How to bring stakeholders with you on your Industry 4.0 journey

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When discussing the Fourth Industrial Revolution – Industry 4.0 – we can’t ignore the fact that manufacturers often struggle to convince leadership that data-led change is necessary, and that financial and cultural investment is required to make digital transformation a success.

Once you have won over the board, you then need to bring the rest of your staff with you.

In this post, I want to talk about some common challenges that manufacturers experience when planning and implementing a digital shift, and how to overcome them.

Start with realizing the value of Industry 4.0

Gaining company-wide support, especially from senior management can be a challenge, but as technologies develop it’s becoming easier to demonstrate the level of payback that digital transformation can deliver and build a business case for change.

The journey towards the utopian dream of Industry 4.0 – the automated factory – is a long one, so it is key to know where you are now and what you want to achieve.

Using case studies of other manufacturers, along with the advice of supportive vendors and consultants will help you to turn a few heads in the boardroom and build your business case for digital change. 

It all starts with education.

Be smart about Industry 4.0

Gaining a clear overview of the smart manufacturing space is important, but researching every technology on the market will slow you down.

I always recommend focusing on the big picture first, so you can hone in on the types of technologies that are most relevant for your operation. Once you know what you want to focus on, you can invest more of your time in detailed research. 

Attending trade shows where you can learn from industry experts and talk to other manufacturers is the fastest way to get an overview of the smart manufacturing landscape and get up-to-speed on different technologies and approaches. It also gives you the opportunity to find out what kinds of challenges other manufacturers have experienced on their digital transformation journeys and how they overcame them.

These face-to-face conversations can prove extremely valuable. For example, I recently spoke to a customer who told me that one of their key learnings was to spend more time with their team developing strategy and setting goals. The customer estimates that they lost around three months, because they didn’t agree on the vision and expectations of their transformation and didn’t write everything down in a strategy document, so everyone was aligned right from the start.

Take an integrated approach: Technology, people and processes

Technology is the driving force of smart manufacturing, so it’s common for manufacturers to focus all their attention on tech during the research stage and forget about the people.

In my opinion, this is one of the biggest – and most expensive – mistakes manufacturers can make.

Problems escalate when manufacturers lose sight of the people involved in digital transformation and neglect to define individual roles and responsibilities.

Avoid falling into this trap by setting aside time to consider your leadership strategy and how you will empower your internal teams. This help you define the policies and training you will need. This is particularly important when planning how you will support non-tech-native staff on your digital transformation journey.

The issues that come up at this stage will often be specific to the individual operation. For example, one of our customers wanted to enable data on-the-go via tablets and mobile devices, yet company policy prohibited the use of these devices on the factory floor. They had to work with Human Resources so that their policies and goals were better aligned.

Be clear on your goals 

Sit down with your team and discuss your overall strategy for the digital transformation.

Setting both short and long-term goals will enable you to start collecting the data-sets you need now and in the future. Start with one or two short-term objectives and set your key performance indicators (KPIs), then do the same with your long-term goals.

Understanding the different levels of a smart factory and what you can achieve with your initial – and future – technologies will inform your planning. We have a variety of quick-read resources that will help. Including a digital transformation guide/manual, which should help you set your short and long-term goals.

Once you are clear on your goals, set everything out on one document, so everyone has the same reference. Together with your team, define everyone’s roles and responsibilities and how you plan to work.x

Run some low-risk pilots to test your processes and technology. You can then demonstrate value, start small and scale up.

Moving forward

People often imagine that smart manufacturing makes operations more impersonal, but in my experience that couldn’t be further from the truth.

Smart manufacturing is all about smashing the silos, connecting machines and people and fostering a collaborative working environment. Getting everyone on the same page will help keep things clear and focused and get people throughout your organization excited about the changes ahead.

Oden can help you make sense of your digital transformation journey. Get in touch to see how we can help you set goals, demonstrate value and understand how technology can transform your business.

Configurable Dashboards for Everyone on the Factory Floor

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Over the summer, we introduced a new feature called Dashboards, which is now available to all Oden users, and we’ll be continuing to add enhancements.

