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Achieving digital transformation in manufacturing is a team sport

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Bridge the physical-digital divide with IoT services and solutions

The explosion in connected devices and platforms, abundance of data from field devices and rapidly changing technology landscape has made it imperative for companies to quickly adapt their products and services and move from physical world to a digital world. Companies will need to leverage Internet of Things (IoT) technologies for this transformation. Looking beyond the connected devices and protocols to drive business value from IoT investments, the success of an IoT initiative hinges upon capabilities in data acquisition, integration, analytics, and an in-depth expertise in enterprise business processes.

IoT is driving four major trends

The evolution of the IoT is driving four major trends. Instrumenting products with sensors and exploiting the operational data received, is disrupting both the marketplace for industrial manufacturers products, and the ecosystem in which they operate, as it:

  • Drives the shift to data-enabled services offerings
  • Transforms an OEM value chain from product-centric to customer-centric
  • Changes relationships through the supply chain to disrupt existing ecosystems
  • Provides valuable feedback into engineering and product design

This ability to disrupt existing business models and move to higher value services has been necessary to address challenges such as:

  • Globalization and reduced-cost manufacturing
  • Natural erosion of profit margins on existing products as they become commoditized
  • Competition from after-market companies wrapping OEM equipment in services-based contracts

Industry 4.0 and cognitive manufacturing

With billions of sensors connecting pieces of equipment and artificial intelligence producing insight about their performance and operating environment, manufacturing organizations are eager to usher in new ways of achieving improved factory productivity. IBM Watson IoT for manufacturing can help improve quality, increase efficiency and optimize performance through the power of the Internet of Things, advanced analytics and cognitive technology.

Why moving beyond basic infrastructure changes to digital transformation can be tough

One major challenge to digitization of manufacturing is return on investment (ROI) – or, more specifically, rapid ROI. According to IDC Manufacturing Insights, manufacturers are challenged to deliver value-added, profitable services through these connected products. They can do this through technology, namely the Internet of Things (IoT), and offer value-added services such as monitoring, predictive maintenance, and guaranteed uptime, each of which creates a potential new revenue stream that extends the lifetime value of a customer [1]; more specifically, ‘operating model transformation’ is among 2016’s top technology priorities, offering the most bang for the buck in terms of change.

However, while many organizations readily accept that leveraging ‘connected assets’ beyond performance feedback and predictive maintenance is the way forward, when it comes to identifying new revenue streams and business models, risk-averse leadership teams are less willing to fund innovation-driven investments (due to uncertain ROI), over initiatives offering more visible results, in factory automation for example.

Think about the amount of cost it takes to actually invest in the digitization of a manufacturing line. The volume of sensors that are needed may vary from 100 to a million depending on the size of the shop and complexity of the machinery, then, multiply this by the connectivity capability required to integrate the sensors and machines. For a manufacturer that is brown field (they have a lot of old machines), the cost of transformation is prohibitive.

Time can be yet another deterring factor. The sheer length of time it can take for new processes to be adopted inevitably prolongs the gap between the investment and its payoff. The sheer scale of the undertaking, especially in a brownfield site is prohibitively expensive, and operationally difficult to achieve. Taking old machinery off line is disruptive – especially when you think about the output a single machine has on a production line.

In addition, a manufacturer will have any number of different machines and processes represented on a plant or shop floor. There is no ‘one size fits all’ solution, no single manufacturing platform of choice. For new plants, or green field plants – a lot of new technology can be embedded from the beginning; but for a brownfield site, many organizations are challenged to find a way to reduce the time and effort it takes to retro fit old sites and machines so they can be IoT enabled.

Overcome potential digital transformation show stoppers by focusing on three key metrics

To overcome some of the showstoppers, customers need go back to basics and consider what the overarching technology driver is. In most cases, the need to improve the overall efficiency of the manufacturing plant is the primary objective. Based on hundreds of customer engagements, market research, and analyst review, the following three core metrics – reliability, quality and yield, and cost have consistently emerged as critical measurement. When considering how and where to apply IoT capability, by focusing on three pillars – intelligent equipment, cognitive process and smarter resource – customers can target their critical metrics areas – reliability, quality and yield, and cost efficiency.

Reliability

Down time and reliability are critical when it comes to the operation of equipment and machines on a shop floor. A production line is comprised of a set of machines, each operating in well-orchestrated process to produce an item. In the instance of an automotive manufacturing line where 60 to 70 cars are produced per hour, losing the operational capacity of even a single piece of machinery can have a knock on effect that results in significant cost or delay. Unplanned maintenance or repair inefficiencies can also contribute to down time. Infusing machine intelligence is one way to boost the reliability of manufacturing equipment and assets. This is exactly what Schaeffler set out to achieve.

