Overcoming Barriers to Implement Industry 4.0: Making Smart Manufacturing a Reality

Industry Revolution and How Industry 4.0 is Changing the Economic World

Between 1760 and 1820, in Europe and the United States, the Industrial Revolution was the move made to develop new industrial processes. The shift from hand to machine production, the use of new chemical and iron manufacturing processes, the increased use of steam and water power, machine tool development, and the advent of the mechanized industrial system were all part of this transformation.

The Industrial Revolution dates back to before the 18th century. With all the products being handcrafted and the production rate low because of the absence of proper resources, the first Industrial Revolution, the base of the Industrial Revolution, was called Industry 0.

Then comes the Industry 1.0 a shift from older manufacturing processes to new manufacturing processes which can also be deemed as the beginning of the industry culture which focused on quality, efficiency and scale.

In the 19th century, the massive use of electric power and mass production using assembly lines added more production and output. This was industry 2.0. In the 20th century, more technologies such as automated production systems, computers, IT technology, and robots were introduced. This not only increased efficiency but also increased production in the industries. These new technologies are the reason for Industry 3.0.

Industry 4.0 includes all the features of Industry 3.0, along with newer and more advanced technologies, such as Cyber-Physical Systems, IoT, and Networks. It also includes advanced analytics or innovative interfaces that boost asset and labor productivity, accelerate time to market, and unleash other efficiencies. Many global manufacturers are already capturing significant value from the Industry 4.0 technologies. 68 percent of companies see Industry 4.0 as a top strategic priority today.

Many global manufacturers are already capturing significant value from Industry 4.0 technologies. Rolls-Royce, for instance, has overturned its model for jet engine sales. Today it sells a real-time diagnostics and maintenance tool for the engine through its fee-based Total Care Program. It constantly collects data from thousands of in-service engines, helping it predict maintenance needs, improve performance, and amass an irreplicable knowledge base. Total Care fees account for more than half the engine maker’s revenues.

Despite these individual efforts, few manufacturers truly appreciate the enormous long-term potential and implications of Industry 4.0 initiatives on their production systems. A global McKinsey survey that included developed and developing markets showed great excitement across the board for these initiatives but a significant difference in the pace of adoption. Germany and the United States were confirmed as clear front-runners in this trend. In these countries, governments have supported the shift with clear policy efforts to encourage companies to adopt these technologies.

Key Technologies Used in Industry 4.0

• Advanced production method: This includes additive manufacturing with integrated and accelerated prototyping and manufacturing.
• Analytics and intelligence: The use of analytics and intelligence have a higher chance of enabling the shift from detecting to predicting.
• Data, computational power, and connectivity: Machine-to-machine and machine-to-product connectivity will help realize mass personalization.
• Human-machine interaction: This includes virtual reality (VR) and augmented reality which allows maximum human-machine interaction.

Therefore, companies that have adopted Industry 4.0 have observed the following characteristics:

• It is digitally mature.
• It digitalizes core business.
• It has clear value capture targets.
• It has larger businesses and can adopt multiple use cases.
• It has C-level support.

ASEAN and the Need for Industry 4.0 in ASEAN

ASEAN, or the Association of Southeast Asian Nations, is a Southeast Asian economic union made up of ten member states that promote intergovernmental cooperation and facilitates economic, political, security, military, educational, and sociocultural integration among its members and other Asian countries. The major goal of ASEAN was to speed economic growth and, as a result, social and cultural development.

Exhibit 1 - Industry 4.0 and its potential to transform the manufacturing system

Industry 4.0 has the potential to transform a company's manufacturing processes, such as excessive work-in-progress inventory levels, wired communication with the control center, labor-intensive processes that can lead to coordination and repeatability issues, individual control room for specific machines, no visibility to upstream or downstream processes into an optimized, world-class manufacturing system with technologies, in the following ways,

• Predictive maintenance - This involves the use of advanced analytics to evaluate sensor data and predict machine failures.
• Robotics - This refers to the use of robotics to increase labor productivity and reduce human failure.
• Advanced analytics for optimization - This comprises yield, energy consumption, and throughput optimization using advanced analytics over machine operating parameters.
• Automatic parameter - The automatic recording of operational data and centralized storage is possible with this system.
• 3-D printing - Reducing essential part lead times by 3D printing critical components is key here.