Using the Dashboards feature, you can create and configure dashboards to reflect targeted KPIs within your factory, so different teams can focus on their specific goals. We expect that these will prove invaluable for your regular reporting or use in standing meetings.

With these configurable Dashboards, you can now create:

  • A shift production dashboard for supervisors to report on production for their shift and the previous shift across each line or product
  • A dashboard focused on utilization and performance for specific zones in your factory over the past month
  • Charts to report on top downtime reasons for a part of your factory, or changeover duration in different shifts
  • Charts to evaluate the performance of a key product group over the past few weeks
  • Other configurations tailored to your factory’s workflow, with more options to come

When viewing any of your dashboards, you can also use the dashboard’s global filters to change the time range for all modules, or focus them all on the same specific lines, states, products, or shifts. For example, you might want to see all of a Dashboard’s modules focused on Line 3 for October 1-7.

Within Dashboards, you can also choose to have your data align with the first shift of your production day. With that option selected, creating a chart that shows data for ‘yesterday’ will show yesterday’s data beginning with first shift (rather than midnight).

Dashboard creation and configuration

Dashboards can be created and modified by any Oden user with Administrator permissions. Each dashboard will be visible to all users on your account, but the data in them will be limited to the areas of the factory each user has access to.

Admins can modify any Dashboard, or add new charts to it, simply by hitting the admin-visible ‘Customize’ button. Any existing chart can then be moved, resized or modified. New charts can be added by hitting ‘Add new chart’ at the bottom of the dashboard, and configuring it to have the right data, filters and time range.

To create a new Dashboard, admins can head to the Oden Settings panel and select the Dashboards option. Here, admins can edit an existing dashboard, or create a new one that will be available to their team.

Check out Dashboards today!

How Oden works with System Integrators to deliver Industry 4.0

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Industry watcher IDC expects nearly $2 trillion to be invested in digital transformation projects worldwide by 2022. Organizations will set aside capital budgets equivalent to 10% of revenue towards their digital strategies, the group predicts.

In manufacturing, much of that investment will be paid by manufacturers to their system integrator (SI) partners to select the right technology that enables them to achieve their Industry 4.0 goals. In fact, IDC expects spending on hardware and services to account for 75% of digital transformation investment in 2019.

Working with System Integrators to deliver Industry 4.0

These predictions make it clear that system integrators are going to play a pivotal role in manufacturers’ digital transformation. And as the manufacturing industry evolves, it is critical that operators seek out the best advice and solutions as early as possible. 

Selecting the right Industry 4.0 technology expert is arguably the most important step of a manufacturer’s digital transformation journey. Often, manufacturers will go to their local service provider and a system integrator will recommend a framework, any required hardware and software, and an installation plan.

How Oden works with System Integrators

At Oden, we tend to work directly with our customers. Oden’s customer success team manages the entire customer journey from the moment a contract is signed, throughout the installation to lifetime support. Our Forward Deployment Engineers are very hands-on at the installation stage, as our system has specific set-up requirements. For instance, they need to ensure that the admin panel and network are correctly configured to Oden’s specifications.

We do however believe that the Oden platform could be a useful solution in a system integrator’s toolkit, and we have just begun working with a selected group of SIs. The SaaS business model makes Oden very easy for systems integrators to set up, and it is on our development roadmap to make it even simpler for SIs to install Oden in the near future.

Are you a manufacturer who needs help on your digital transformation journey? Read our Digital Manufacturing Manual, get in touch with us directly or ask your system integrator to investigate the potential benefits of deploying Oden at your factory.

Are you a system integrator? Contact us to learn how our technology can help your customers realize their Industry 4.0 ambitions.

How Oden Fits Into The Manufacturing Process Alongside Your MES

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We’re often asked by manufacturers if Oden is a manufacturing execution system (MES). The quick answer is no. While Oden has functionality to monitor production in real-time and react with alerting, our technology also complements and enhances existing MES frameworks, but it is not an MES offering. 

