Using Operational Data to Monetize 5G Digital Transformation for Industry 4.0

Being prepared for the increased capabilities of 5G, and how they stand to change operations, is the best way for companies to move forward into a new age and capitalize on the volume of new data generated. As businesses grow more familiar and comfortable with Industry 4.0, operational data does not just present monetization opportunities for profitability but also indicates the exact moment a threat or intrusion is in progress to help prevent revenue loss.

Using Operational Data to Monetize 5G Digital Transformation for Industry 4.0
Using Operational Data to Monetize 5G Digital Transformation for Industry 4.0. Photo by Science in HD on Unsplash

Having access to real-time data opens up opportunities for business process optimization in ways that once seemed impossible. Read on this article which covers everything you need to start planning including:

  • What is Industry 4.0?
  • Identify and avoid Common Pitfalls in Industry 4.0’s Path to Digital Transformation.
  • Learn What Kind of Digital Transformation Should Businesses Prepare For.
  • Learn the 5 Key Questions to Ask When Planning for a 5G Digital Transformation.

Table of contents

Understanding Industry 4.0
What Kind of Digital Transformation Should Businesses Prepare For?
The Dual Role of Machine Learning
Low Latency Brings Real-Time Data to Decision-Makers
Identifying Pitfalls in Industry 4.0’s Path to Digital Transformation
Asking Key Questions When Planning for a 5G Digital Transformation

The challenges presented by the needs of Industry 4.0 require businesses to undertake a digital transformation or risk being left behind. Being prepared for the increased capabilities of 5G and how they stand to change operations is the best way for companies to move forward into a new age and capitalize on the enormous volume of new data generated. Being prepared for the increased demand for real-time and higher digital consumption rates is a must for companies that want to improve their competitiveness. As businesses grow more familiar and comfortable with Industry 4.0, operational data does not just present monetization opportunities for profitability but also indicates the exact moment a threat or intrusion is in progress to prevent revenue loss.

Before a business considers monetizing data, it’s important to understand what Industry 4.0 means, what the key components are, and how the coming changes will impact all companies, regardless of industry. Business owners can prepare for the upcoming digital transformation by identifying common pitfalls and ways to avoid them. With a clearer picture of the changes ahead, businesses may want to learn more about the tools that can help with a holistic digital transformation rather than attempting to cobble together technologies of yesteryears.

Understanding Industry 4.0

Industry 4.0 represents the fourth industrial revolution and promises to disrupt the global market in industries that didn’t make the digital leap yet. The increased deployment of 5G networks around the globe is an important catalyst for this new industrial revolution. The increased level of connectivity, reduced setup, and operational costs, and the potential for more insightful data means that businesses are poised to understand their consumers better than ever before. With this transformation complete, the way companies in manufacturing and logistics operations, for example, can change through the use of real-time artificial intelligence, big data, and machine learning. Previously, this mode of operation was considered impossible.

While the deployment of 5G networks and the full impact of Industry 4.0 is new to many markets around the world, the ideas that it makes possible are not. The familiar part of Industry 4.0 is the potential for automation and how it can optimize long-accepted processes by removing the need for manual intervention. Smart machines that can operate themselves and communicate with each other is a popular example often cited in the mainstream media. This is an idea that most businesses and consumers have come to understand and accept over time, but one that hasn’t come to fruition in most places. While automation was introduced as a concept during Industry 3.0, this next stage will bring a level of intelligent connectivity to more industries and empower artificial intelligence and automation where it may not have been possible before—through the power of 5G.

With the widespread availability of 5G connections, businesses can anticipate seeing lower latency, higher security, and better connection speeds. Lowering the latency requires more computing to be done at the edge of the network, opening up the potential for processing increased amounts of data coming in and optimizing the data required for machine learning analysis. The use of edge computing, however, remains a topic of much speculation across the industry as developers test and implement machine learning models that power change.

Examples of possible changes that increase the availability of data and faster speeds with lower latency might include:

  • Supply chain optimization that prevents long lead times or communication breakdowns. Using predictive analysis models, machine learning can anticipate supply chain needs ahead of time and incorporate current market trends to refine those predictions.
  • Connection of Smart devices to power the Internet of Things. To fully see the impact of the Internet of Things and how it may change the factory environment, these Smart devices need to exist in each part of the environment.
  • Incorporation of autonomous vehicles and tools to make logistics even faster. Autonomous freight shipments and deliveries can be much simpler and cost-effective than traditional methods.
  • Incorporation of automated 3D printing allows for faster manufacturing in any location, which no longer requires large investments toward a separate manufacturing environment.
  • Capturing new 5G revenue streams through the use of network slicing. By dividing the network into designated virtual slices, providers can capitalize on the profitability of each one.
  • Fraud prevention using real-time data prevents the amount of damage that can be done by criminals, reducing the company’s liability and damage to the public reputation.
  • Customer value management will yield better engagement since real-time data and real-time actions tap into the moment of engagement.

