Industry 4.0

The 5 Steps to Becoming a Smart Manufacturing Firm

ThinkIQ is the revolutionary manufacturing data platform that transforms companies into Industry 4.0 Smart Manufacturing firms.

But what exactly is Industry 4.0 Smart Manufacturing? More importantly, how can you become an Industry 4.0 Smart Manufacturing organization and reap the benefits?

What is Industry 4.0 Smart Manufacturing?

Due to the digitization of commerce, the manufacturing industry is in the midst of a massive transformation.

It’s such a large change in how manufacturers work, that it’s being related to the earlier 3 industrial revolutions. (The First Industrial Revolution was through water and steam, the Second Industrial Revolution was mass production and assembly lines, and the Third Industrial Revolution was considered computerization, by the way.)

The Computerization of Industry 3.0 was disruptive — an entirely new technology was added to existing manufacturing lines. Now, though, those computers are connected, not just to each other, but to manufacturing system (also called cyber-physical systems), to a broad network of devices (called the Internet of Things, or sometimes the Industrial Internet of Things — IoT and IIoT), and to a connection of systems (called the Internet of Systems).

 This interconnected world of manufacturing machines, IoT and IIoT sensors, and supply chain data — once communicating together — leads to the Fourth Industrial Revolution — commonly referred to as Industry 4.0, Industrie 4.0 (referencing the term’s French origins), and Smart Manufacturing.

If a manufacturing machine was somewhat smart with its Industry 3.0 computer, it’s logarithmically more useful when they are all interconnected in Industry 4.0, and empowered to share the same language, context, and goals.

This revolution goes far beyond analytics. While ERP, MES, MOM and other manufacturing analytics were a first step to connecting data, they do not create truly connected, truly maximized Industry 4.0 manufacturing systems. In fact, until recently no platform has implemented traceability and supply chain data that creates proactive, actionable end-to-end data insights with the petabytes of manufacturing data created daily.

What are the Benefits of Industry 4.0 Smart Manufacturing?

While many organizations deny the coming changes of Industry 4.0 — or consider it a marketing term — the benefits are vast and undeniable. Connected supply chains, sensors, and manufacturing lines can and do outperform “dumb manufacturing” in every instance.

Examples of Industry 4.0 Smart Manufacturing are many. But imagine it in your own manufacturing facility: you can see all the data, from supply chain source data to shipping, on one screen. That data has been AI connected to form cause & effect associations, which alert you, perhaps, that a temperature change at a grain silo may cause a manufacturing problem in the future, or that the way that you are cutting your product is inefficient.

In fact, an alliance of research & government labs and manufacturing organizations — the US Smart Manufacturing Leadership Coalition — has predicted that the benefits of Industry 4.0 Smart Manufacturing include a 10% overall improvement in operating efficiency, 10% faster time to market, and a 25% reduction in factory safety incidents.

So, how does one transform to the next generation of manufacturing efficiency?

The 5 Steps of Transformation to Industry 4.0 Smart Manufacturing

Every manufacturer must go through these 5 steps to reach Industry 4.0 Smart Manufacturing status:

Data Capture
Visualization & Integration
Material-Centric Insight
Transformational Intelligence
Industry 4.0 Smart Manufacturing

Thus, with incremental changes an organization can transform their safety and efficiency dramatically, fulfilling the promise of Industry 4.0. A Smart Manufacturing organization can trace a product from supply chain through manufacturing and delivery, all on one screen in any location. Data that was unused is not only visible, but connected to proactively inform you of both problems and opportunities. And you have a significant advantage over your competitors, in speed, flexibility, safety, and product costs.

Let’s look at each step towards Industry 4.0 manufacturing efficiency in detail.

Data Capture: Beginning the Path to Smart Manufacturing

Step One of becoming a fully automated Smart Manufacturer is to collect data — all data, from anywhere. Figure out what digital data you have, from IoT and IIoT devices, to HMIs, PLCs, ERPs, your CRM, and even manual data captures. Begin the conversations with suppliers and partners to figure out what data they have, and what step they are in.

If you’re not yet at this step, this is the most urgent requirement. It’s required to have basic digital access to the majority of your data before moving deeper into Smart Manufacturing.

Begin the process of gathering data — or at least knowing who has the data — about raw materials, processing, manufacturing, delivery & distribution, transportation, even down to the consumer level.

Once you’ve collected this data, you’ve completed the first steps towards Industry 4.0 benefits. Your data can be seen. However, at this step it’s almost certainly still on a wide variety of screens and in different data silos. Usually, manufacturers at this step are ahead of some competitors, but still cannot aggregate and correlate what’s important to their organization in a useable manner.

Visualization & Integration: Step Two to Industry 4.0

Step Two brings standardized metrics and views, bringing wide visibility and context to the data of an organization on the path to Industry 4.0 Smart Manufacturing.

