process monitoring strategy, and improve the quality of business outcomes.
SAP Signavio and SAP LeanIX solutions are core elements of the Business Transformation Management portfolio, which leverages SAP Business AI technology, initiatives, and principles to help customers on their transformation journeys, and deliver generative AI capabilities that are specific to the process world.
The latest release of SAP Signavio Process Transformation Suite features the following:
An AI-assisted process recommender provides process owners with preconfigured process models suggestions out of a database of more than 5,000 best practices from SAP. This new generative AI capability will help process owners move fast from the initial exploration phase to process design by instantly narrowing down hundreds of options to those that present a best match to their business.
Users are suggested preconfigured process models which serve as a great foundation they can save, edit, and further perfect, saving weeks, if not months, of stakeholder interviews and lengthy workshops currently required to design a new process or redefine an existing one.
An AI-assisted performance indicators recommender delivers instant recommendations on the most relevant process performance indicators (PPIs) to be applied to a specific process, based on a database of thousands of KPIs and PPIs.
A capability to identify key process metrics among a variety of options, which is critical to measure success, can recommend the most relevant measures to assess the performance of a business process, providing companies with much needed insight into how they can streamline operations and reduce overhead.
“Generative AI is impacting every facet of modern organizations, and every process — from finance to sales, procurement, and supply chain management — can benefit from this transformative force,” shared Dee Houchen, head of Market Impact for SAP Signavio. “With generative AI serving as a catalyst, we are developing the third generation of our business process management solutions to make people’s work easier and help them perform it faster. When it comes to process management, generative AI can also extend the horizon of our thinking, by providing suggestions that experts can build on. At the same time, we can see generative AI lowering the entry barriers to disciplines such as process analysis, acting as a knowledge multiplier and helping companies to scale.”
In addition to these newly released capabilities, a process mining co-pilot currently in beta testing is expected to be released in November this year. The AI-assisted process analyzer, also known as text-to-insights, will empower users with any skillset to extract deep insights and obtain valuable knowledge from process information simply by using natural language and a question/answer approach. Along with improving the time to insight, this new co-pilot will help continue the trend of democratizing process mining, ensuring as many people as possible within an organization can contribute to process excellence.
Lucas de Boer is Global Marketing Program lead for SAP Signavio.
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