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

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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.

Digital Transformation Is The Wave of the Future – Are You Ready?

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The manufacturing industry is becoming increasingly focused on digitalization with many companies already looking ahead and adopting new technologies. In fact, IDC predicts that organizations worldwide will invest $5.9 trillion in digital transformation between 2018-2021*. 

Now, more than ever, manufacturing executives and decision makers are looking to leverage digital transformation in smart manufacturing to cut costs, drive efficiencies and meet ever-changing customer demands, and many have already started their journey.

But what is digital transformation and how does it impact manufacturing?

Digital transformation in the manufacturing industry

In manufacturing, digital transformation involves a wide range of technologies such as connected machines and services, networked sensors, data analytics, Artificial Intelligence (AI) and Machine Learning. The goal of digital transformation is to improve processes and enable manufacturers to create a sustainable, competitive advantage. 

Like all organizational change, successful digital transformation is driven by sound leadership, planning and knowledge. Identifying the right level of digitalization for your organization is vital, so working with a trusted partner at the very start of your journey is critical.

Let’s explore the benefits of digital transformation.

Predictive maintenance: a key benefit of digital transformation

One of the key benefits of smart manufacturing is the ability to reduce machine downtime with predictive maintenance. When issues such as unusual temperature change, vibration or machine slowdown start to occur, data can alert operators so any problem can be addressed and fixed before the situation escalates.

In an automated factory, some maintenance issues can be solved and corrected without the need for human intervention by using a variety of AI-based technologies. Alternatively, machines can act as a co-pilot, alerting factory managers, engineers and operators to potential problems, so they can decide what action to take and address issues before the machine breaks. 

Reducing quality and waste issues with smart manufacturing 

Many manufacturers have experienced the negative impact of quality issues. Estimates find that the cost of poor quality is typically between 10% and 20% for most manufacturing companies, but can be as high as 30%.

Digital transformation technologies enable manufacturers to reduce issues around poor quality, ultimately saving money on costs associated with repair, rework, scrap, service calls, and warranty claims.

It’s also important to acknowledge that poor quality can also lead to issues around public health and safety, regulation breaches and other serious risks. In this context, improving quality with digital transformation could be viewed as a risk management activity as well as an area of cost reduction. 

A dynamic working environment for a dynamic workforce

A smart factory is a forward-thinking, dynamic work environment and manufacturers that adopt new technologies are extremely well-positioned to engage and retain a strong workforce

In a connected factory, individuals can access the same information wherever they are in the network by working from one single source of truth instead of a collection of separate databases. This fosters a culture of collaboration and enables people to innovate and problem solve together, rather than working in separate silos.

Because of digital transformation, smart factories can future proof themselves from issues relating to manual labor shortages. 

Seeing digital transformation in action

So, how are some businesses currently implementing digital transformation in their factories? One of our customers is a global pharmaceutical and consumer packaged goods company that has engaged in a long-term digital transformation journey aiming to deliver increased personalization, improve access and affordability of products, achieve more agile, responsive and flexible manufacturing, and operate more sustainably and profitably. 

At the outset, the business turned to academics, technology providers and other experts for advice that would inform its strategy. They presented the business case and transformation plan to leadership to get financial and cultural support before a rigorous Test & Learn phase, starting small and scaling up.

Internal and external collaboration was key to helping the company transform digitally and it is now operating more efficiently and sustainably, gaining a competitive edge.

Getting started on your digital transformation journey

Digital transformation is an organization-wide process. It’s a journey that requires buy-in from all levels, an understanding of your business objectives, and the right Industry 4.0 technology partners to help you achieve your goals.  The sooner your organization gets started, the sooner it can start gaining insights and become more competitive and, ultimately, more profitable.

*IDC FutureScape: Worldwide Digital Transformation 2019 Predictions (Oct 2018)

In our next post, we’ll break down the simple step-by-step approach you need to transform your business.


Ready to start your digital transformation journey?

Download our comprehensive Digital Transformation Manual: The Practical Path to Smart Manufacturing

In this guide, we offer practical recommendations on how to benchmark your organization on the digital transformation curve and what steps to take to kickstart your journey and progress. 