ASEAN countries can reclaim a larger portion of global industrial activity thanks to Industry 4.0. Because of China's rapid manufacturing rise, the ASEAN region has been overshadowed. As a result, ASEAN members can make use of Industry 4.0 technology to stake their claim to being the world's factory. As an example of the potential of Industry 4.0, a semiconductor manufacturer in Singapore is leveraging advanced analytics to predict failures and optimize its maintenance operations. The expected result is a 7 percent cut in maintenance costs. ASEAN manufacturers, however, must first overcome their productivity gap. Even though ASEAN's annual exports increased 5.6 percent between 2010 and 2015, and the region is experiencing a strong influx of global investments, its ambitions could be hindered by low labor productivity. Labor costs in most ASEAN countries are lower than those in China, and in many cases, much less than half of China’s costs. However, low labor-productivity rates, except in Brunei and Singapore, eliminate this advantage.

The revolutionary potential of Industry 4.0 was affirmed in a recent McKinsey poll of more than 200 business leaders from the ten countries that make up the Association of Southeast Asian Nations (ASEAN). Almost all the respondents (96%) believe Industry 4.0 will provide new business models to their industries, and a slightly smaller percentage (90%) believe enhanced performance will be one of the key benefits of these new technologies. In addition, respondents in manufacturing-based nations such as Indonesia, Thailand, and Vietnam were largely optimistic about Industry 4.0's potential.

Exhibit 2

In ASEAN, the impact of industry 4.0 could be $0.2 trillion to $0.6 trillion per year by 2025. This can be seen in the chart below-

Globally, companies are investing funds and talent into understanding and adopting the myriad elements of Industry 4.0, which can be grouped into four clusters:

• Data, computing power, and connectivity
• Analytics and intelligence
• Human-machine interaction
• Advanced production methods

Industry 4.0 technologies can be thought of as manufacturing procedures that deliver more efficient operations and seamless data flow across product life cycles and fully automated production systems based on sophisticated analytics as the final result.

Exhibit 3

ASEAN’s labor costs are lower than China's, but this competitive advantage is compromised due to low productivity in the industries. The stats recorded in 2016 show low productivity.

Although ASEAN productivity has increased statistically in recent decades, much of this growth has been slowed due to a movement of labor from agricultural to more efficient industries, rather than increases within particular sectors. Manufacturing productivity remains low across the region, regardless of the overall parameters. Due to its daily output per daily wage, Vietnam, for example, is 87 percent less productive than China. Low productivity, in addition to lowering the region's competitiveness, stifles growth. If the region does not develop a more globally competitive manufacturing sector in the future, it will miss out on a vital opportunity to boost its overall wealth and well-being.

When it comes to ASEAN's potential, the region's five main industrial industries stand to benefit significantly from Industry 4.0. Adopting parts of Industry 4.0 has resulted in productivity gains of 10 to 50 percent and improvements in overall equipment effectiveness of 10 to 20 percent in many industries around the world.

Overcoming Barriers to Implement Industry 4.0

The poll not only cataloged aspirations for Industry 4.0 but also identified specific barriers to implementing the new technology. Only 13% of respondents stated their organizations had started implementing Industry 4.0-related initiatives, which is a significant finding. Despite proclaimed excitement and, in many cases, implementation strategies, ASEAN businesses are finding it difficult to achieve their goals.

Understanding Use Cases

Companies in ASEAN are having a hard time deciding what to prioritize based on the potential impact of Industry 4.0 use cases. The challenge comes from a lack of familiarity with the available technology and data gaps that impede precise economic impact evaluations. Companies should aim to isolate use cases that pertain directly to their industry and focus on understanding those that could have a major or immediate impact as a way to move forward. Companies in ASEAN with resource-intensive processes, for example, might concentrate on situations that maximize asset productivity. One mining business concentrated on predictive maintenance for its freight vehicles, which resulted in a 12 percent reduction in maintenance costs, increased output, and avoided unnecessary capital expenditures. For example, advanced analytics for yield, energy, and throughput optimization can maximize return on assets in the energy industry.

