The growth of the Internet of Things (IoT) has been accelerating over the last few years and has no slowdown in sight thanks to more and more things/machines coming online. It is also one that closely leverages the existing Industrial Machine-to-Machine (M2M) topic.
The main difference in the definition is that M2M is a foundational component for thing interaction and IoT is the orchestration of this interaction across multiple M2M nodes. In order for a thing to exist in a connected network then it needs some context. The connectivity, purpose, and user personalisation need to be well-defined so it can know its role and the way it is supposed to interact with the world around it.
The first step in achieving this is through a common set of standards but also extensibility to include native and proprietary ones as well. There will always be exceptions that hinder the IoT Platform goal of “Connected Everything” and if you don’t account for them up-front in the design then it will be a constant struggle to keep up with the pace of thing technology. Aside from the IoT and M2M definitions being very similar in nature, of one system built on another, the lessons learned from M2M used in industrial environments for years remain fundamental to IoT orchestration around discovery, management, and standardised interfaces.
An industrial IoT scenario essentially can be defined as anything that helps to drive and manage manufacturing operations business processes. To achieve end-to-end process visibility on both the supplier and customer sides of their businesses, manufacturers must be connected across company lines and have a way to exchange data that has different names or IDs in order to standardise visibility. The figure below shows the interconnectedness of the various industries today:
The Industrial IoT solution needs to provide a means to quickly define and view a business process and simulate the live scenario. This way end-users and process developers can react to problems that haven’t actually occurred. The IoT solution will be able to say which machine will likely fail first, how long a typical repair for that failure can take, parts required for repair, and model this for machines whose parts and production materials span across the boundaries of multiple companies.
The concept of inventory management (production volumes, repair parts) based on varying production volumes or defect codes detected may sound simple, but when you throw in purchase of 3rd party products or components and customer order specifications the task grows exponentially. The IoT processes does not have to care what the device nuances but rather only about its purpose in the business network and the data it can provide that influences the orchestration. An emerging area is the ability for the device to auto-discover and auto-manage or classification of available data and tagging it. with possible data relevance. This matters when various systems are “looking” for useful information available in the infrastructure.
There is a clear need for a centralised master catalog that can provide query visibility across the enterprise for visibility and reporting across various business domains. Many manufacturers are challenged with a wealth of disparate data across multiple systems with no coordinated or coherent information about their supply chain activities. M2M provides a way for companies to improve asset and plant performance, optimising inventory, and taking advantage of trade opportunities.
Finally, data has to propagate both downwards and upwards in a business so information from the business systems can travel seamlessly down to the physical systems and back up again. This asynchronous messaging scenario has typically been the focus of many discussions around enterprise messaging down to the operations layer for production scheduling, bill of material updates, master recipe management, etc. There has been little in the way that has standardised the process of device messaging back up to the enterprise. This is typically the case because raw values from a sensor lack the context required to be relevant for a business application to directly consume.
There are many scenarios that an investment in the IoT space shows promise in and there are ways that both end users at both a consumer and industrial scale can benefit. As an example in discrete manufacturing industries, we have customers continually asking for a way to monitor their assets post sale to their customers (Consumer & Industrial). The ultimate goal of these types of scenarios is to ensure reliability of operation, streamline the repair process, and lowering costs around operating or owning equipment (Pay by Use). The discrete manufacturing companies can also see great benefits from this as well in having direct feedback for their internal design engineers, guarantee parts and people are available for repair work, and track the complete history of how their products are being used in order to better adapt their designs to changes in customer demand. The ability to tie together the as-designed, as-built, as-used, and as-maintained records is of tremendous value for these types of manufacturers and for their customers.
By Salvatore Castro
Director, SAP Connected Manufacturing
