Digital Transformation for Manufacturing: Opportunity and Approach

By John Kaczmarowski

Introduction to the Digital Factory

A digital factory is a facility in which people, machines, materials and products share data about all stages of the production process.  Enabled by the internet of things and Big Data analytics, the digital factory enables real-time integration between previously siloed IT and operational systems.

Digital factories leverage people and technologies to continually improve productivity, quality, flexibility, and service. The successful digital factory is built upon an evolutionary adoption of technologies and approaches along a path toward full digital realization:

Connectivity. The first step in digital transformation is the leveraging of Industrial IoT technologies to collect data from manual processes, equipment and sensors. A key here is differentiating between data that is useful in supporting business objectives and data that is “noise“, that is, unneeded to explain an event, phenomenon, process, or object.

Data management at scale. The second step in digital transformation is moving from data reporting to data analytics and finally the use of data to predict, act and react. Having the right data, at the right time in the right person’s hands enables an organization to understand, analyze, predict and react to factory, supply chain and market events and trends as they happen.

Intelligent automation. With a proper understanding of current state, real-time events and trends it is then possible to turn to the power of advanced analytics to support intelligent automation; the application of Artificial Intelligence, cognitive automation, machine learning and Robotic Process Automation. This convergence of technologies produces automation capabilities that dramatically elevate business value and competitive advantages.

As the factory progresses along this evolution, end-to-end digital continuity (digital twinning) can be achieved, enabling the factory to constantly adapt to demand, variations in supply, and process deviations from model to shop-floor.

Trends in Digital Transformation

In survey after survey, manufacturers have reportedly increased their level of participation in digital transformation initiates across both horizontal and vertical value chains. With 43% of manufacturers reporting digital initiatives in 2017 to over 70% just three years later, this participation is estimated to reach 85% over the next five years.

OEMs are 5 times more likely to have digitized manual processes and 4 times more likely to have shop floor IIOT initiatives over tier 1, 2 or 3 suppliers.

Organizations are digitizing both their horizontal and vertical value chains; with participation rates expected to increase from 20% to 83% over the next five years¹.

However, countervailing research is uncovering a different view of the digital factory, one that is quickly moving from being intrigued by its promise to being suspicious of marketing hype and incomplete product offerings that are not addressing what it truly takes to achieve tangible benefits.

In study after study, the biggest complaints from manufacturers are that they are drowning in unusable data and lack the resources and skills necessary to execute on digital transformation projects. These challenges, in turn, require organizations to hire more resources, pay more money, invest in ever growing and changing infrastructure and delay any anticipated benefits, according to 2019 research by AEGIS Software / LNS.

Let’s consider both the promise and challenges offered by digital transformation of manufacturing processes next…

The Promise

Companies with IIoT initiatives report an average of over 3% increase in efficiency per year

Manufacturers realize an average of 2.5% reduction in cost per year; a 13% percent reduction over a five-year period.

Using current trends as a guide, manufacturers globally will see between $1 and $3 trillion in productivity gains over the next five years.

Digital factory initiatives are delivering benefits including real-time understanding of the manufacturing process, data-driven decision support, predictive analytics of markets, products and manufacturing processes and intelligent, coordinated automation across the manufacturing floor.

All of this drives several measurable benefits anticipated by the adoption of digital factory initiatives and technologies including:

• Increased safety
• Productivity and capacity gains
• Enhanced operational efficiency
• Cost containment
• Organizational and supply chain agility
• Increased ability to innovate

These benefits are realized by developing and integrating capabilities across factory planning and operations efforts with focus on six core topics:

Realtime Information Management
By integrating data and processes from Product Lifecycle Management (PLM), Manufacturing Execution (MES), Materials Requirements Planning (MRP), and Enterprise Requirements Planning (ERP) systems with manufacturing floor data (SCADA), IIOT streams and staff / operator data organizations can reach a level of real-time information management that supports quicker decision making and rapid escalation of issues or opportunities which can drive greater productivity, efficiency, agility and safety.

Quality Analytics and Testing
Realtime collection of data from smart manufacturing technology, combined with automated and manual test data, repair data of defective units and shop floor maintenance records enables organizations to visualize and understand testing and production data, support SPC models and efforts and conduct root-cause analysis while forming the basis for predictive process and component analysis and action.

Energy Management
By establishing the connection between energy management and facility control systems, operational data can be evaluated in real time. This enables the identification and implementation of energy-saving measures through monitoring, energy-vs-performance analysis, demand planning and scheduling. These efforts lead to the measurable optimizations of energy operations throughout the facility.

