What are the Risks for Manufacturers of not Embracing Industrie 4.0?

What are the Risks for Manufacturers of not Embracing Industrie 4.0?

Component Manufacturer, Industrie 4.0 Risks

What are the Risks for Manufacturers of not Embracing Industrie 4.0?

26 Jul, 2019

Making a case for the implementation of Industrie 4.0 compliant business models has never being easier in today’s data driven world. The insights that can be derived from the data that businesses produce has led to large scale innovation and increased productivity across multiple industries. As major industries continue to receive business insight from data, a few others have momentarily been left behind. Component manufacturers, that comprises of original equipment manufacturers (OEMs), discrete manufacturers, and hardware manufacturers are examples.

In this post, a dispassionate analysis of the risk component manufacturers’ face by shunning a data-driven factory will be provided. This article will cover:

  • The 8 Industrie 4.0 models and the benefits that come with implementation
  • The risks associated with overlooking Industrie 4.0 as a component manufacturer
  • Guidelines on integrating Industrie 4.0 models and the benefits component manufacturers stand to gain.


An overview of the 8 Industrie 4.0 business models

The factory of the future seeks to increase productivity, simplify supply chains, maximize capital, and exceed customer expectations. To achieve these, facility owners must embrace the business models of the future, or risk being left behind. These models are being defined by Industrie 4.0:

  1. Data-driven Plant Performance – This business model and Industrie 4.0 concept is centered on efficiency. A data-driven plant ensures every aspect of a production cycle is optimized. It cuts across overall equipment effectiveness, enhancing workstation output, product quality, and the ability to meet production demands. This concept drives optimal effectiveness or efficiency through the manufacturing insights from a facility’s data.
  2. Data-driven Inventory Optimization – In component manufacturing, available inventory is a double-edged sword. This is because too many or too little items in a facility’s inventory, carry negative financial implications. Data-driven inventory performance models focus on delivering inventory accuracy throughout a production cycle.
  3. Data-driven Quality Control – This industrie 4.0 business model focuses on fine-tuning the different variables behind component manufacturing. The end result is a data-drive process that eliminates or minimizes production errors. Thus, enhancing the quality of a product.
  4. Machines as a Service – This concept involves taking advantage of IIoT connectivity to increase sales. 
  5. Human Data Interface – This concept focuses on making data produced by factory equipment accessible and understandable to humans.
  6. Predictive Maintenance – Keeping track of the health of manufacturing equipment with the aim to forestall unplanned downtime is what predictive maintenance is about.
  7. Remote Servicing - From a distance accessing manufacturing equipment to give service
  8. Virtual Training and Validation This concept involves the use of augmented reality to safely train staff and try out new manufacturing concepts.

These Industrie 4.0 models are currently defining how manufacturing businesses and facilities are run. Integrating these models bestow certain advantages such as increasing the production capacity of legacy machines, reducing waste, and enhancing shop floor safety. Industrie 4.0 business models have the capacity to enhance every aspect of component manufacturing, and facilities that don’t measure up will lose their competitive edge.


Understanding the risks sssociated with overlooking Industrie 4.0 business models

Component manufacturing relies heavily on maximizing the performance of manufacturing assets. In traditional manufacturing outfits, legacy assets are equipped with production features without the ability to capture data. In these facilities, although the speed settings, dimensions, and output levels of production assets are known, there’s a limit to what can be accomplished.

An example is forecasting downtime due to machinery breakdown. In a non-Industrie 4.0 environment, maintenance is done by following the machinery’s service guide. Log books are used to manually record previous servicing activities and create a timeline for the next service date. This process has proved inefficient in component manufacturing, and according to the International Society of Automation; the manufacturing industry loses 650 billion dollars yearly. Statistics from a GE study also showed that approximately 75% of manufacturers are not aware of when production assets are due for maintenance. This goes to show that log books and service manuals are not enough to handle predictive maintenance.

Although lean manufacturing models and Six Sigma have helped reduce waste, a Mckinsey report shows that component manufacturers still struggle with reducing waste. This is because of the number of complex variables and activities involved in manufacturing components to meet specific requirements. Thus, not having a model in place that can identify production patterns, manage real-time relationships, and optimize production at a granular level affects output and therefore, the company’s bottom line.

An example of this occurs in the production of complex electronic converter hardware for automobiles. If the production cycle consists of 20 different variables in order to ensure quality, tracking these variables and standardizing them could prove challenging. The end result of not-applying a statistical approach to standardize production variables is hundreds or thousands of products of varying quality and usability. This leads to resource waste, and in sensitive industries like the automotive industry, thousands of product recalls may occur. As Volkswagen’s experience has shown, a faulty catalytic converter or something as little as a defective air bag module can lead to heavy financial losses and a production ban. 


How can component manufacturers mitigate loses?

Industrie 4.0 provides granular solutions to the problems associated with waste, downtime, and standardizing production variables. The implementation of the Industrie 4.0 models listed above creates an interconnected shop floor, where data from every production process can be tracked. In the case of maintenance, sensors can be used to track the function-levels of tool bits and the effects of vibrations on the machinery’s structure. The collected data can be stored and analyzed using Industrial cloud solutions.

This interconnected ecosystem also works with a ticketing system that ensures maintenance activities are not overlooked due to human error. The data collected can also be used to analyze multiple production variables in real-time. This supports the development of standard production models that can react to and accommodate external input or information. Standardizing the production variables in component manufacturing drastically reduces the persistent issues with product quality that component manufacturers’ face. 



The risks associated with not embracing Industrie 4.0 include, but are not limited to:

  • Heavy financial losses for the company
  • Excess waste and inefficiencies
  • Falling behind the competition
  • Lower quality output
  • Potential loss of business opportunities
  • Censorship, in extreme situations

To eliminate these risks, the Industrie 4.0 models discussed here must be reviewed in detail, considered, and properly implemented. Implementing any of the Industrie 4.0 business models highlighted in this article, is a technical process that involves integrating sensors in machines, collecting data, and managing Industrial cloud solutions. To avoid technical challenges, component manufacturers are advised to choose the managed services route or seek the expertise of Industrie 4.0 experts.


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