Technologies including industrial internet of things (IIoT), data platforms, artificial intelligence (AI), edge and could computing can be threaded together in Smart Manufacturing (SM) systems for streamlined semi-autonomous processes in the factory, enhanced manufacturing operations management, and new data-centric services for customers that are helping manufacturers achieve significant increases in productivity and competitiveness. However, the use of these SM technologies and techniques remains concentrated among industry leaders and specific industries due to several challenges that are holding up wide adoption. The following are top challenges identified by industry analyst reports and a 2022 survey of manufacturers performed by SME and CESMII – The Smart Manufacturing Institute.
Lack of a Link Between Technology and Business Strategy
Over 50% of manufacturers admit to not having a corporate-wide strategy for their adoption of digital technologies and data-driven processes. However, industry analysts and consultants have found that a strategic approach is effective. Businesses should have a strategy with a clear vision, goals, milestones and a clear link between the technology adoption plan and the business goals for future revenue and growth. Without a clear technology-enabled business strategy the investments in technology will probably be focused on short-term localized pockets of process improvement instead of focused on achieving a future competitive edge in the marketplace for the whole business.
A digital transformation of the business will not be achieved through independent departmental efforts. Different divisions and departments may be tasked with implementing different parts of the strategy at different times, but the whole company should be working towards a common corporate business strategy and technology adoption plan—a plan created with all key stakeholders at the table. Key stakeholders with experience and expertise in engineering, factory automation, IT, shop floor operations, inventory, quality, and supplier management should be recruited into the innovation team developing the roadmap and plan.
Legacy of Processes and Systems
To connect everything digitally, organizations must move beyond traditional paper-based processes and departmental silos; there is no longer a place for manual, time-consuming processes. To be efficient and provide front-line industrial workers the necessary information at the right time and at the right place, companies need to reimagine and reinvent their outdated processes with digital solutions and workflows that replace outdated and error-prone paper-based processes.
An old system infrastructure can make it harder to implement the newer smart manufacturing technology solutions that continue to evolve at a very fast pace. Business platforms and technologies that are over ten years old may not be able to share data as needed. Many older machines have difficult proprietary interfaces or do not have any digital interface at all. New IIoT sensors can be layered on top of older machines and edge gateway devices can communicate data to analytical and data platforms via Wi-Fi, the local network, and the internet.
Updating technology can be extremely challenging since numerous interdependencies must be considered. A carefully phased and tested implementation approach can de-risk upgrades and minimize impact on the production line. Leveraging a more modular approach to technology with standard application programing interfaces (APIs), can reduce these concerns for future solutions implementation.
Cost and Complexity of Implementation
Smart manufacturing technology can automate control processes and implement techniques that make processes more productive, faster and collaborative. However, a significant constraint to scaling the use of these practices has been the complexity and cost of implementation; implementations that have traditionally depended on system integrators with specialized skills on proprietary data acquisition and integration methods specific to each machine and software vendor.
In many cases, the result has been a web of fragile integrations that many manufacturers are afraid to break in the process of implementing new systems. In other cases, manufacturers have been locked in by their selection of a preferred vendor and cannot combine technologies from different vendors into their systems landscape. Either way, the result has been a slow adoption process for new techniques and companies with many “blind” spots where production cells are not connected to information systems and rely on manual data collection and paper-based methods.
Smart Manufacturing practices need a paradigm shift to interoperable ways of integrating machines and systems. System integrators can leverage IIoT gateway devices and data platforms as connecting intermediaries between data sources, applications, and enterprise systems. Information models based on industry open standards can be used as data contracts and APIs among these systems to prevent vendor lock-in and make future upgrades and changes easier.
Budget for the Investment
Smart manufacturing initiatives may require significant upfront investment to establish a good information platform and systems architecture foundation. Over the long term, the enterprise should develop the system and organizational capabilities that make incremental and modular changes simpler and faster. For example, a platform-based system with an open API strategy can make it easier to connect data sources, organize data in information models, and connect applications that leverage information to improve processes in the organization. The investment might also include changes in organizational structure and agreements with ecosystem partners.
Without a strategic investment plan, an organization’s efforts to increase the use of digital technology can lead to unbounded investment costs that do not achieve transformative competitive advantage. This is especially true where investments are reactive to solve specific problems and tactical process improvements. When a roadmap plan is established, it enables SMMs to sensibly invest in smart manufacturing technology and capabilities over time to get closer to a future state vision for the business and realize benefits beyond increased efficiency such as improved agility to adjust to changing market demands and suppliers in the network.