Process Simulation
Process simulation is a 2-step approach where integrating factory-floor process models with real-time data from factory systems, tools and operators (step 1) enables the “what-if” scenarios and models to be measure against real-world workflows to achieve ever more accurate and simulations supporting factory optimization while discovering and mitigating critical weaknesses in the factory floor flow (step 2).

Predictive Maintenance
Predictive maintenance uses real-time data from the factory floor to monitor the actual condition of the machinery.  Matching current equipment condition and operating profile to historical operating profiles and maintenance event logs can be used to predict interruptions and failures before they happen.

Plant Control
Driven by advances in connectivity, availability of real-time sensor data and integration with enterprise data and manual data collection on the shop-floor, plant control systems are reducing exposure to failures, increase health and safety in the plant and optimizing asset productivity and operational lifetime.

Evolutionary Promise
Extending beyond the areas of monitoring, operational control/optimization, energy management and plant control the digital factory model can lead to the introduction of advanced real-time systems including:

• Intelligent automation to more effectively and responsively orchestrate users, tasks, systems and robotics
• Operator support and enhancement providing digital tools including work instructions, artificial reality/virtual reality (AR/VR) and robotized production assistants such as cobots to optimize logistics flow, reduce unnecessary movements or actions and improve safety and reliability of the shop floor.
• Market-drive production which marries supply and distribution chain data with real-time manufacturing data to develop and deploy manufacturing strategies that optimize production around market demand, inventory and the distribution chain.

To take advantage of these promises, the manufacturer requires four foundational prerequisites. The first is a scalable technology architecture that leverages industry standards where possible while supporting a JIT approach to deployment as the organization’s digital transformation evolves. Second, a focused, but agile mindset across software and analytics that fosters targeted, measurable, innovative solutions. Third, engagement with internal and external partners to fill capability gaps, learn from and leverage existing function in the supply chain and engage cross-functional and integrative expertise. Finally, an organizational ethos that promotes internal collaboration and shared success.

The Challenge

Transformation (n): a thorough or dramatic change in form or appearance.

Digital transformation, by its very definition can introduce dramatic change. This change isn’t limited to a single aspect of the organization; instead offering challenges that are cultural, technical, operational and personal.  Without clear planning and skillful collaboration, extending this transformation to partners and organizations up and down the value chain can increase these challenges exponentially.

Manufacturers may see challenges along the path of digital transformation from many different quarters of their business and their ecosystem including:

Complexity of system integrations. The integration of enterprise systems (MES, ERP, MRP, PLM, HRIS) with factory automation systems, big data, robotics and human operators and operations can be a dauntingly complex and expensive undertaking. One recent study rated this the biggest challenge to ongoing digital transformation efforts.

Supply chain integration. OEMs are 5 times more likely to have digitized manual processes and 4 times more likely to have shop floor IIOT initiatives over tier 1, 2 or 3 suppliers. This gulf in adoption can challenge full realization of the benefits of a digital transformation.

Data deluge. The deluge of data streaming from everything from smart-building and real-time production sensors to robots and cobots makes it difficult to differentiate the data from the noise. It is imperative that all data considered during digital transformations has a well-defined and well-understood purpose lest your data gets lost in the noise.

Overreach and lack of focus. Organizations have a fundamental choice when approaching digital transformation. Approach it as an evolution or as a revolution. While each has its advantages, the factors that should help make this choice include an organization’s ability to absorb change, the current state of the organization’s skills and infrastructure and the business drivers which support transformation. More companies fail due to overreach than any other single factor.

Corporate culture shock. Transformations can be dramatic, causing stress on individuals and the organization as a whole.  A culture that is not considered, engaged or informed can be one of the biggest impediments to successful transformations.

Financial justification. Finally, digital transformation does not come without cost. Developing, tracking and proving financial justifications is a critical component to success. Any investment in technology should adhere to (at least) one of three simple metrics for success:

• Does the investment make money?
• Does the investment save money?
• Is this investment a result of a clear competitive imperative?

Digital transformation is not a destination. It is a journey leading to increasingly higher levels of maturity and benefits. This journey starts with a well-defined strategy and is built from ground-up. With a focus on continuous improvement and agility companies undertaking the smart manufacturing journey will definitely succeed.

Want to learn more about digital transformation? Contact Actalent now.