Industry 4.0 implemention steps

When you have decided to implement Industry 4.0, you may have a bit of a challenge on where to start. That is so since every time a new technology is introduced, significant disruption is bound to occur. Therefore, there is the need to get it right from the very beginning. This chapter provides a detailed discussion of how to go about the implementation of Industry 4.0.

Needs Assessment

Just like any other project, it is vital that you have a better understanding of what you are looking for in Industry 4.0. Your needs serve as the guiding light. You will keep on referring to them as you go about the implementation of different components of technology (IIoT World).

In simpler terms, needs assessment refers to the process of determining and addressing gaps in the company’s current conditions and the desired wants. It is important to measure the discrepancy between current state and the most appropriate state. In most cases, companies adopt Industry 4.0 with the aim of improving efficiency. The need can also be related to correcting deficiencies in the production process.

Needs assessment is essentially a planning process. It is important as it bars you from taking on the project without a clear picture of what you want to solve. Even though the general need is to always improve results, being as specific as possible goes a long way in making sure that the issue at hand is addressed correctly.

Pilot Project Definition

Closely related to need assessment, pilot project definition involves giving the scope that your project covers. Scope management entails the deliverables and work needed to produce them. Project definition must clearly state what is included in the scope and what is not included.

Remember that implementing Industry 4.0 is just like any other project that the company may have undertaken in the past. In such a project, stakeholders were involved. Thus, make sure that all the stakeholders have knowledge of what the current project entails and that the project definition should clearly highlight them. The stakeholders are crucial since they determine the scope of the project. Do not just make assumptions that they know or would want a given item. Base on the needs that the provided under step 1 and create a scope that best captures all the project’s details. Having taken care of project definition, you are set to move to the next phase (Sands, 2018).

Connectivity and Network Accessibility

Industry 4.0 is all about things; (people, devices, and machines) being connected. That is why the term Industrial Internet of Things is synonymous with this technology. That is everything is interconnected in such a way that they can freely exchange data which is then analyzed for improvement of processes. As a result, connectivity and network accessibility are crucial elements to the success of your Industry 4.0 implementation initiatives. You have to ensure that both elements are taken care of.

Connectivity entails determination of the manner in which various components of the network are connected. The term network topology is also used to describe network connectivity. On the other hand, network accessibility refers to the ease with which users can access the network. While network connectivity may have a high reliability rate (remain unchanged), network accessibility easily changes with time. Make sure both items are evaluated while implementing Industry 4.0.

Collection of Data from Machines

If you do not collect data, you will not be able to perform data analysis. Therefore, a key step towards ensuring that digitization is brought about in your company is to harness data from machines. This data may be collected from, for example, sensors and PLCs. Use a secure Cloud solution to transmit your data in specially-designed IoT software. The data is important as it paves the way to get a detailed snapshot of your production.

As you go about the data collection, it is important to ensure that the data has no gaps. Gaps in data make it hard to perform data analysis. In case you want to build a model to best describe your situation, the gaps hinder you from coming up with an accurate model. If there are data points which are not collected, restoring them will prove to be an impossible thing to do.

Data Visualization and Dashboard

With the data properly collected and stored in the Cloud, visualization paves the way to gain insights from your data. Through data visualization, you are able to see things such as why two identical machines have varying performances. Why is it that two different production sites lead to varying results for identical manufacturing processes? When you perform data analysis, you shed light on the blind spots in the production as previously unknown factors are revealed.

Data visualization could also be seen as way to gain more confidence in the new technology. The information obtained from analysis reveals that there are problems and that these problems can only be addressed by Industry 4.0. It is important to understand that installation of new technology is always met with some form of resistance. Therefore, having data to back up your arguments regarding a problem at hand and how it can be solved gives you an edge.

Processes and Tools/Machines Digitalization

This step entails digitizing the current production tools, processes and machines. OEE is often used in the production, management, and monitoring processes. When you use these as analogue tools, there are high chances of making mistakes which may turn out to be costly. That is why you have to digitize OEE as a way of simplifying the production process. In so doing, it you are able to easily create concrete actions (Sands, 2018).

A bigger part of machine digitalization has to do with massive collection of data. Therefore, data collected from machines in section 4.4 must be vast enough to make this a reality. When sent to the Cloud, the data is converted to a virtual replica. Having enormous amounts of accurate data makes it possible to create a more accurate digital twin of the real-world machine. The digital twin is then used in simulation of the physical environment.

Predictive Maintenance Implementation

One of the main goals of machine digitization is to predict downtime before it happens and schedule appropriate maintenance actions. Predictive maintenance is easier said than done. It requires that digitalization be done as accurate as possible so that you can come up with accurate predictions based on which actions are taken. For predictive maintenance to be done well, there is a need to combine data and effective software. There are cases where human observation may be necessary. It is worth noting that Industry 4.0 is not here to eliminate human involvement but rather to facilitate better actions. Thus, do not hesitate from making human observation as part of your predictive maintenance steps.

Predictive maintenance needs a whole ecosystem of technology and creating a balance with various other maintenance strategies. If there is lack of a solid foundation, the vast investments put into it may be for nothing.

Machines to Machines Communication Self-Adjustment

With digitalization already enforced and the ability to predict setbacks established, you have to make sure that machines can communicate to each other. In this step, you try to create different scenarios when machines may have to exchange data, even though they are in different locations. Remember that Machine Learning is part of Industry 4.0, In this regard, a machine receives vast amounts of data and learns out of it. Even though machine A and machine B are connected, it does not mean Machine A learning automatically translates to Machine B also learning. But with machine to machine communication, whatever A learned can be shared with B.

Thanks to the self-adjusting feature of these machines, whatever information is shared results to automatic adjustment without the need for human intervention. The machine to machine communication may be through wireless means as well as wired strategies. Under Industry 4.0, M2M consist of a system of networks which send data to personal appliances. As IP networks expand globally, machine to machine communication has been made to happen at a faster rate and easily. Similarly, it uses less power while opening new business opportunities for suppliers and consumers.

Measurement and Evaluation

Measuring and evaluation allow for the prediction of whether a project is successful. When you measure the success of Industry 4.0 implementation, you are able to learn new insights for future undertakings even as you discover the effectiveness of your project. While measuring and evaluating the project, you have to keep referring to the scope, schedule, team satisfaction, budget, and quality.

Your final Industry 4.0 implementation must be able to do what it was meant to do as captured by the scope. The project stakeholders must also be happy with it, while at the same time it should have been delivered within the set budget.


The business takes some time before getting to this step. With optimization, one determines which technologies are working as desired, which ones are exceeding expectations, and which ones are still lagging behind. Data is collected and quantified after which processes are adjusted as necessary.

Close assessment of the data shows variances which are used for developing corrective action, thereby optimizing all the processes involved. The main goal of this last step is to make sure that the final product is better than the one which was initially created. Successful execution of optimization requires better insights obtained from measurement and evaluation. Such insights are best obtained from accurately corrected data. As the last step to your journey in Industry 4.0 implementation, you have to be careful not to mess up items that are working just fine.

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