The manufacturing industry is going through a shift driven by technological advancements and ever-changing market dynamics. Companies have accepted that transformation is inevitable in the manufacturing sector due to several industry trends. One of these trends is smart manufacturing. The concept of a smart factory falls under this umbrella. Several companies are realizing the need and importance of implementing solutions to make their factories smarter. Transitioning to a smart factory is a multi-year process for any company. The entire value chain gets affected and may have to be re-optimized. This first question that needs to be resolved is - what is a smart factory. This term has been loosely used in the recent past, but there is some commonality about what it entails.
What is a smart factory?
On a fundamental level, a smart factory can be defined as a factory where cutting-edge technologies are used in a highly advanced and interconnected manufacturing facility to optimize manufacturing operations, enhance productivity, and achieve higher levels of efficiency. By integrating several digital tools and technologies, the manufacturing processes are made intelligent so that several actions and responses can be handled by the system directly, rather than wait for resolution from any operator or manager. In a smart factory, various components, machines, and systems are interconnected through the Internet of Things (IoT), allowing seamless communication and sharing of data. This enables real-time monitoring, control, and analysis of production processes, equipment performance, and supply chain logistics. A detailed smart factory framework has been described in detail.
The various steps involved in making a factory smarter are listed below:
Define needs and goals: Just stating that a smart factory is needed is not enough. The organization needs to clearly lay out the business needs and goals which need to be achieved. The "why" needs to be established very well. This needs to be done from both a top-down as well as a bottom-up approach. Different goals will need different strategies and technologies to be implemented, hence defining the objectives correctly is very important.
Identify technology/tools/solutions: A detailed roadmap of various technologies, tools and solutions that need to be implemented to achieve the stated goals needs to be done. A detailed gap analysis of various functions and a study of their KPIs and patterns will yield the gaps that exist in the current system and processes. From this, the right set of technologies and tools can be selected so that the desired results are achieved. Among the latest technologies, connected machines, IIOT, automation, AI/ML, additive manufacturing, cloud computing, etc. are the most prominent ones.
People: This is one of the most important pillars for any transformation. Without taking the people into confidence and considering changes that they have to endure, the transformation project will not be sustainable. Providing training, feedback and growth avenues for employees is important, and so is assuaging their fears about how the transformation will affect the way they work. There needs to be a mindset change in the people involved. Taking the people onboard for any transition is often the most difficult part, and yet most rewarding.
Security framework: With new technologies and data being used, the security layer needs to be upgraded as well. No company would want to be caught off-guard, in case there is a data breach which may potential leak their confidential product information, financial or employee data. Cybersecurity, employee training, etc. need to be looked at more closely to ensure the security framework is well defined for the company and its employees.
Equipment upgrades: Among the machine tools and equipment being used on the shop floor, the ones which need to be upgraded first need to be prioritized. Adding smart sensors to existing machines or replacing them with newer ones completely needs to be understood well before embarking further on this. Infrastructure upgrades such as increased internet bandwidth, better Wifi connectivity over the shop floor, etc. will also need to be considered. Since a lot of data will get generated, appropriate data handling and storage policies as well as computer systems and servers will need to be looked into.
Data driven processes: Since data will be available from each step of each process, decisions and insights can be derived from this. All the systems, processes and people involved will need to realize that real-time information is now available, so their response strategy needs to be adapted to this as well. For example, rather than waiting for a process to finish, in-process adjustments might need to be made. Data can be used for post facto analysis, however the real value is realized when data is used to generate insights on what may happen soon and any needed corrective actions can be taken.
Change management: Various stakeholders need to realize that change is a continuous process and appropriate improvements will need to be identified and adapted as everyone becomes more familiar with the new systems and processes. Managers will need to be cognizant of what changes the workforce and shop floor employees might be subject to during each phase of the smart factory implementation, and they will need to refine their production targets and KPIs accordingly. Change is always difficult for organizations, and the right culture will need to be developed within the organization so that the maximum benefits can be reaped from such a transformation.
In conclusion, a holistic approach is needed to make the operations of a factory smarter. Though some of these steps are shown separately, they might have to be done in parallel and not necessarily one after another. Transitioning to a smart factory is not just about adding some smart sensors on the shop floor and getting data plotted on a dashboard, or just implementing an ERP solution in the organization. These are just some of the pieces that are needed to complete a smart factory - and the requirements change as per the specific needs and objectives for each organization. This involves various stakeholders, and enables data driven decision making across all levels and functions in the organization. It also leads to a mindset change among the organization and a revamp of the business processes to adapt them to current and future needs of the organization and the market.
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