So, what is the difference between Oden and an MES? In this post, we’re going to answer this question and outline how Oden fits into the factory technology ecosystem alongside your MES.

Accelerating the full value from your MES 

Many manufacturers use an MES because they want to capture data throughout a production lifecycle and make processes more repeatable. Having access to this kind of high-level information enables manufacturers to identify bottlenecks, work faster, saving both time and money.

However, a typical MES has limitations around the depth and breadth of data and analytics, and this is why many manufacturers are turning to Oden. In terms of functionality, Oden uses advanced analytics and machine learning to improve quality and efficiency in real-time. In the future, by understanding the root-cause of process issues, the Oden platform actively learns and suggests optimized setpoint improvements, while an MES typically focuses on helping people on the factory floor with day-to-day processes. 

Using Oden alongside your MES allows you to release more value from your MES data and get to those money-saving efficiencies that you wouldn’t be able to access without Oden.

How Oden and MES work together

When it comes to optimizing production quality and efficiency, we like to think of Oden as the central hub with MES’, Enterprise Resource Planning (ERP) and offline quality systems as spokes within that hub. 

Information from all these systems stream into the Oden platform, providing a comprehensive view of your operations and processes. As you’d expect, the more high-quality data the Oden platform has to work with, the more precise the recommendations will be. 

It’s within these finely-tuned insights that manufacturers find their greatest savings. 

Your MES can give you instructions to help you execute a job, and the Oden platform can take that information and layer it on top of detailed process data. By doing this, you can improve process performance, quality and perform root-cause analysis on issues impacting your OEE, such as unplanned downtime or quality.  

Quality and maintenance are two key priorities for manufacturers, so let’s take a quick look at how an MES and Oden could combine to make improvements in these areas:

1. Combining your MES with Oden to improve quality 

When data from an MES and machine metrics are fed into the Oden platform, real-time metrics can be accessed by anyone on the factory floor. With predictive quality, engineers are quickly alerted to any potential issues around quality, so they can act fast, make adjustments and improve results. 

Of course, improving quality reduces costs associated with waste, so these insights from Oden mean that manufacturers save money on materials and scrap. When you consider the environmental benefits of reducing waste, the effect of improving quality spreads even further, which is great news for manufacturers that are looking to work more sustainably. 

Combining an MES with Oden essentially means that manufacturers can gain even more value from their MES data by enhancing it with Oden’s detailed process metrics and analytics. Working with an MES alone would be unlikely to produce the same cost-saving insights and opportunities.

2. Combining MES and Oden to gain benefits from preventative maintenance

Wear and tear is a reality of manufacturing and operators are all too aware of the costs associated with machine breakdowns and unplanned downtime. 

Drawing on data-sets from your MES, Oden’s predictive maintenance can anticipate impending problems, so they can be addressed before they occur. And again, these are insights that you would be unlikely to gain from your MES system alone. 

3. Improving day-to-day processes on the factory floor

An MES will provide an operator with changeover instructions and tell them how to get the line ready. When this information is combined with the added functionality that Oden brings, manufacturers can learn how to reduce the time it takes to set up, so they can reach the required setpoints much faster and more consistently across lines and shifts.

So, while and an MES will capture the qualitative aspects of, “hey, I had a machine breakdown,” or “hey, I’m doing a changeover,” the Oden platform can layer the quantitative aspects of process data to deliver real-time information and insights about these processes. 

For example, if the set-up instructions from an MES system say that the temperature should be set to 150 degrees, Oden will show the targeted temperature for that product, show the temperature change happening in real-time and alert you if the temperature is not within the desired range.

Faster problem-solving and clearer insights 

Manufacturers can access important data with an MES. Oden technologies takes that data to the next level. This “supercharging” of your data allows you to save time, increase productivity, reduce wastage, improve your environmental footprint and save significant dollar amounts.

Adding Oden to your tech ecosystem, gives you the ability to identify insights and efficiencies that you would not have found with MES alone. If you would like to find out more about using Oden in your operation, get in touch with us today!

