“I learn best by doing.”
I made this claim to our Chief Research Officer, Riz Ahmed, on my first day as the new Vice President of Research at SAPinsider last week. Ten days later, I find myself writing my first blog post after already having written a full benchmark report. Keep an eye out for that report — titled “Digital Transformation of the Supply Chain” — in the coming weeks.
I joined SAPinsider after serving as the Research Director for the Industrial Solution at ABI Research. While ABI had been covering industrial technologies for a couple of years before I arrived, I helped start its Smart Manufacturing service, which evolved into the Industrial Solution this past year. Before ABI, I started my career with the boutique Internet of Things (IoT) analyst firm Machina Research, where I covered Connected Cars and a wide variety of IoT applications. The firm was then acquired by Gartner, and I continued on with Gartner for a few months after the acquisition before the opportunity arose to join ABI.
I hope to leverage this experience to serve the SAPinsider membership and provide you with helpful insights and strategic advice. Therefore, with no further ado, I present you with the an introduction to and brief analysis of the Industrial IoT (IIoT) landscape.
Demand for IIoT
SAP has many customers using its enterprise software and middleware for manufacturing and throughout the industrial sector. Over the past several years, the Industry 4.0 movement has swept up many of these customers as they try to keep up with innovations in technology, and many of these innovations come down to data collection, integration, processing, and analyzing on IIoT platforms. SAP has invested heavily in IIoT technologies over the past several years and, in 2016, coalesced these capabilities under the SAP Leonardo brand. SAP built SAP Leonardo on SAP Cloud Platform to connect industrial automation equipment and other devices to SAP Cloud Platform and bring transformative technologies to IIoT.
SAP customers that bought into the Industry 4.0 movement did so because leveraging IIoT data can lead to real business benefits. Many factors, applications, and technological developments drive these business benefits and therefore demand for IIoT, including:
- Predictive maintenance to minimize downtime: Already, predictive maintenance has proven results when it comes to reducing downtime and increasing productivity in manufacturing. One minute of downtime can cost tens of thousands of US dollars, depending on the industry. These results clearly affect the bottom line. SAP offers predictive maintenance software that combines IIoT data with information from its ERP, customer relationship management (CRM), and enterprise asset management (EAM) systems to predict equipment malfunctions.
- Identifying the root causes of quality issues: Many production processes face quality issues that cut into productivity and, if they sneak through inspections, lower customer satisfaction. Edge-to-cloud closed-loop machine learning (ML) and advanced manufacturing execution systems (MES) have proven use cases in reducing quality issues.
- Developments in cloud computing and ML: Cloud environments offer almost unlimited compute and processing power and can provide a similar interface for work from the different points of view of different employees, engineers and senior management. Companies can assign user roles so that employees can only see and modify the details and information that they need, and engineers in different locations can work on different parts of the same project. Already, firms have started to adopt ML models and apply them to smart manufacturing data to minimize repeatable tasks capable of being performed by software, improve the accuracy and predictability of maintenance schedules, and drive first-time-right results across the organization. While these models often run and evaluate data at the edge, they often need cloud computing for training due to the large amounts of data and processing required (a shortage of ML training data puts the validity and reliability of a given model in question).
- New “as-a-Service” (aaS) business models: Many SAP customers have started or plan to offer maintenance-aaS or products-aaS. IoT platforms empower these business models by sending alerts if a product requires maintenance or attention.
- Continuous product improvement based on real-world data: An enterprise can leverage IoT usage and performance data to continuously improve its products. Right now, this requires engineers to analyze the data, but as more products get connected and companies leverage more AI techniques, generative design software could automatically create improved designs based on IoT data.
A Leader or a Follower?
Measuring by its support for IIoT apps and other new technologies and adoption to date, SAP Leonardo ranks among the leading IIoT platforms but not as the leader. Industry 4.0 started as the synergies between many emerging transformative technologies presented themselves. SAP has made conscious decisions to identify these technologies and invest. It has also recognized where it needs to acquire or partner with companies to fill any gaps.
For instance, SAP’s most exciting digital twin capabilities come from an acquisition and a partnership. First, it acquired Fedem for exact, high-performance 3D digital twins that capture motion dynamics. Fedem’s software captures sensor data for tension and movement and performs 3D simulations in near-real time. Later, SAP partnered with ANSYS and now embeds ANSYS’s simulation solutions on top of its existing digital twin capabilities. SAP brought these capabilities together for its SAP Predictive Engineering Insights enabled by ANSYS solution for structural analysis.