Eliminating Compartmentalized Data

From the 1980s onwards, several established ASEAN manufacturers created their IT systems, gradually adding more as technology advanced. Processes and data become segregated as a result. Industry 4.0 systems rely on data, and to get the most out of the data, an ongoing and dependable flow is essential. Integrated data helps create a consistent view of a company's performance, such as customer satisfaction ratings, by preventing disputes in the findings that can arise from disparate systems.

Mitigating Security Risks

Even though centralized IT systems are frequently required for Industry 4.0 changes, recent cyberattacks throughout the world have revealed a vulnerability in these IT systems. Companies in ASEAN have not been exempted from these attacks, which has led to a reluctance to fully implement Industry 4.0. Cybersecurity concerns should not deter ASEAN firms from using these technologies. Cybersecurity threats can be mitigated by putting in place adequate measures in three areas:

• Controls: Security postures and advanced capabilities, such as stronger encryption, campaign planning, and security-control assessments for high-risk vendors are used to protect the information of the highest value.
• Organization and governance. For example, by implementing intelligence coordination and developing an advanced forensics unit, reviewing governance processes and organizational structure to enhance engagement, coordination, execution, and capability creation are possible.
• Process and operations: Integrate security measures into routine procedures and, among other things, use training programs and simulations to ensure that all employees understand the importance of cybersecurity. New processes will be established when organizations adopt Industry 4.0 technologies, and new positions will be created. These changes will necessitate competencies that traditional manufacturers should possess, such as data mining and machine-intelligence expertise.

?Eliminating Compartmentalized Data

From the 1980s onwards, several established ASEAN manufacturers created their IT systems, gradually adding more as technology advanced. Processes and data become segregated in siloed systems as a result.

“Data is the lifeblood of Industry 4.0 systems, and ensuring that it flows consistently and dependably is key to reaping maximum benefits.”

Integrated data eliminates the potential for contradictory results from different systems and gives a unified view of a company's health and performance, from customer satisfaction levels to quality-control indicators. Without investing in integrating old systems and consolidating their data under one roof, ASEAN enterprises adopting digital technology as key to their operations will be hampered.

Finding Scarce Talent

New processes will be established when organizations adopt Industry 4.0 technologies, and new positions will open. These developments will necessitate competencies unlikely to be found in traditional organizations. Bringing in these abilities from outside the organization can be a quick method to get what you need. However, a huge flood of new personnel might destabilize the corporate culture and reduce productivity. Employee motivation is important. Internally developing these qualities may help avoid issues with morale or culture. It also causes some disruptions, but it extends the transition time, allowing competitors an opportunity to surge ahead. A fine balance between external hires and internal development is found in the middle ground. External hires can serve as role models for change, while existing employees motivated by the possibility for development can provide a strong support network to new hires. Companies often fill 50 to 80 percent of the new roles required by Industry 4.0 through external hiring, according to our experience.

Exhibit 4

While using the industry 4.0 features, a company might also need new capabilities for new roles. A few of these roles include:

• Data scientist - An industry will require a Data Scientist with a background in applied mathematics, data mining, statistics, machine learning, computer science, physics, and can build mathematical algorithms.
• Analytics engineer - An Analytical Engineer who has a data-scientist background focused more on computer science and programming and can take data scientist’s algorithms and make them more efficient will be needed.
• Analytics translator - An Analytic Translator with a data science background plus business experience, someone who understands business problems and translates those problems into technical language and vice versa is also required.
• Transformation coach - A transformation coach in Industry 4.0 should be able to line up top talent with 5–10 years of business and operations experience and an open mind to learn digital and IT. He should also be able to coach line operators and managers on driving integrated performance and digital transformation.