How digital manufacturing technology improves quality in the medical device industry

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In the fast-changing and increasingly complex medical device industry, manufacturers are turning to digital tools to ensure their production practices and products meet the highest quality and safety standards. With an increased demand for personalized care and a rising elderly population, the medical device industry is expected to grow at a compound annual rate of 4.5% through 2023. Increasingly, those devices will feature more of the latest embedded technologies. Predictive analytics, apps that integrate with personalized medical devices via Bluetooth, and magnetic stimulation devices are just a few of the innovations the industry sees on the horizon. However, the increased complexity that comes with such innovation threatens to exacerbate the challenges companies face achieving the strict quality standards the industry demands. 

The Crippling Cost of Poor Quality

Because quality in medical devices often directly affects patient safety, the risks go beyond financial concerns. Quality manufacturing issues can cost a company, on average, up to $300 million a year, should they face a recall or other quality-related challenges. The amount includes spending to ensure good quality and the direct cost of poor quality. Worse, dysfunctional medical devices reportedly have caused more than 1.7 million injuries and nearly 83,000 deaths since 2008. 

Quality lapses arise in a variety of areas. Inaccurate or incomplete medical device records; cyberattacks on networked devices; and uncaptured, lost or expired charges make up most of the cost of poor quality in the industry. In total, estimates indicate that the industry is losing anywhere from $17 billion to $26 billion per year on average as a direct result of devices that don’t meet quality standards.

Improving Quality in the Medical Device Industry

Many—if not most—medical device industry quality issues result from mistakes that occur somewhere along the supply chain or during design, testing, or manufacturing processes. Fortunately, that’s where the latest digital manufacturing technologies can make the most significant impact. To ensure quality, manufacturers must automatically track medical devices through every stage of production and, increasingly, once they’re in use by a patient. Among the most effective technologies are the following: 

Intelligent automation: With intelligent automation systems, companies can implement more robust process and product controls. Such automation provides continuous visibility into factory operations and processes, which drives improvements in productivity and quality.

Intelligent systems also automate the all-important tracking and tracing capability necessary to meet ever-stringent medical regulatory requirements, as well as monitor production processes. Instead of using time-consuming manual methods, manufacturers use new systems to record data from every step in the process automatically. With electronic records, companies can more efficiently deliver requested materials to auditors. More importantly, they can more quickly trace any sign of a potential quality issue to its root-cause and address it before product quality is affected.

Data analytics: With data automatically collected from connected intelligent production systems, companies can deploy predictive analytics to improve production processes and product quality. Predictive analytics monitors every machine in the production process in real time, predicting outcomes, and preventing medical device defects. The system alerts operators when predetermined Key Performance Indicators (KPIs) begin to slip out of spec. With early warning and prompt action, manufacturers ensure that no devices are produced by machines that are not in acceptable condition. Also, operators can use predictive analytics to make better decisions.

Artificial Intelligence (AI) and Machine Learning (ML): When predictive analytics is powered by AI and ML, the system continuously learns and optimizes production processes. By implementing AI- and ML-powered predictive analytics, manufacturers can eliminate a great deal of human error in record-keeping, and in-the-moment production decisions. It can also improve patient outcomes and the quality of their care, as predictive models assess resource use and patient risk. 

Remote Device Monitoring: Once in use by a patient, a medical device generates massive amounts of data. With a big data and predictive analytics solution, manufacturers can capture and analyze this data with unprecedented speed, leading to powerful, actionable conclusions. Device data can be used by the manufacturer to improve device quality and, in partnership with physicians, to personalize the device based on patient vital signs, lifestyle, and other information. 

A Higher Standard of Quality is Good for Everybody

Fixing the medical device industry’s quality manufacturing issues isn’t a simple fix. It requires effort on multiple fronts, such as improving record-keeping, optimizing production, and managing complexity. However, the latest digital technologies offer manufacturers a robust, comprehensive solution. By adopting them, they’ll increase operational efficiency, reduce device malfunctions and recalls, and more effectively meet regulatory requirements—even as they become able to offer more innovative and more useful products. They’ll save potentially billions in lost revenue, capture market share, and grow profits. More importantly, however, they’ll reduce—or eliminate—the injuries and death caused by poor-quality devices.  