IBM and Schaeffler: Using IoT to take advantage of Industry 4.0

Schaeffler’s precision engineered automotive parts will be manufactured using IBM Watson IoT, enabling industry transformation by creating and analysing some of the largest automotive data sources of connected devices. Schaeffler was very interested in connecting their machines. However, like many other manufacturers, Schaeffler has a lot of different machines on the line and could not silo themselves into one specific platform that’s dedicated to one specific machine. They needed a platform to which they could connect different machines in order to begin harvesting the information from the machines. In this use case, Schaeffler’s objective was condition monitoring – which means what they wanted to do was effectively have a controller dashboard that provided insight into how well their manufacturing equipment was performing. Monitoring the condition of equipment in real time can be complicated, however, their objective was clear – connect the equipment, get the data into IBM Watson IoT Platform, and subsequently, monitor the status of that equipment in real time using a visual dashboard to see how the data flowed, and how the equipment was functioning.

Once connected, the use of the data enabled Schaeffler to progress to more advanced actions such as predictive maintenance. With Watson IoT Platform as the platform that joined all of the different sensors and machines together, Schaeffler first gained their desired baseline understanding of how their equipment was running, and, from there, they were able to analyse the data, and arrive at more advanced capabilities – more insight from the data, more analytical capabilities, and in the future, cognitive capabilities. Even though the connectivity of the equipment is fundamentally important – what Schaeffler really wanted to see was the visualization above the connectivity. Watch the video to learn Shaeffler’s vision for digital transformation.

Data streaming shows how machines are operating using raw data. This raw data needs to be translated, and transformed into something that provides a visualization of what’s happening in a manufacturing plant. These are the advanced capabilities that analytics running on top of the solution can bring that allow clients to learn more than how the equipment are performing, but also if it’s likely to fail, and if it fails, what impact will that happen on the broader process, and the people or machines that exist in the chain. These are the real distinguishing features that IoT can bring to manufacturing – the visualization of data, the analytics and the predictions of the data. And then – in the future – as the technology progresses, we can move toward the cognitive capabilities of the data.

Quality and yield

Quality and yield is directly related to manufacturing processes – how raw materials are used, inspected, manufactured, and how everything comes together, really determines the quality level of the products. There is a direct correlation between how much yield can be generated out of the amount of raw material that goes into the products’ creation. This is an area where the application of cognitive capacities can really make a difference. For example, IBM research has developed a set of algorithms – Quality Early Warning System (QEWS) – that detect and prioritize problems and parametric shifts earlier and more definitively than can be done using traditional techniques of statistical process control. These algorithms are used by IBM throughout its own supply chain and manufacturing processes to meet established quality standards. The result: earlier identification of nascent quality problems, increased production yield, and reduction of problems that lead to service and warranty costs. IBM has incorporated the QEWS capability into IoT for Manufacturers quality analytics solution to help address quality control throughout the supply chain.

Cost

Cost efficiency is the third metric. Cost efficiency relates to how well an organization is using its resources – things like energy consumption, worker safety, and employee resource efficiency. For example, despite safety regulations and procedures work accidents are a cross-industry problem. In the United States alone, workplace injuries and illnesses cost employers upwards of $220 billion dollars with 27 million working days lost per year [2]. Even a non-fatal injury can have a long-lasting impact on both the organization and the individual employee, causing distress, suffering, productivity issues, not to mention the potential for significant personal and organizational financial loss. These rising costs are felt by individuals, employers, as well as insurance companies through increased coverage rates.

Implementing smarter resource and optimization strategies can help to improve the cost efficiency of these resources. For example, in a project with IBM Research, Haifia, North Star BlueScope Steel is applying IBM Watson Internet of Things technology and wearable devices to pioneer novel approaches to help protect workers in extreme environments. Collaboration between North Star BlueScope Steel and IBM using Watson IoT technology and wearable devices is paving the way for pioneering approaches that can help protect workers in extreme environments, while also helping to drive down costs associated with risk and workplace accidents. The piloted solution, IBM Employee Wellness and Safety, identifies potentially problematic conditions by collecting data from various sensors that continuously monitor the worker’s skin body temperature, heart rate, galvanic skin response and level of activity, correlated with sensor data for ambient temperature and humidity. The solution then alerts North Star management so they can provide personalized safety guidelines to each individual employee.

Why tap into a rich ecosystem to transform digital manufacturing?

What role does the ecosystem play in digital transformation? To manage change across an ecosystem of ecosystems requires a host of partners and stakeholders working together. Innovation doesn’t happen in a room of one. To identify gaps, share depth of experience, and breadth of resource, IBM actively engages clients and business partners to create and deliver business, technology and digital solutions that fit the needs of the industry. The end goal is to enable customers to achieve new levels of innovation and competitive edge.

Analyze real-time data, without the bandwidth cost of sending data offsite

Edge analytics can increase an organization’s ability to monitor and react to equipment health, resulting in lower costs.  To help organizations get a more cost-effective way to obtain real-time insights, IBM has joined forces with Cisco to handle the vast amount of data being created at the edge of the network. The key advantage of analytics at the edge of the network is to leverage the benefits of analyzing real-time data, without the bandwidth costs that come with sending that data offsite (to the cloud or the data center) for analysis. Evaluating asset performance at the point of monitoring helps drive corrective action and reduce premature degradation.