Important Agents Required to Fully Deploy the Potential of Industry 4.0 in a Country

Government

National governments play a crucial role in the early phases of the adoption of Industry 4.0 technologies in nations that have them. Successful government support hinges on five key activities in particular:

• Adjust taxes and provide financial support: Governments can assist enterprises transitioning to Industry 4.0 by providing advantageous tax policies and setting up specific funds to encourage industrial transformation and upgrade. Governments in Germany and the United States, for example, have put in place strong national policies to accelerate the reforms.
• Promote industrial alliances: Startups and small- and medium-sized industries receive insufficient practical support from ASEAN governments. Central governments could assist in the creation of innovation platforms that connect local businesses with IT professionals or industry groups that can assist in the promotion of demonstration projects.
• Cooperation: Governments should leverage their ties with other ASEAN countries to promote international exchange and collaboration in areas such as pilot and demonstration projects. Supporting talent development and training programs is key. Pilot and demonstration projects should be encouraged by governments to boost the development of top-tier design and other specialized talents for intelligent manufacturing.
• Establish national standards: Governments should create standards and protocols for important parts of new technologies as they arise, allowing for more innovation but avoiding "winner-take-all" results.

Corporations

Companies in ASEAN have only recently begun to investigate Industry 4.0. Only 30 to 40% of production lines in the region are automated, with the rest relying on sporadic, labor-intensive operations that frequently result in quality difficulties. Businesses have played a key role in introducing the Industry 4.0 technology into the actual economy in leading countries, actively seeking innovative ways to employ these technologies to seize emerging-market possibilities. To accelerate the implementation of Industrial 4.0, many industry actions are required.

Academic Institutions

Universities and professional training institutes play critical roles in helping manufacturers implement Industry 4.0 technologies. Academic institutions, in particular, might help speed up the adoption of Industry 4.0 technology in the region by taking two steps. These are:

• Teach appropriate capabilities: Universities and professional training institutions should alter their curriculum to meet the unique talent demands that Industry 4.0 has developed. They should place a strong emphasis on vocational education and other customized training programs that impart essential new skills to new students and workers who have been displaced by technology.
• Lead innovation: Universities should pursue technology development from conception to completion, bridging the gap between basic research and the final implementation of an idea in the industry.

Smart Factory

A smart factory is a versatile system that can self-optimize productivity across a wider network, self-adapt, and learn from emerging situations in real or near-real-time and run complete manufacturing processes autonomously.

The phrase "smart factory" also implies an integrated IT/OT landscape that connects shop floor choices and insights with the rest of the supply chain and the broader organization. This has the potential to significantly alter production processes and improve relationships with suppliers and customers.

It is evident from this explanation that smart factories go beyond simple automation. Smart factories can work within the factory's four walls, but they can also connect to a global network of comparable production systems, as well as the wider digital supply network. One important point to note is that a smart factory is not considered an ‘end state' since it constantly evolves while building a learning system that is quite flexible.

Smart factories can evolve and adapt to needs such as shifting customer demand, expansion, new products, and many more processes that an organization goes through. Smart factories can enable businesses to respond to changes in ways that would have been difficult earlier, if not entirely impossible. This is now possible due to more sophisticated processing and analytical capabilities.