Oden Technologies and WorldWide Polymer Compounding partner to deliver Industry 4.0 solutions to polymer and food compounding industries

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We’re delighted to announce that Oden Technologies has partnered with WorldWide Polymer Compounding (WWPC),  a leading specialist in process development and troubleshooting for the compounding industry to offer Oden’s products to polymer and food compounding manufacturers in North America.

Today, companies operating polymer, food and chemical compounding facilities face a number of technical and operational challenges that have the potential to be resolved by Industry 4.0 solutions. Specifically, manufacturers are struggling with maintaining the highest throughput rate and product quality on a continuous basis as well as hiring operators and process engineers with the right skills to oversee their operations.

Established in 2018 and based in New Jersey, WWPC is led by Steve Jackson, an industry veteran who spent his 25+ year career working for Coperion, the market and technology leader for compounding and extrusion manufacturing. Steve has extensive experience in identifying and solving operational issues with twin screw compounders from lab scale to world scale polyolefin production. His background in both process engineering management and field service management is essential in providing a holistic approach to process development, troubleshooting and training.

“We are excited to be working with the Oden team to implement this new technology into the compounding area. The number one challenge we hear from our clients – and as documented in the industry press –  is the need for more skilled technical support on the floor and in the workers’ hands. The ability of the Oden system to work with controls technology that is 30+ years old shows that they are targeting the entire market of 10,000+ compounding lines and not just looking to work on the 100-200 new lines sold every year,” said Steve Jackson.

Test case: How the Golden Run recommendation engine helped a manufacturer achieve peak production

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In the first of this two-part series, we looked at Oden’s Golden Run recommendation engine and how it uses data to identify the most efficient way to make a product to a manufacturer’s specified quality. In this post, we will look at the Golden Run in action and share the results of a test we conducted recently over a six-month period. 

Just to recap, Oden’s Golden Run recommendation engine considers the product quality levels required by the manufacturer and uses Artificial Intelligence (AI) and Machine Learning to calculate optimum settings for making a product as quickly and efficiently as possible.  

Achieving peak production with the Golden Run

We conducted a test simulation where we ran a six-month data gathering test at an American industrial manufacturer. We measured metrics relating to the quality, performance and control parameters of the factory’s core product. 

In this simulation, the metrics included:

  • Quality: Diameter of product
  • Performance: Line speed
  • Control parameters: Temperature, Motor revolutions per minute (RPM), Pressure

The manufacturer had a minimum goal of 0.75% in CPK (Process Capability Index), which is a statistical measure of a factory’s ability to produce output within specification limits deployed by many manufacturers.

Using a complex system of data analysis, segmentation and extraction over that six-month period, we identified how much the manufacturer could save by using our Golden Run settings.

The optimum conditions we identified delivered a CPK of 0.83, some way above the target minimum. Also, we now had on record the best duration, line speed, temperature, motor RPM and pressure conditions that the manufacturer needed to create its most efficient production run.

We found that by operating at the optimum Golden Run settings, the manufacturer could have saved more than 230 hours over that six-month period – that’s almost ten days of full-time production. This means that the manufacturer could use the new Golden Run settings to execute the next run 15% more efficiently than previous runs.

Providing a dollar value to Golden Run savings

How do we put a dollar cost on savings? Savings vary from company to company, as every organization’s overheads are unique to them. The machine cost, material cost and required human resources will all need to be considered.

There are other variable costs associated with savings particularly in the areas of power, maintenance and wastage. The longer you run a machine, for example, the higher the costs of maintenance and power. 

What if you could control the cost of power without sacrificing the product quality? Operating with the optimum Golden Run settings to reduce production time will result in savings around maintenance and power. Similarly, if you’re prepared to be flexible with your product quality parameters, you may be able to deliver more products faster. 