Introducing Oden’s Golden Run recommendation engine for achieving peak production

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In manufacturing, finding the most efficient way to make a product has always been the utopian dream. But not any longer.

Today, we’re announcing the launch of the Golden Run, the Artificial Intelligence (AI)-powered recommendation engine that uses data to identify the very best way to make any product. Its goal is to boost profits for manufacturers by helping them identify the most efficient way to create their products.

Why look for the Golden Run?

One of the most common questions we are asked is “where is the return on investment from Digital Transformation and smart manufacturing, and how do I get it?”

Manufacturers work to tight margins. Any maximizing of efficiency within the production cycle can help save money, reduce waste, improve output and increase overall productivity which, over the long-term, will deliver significant margin improvements.

Using data to find the optimal conditions for production and giving operators the settings they need to replicate those conditions, allows manufacturers to improve their processes run after run, until they are working at the very best possible efficiency.

Oden’s Golden Run recommendation engine effectively hands manufacturers the key to unlocking the true potential of continuous improvement. It’s easy to use, it delivers fast results, and there’s nothing quite like it on the market today.

Let’s look at how it works.

Start with quality

We start with quality, because if increasing output means decreasing quality then you are not making the right trade-off. Manufacturers using the Golden Run recommendation engine start by identifying the product quality benchmark level they want to achieve.

For example, products that require a high level of precision, such as medical devices, computer components or automotive components, will naturally need to be made with higher specifications, while other products can accommodate a wider range of variation and quality level.

Once you know the quality level you need to hit, the Golden Run gives you the settings you need to achieve your specified quality in the most efficient way.

How the Golden Run works

Most factories experience a wide range of variability when making products. The Golden Run recommendation engine makes use of all those variables, analyzing different shifts and different sections of each run to gain insights.

As the Golden Run looks at how a manufacturer previously made a product, it identifies the best sections of each run and uses those insights to create perfect run settings. With the Golden Run, there is no need for human analysis or input – the calculation is all done through the algorithm.

For example, if you ran a stable production for two days and the process ran at optimum efficiency for just 15 minutes, the Golden Run engine would find that 15 minutes and define the perimeters. Using these new settings, you could replicate the same optimal conditions for the full duration of your next run. In other words, instead of running at maximum efficiency for a short burst of time, you can now do the full run using these “golden” optimum settings.

The more historical data you have for a product, the more there is for the AI algorithms to crawl through and the better the insights and optimal settings will be.

The Straight Out-of-the-Box AI Solution 

The beauty of the Golden Run recommendation engine is that it’s designed to work straight out-of-the-box. There’s no need for manufacturers to employ new internal expertise, such as data scientists, or shake up existing teams in order to gain insights and benefits.

Creating a similar AI-powered models in-house would likely run into the millions of dollars and take years of research. Our straight out-of-the-box approach saves manufacturers time and money, and delivers rapid results.

New Oden customers can use the Golden Run engine as soon as sufficient data has been collected, which can take as little as a month if the product is made frequently. Existing clients should already have enough data to make use of the Golden Run, which is currently accessible through Oden Labs.

Grow Your Profits with the Golden Run

Every manufacturer wants to create more output for less input, and the Golden Run recommendation engine helps them achieve that perfect run goal in the easiest way possible. In every sector, small margins can make a significant difference and using the Golden Run can give forward-thinking manufacturers a strong competitive edge.

If you’d like to learn how you can use the Golden Run recommendation engine in your operations, please get in touch with us to discuss your requirements.

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Smart Manufacturing: The Rise of the Machines

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Willem Sundblad is featured in GP Bullhound’s latest research report, Smart Manufacturing: The Rise of the Machines. You can access the full report here. Below is a snippet:

Martin Lorentzon, co-founder of Spotify says, “The value of a company is the sum of the problems you solve.” I think it’s true for all businesses, but especially true for manufacturers. Manufacturing has always been competitive in nature, but due in part to globalization, the competition has intensified tenfold. Improvement methods such as Lean, Six Sigma, and Kaizen, that emerged as a result of the competitive landscape, are now considered table stakes for everyone, forcing manufacturers to look to a new frontier to gain the competitive advantage. They’ve found this new frontier in digital manufacturing solutions.