An investment plan considers not only the cost and benefit of implementing transformative changes, but also when those changes should occur. Companies may struggle with the investment cost, but the cost of not transforming and updating practices might be higher in the long term. Manufacturers must keep pace with market expectations and competitors. Sometimes small and medium manufacturers (SMMs) can be more aggressive in adopting new technology than larger ones with a significant legacy of machines and processes; but SMMs can also be challenged by a lack of capital to invest. A company, small or large, can use a strategy of phased investments to implement modular solutions and incrementally achieve benefits as the company incrementally achieves their vision.
Expertise to Lead and Implement
It is easy for business leaders to get caught up in the potential of new technology for the business and underestimate the change management effort needed including the skills training required for the staff to champion, implement, and sustain new processes that leverage the new technology. Technology solutions like data, low-code, and machine learning platforms are becoming easier to use every day but they still require some specialized skills and training.
Manufacturers can use a combination of three strategies to get the skills they need:
- Hire new talent. Manufacturers used to require a four-year degree for occupations involved in Smart Manufacturing systems, but thanks to the advances and ease of use of modern tools, manufacturers are now lowering their requirements to a two-year degree, or a high school degree with additional industry credentials.
- Upskill the workforce. Some of the needed skills can come from new talent, but it is likely that the current workforce will require training to become digitally literate and build the skills needed for innovation including skills with IIoT data capture, edge data gateway devices, cloud software, data platforms, AI algorithms, and mobile applications. By making early investments in people, organizations can stay ahead of the game.
- Contract specialized expertise. The organization might want to look outside and find business partners that will help on the digital journey and supplement the internal team with specialized skills on specific machines and processes. The organization can reevaluate technology partners and see what new services they offer. Outside experts can shorten the learning curve by providing immediate implementation guidance and training in areas like IIoT data capture, automation, AI, cloud computing, data platforms, systems integration, cybersecurity, augmented reality, and digital twins.
Cybersecurity Concerns
Automation systems networks in the plant used to be “air gapped” with no access to the office systems network, but Smart Manufacturing practices require a bridge between the two. Smart Manufacturing aims to provide broad, secure connectivity among devices, processes, people, and businesses in the ecosystem leveraging the internet, Wi-Fi, and cloud services to share data across industrial automation and enterprise information systems. To make this possible, manufacturers must also implement proper cybersecurity measures and mitigate the risk of cyberattacks.
Cybersecurity measures must secure data integrity, protect intellectual property, shield against cyberattacks, and maintain business continuity with minimal impact to performance of the overall network of networks. Cybersecurity measures must cover the connection points between automation controls, data platforms, enterprise systems and the Internet. Cybersecurity tools include identity verification schemes for every “thing” in the ecosystem, schemes for access control, data traffic monitoring, fault-tolerance, high availability, anomaly detection, issue containment, and seamless data recovery.
Even with cybersecurity tools implemented, employees are ultimately the main mitigators of cybersecurity risk. Proper training, guidance, and support can prevent them from inadvertently increasing risk and help make the whole enterprise more secure.
Organizational Culture and Resistance to Change
Organizations can become very comfortable with their legacy processes and systems. However, to take full advantage of Smart Manufacturing techniques it is important to embrace innovation that streamlines collaborative processes across the enterprise and supply chain. It is natural that the business will experience some employee pushback against transformative changes. Employees will be comfortable with their existing duties and proud of achievements towards incremental improvements in their departments. It is important to explain to the team how change is needed to keep up with technological innovation and that not changing can be riskier to the competitive future of the business. The management team must also clearly communicate how the technology and organizational changes are linked to the strategy, and how these changes will benefit everyone in the company.
Concerns about ownership and control of processes and systems can make people reluctant to share their information and knowledge across organizational boundaries. Digital innovation requires an organization to adopt a different approach bringing together people, processes, and technology in new ways to create new business models and services. The legacy of clearly defined areas of responsibility needs to be challenged with collaboration and innovation across old departmental boundaries.
Managing the transition is not trivial. Transformative business changes may require changing employee roles and departmental boundaries. Highly hierarchical and slow traditional processes can be a constraint to obtaining all the benefits of speed from new smart manufacturing methods. The organizational structure should be considered fluid to achieve streamlined processes and new operating models. For example, in some organizations the industrial controls team and the IT team have been merged to accelerate the integration of production and enterprise systems. Other organizations have eliminated a few management layers and enabled the frontline team to make more decisions by equipping them with better real-time data that highlights non-routine situations.
Employees may feel threatened and concerned about changing roles and job security. It is important to be transparent with employees about their changing roles and the training required for new processes and equipment. Employees should be engaged through the whole implementation process.
The challenges listed above should be addressed proactively as organizations pursue Smart Manufacturing but should not be considered showstoppers. Organizations can also consider joining organizations like CESMII, the Smart Manufacturing Institute, which offers support for manufacturers through an ecosystem of peers that are pursuing similar technology adoption journeys.