The Golden Run recommendation engine gives you the flexibility and control to make decisions that will drive the most revenue for your organization.

How you can achieve your optimal Golden Run

The Golden Run recommendation engine is available as a part of the Oden platform, and it’s powered by its Artificial Intelligence framework – Mímir.

Our goal is to enable manufacturers to release even more value from their data by expanding the applications of the Golden Run. In the near future, the Golden Run will also process environmental data, adding yet another dimension that you can control to drive revenues from your production.

Ultimately, the Golden Run recommendation engine will execute machine control – manufacturers’ machines will automatically identify their optimum performance settings and seek to achieve them without human intervention.

If you’re interested in learning how the Golden Run recommendation engine can drive revenues for your factory, please get in touch with one of our experts.

 

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How the Golden Run recommendation engine delivers measurable Digital Transformation

By | Blog

As Industry 4.0 gains momentum, manufacturers are not just seeking out more information about the benefits of Digital Transformation – they also want to understand the guaranteed return on investment (ROI) they can expect. 

We’ve already introduced the Golden Run recommendation engine in our recent blog post as the most efficient way for you to manufacture a product. Today, we’d like to focus on the measurable ROI it delivers for manufacturers.

The future of production is prescriptive

According to Ventana Research, by 2021, two-thirds (66%) of analytics processes will no longer just uncover what happened and why, but will also prescribe what actions should be taken next by organizations such as manufacturers. Oden is at the forefront of this shift, delivering Artificial Intelligence (AI)-powered prescriptive analytics to manufacturers today.

The Golden Run recommendation engine was designed specifically for manufacturers who want to predict and measure tangible short-term results from Digital Transformation, as well as long-term benefits. It prescribes the most efficient way to manufacture a product and to generate additional revenue.

The Golden Run is powered model by AI and Machine Learning to identify the fastest and most efficient way to make any product to a specified quality. Effectively, it is optimizing a factory’s run – so a quantity of units that are produced for a period of time by a production line – in terms of what are the best speeds that you can achieve while meeting a certain quality level, especially when you’re in a stable period of the run.

Using a complex system of data analysis, segmentation and extraction, the Golden Run identifies how the manufacturer could make its production more efficient, what measurable results they should expect, and generates new settings to realize those efficiencies. As a result, it offers manufacturers the ability to test their business case for Digital Transformation in a record time.

Collecting Data for the Golden Run 

In order to deliver very specific recommendations, the Golden Run model requires very specific datasets, including:

  • Quality metrics
  • Speed metrics
  • Controllable metrics

Manufacturers often ask how much data they need to collect before they can start using Oden’s Golden Run. The answer varies depending on production activity, but as a gauge, we generally recommend that organizations collect at least one month’s worth of data before using the Golden Run recommendation engine.  

As you’d expect, the more data the algorithm has to crawl through, the more finely tuned its recommendations will be. A manufacturer with three months of data, for example, can expect a more finely-tuned recommendation than a manufacturer that’s working from one month of data. 

It’s also worth noting that the Golden Run can utilize historic data collected prior to using the Oden platform, as long as the datasets fulfill the requirements of the algorithm. 

How Does Oden Use the Data to Create the Golden Run Settings?

Once data has been collected, it is organized in groups of time-stamped metrics. We then assign a label to each metric – a process known as taxonomy – so we can differentiate between quality metrics, speed metrics and controllable metrics. 

We merge all this information with the contextual data, which includes metadata associated with the product, the product name, targets, line numbers and so on, as well as time-stamped data about various states of the product run. 

After the information has been merged, it is organized into five-minute chunks of time known as ‘intervals’. This enables Oden to identify the metrics and summary statistics for each five-minute interval of any specified run. 

Next, a segmentation algorithm crawls through all the data and identifies periods of stable performance. It’s worth noting that the defining parameters for stability vary depending on the manufacturer’s priorities. For example, one manufacturer may only be interested in line speed, while another may have a much longer list of metrics.