At this step, manufacturers generally setup on-premise gateways & connectors along with a system to centralize data. Their existing sensors continue to collect the same data as in Step One, but now they also aggregate and securely send the data to a common location (often the cloud).

Upon completion of this step, you should be able to view all your data on one screen. This is often a revolutionary moment for production teams.

This then empowers your data scientists. Alerts and notifications bring problems to your attention, which begins to mitigate your risk of a recall, and often results in yield improvements.

Imagine what you can do with all your manufacturing and material data – including from suppliers – available on one screen and across multiple facilities.

Material-Centric Insight: Step Three on the Path to Industry 4.0 Manufacturing

Step Three of your transformation is to enable a material-centric view of operations. This most often uses advanced AI and ML to correlate data.

Key deliverables of this phase might include advanced visualizations of your manufacturing line (including supply chain), cause & effect identification, industry benchmark reporting, and cross-plant KPIs.

An ideal outcome of Step Three would be for an organization to identify previously unseen correlations — even root issues — from their supply chain through the internal manufacturing process and outward towards the end-user.

To reach the Insight step, your Industry 4.0 platform will require strong Artificial Intelligence / Machine Learning abilities. It uses AI/ML to create associations that lead to cause-effect determinations, which are ultimately what lead to the most useful predictions, warnings, and suggestions.

However, for the AI to work properly requires several somewhat complex elements:

1

The data must be able to coexist as a wide variety of data, from traditional supply chain and manufacturing data and traditional data analytics tools, to the new, modern inputs. Traditionally, a “historian” element has handled that. This historian must be scaleable for vast amounts of data, and must be able to not only accept traditional manufacturing analytics, but also new inputs from images to geo-location tags.

2

Terminology must be enabled that allows the AI to relate sensor data to physical plant equipment, manufacturing processes, raw materials, and finished goods. This requires a linguistic and logical model (“semantic model”) designed for manufacturing and able to “talk about” each different type of information.

3

Manufacturing data isn’t useful unless it can be queried. These queries, though, must be able to also interact and “speak with” the Semantic Model, which often requires a rewrite of existing queries, along with additional algorithms.

4

In addition to the above, a methodology — such as a ledger — must be created that allows for manufacturing data to digitally flow along the supply chain. For example, sensor data that a partner gathered needs to also flow along logistics pathways and through your manufacturing line, still digitally connected to the actual product itself. This then allows product tracing on a deeper level, which contributes again to the AI’s ability to make sense of causes and effect.

Once a manufacturer has data, integrated, and visible, processed by AI/ML that can truly understand that data … it becomes potentially transformative, and you’re within each of Industry 4.0 benefits.

Transformational Intelligence: Step Four to Industry 4.0

Step Four of the path to Smart Manufacturing utilizes all of the efforts of the prior steps to begin supplying transformational intelligence to manufacturing organizations.

A primary goal of Step Four should be for Machine Learning to begin uncovering root causes and effects. (This, of course, requires the completion of Steps 1-3 and then a wide variety of real-life data inputs through the system.)

In the ultimate scenario, your data points from suppliers through customers are processed and related to your KPIs.
 You will then be able to rapidly spot operational anomalies, and will address problems much earlier in the process — saving you both time and money.

Also, this is where opportunities for improvement surface in your manufacturing process and supply chain. You may see how a supplier delay affects you, or where there were quality issues you were previously unaware of. The analytics at this step transform your viewpoint, with fully transparent data … and most importantly, context for that data.

By feeding all data into one continuous location, assuring the data is standardized and correlated to KPIs, then processing that data with specialized machine learning, you will transform the intelligence your organization receives. From plant manager to CEO, you’ll have an instant, intelligent view of operations, from the beginning of the supply chain, through your manufacturing line, and onward to customers.

Once this cause & effect processing has begun, and continuous improvement procedures have been implemented, you will see the ongoing benefits of Industry 4.0 manufacturing at the enterprise level.

The Final Step to Industry 4.0 Smart Manufacturing

Digital Manufacturing — often called Industry 4.0 Manufacturing — has arrived, combining technology and manufacturing to create the most efficient, safest manufacturing line in history.

With fully autonomous Smart Manufacturing, your manufacturing process now includes traceability — from raw materials to product delivery — optimized supply chains, and fully transparent, realtime contextualized data. It can warn you of trouble, early on. It can surface numerous opportunities for improvement. It increases plant and product safety. And it can speed products to market.

The results are truly revolutionary, establishing users as market leaders. Smart Manufacturing companies are more profitable and more competitive. In addition, employees are happier — with better collaboration and connections — and are more empowered and able to make changes that support the organization’s goals.

It’s Time to Transform Your Data

The exact path to becoming a Smart Manufacturing firm is different for every company, but the direction is the same –– to modern, optimized manufacturing. ThinkIQ has aligned our Solutions roadmap with the 5 Steps above, combining the best of supply chain data analytics, traceability, and manufacturing analytics, into the next generation manufacturing data platform.

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