Digital Transformation in Manufacturing

Better connectivity, seamless integration, and the flow of real time data brought about by the IoT and Industry 4.0 has allowed manufacturers greater visibility into their processes and outputs. Manufacturers are all about efficiencies and throughput. By leveraging new technologies they have gained more insights into each stage of the manufacturing and distribution processes. Companies that don’t keep up with these changes are in danger of being left behind in the long run.

Using Real Time Data in Manufacturing

Manufacturing innovations began with the invention of basic assembly lines then decades later came automated assembly lines that have now advanced to the use of robots, new materials, smart machines and other ground breaking technologies. IoT combines real time data with equipment to improve outcomes in the manufacturing process and the supply chain.

Real time data is the delivery of information immediately after collection – there is no delay in the timeliness of the delivery. This is possible because IoT enables a physical-to-digital-to-physical connection which is the collection and delivery of information, and then an instantaneous relay of information back.

For example, you could use sensors that send an alert from the assembly line that equipment is beginning to overheat. A command is immediately sent back slowing down that part of the assembly line before there’s a problem. Before IoT, this would have taken several minutes. Now it takes seconds.

IoT Will Increase Manufacturing Throughput

At its core, the IoT means seamless integration. It allows devices to connect and communicate in real time without the need for special programming. The continuous influx of real time data creates visibility into manufacturing processes and distribution so teams can react and adjust for unanticipated changes.

Computing and robotics in automated assembly lines have created an enormous increase in real time information during the manufacturing process. Improvements include:

  • Instant alerts for scheduled maintenance or equipment repair

  • Notifications for re-orders of materials and supplies

  • Alarms for equipment malfunctions and errors

All of these are immediately transmitted to help prevent and reduce equipment malfunctions, lost production time and defects in manufactured goods. The inclusion of blockchain technology makes it simpler to securely store and access critical data making it easier to trace defects back to a specific factory, assembly line or supply chain.

How Will IoT Affect Distribution Systems?

IoT combines data, analytics and blockchain technology to track thousands of orders that are scheduled to be picked, packed and shipped. Inventory levels will be tracked and updated continuously to help reduce orders that can’t be filled. Packing and shipping supplies will also be monitored to ensure that orders are packed properly and shipped to the correct address. IoT will also allow you to track your order in real time and be alerted as soon as it has been delivered.

The Industry 4.0 Ecosystem

Setting stringent future plans and long-term goals are essential to day-to-day operations. For this, the operation needs a strong hand, which may not act autocratically and openly selfish. The balancing act between authority and open-mindedness is difficult for many founders and turns out in hindsight as one of the biggest hurdles.

Organization and coordination

SME often have a barely defined hierarchies and distribution of tasks, especially in the initial phase. However, it is of vital importance that different areas of responsibility and responsibilities are clearly determinable and selective. The definition of a clear and organized corporate structure is often subject to major obstacles due to poorly defined responsibilities. This problem weakens the economic capacity of companies to act.

Information, Data Maintenance and Interaction

One of the most important elements in business management is a functioning infrastructure. Incoming information must be carefully documented in order to be used again for later projects. Unfortunately, the maintenance of data within SMEs is too often unprofessional, so many important information is lost on the various networking paths. Since most companies, at least successful, now rely on digital networking across the board, a flawlessly functioning IT network is needed. However, there are deficits in many smaller companies. Too seldom do the individual systems in the IT area make sense, are networked in a secure and fruitful way.

Original equipment manufacturers (OEMs) are no strangers to boom or bust sales cycles. Traditionally, they’re either ramping up production to meet demand or seeking ways to slash costs when sales are down. But Industry 4.0, or the Industrial Internet of Things (IIoT), allows the OEM to look differently at customer’s needs. A customer ultimately is not interested putting a lump sum of money in the table buying expensive machinery - the technical solution is just the means to an end. OEM’s job is about satisfying customers which can only be achieved with reliably running machinery and a repeatable, reproducible production. That too level finding is enabling new sales models that also generate much more consistent revenue streams for OEMs.

There are considerable benefits for forward-thinking manufacturers that transition from selling a product to offering, “machines as a service.” Rather than relying on a one-time sale, they’re charging customers based on machine use and service. Machines as a service can revolutionize the way OEMs design, sell and service products. It will be a win-win for OEMs and their customers, as both partners benefit from increased predictability.