Oden estimates the quality and duration for each segment and configures it with the minimum acceptable quality threshold stipulated by the manufacturer. For instance, if a manufacturer wanted the quality of a product to be in the 90 percentiles, we would only isolate the segments that could deliver that specified quality. 

The system then combines the isolated segments to create the metrics needed to generate the optimal Golden Run settings. In other words, the Golden Run recommendation engine tells the manufacturer exactly which settings they need in order to produce a product as fast and efficiently as possible to the desired level of quality

 

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Where Is Your Factory on the Path to Smart Manufacturing?

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Before you start planning your smart manufacturing strategy, it’s critical to identify where your business currently is in its digital transformation journey. As we briefly discussed in a recent post, the path to smart manufacturing can be broken down into four different levels. 

Let’s unpack those four levels in more detail, so that you can determine where you are now and what you need to focus on next.

Level One: The Unconnected Factory

A factory operating at level one does not have a central database. Data that is generated on the factory floor via various sensors, machines and systems is held separately in different locations. 

A level one factory might use technology to improve efficiency and productivity, but since there is no central database, it is difficult for decision makers to get a clear overview of the operation and develop new strategies for evolving processes.  

Arguably, this unconnected way of working describes the experience of most manufacturers today, so if your operation fits this description, you’re not alone. 

At level one, the challenge for CIOs and the C-suite is to understand the objectives of their future digital transformation and work with partners to plot a path forward. Our recent post on the path to smart manufacturing covers this in more detail. 

Level Two: The Connected Factory 

In the connected factory, sensors, machines and systems are linked to a central database, and may include solutions in a cloud or hybrid environment. At this level, decision makers can work in a more collaborative and cohesive way, accessing data from one single source of truth. Potential benefits can be seen in multiple areas of the manufacturing process, including innovation, supply chain management and problem solving, as well as logistics, sales and planning.

Collecting enough of the right data is key at this stage. Organizations need to have a clear vision of what they want to achieve in the long term, because long-term goals determine which data-sets are collected at level two. 

Level Three: The Predictive Factory

Level three describes a factory that uses data-led insights, Machine Learning  and Artificial Intelligence (AI) to identify where efficiencies can be made. Working at this level, decision makers have access to data-driven AI recommendations and can solve problems and identify opportunities rapidly. Very often, predictive maintenance and quality are the first areas of focus when transforming from level two to level three. 

In a predictive factory, manufacturers can identify their ‘Golden Run’, when AI and Machine Learning use data to identify the very best way to make a product to the manufacturer’s specified quality.

Level Four: The Automated Factory

At the very top level of Industry 4.0, manufacturers enjoy all the functionality of a level three environment with the added benefit of automation. Here, AI and Machine Learning will identify opportunities, generate new settings and send them out to devices on the factory floor in order to implement changes. The automated system can then keep track of the changes, creating and implementing further efficiencies when necessary.  

Decision makers in an automated factory can rest assured that their products are being created in the most efficient way possible.

So, where are you?

When entering the smart manufacturing space, it’s all too easy to become lost in the jargon, but the truth is that smart manufacturing technologies are all related to data and how it can be used. Using the four levels to benchmark where your factory is currently operating at allows you to visualize the next step of your journey and the technologies involved. 


If you’d like to read our comprehensive guide to smart manufacturing, download our Digital Transformation Manual.  

Request a demo to see how we’ve helped several customers get to a predictive factory. 

The Race to Smart Manufacturing: 7 Essential Steps For Digital Transformation

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Industry 4.0 is set to drive significant, measurable change in the competitive environment of manufacturing. However, the route to becoming a smart manufacturer can often seem overwhelmingly complex. 

Simplifying the process enables manufacturers to cut through the confusion and fully leverage new technologies and the opportunities they bring. 

Let’s take a closer look at the seven steps every manufacturer should follow to turn their smart manufacturing dream into a reality. 