While these concepts may exist in some form in the current business environment, lack of real-time data means that decision-makers are forced to use outdated figures. With the changes Industry, 4.0 is set to bring, the information available becomes more accurate and revenues can increase quickly. Errors and missteps can also be corrected on time to minimize any operational costs or revenue loss related to unplanned downtime.

What Kind of Digital Transformation Should Businesses Prepare For?

The availability of real-time data, artificial intelligence, and machine learning as 5G spreads is allowing long-anticipated concepts to become reality. The augmented capabilities of machine learning, powered by low latency connections, can truly make the manufacturing or fulfillment environments smart in ways never before seen. These changes are powered by important facets of Industry 4.0, such as machine learning, low latency connectivity, and greater data collection and analysis.

The Dual Role of Machine Learning

Harnessing the power of machine learning models lets companies change daily operations, bringing them much closer to fulfilling larger business objectives. By using machine learning models based on real-time data, companies can reduce costs, improve efficiency, and unlock deeper data-driven insights.

Machine learning is not a one-time activity. With real-time data being continually fed into the machine learning layer, new insights are generated frequently by retraining the model. These retraining exercises will result in generating new predictive and prescriptive insights better aligned with reality. This allows combat the false-positives and false-negatives more effectively. This continual retraining is only effective when these insights are immediately assimilated into a real-time decision-making process to ensure process optimization doesn’t need to compromise customer satisfaction or experience.

Low Latency Brings Real-Time Data to Decision-Makers

Continuous low latency connectivity makes it possible for businesses to make real-time decisions empowered by data, rather than using “near real-time” information. The consequences of using outdated data can lead to revenue losses. Having access to true, real-time data opens up opportunities for business process optimization in ways that previously seemed impossible, including:

  • Machine-to-machine communications that enable intelligent connectivity. With machines that are capable of communicating with each other to perform simple tasks, automation becomes more powerful than before.
  • Automation of monitoring, measuring, and intelligent decision making. With artificial intelligence in place, automated intelligent decisions can be made, removing human errors. Machine learning models enrich the intelligence embedded in the business logic to make better decisions that align with the objectives of the business over time.
  • Increased telemetry data that enables finer granularity of view into operational data. This data is ultimately useless unless put into action. Using these insights to power new machine learning models will allow a company to get the desired information and start making deeper connections between the data and how consumers behave, what they want, and how to meet their needs.

When done right with all the above-mentioned considerations, a true digital transformation can be a significant competitive differentiation of enterprises. However, many businesses, in their digital transformation efforts get caught up in an innovation paradox of doing the same processes of yesteryears with new technology.

Identifying Pitfalls in Industry 4.0’s Path to Digital Transformation

While some businesses view the potential for digital transformation with excitement, others find themselves intimidated or overwhelmed by the sheer volume of work to be done. Experts identify the gap between business people and AI experts as one of the biggest issues, with the two roles needing to communicate about business objectives and how to best accomplish them in a common language.

To be successful at digital transformation, companies should be aware of common pitfalls and how to avoid them, including:

  • The desire to do more of the same instead of undergoing a true transformation.
  • Using talent that is skilled at yesteryear technologies instead of disruptive technologies.
  • Inability to bring intelligent decisions into the automation of daily operations at the scale that 5G requires.
  • Too much reliance on CSPs and operators’ schedules to launch the exercise.

Asking Key Questions When Planning for a 5G Digital Transformation

To plan a digital transformation that effectively incorporates data to drive higher revenues and greater efficiency, companies have to ask themselves some questions about how current operations can change or be refined. By customizing how the business will adopt new technologies and processes, companies are likely to have a higher rate of success than taking a one-size-fits-all approach.

Some of the key questions to ask when planning for a 5G-based digital transformation include:

  • What smart-resources exist already and how can new ones be incorporated? In a recent survey, executives reported that nearly half of the equipment presently being used will need upgrading or replacement to work effectively with the new technology. Allocating resources to upgrading the right pieces will help develop the processes that will make the business environment more effective.
  • What decisions can be automated by incorporating machine learning? While traditional approaches may involve having a qualified business person analyze reports and make recommendations, machine learning models can now perform the same process and come to the same recommendations in a fraction of the time.
  • What latency expectations should there be when optimizing digital processes? What needs to be done within that latency SLA to build automated intelligence into the business process? Can machine learning models be incorporated into these decisions to ensure that they reflect the most up-to-date understanding of the business events and impact?
  • What data stays close to the edge and what data goes for retraining my machine learning models? The amount of data coming in, along with the disparate formatting of the data, can be daunting. Understanding which of these to include in machine learning models as you retrain has a real impact on the model’s accuracy.
  • How often should my machine learning models be retrained? Without proper retraining, you risk model drift creeping in overtime. Without accounting for the changing realities of the real world, a model can become less accurate and provide less effective recommendations for the current consumer. Retraining the model often, if not continuously, helps to keep it accurate and accounts for the changing interest and preferences of users.

Source: VoltDB