Five Key Features of a Smart Factory

• Connection - The most important attribute of a smart factory is the way it can remain connected. This is also one of the most important reasons for its value. To create the data needed to make real-time decisions, smart factories require the relevant processes and materials to remain connected. Smart sensors are installed on assets in a genuinely smart factory so that systems may continuously pull data sets from both new and conventional sources, ensuring that data is always up to date and reflects actual conditions.
• Optimum Results - Data from operational and business systems, suppliers, and customers is combined to provide a comprehensive view of upstream and downstream supply chain processes, resulting in increased efficiency of the total supply network. In an optimized smart factory, operations are executed with minimal manual intervention.
• Transparency - In the smart factory, the data captured are transparent. Data acquired from processes and fielded or still-in-production products can be transformed into actionable insights through real-time data visualizations, which can be used by people or automated decision-making. By providing tools such as role-based views, real-time alerts and notifications, and real-time tracking and monitoring, a transparent network can enable more visibility across the facility and ensure that the business can make more accurate decisions.
• Proactive System - A proactive system works when employees can anticipate, understand, and act on the challenges that arise. This requires identifying mistakes, monitoring anomalies, and acting on those accordingly. A smart factory can predict their future outcomes based on the data, and hence, prevents safety issues.
• Agility - Agile flexibility helps a smart factory learn how to schedule with less intervention. Advanced smart factories may also self-configure equipment based on data from the product being created, schedule modifications, and track the impact of those changes in real-time. Agility can also improve factory uptime and yield by reducing changeovers due to scheduling or product changes and allowing for more flexible scheduling.

Why is a Smart Factory Needed Now?

There are five main reasons why smart factories are needed now. Those are:

• Rapidly evolving technological capabilities - Due to the limitations of digital technology capabilities and prohibitive, computing, storage, and bandwidth costs, the realization that we need smart factories has remained elusive. However, such barriers have faded in recent years, allowing for more labor employed for less money across a more extensive network. Manufacturers can move beyond task automation to more complicated, networked processes due to the flexibility to evolve and powerful data processing and storage capabilities.
• Increased supply chain complexity and global fragmentation of production and demand - Production has become fragmented as manufacturing has become more global, with stages of production distributed over various sites and suppliers across multiple geographies. Supply chains have become more complicated due to these transformations, along with the rising desire for regional, local, and even individual customization, strong demand fluctuation, and increasingly restricted resources, among other factors. To accommodate the ever-shifting demands due to these changes, many manufacturers have found it necessary to be flexible, connected, and proactive.
• Growing competitive pressures from unexpected sources - The rise of smart digital technologies has ushered in the threat of entirely new competitors who can use digitization and lower entry costs to gain a foothold in new markets or industries where they previously had no presence, bypassing their more established competitors' legacy of aging assets and dependence on manual labor.
• Organizational realignments resulting from the marriage of IT and OT - Factory automation decisions are usually made at the business unit or plant level, resulting in a patchwork of different technologies and capacity levels throughout the manufacturing network. As linked firms expand beyond the four walls of the plant and into the more comprehensive network, they are gaining a better understanding of these discrepancies. The shift toward Industry 4.0's connected digital and physical technologies heralds solutions to this problem—the ability to collect data and evaluate and act on it in the real world.
• Talent Challenges - Many traditional manufacturers have struggled to find skilled labor to keep their operations running due to various talent-related challenges, such as high competition for a position and a scarcity of younger individuals interested in or prepared for industrial jobs. These are all factors to consider.

The Benefits of a Smart Factory

Quality - Self-optimization of smart factories can help one foresee and detect quality. You can also recognize defective quality and pinpoint various human, machine, and environmental sources of poor quality. This will result in lower scrap rates and also lead to a shorter lead time.

Asset efficiency - Every part of a smart factory creates reams of data that can show asset performance concerns requiring corrective optimization through ongoing analysis. Indeed, this self-correction distinguishes a smart factory from traditional automation, which can result in higher total asset efficiency, one of the most important advantages of a smart factory. Lowering asset downtime, optimizing capacity, and reducing changeover time are just a few of the possible benefits of asset efficiency.

Paving the way for safety and sustainability - With the help of smart factories, one can also see a lot of tangible benefits in terms of the health of the workers and environmental sustainability. A smart factory’s savings will also ensure a smaller environmental footprint. With the help of a small factory, it is true that worker roles may need to be evaluated, which can increase workloads and reduce turnover.

Impact of a smart factory on manufacturing processes - Manufacturers can deploy a smart factory in various ways, both inside and beyond the facility's four walls, and rearrange it when priorities shift, or new ones emerge. One of the smart factory's most essential features provides producers with several options to leverage digital and physical technology, depending on individual requirements. A smart factory's impact on manufacturing processes will most likely be different for each company. Today companies have identified a collection of innovative technologies that lets people navigate between the physical and digital worlds and facilitates the flow of information.