SAP also partners with Telit to connect to machines and robots on factory floors with native drivers and for the SAP Device Management for IoT application by Telit. This application empowers SAP customers to connect previously siloed operational technology (OT) equipment to SAP Leonardo.
SAP Leonardo Machine Learning Foundation provides ML capabilities such as image recognition, natural language processing (NLP), and custom models that adapt to use cases. The ML models can adapt to livestreamed data from the edge or batched data in the cloud. SAP provides SAP Edge Services for the streaming analytics and local storage, but most commonly, customers deploy and manage from the cloud. It does offer on-premise deployment of some IoT solutions on the SAP HANA platform for hybrid deployment or private cloud.
SAP has also launched the SAP Cloud Platform Blockchain service to empower enterprises to develop blockchain-based solutions. About 65 companies participated in its blockchain co-innovation initiative to help improve transparency and collaboration in manufacturing and supply chain. SAP also founded a blockchain consortium with six other companies including Intel and HPE.
Unfortunately for SAP, some of its manufacturing customers may find that deep integrations between the IIoT platform and industrial software portfolios with product lifecycle management (PLM), MES, manufacturing operations management (MOM), computer-aided design (CAD), and plant simulation software provides more value than deep integrations with the ERP, CRM, and EAM systems. Therefore, companies with a broad industrial software suite — such as Dassault Systèmes, Siemens, and PTC —have found more traction with their IIoT platforms than SAP has with SAP Leonardo. PTC likes to point out that the P in PLM (Product) represents the T in IIoT (Things). Of course, this simplifies the issue, but it also holds water to some extent.
The integrations with the industrial software mean that companies can use the advantages of cloud computing to design a product in CAD software; simulate its production in plant simulation software; put it into production with PLM, MOM, and MES software; and then leverage the IIoT platform to train and deploy ML models, all on the same platform. They can then feed results and usage data from the IIoT platform back to the appropriate tools and users.
The largest industrial software vendors offer all these tools as parts of large smart manufacturing platforms. For example the Dassault Systèmes 3DEXPERIENCE platform has DELMIA for production simulations and DELMIA Apriso for MOM; however, starting in the design process, engineers can use CATIA to generate virtual visualizations of any part they need to design and put it through simulations on SIMULIA to ensure it meets all functional requirements. CATIA can also incorporate and adjust for findings from BIOVIA, Dassault Systèmes’s material science brand. Again, Dassault Systèmes brings all these tools together on the 3DEXPERIENCE platform with its IIoT capabilities. Likewise, Siemens has the Simcenter with many simulation tools such as Tecnomatix on top of its own PLM software portfolio, which includes several design products and the MindSphere IIoT platform. PTC has Creo for design, Vuforia for augmented reality, Windchill for PLM, and ThingWorx for IIoT. SAP Leonardo does not come with these types of manufacturing purpose-built integrations.
What Does This Mean for SAPinsiders?
To make the most of IIoT data with scalable applications, SAP customers must:
- Create cross-functional technology transformation teams. IIoT projects cannot succeed if IT and OT do not communicate. IT professionals need to understand the operational goals of OT, and OT needs to understand the security policies and strategies of IT. These cross-functional teams should work together to identify relevant technologies, run proof of concepts (PoCs), and scale technologies in a way that provides long-term value to the company.
- Evaluate internal capabilities and form a strategic vision. Most industrial companies and industrial engineers still try to solve problems one by one. For instance, they try to figure out how many automated guided vehicles (AGVs) they need. Then they try to figure out what types of robots to purchase. Then they try to figure out where to place the computer numerical control (CNC) machines, and so forth. Scaling IIoT requires a more holistic and more collaborative approach with a long-term strategic vision.
- Choose the IIoT platform that best fits this vision. Once a company knows where it wants to go with its technology transformation, then it can decide what platform can best help it get there. For many manufacturing firms, this means choosing an IIoT platform from a vendor with a comprehensive industrial software portfolio including CAD, plant simulation software, and PLM rather than SAP Leonardo. For others, the integration with other SAP products and capabilities will provide more value than integrating with other PLM vendors — especially, of course, if they use SAP Portfolio and Project Management or other SAP products.
- Integrate that platform with existing SAP products. Even if SAP customers choose other vendors for their IIoT platforms, that does not diminish the importance of SAP enterprise software. As soon as they have mastered data collection and processing, SAP customers must ensure that they can deliver relevant IIoT data to wherever they need it within their SAP systems.
Following this strategic guidance should help SAP customers implement scalable solutions.
Pierce Owen can be reached at Pierce.Owen@wispubs.com.