STEP 1: Identify where you are on your digital transformation journey

The digital transformation journey can be broken down into four different levels, from Level One where systems, machines and sensors are not connected, right up to the autonomous Level Four, where Machine Learning and Artificial Intelligence (AI) work to identify efficiencies, generate new settings and send instructions to machines.

Understanding the four levels of smart manufacturing will enable you to form a clear overview of the digital transformation journey and the potential benefits each stage could deliver. 

STEP 2: Make the business case for digital transformation

Digital transformation technologies have the power to streamline multiple processes and make significant efficiencies within an operation. Ultimately, your level of investment will determine your digital transformation strategy, so presenting a strong business case to decision makers who control the budget is vital.

The quickest way to get people onboard and excited is to focus on the monetary benefits of smart manufacturing. For example, new technologies can enable manufacturers to be more agile and responsive to changing customer and market demands, which can lead to a more lucrative business.

Reducing the unnecessary costs around waste is another key benefit of smart manufacturing. While waste reduction can help you meet environmental regulations and company targets, it also typically leads to a more sustainable operation. This will boost your business’ appeal to today’s environmentally-conscious customer and creates potential marketing opportunities. 

STEP 3: Educate yourself and your team

The quickest way to learn more about digital transformation is to attend tradeshows and conferences, and speak to peers and experts. 

Understanding some of the common challenges other manufacturers have experienced will help you  establish best practices. There’s no one-size-fits-all approach to digital transformation, every manufacturer is different, but learning from others’ failures and successes can help inform your own strategy. 

Many manufacturers make the mistake of focusing exclusively on new technologies, but the truth is that people also play a vital role in digital transformation. Look for key learnings around human resources and team structure, as well as technology.

STEP 4: Develop your strategy in collaboration

Work together with your team to develop your strategy. Identify the types of problems you want to solve with smart manufacturing, agree on a clear timeframe and set your expectations and key performance indicators (KPIs) accordingly.

Set out your plan on a master document, so that everyone is aligned right from the start. Planning your strategy as a team will help you avoid communication issues and encourage stronger collaborative relationships. 

STEP 5: Identify people and policies impacted

Once you have established your objectives and KPIs, you’re ready to build your digital transformation team.

At this stage, it’s crucial to have the relevant expertise for your specific Test & Learn project or ‘pilot’.  If your pilot involves Machine Learning and AI, for example, a data science team would be required. 

You might also consider working with outsourced technology experts or recruiting in-house specialists. Alternatively, you could consider working with an Industry 4.0 partner that can offer a production-ready solution.

Once you have your team in place, create a clear strategy and rollout document that outlines everyone’s roles and action points.

STEP 6: Select the right pilot partner 

Now that you’ve established what kinds of problems you want to solve and your team is aligned, you’re ready to identify a goal for your pilot and choose an appropriate Industry 4.0 technology partner.

When selecting a partner be sure that they have technologies and expertise relevant for your business. Our Digital Transformation Manual: A Practical Path to Smart Manufacturing has a comprehensive list of considerations for this step. 

Looking beyond the pilot, check if vendors can provide your business with support for every level of smart manufacturing, including advanced features like Machine Learning and AI. Successful digital transformation requires momentum and continuity, so working with the same technology partner throughout your journey is best practice.

Creating a successful pilot requires the right mix of people, processes and technology, so choosing the best partner for your operation is key. And don’t be afraid to do a test pilot with several providers. It’s encouraged and is the more effective way to see which ones can deliver on your specific needs. 

STEP 7: Start small, then scale fast to continue to evolve

Finally, after you have completed your first successful pilot, creating follow-up test-and-learns becomes easier, as the initial pilot informs the working structure. 

Expanding your digital transformation efforts adds incremental value and allows you to move forward in your journey. Aim to scale fast and build up your wealth of data rapidly, ensuring you have enough of the right data about the processes you want to improve.


If you’d like further information about the path to Smart Manufacturing, download our Digital Transformation Manual: A Practical Path to Smart Manufacturing  or request a demo to see how we’ve helped several customers on their path to digital transformation. 

Read more on what digital transformation is and find at where you are on your digital transformation journey.