There are two ways that a digital system can deliver value to users. It can help them solve problems faster than previously possible. This has immediate value, given how time consuming the process of solving quality, performance or downtime issues is in manufacturing.

However the second way a digital solution delivers value is more long-term and transformative: it allows users to solve problems they would never be able to solve previously. Take the environmental algorithms we have delivered at Oden Technologies as an example. The factory environment (e.g., temperature, humidity, etc.) plays a sizeable role in material processing. But, in order to understand and adjust process parameters to account for the impact of environmental factors, one has to first analyze an abundance of data. The volume is typically too great for skilled engineers to handle, and since many do not have the experience to train models, the task is nearly impossible.

However, digital solutions like Oden have algorithms to analyze millions of historical data points and make recommendations on the optimal settings that will drastically improve quality and output. These trained models do what even your most skilled engineer cannot. Getting to a Smart Digital Factory is a journey. At Oden, we educate the industry on the four levels to that journey towards data-driven, intelligent manufacturing.



Oden and I-IoT Solutions team up to deliver predictive production analytics to manufacturers in Latin America

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Excited to announce that we’ve partnered with I-IoT Solutions, the industrial IoT solutions provider from Tucson, Arizona to offer Oden’s products to manufacturers in Latin America. Under the terms of the agreement, I-IoT Solutions will be offering Oden’s intelligent industrial automation platform and AI-powered, predictive data analytics to manufacturing industries in Mexico and Brazil. The partnership will expand into the rest of LATAM countries later this year.

Oden’s mission is to help manufacturers worldwide achieve predictive production and optimized plant performance, and we’re very excited to partner with I-IoT Solutions to deliver on this mission in Mexico and Brazil. The company founders have a proven track record of supplying data analytics solutions in Latin America and conveying the value and benefits of pioneering technologies to manufacturers.

We begin our expansion in Latin America by offering Oden’s products through the partnership with I-IoT Solutions to manufacturers in Mexico and Brazil. The Mexican manufacturing industry is closely tied to U.S. manufacturing, with most U.S. manufacturing companies operating plants in Mexico, while Brazil has the third most advanced industrial sector in the Americas.

The Oden intelligent industrial automation platform provides continuous visibility into factory operations and processes, with customers seeing improvements such as a 20 percent increase in monthly output. Its AI-powered, predictive data analytics help manufacturers achieve as much as a 50 percent decrease in total scrap, resulting in millions of dollars in savings and additional revenue each year.

Through the partnership with I-IoT Solution, we hope to transform manufacturing industries in Latin America by improving manufacturers’ processes and increasing efficiency across their operations.

Introducing Oden’s New Product Demo Summer Series

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Explore our newest features that take your plant’s performance optimization to the next level, plus everything you expect from a first-class data exploration tool. Learn how to improve processes and product quality by solving production problems immediately, or before they even occur.

In part 1 of our three-part demo series, our CEO Willem Sundblad and Customer Success Manager Mike Monroe dive into our first product feature release, Oden Discover.

Oden Discover is an interactive visualization of your factory’s performance. Discover quickly shows you production issues that you haven’t been able to identify yet with exactly where production is failing and what variables to adjust. Focus on the biggest causes of downtime and prioritizes improvements through the most actionable areas—lines, products, and shifts.

Now you can visualize production trends and identify constraints across your factory floor!

In part 2, Willem and our Customer Success Manager Phil Typaldos introduce Oden Now.  A new feature that offers live intelligence on your factory’s performance.

Oden Now enables you to gain in-depth visibility of your factory processes in real-time, with detailed performance metrics for each line. Set up customized Alerts to notify the team when your lines reach beyond acceptable limits, so you can optimize your production and increase product quality.

Register here for the Oden Now demo, August 7, 10am EST.