Digitising your manufacturing process – Industry 4.0

Industry 4.0 - Industry 4.0 refers to a new phase in the Industrial Revolution that focuses heavily on interconnectivity, automation, machine learning, and real-time data. Industry 4.0, which encompasses Industrial Internet of Things (IIoT) and smart manufacturing, it marries physical production and operations with smart digital technology, machine learning, and big data to create a more holistic and better-connected ecosystem for companies that focus on manufacturing and supply chain management.

Production systems have moved from being just digitally controlled to being interconnected, automated and smart. From raw material transportation, machining, packaging to shipping, all the systems are like “meshed” gears that allow factories to “output” like clockwork. At the same time, the data available from production is continuously analysed to find better solutions for every sub-system using big data analytics and cloud computing. The result - modern factories with smart manufacturing and higher efficiencies.

The first step on the journey to the digitisation of any factory should always be to define the problem(s) in the plant, taking into account key processes and tasks that need to be improved. Once this is done an assessment is made of the scale of each identified problem to understand what level of optimisation is actually needed and should be undertaken.

With better visibility of the problems, it becomes easier to decide which problem to address first to improve production and move towards smart manufacturing. The gaps and shortcomings of current systems, which are of a low priority, should be addressed at a later point when the high impact problems have been addressed and optimising becomes the goal.

Once the issues within the production system are known, the next step would be to create a business-led technology road map that would incorporate a comprehensive solution involving digitisation, security, onboarding support and training, post-implementation support, data analytics, scaling and compatibility. Responsibility for this lies with the business leaders who must first develop and align on a digital vision for their organisation - looking at both the overall digital strategy and value proposition for their company. They should begin by assessing their capabilities, estimating the resources required, and contemplating potential partnerships that could help them achieve their goals, asking the question “How can “digital” help us transform core business processes or generate new opportunities?”

With the strategy in place and a suitable partner engaged, the process of digitising can start. The key is to start small so as not to disrupt the whole production system.

By implementing new technology or a few tech solutions to the most sensitive parts of the production process thereby potentially solving multiple problems, costs are kept low, and the implementations will have a visible effect on production, and provide enough data to analyse the effects on the rest of the system. Analysis of this small start will help optimise the solution and also highlight the areas that can be targeted next to improve production. This is an iterative process and should be adhered to when moving on to the next area for digitisation, with the results being carefully analysed to expose any unwanted side-effects that might have arisen.

New technology capabilities form the foundation of any digital transformation. Ideally, these capabilities will be described in the road map, providing specific details about various areas including commercial backbone services, front ends, integration architecture, front- and back-end integration, digital platforms for development and operations, software as a service, custom (micro) services, and data-intensive services.

Challenges to Digitisation

One of the most common challenges to manufacturing digitisation is human error which represents one of the most common risks that organisations face, including safety, quality, and cybersecurity risks.

Humans can:

• configure machines incorrectly or insecurely

• forward unprotected business sensitive information to outside parties for quoting

• mishandle equipment, or

• open unknown attachments.

Digitising manufacturing processes may also require new processes and re-training on equipment, humans are especially resistant to change (lack of awareness about the reason for change, change in job role, fear of the unknown, lack of support from or trust in leaders, exclusion from change-related decisions. Etc.) so where possible, change should not be imposed but should instead be the outcome of a continuous discovery phase that empowers employees to test current processes and identify room for improvement. The discovery process requires engagement at all levels of the business. It should begin with the company’s president or leader and then continue through key multi-departmental staff, including decision makers at every level of the organization. This process should consider the experiences and expertise of all employees, including but not limited to: administrative staff; engineering staff; IT managers and staff; operations managers, and shop floor employees.

The older a technology infrastructure is, the more difficult it is to make it compatible with a digitised manufacturing environment. In a fully realised digitised environment, appropriate data can be shared between a variety of systems quickly and efficiently. If on the other hand, business platforms and technologies are over of a significant age (say five years or older), they may not be able to read, write, or share data as required thereby posing significant challenges to the digitisation program.

Due to the numerous interdependencies, updating technology can be extremely challenging and a carefully tested parallel, phased, or piloted implementation approach can be employed to upgrade systems without impacting the production line. Leveraging a more modular approach to technology, such as by using standard application programming interfaces (APIs), can reduce these concerns for future innovation implementation.

Privacy concerns are almost always included in digitisation challenges as digitised manufacturing capabilities can provide a window into every aspect of a manufacturing operation. Privacy should be considered whenever information is collected especially where it could be used to identify an individual. This information may include employee or customer contact information as well as the data collected by certain IoT sensors, cameras, or biometric authentication devices.

It is important to note that while data from one device might not present a privacy concern, that data combined with data from other devices could.

Cybersecurity should be planned, implemented and core to the digitisation program as it not only helps manufacturers identify and improve current security protocols, it also positions them to manage future risk. Key stakeholders would identify the most critical information assets to protect, map how that information flows through the organization (currently and with any proposed technology or process changes) and determine the level of risk if that information was lost or compromised.