AUTOMATION → INNOVATIVE DATA PRODUCTS → BUSINESS MODEL TO OPERATING MODEL → PRODUCT DEVELOPMENT STRATEGY
In Part 2 of this series, I shared with you the tools like the canvas and techniques like Freshwatching to learn from other company’s’ products to establish your own product roadmap using three core assets: Data, Presentation, and Logic. What are the next steps to generate multiple project scenarios to increase the chances of a product’s financial success? What organizational components should be developed for a strong foundation to execute on this new innovative product roadmap?
The next step in the process is to identify a Target Operating Model that is well-suited for the Business Model characteristics. The process is defined by evaluating the standardization and integration necessary in certain elements in the Business Model. Elements in the Business Model that play a significant role in determining the Operating Model include Customer Segments, Key Activities, Key Resources and Key Partners. Diversification (low standardization, low integration), Coordination (low standardization, high integration), Replication (high standardization, low integration), Unification (high standardization, high integration) are four types of Operating Models.
For the mapping activity, input should come from the intrapreneurs and enterprise architects. The goal is to understand the evolution of the enterprise system landscape for optimized, agile ecosystem that can provide the responsiveness needed to implement higher velocity initiatives in the age of Internet of Things. The intrapreneurs can provide data and characteristics from the Business Model to the mapping matrix. Enterprise architects can analyze the business processes, system linkages, and data necessary to support that aspect of the Business Model to lead to a choice of one of the four Operating models.
For the mapping activity, input should come from the intrapreneurs and enterprise architects. The goal is to understand the evolution of the enterprise system landscape for optimized, agile ecosystem that can provide the responsiveness needed to implement higher velocityinitiatives in the age of Internet of Things. The intrapreneurs can provide data and characteristics from the Business Model to the mapping matrix. Enterprise architects can analyze the business processes, system linkages, and data necessary to support that aspect of the Business Model to lead to a choice of one of the four Operating models.
But what is a Target Operating Model? It is a mixture of Business Processes, Business Functions, and Business Capabilities with some Business Services thrown in for good measure.
- A Business Capability is defined in terms of the outcome of the course of action, one that has a business value. A Business Capability is used for managing units of strategic business change, and is named after the desired outcome.
- A Business Function defines the ‘What’ behavior that is associated with an organizational unit and modelled in a target operating model. A Business Function is typically named with a suffix of ‘management’ (i.e. ‘Customer Relationship Management’), but could also be a single noun (i.e. ‘Billing’). Business Functions can be decomposed into component business functions, resulting in a business function hierarchy.
- A Business Process is defined as the behavior from the ‘How’ (how work is done) perspective. It represents both a complete Business Process Flow and the elements in a Business Process Flow.
- A Business Service represents an external view of the services an organization provides or sells to its customers alongside the sale of a Product. A Business Service may be realized by a Business Function or directly by an Application Service, but is more usually modelled as being realized by a Business Process.
REMOTE MONITORING SERVICE
I recently attended the Offshore Technology Conference (OTC) in Houston. The software-related capabilities that most big vendors at OTC talked about were in the areas of Analytics, Dashboarding and Remote Monitoring Services. Most initial Industrial Internet initiatives are focusing on what many consider the low-hanging fruit for their initial trials. A couple of goals that you’ll see mentioned repeatedly are the concepts of doing predictive maintenance and anomaly detection. This tends to be because these are the problems that are the easiest to tackle and obtain a high return on investment. The OTC was also an opportunity to share the two solution offerings of Divergence Data in the areas of Text Analytics (convert unstructured text through our Clean and Catalog Service offering) and Industrial IoT Dashboards (a remote monitoring solution).
In a manufacturing setting, machine learning is used mostly for finding patterns in industrial data for the purposes of anomaly detection and predictive maintenance.
Anomaly detection is certainly not specific to manufacturing, but it is used differently when applied to manufacturing-specific problems. Manufacturers can benefit from anomaly detection in a number of ways; a prime example is by using it to discover defective products early in the production pipeline. Early anomaly detection can give machine operators advance warning of issues downstream in the manufacturing process so that these issues can be resolved quickly and without shutting down the production line.
Predictive maintenance is a subset of anomaly detection that focuses on determining the mechanical status of a machine—for example, whether a machine is approaching its maintenance window or if failure is imminent. By comparing current sensor readings to historic data, the system can use predictive maintenance to detect issues early on, letting the company handle repairs at a time when overall impact to the system is minimal. This level of prediction can prevent costly and unplanned maintenance as well as lost earnings that might otherwise arise and affect service agreements.
In the report “Industrial Internet” (O’Reilly, 2013), Jon Bruner states: The Industrial Internet is the union of software and big machines— what you might think of as the enterprise Internet of Things, operating under the demanding requirements of systems that have lives and expensive equipment at stake. It promises to bring the key characteristics of the Web—modularity, abstraction, software, above the level of a single device—to demanding physical settings, letting innovators break down big problems, solve them in small pieces, and then stitch together their solutions.
Beyond the low-hanging fruit of Predictive Maintenance and Anomaly Detection, there are opportunities to deliver Business Services in the areas of Asset Simulation, read error codes and sensor readings using Natural Language Processing, Additive Manufacturing for rapid prototyping technology in the design phase of products, Augmented Reality to see diagnostic sensor readings and many more. So, whereas the IT industry has now evolved to the point where commodity hardware and protocols have become the de facto standard for nearly all except the most specialized applications, the manufacturing world remains extremely fragmented. Regarding the variety of machines, protocols, and data formats in the industrial space, Nathan Oostendorp, CTO and cofounder of Sight Machine, explains:
“As far as the number of types of machines, tens of thousands to hundreds of thousands is pretty conservative. There’s a lot of automation that is built to spec. With transport mechanisms and protocols, that’s covered by a couple dozen cases. But in terms of what the data says and how it needs to be mapped, that is really a massive variety problem and I don’t think it’s something where you’re going to be able to compile the list of all known machines and walk into a plant completely cold and have a turnkey solution that takes all of its data automatically and gives you a perfect digital picture of what’s going on. There’s always going to have to be some amount of modeling that goes on, and some information that comes from the process itself that will inform how you’re going to report on that data.”
Building a strong foundation requires good resources – especially people. When dealing with industrial size variety and velocity challenges, it has also been said that “good help is hard to find“, but when you’re dealing with emerging technologies, it can appear almost impossible to recruit the talent necessary to get these complex Industrial Internet projects off the ground successfully and running smoothly. The good news is that there is an equally prosperous alternative path (beyond startups) for new generation of young talent that is interested in working on large-scale enterprise systems. As the Industrial Internet transitions from its infancy to maturity, the industry will be able to recruit talent that has more than computer science and statistics backgrounds. Not everyone has to have that PhD in computer science (Source, Fast Company, Machine learning could create more tech opportunities for women).
Next step – let’s pick the right platform and get the product development started.
Original post on LinkedIn Pulse