Making the Transition to the Smart Factory: Areas for Consideration

Data and algorithms

A smart factory's lifeblood is data. Data drives all processes, detects operational faults, provides user feedback, and when efficiently gathered, may be used to predict operational and asset inefficiencies and sourcing and demand fluctuations. All this is made possible by the power of algorithmic analyses. Within the smart factory environment, data can take numerous forms and serve many purposes, such as discrete information about environmental variables, including humidity, temperature, or pollutants. What makes data important is how it is combined and processed and the actions that follow.

Manufacturers should be able to create and collect continuous streams of data, handle and store the huge amounts of data generated, and analyze and act on it in various sophisticated ways to power a smart factory. To progress to greater degrees of smart factory maturity, the data sets gathered will certainly grow over time to capture more and more processes.

For example, implementing a single-use case might necessitate the collection and analysis of a single data set. Expanding the acquisition and analysis of larger and diverse data sets and types (structured vs. unstructured) and extending an operation to an industrial level, would often necessitate considerations surrounding analytical, storage, and management capabilities. Data could also be a digital twin, which is a part of a more advanced smart factory arrangement. A digital twin, at its most basic level, is a digital representation of an object's or process's past and current activities. The digital twin necessitates the collection of real-world data across diverse variables, including production, environmental, and product performance.

Technology

Assets—defined as plant equipment such as material handling systems, tools, pumps, and valves—must be able to interact with each other and a central control system. A factory execution system or a digital supply network stack are examples of these types of control systems. The latter is an integrated hub that aggregates and combines data from across a smart factory and the larger digital supply network to drive decisions. Other connected manufacturing technologies, including transaction and enterprise resource planning systems, IoT and analytics platforms, will also need to be considered. To connect assets and facilities, make sense of data, and digitize business operations, companies may need to implement the various digital and physical technologies inherent in Industry 4.0, such as analytics, additive manufacturing, robotics, high-performance computing, AI and cognitive technologies, advanced materials, and augmented reality.

Process and Governance

A smart factory's ability to self-optimize, self-adapt, and autonomously run production processes is one of its most useful qualities, and it has the potential to dramatically alter traditional processes and governance structures. Many decisions can be made and carried out by an autonomous system without the need for human interaction, moving decision-making responsibilities from humans to machines in many cases or concentrating decision-making power in the hands of fewer people. Furthermore, a smart factory's connectivity may expand outside its four walls, allowing for greater interaction with suppliers, customers, and other factories.

This kind of collaboration could generate new concerns regarding processes and governance frameworks. To account for these transformations, organizations may wish to reconsider, and maybe redesign, their decision-making processes.

Cybersecurity

The smart factory is, by definition, linked. As a result, cybersecurity is a bigger concern in a smart factory than a typical manufacturing plant, and it needs to be handled as part of the overall smart factory architecture. Cyberattacks in a fully connected system can have a broader impact and may be more difficult to defend due to the numerous connection points. As a smart factory scales and potentially expands beyond the firm's four walls to encompass suppliers, customers, and other production sites, the cybersecurity risk might become more pronounced. Manufacturers should prioritize cybersecurity in their smart manufacturing plan from the beginning.

People

People are expected to continue to play an important role in business. A smart factory, on the other hand, can result in significant changes in operations and IT/OT organizations and a reorganization of roles to accommodate new processes and capabilities. As previously stated, some roles may become obsolete as a result of robotics (both physical and logical), process automation, and artificial intelligence (AI). New roles to operate the managing and other sectors will emerge, and these processes will require dedicated workforces and different recruiting processes, and a cross-functional role management system. It's conceivable that new, unknown roles may develop. Managing people and process changes will necessitate an agile, adaptable change management strategy. Any smart manufacturing solution's adoption could be hampered by a lack of organizational change management. A motivated staff that understands the wider significance of their tasks, applies innovative recruiting strategies, and focuses on cross-functional positions is required for a successful smart factory journey.

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