Infuse intelligence into plant management

Another example where two organizations’ IoT expertise comes together to transform digital manufacturing is in the area of plant management. Leveraging best-in-class IBM technologies, Capgemini’s Smart Plant Supervision solution delivers a 360-degree view of an organization’s production, connecting and monitoring assets in real time – from anywhere.

Capgemini’s Smart Plant Supervision (SPS), an end-to-end solution that can be integrated with existing systems, uses the Watson IoT Platform to enable machine-condition monitoring, predictive maintenance, and self-correcting equipment. Capgemini’s Smart Plant Supervision (SPS) solution can improve plant and facilities management using the Watson IoT Platform to leverage data from a wide range of digital and analog sensors. The end-to-end solution for data aggregation, analytics and action—in near real time— is offered through Capgemini’s own Bluemix recipe. By leveraging Watson IoT Platform running on IBM Bluemix, SPS enables predictive and preventive maintenance, higher machine uptime and maximum efficiency.

Watch the video to learn how the Watson IoT Platform, other IBM products and Capgemini’s systems integration expertise, enables companies to take advantage of IoT to transform plant operations and facilities management.

With more than 180,000 people in over 40 countries, Capgemini is a global leader in consulting, technology and outsourcing services in a number of domains and industries including manufacturing and product engineering.

“The Internet of Things is a massive accelerator for digital transformation. Building a consistent strategy and providing an innovative platform for IoT services is an asset that companies can leverage for the benefit of their clients.” Olivier Sevillia, Member of the Group Executive Board and responsible for Digital Services at Capgemini.

Working together to create a smarter factory

Digital convergence is transforming manufacturing. IoT, M2M, data, analytics and automation are blurring the lines between the ideal and the possible in industrial environment. Factory shop floor(s), especially, present a significant scope for connectivity, traceability, automation and remote management.

To address these opportunities in Manufacturing, IBM is also teaming up with Tech Mahindra, another global partner that brings deep domain expertise in this sector. The two organizations have collaborated to create a solution called a Smart Workstation that manages smart tools on the shop floor. Smart Workstation leverages Industrial IoT, convergence and analytics on the shop floor, to deliver tangible operational improvements in productivity, efficiency and availability.

The solution is aimed at improving the quality and productivity of existing manufacturing process using IoT. Smart Workstation connects workers, tools and processes to bring a new level of automation and control into the shop floor.

Shop floors, even today, experience a range of issues such as time wasted in tracking the exact location tools, gaps in defining and monitoring status of work packages and limited data availability for continuous process improvements. The Smart Workstation solution presents immense potential for global industry in addressing these issues and improving manufacturing quality, traceability and productivity.

Here’s how it works:

  1. Smart Workstation allows planners to create Work Packages** for each worker mapped to them. The created Work Packages are sent to the Smart Workstations IoT application.
  2. The IoT application is configured to individual Local Gateways. Local Gateways are mapped to individual workers through their cell phones.
  3. The IoT application verifies the Work Packages and publishes them to the appropriate Local Gateways. All Local Gateways that are connected will receive the Work Packages immediately.
  4. Workers’ cell phones frequently poll the Local Gateways for new Work Packages and updates. Work Packages, if any, are downloaded to the worker’s cell phone.
  5. When the worker accepts the Work Package, the Local Gateway pre-validates the task and sends a command to the worker’s phone and the connected tool needed for the task.
  6. When the task is completed with the tool, the worker ends the task and the Local Gateway validates completion

** Work packages and work tasks are generated using planning software like Maximo or any other ERP tools, and are then pushed down to the Watson IoT Platform.

Here’s what the solution setup looks like:

Figure 1: The Smarter Workstation set up

Figure 1: The Smarter Workstation set up

A rich interactive user interface allows various personas, e.g. Plant Manager, Line Manager Quality Manager, etc. to view various stages of the manufacturing line in real time. This solution has provided significant reduction in the processing time of a work task. Further, using a cognitive technology solution can also help to match worker skill sets and past performances to quantitatively assign the right kind of work package to complete a task more efficiently, increasing the quality of the job at hand.

Achieving business transformation through cognitive, connected manufacturing

As more factories and equipment are instrumented for the Internet of Things (IoT), data volume will only grow larger. IBM and its ecosystem of partners can help harness and mine this influx of information— ensuring both brown and green field organizations are able to take advantage of new cognitive capabilities through effective processing, analysis and operational optimization.

Where to find additional information:

End notes

[1] IDC IDC MaturityScape: Manufacturing Service Innovation, Sep 2016, Doc # US41758816

[2] CDC Foundation, Worker Illness and Injury Costs U.S. Employers $225.8 Billion Annually

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