From real data to digital models

A semantic enrichment is required for the mapping of real data and virtual models. The data must be made easier to use via semantic information models. Visual models, such as geometry models, are becoming increasingly important in order to be able to represent the process and the results of the test accordingly. The simulation coupling has not yet been resolved. What is required is the coupling of data analytics, including the knowledge gained from data with the help of AI methods and processes, with physics-based deterministic simulation processes of behavior as well as with desired event-based simulation models of the entire factory. The semantics also form the basis for the Industry 4.0 skills required for autonomous decision-making in Industry 4.0 solutions. Without knowing what form the data is in, it is not possible to make a machine-based decision as to whether, for example, maintenance needs to be carried out or whether there is an incorrect entry. By substituting physical components in Industry 4.0 solutions with model-based components, such as a virtual controller, physically induced delays and disruptions are eliminated and greater adaptability is achieved. This is also helpful when making Industry 4.0 solutions more flexible. Both in operation and in engineering, it is important to be able to consider Industry 4.0 solutions in a system network as well as at the system element level. This requires a link, for example using system models that combine all the components of the solution with one another. It is also relevant to keep the modeling effort low in order to achieve the highest possible efficiency. This can be achieved, among other things, by reusing models as models of reduced order and automatically converting them into models for digital twins.

Selling uptime as a differentiator

Comparing that service model to the more common fail-and-fix approaches in which OEMs sell the equipment outright and only provide service when a machine breaks down. OEMs that adopt machines as a service differentiate themselves from competitors by guaranteeing 100% uptime and only charging for actual usage.

The benefits to performing an ongoing service are staggering. OEMs form tighter relationships with their buyers, customer satisfaction and loyalty increases, and the OEM has an ongoing stream of income, which is especially critical during down business cycles.

How Industry 4.0 enables new service models

Industry 4.0 software provides analytical tools that help OEMs understand the mechanical factors and environmental conditions that lead to machine failures. Whether it’s vibration, temperature, pressure or other performance indicators, OEMs can use this software to analyze the machine data gathered from their IoT-enabled machines to perform predictive maintenance.

Traditional maintenance programs are typically based on machine hours or predetermined service intervals. It’s a process that’s often wasteful because the maintenance team has little visibility as to what parts of the machine actually need servicing, if any at all, resulting in wasted man-hours. Additionally, it requires companies to have more inventory for equipment repairs on hand than necessary.

Predictive analytics helps manufacturers plan for repairs, so they have parts on hand only when and where they need it. In turn, they minimize the cost of carrying excess equipment inventory and can accurately schedule repairs around customer production schedules to avoid any disruptions.

The future of IoT-enabled service models

For OEMs, machines as a service will have additional benefits beyond service agreements. By leveraging IIoT technology, they will be able to analyze how customers are using their machines in the field to improve product designs. In some cases, they may be able to use the information to customize products based on usage patterns or other factors observed in the field.

To get there, OEMs need to take more proactive steps. Many have started incorporating some form of Industry 4.0 into their marketing and sales pitches, but many companies have not integrated the technology into their products. Many early-stage IoT initiatives are not bringing truly smart, connected machines to the factory floor. In many cases, OEMs are simply installing plug-ins that offer some level of connectivity but don’t provide the analytics needed to make machines as a service a reality.

Digital business model development

Successful companies for sure need to have great technologies, platforms, and demographics, but the secret to being successful turn out to be much more prosaic. One able to satisfy real customers who needed real jobs done - and by jobs, I mean a fundamental problem in a given situation that needed a solution - turn out to be successful. In other words, they had great business models.

A digital platform, or a digital solution, may enable a new epoch of transformative growth, but when you get under a company’s hood and look to see what’s really driving it, the engine of transformation turns out to be its business model.

Lean.IQ identified four interlocking elements that, taken together, create and deliver value to customers:

  1. Customer Value Proposition (CVP), which is a way to help customers get a job done. The more important the job, the lower the level of satisfaction with other companies’ attempts to solve it, and the better and cheaper your solution is than theirs, the more potent your CVP.

  2. The second is a Profit Formula, or how you create value for yourself while providing value to a customer. There are four essential elements to the formula: revenues, cost structure, margins, and resource velocity. The best way to create a profit formula is to work backwards, either starting with the price for lower cost businesses that is required to deliver the CVP, and then determining what the cost structure and other factors need to be or in highly differentiated businesses, start with the needed cost structure and margins that leads to the required price.

  3. Key Resources are the assets that are required to deliver the CVP to the customer at a profit, meaning the people, technology, products, facilities, equipment, channels, and brand.

  4. Key Processes are the operational and managerial capabilities that allow a company to deliver value in a way that can be repeated and scaled. These include manufacturing, budgeting, planning, sales and marketing, and customer service.

Successful business models have an exceptionally strong CVP, and a stable, scalable system in which all the elements mesh together seamlessly while complementing each other. As simple as this framework may seem, its power lies in the complex interdependencies of its parts. Major changes to any one of these elements affect the others and the whole. Any consumer or service company that doesn’t have a digital component certainly should; this is 2021, after all. But the key to transformational growth is still a powerful and coherent business model.

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