OEE benchmarks in manufacturing

The drive to measure OEE: benchmarks, business goals and more

Overall equipment effectiveness (OEE), a term coined by Seiichi Nakajima in the 1960s, has become the golden measure of measuring manufacturing productivity. But OEE is not only a way for manufacturers to judge their operational efficiency; it’s also a benchmark for where they stand in their industry.

Measuring OEE gives manufacturers improved visibility into individual asset health, helps drive down costs, and improves both asset and process effectiveness.

However, OEE is notoriously difficult to measure objectively, and percentages vary wildly. Process and discrete manufacturing benchmarks for OEE look nothing like each other.

We’ve compiled results from different surveys and reports to present benchmarks for OEE in manufacturing today: What’s driving OEE, where different industries stand, and what measures manufacturers are taking to improve operational efficiency and effectively in their plants.

Here are the top business goals driving adoption of OEE measurement:

Manufacturers are driven to greater OEE efforts thanks to the high demands on production.

Consumers worldwide are demanding more. To keep up, manufacturers need to increase yield and reduce downtime, while reducing or preventing rapid increases in cost.

What is OEE?

Measuring OEE is closely linked to improvements in performance, availability, and quality – because these are the elements by which it is measured.

OEE benchmarks: Where do industries stand today?

It’s risky to make generalizations about OEE, in part because every industry has completely different standards. Process and discrete manufacturing, for example, have drastically different production times and processes; a food and beverage manufacturer shouldn’t base OEE expectations on a benchmark made for an automotive parts manufacturer.

Another factor that complicates benchmarking OEE is that measuring OEE is not standard practice yet across all industries.

Compare these two industries, for example:

OEE: Expectations vs Reality

Another interesting point is the gap between a manufacturer’s target OEE, and their current OEE (as reported).

Take a look at the food manufacturing industry.

For these manufacturers, the average target OEE was 74%. The average current OEE – only 68%.

In order to understand these OEE benchmarks, it’s important to look at the 3 variables that affect OEE for manufacturers: Availability, performance, and quality.

Where are the losses coming from?

The Next Step

Measuring OEE is only the first step. Every business problem must be addressed individually.

So, if you want to improve overall productivity, start by targeting one or more of these big losses – for example, downtime:

Decreasing downtime is one way to increase OEE. But improving productivity and increasing OEE is only the first step in a journey to optimal manufacturing processes today.

To continually optimize results and reach the ideal 85%, be prepared to progress from monitoring and measuring OEE, to deploying predictive – and later, prescriptive – manufacturing solutions. 

Want to learn more about predictive maintenance? Access the Free Guide to Predictive Maintenance:

Why predictive maintenance is driving industry 4.0 - iot resource

Seebo pioneers process-based Industrial AI to Predict & Prevent Manufacturing Disruptions

New Industry 4.0 technology, specific to process manufacturing, increases accuracy and accessibility of predictive insights into downtime and quality issues.

TEL AVIV, November 15, 2018 - Seebo today announced the launch of its unique process-based artificial intelligence (AI)  technology. The new AI-based capabilities for production line data introduce unmatched accuracy and ease of use of the company’s predictive quality, predictive maintenance and production line intelligence solutions.

Process manufacturers today face rising demands on production capacity and continuous disruptions that affect uptime, quality, and throughput. Increasingly, they are turning to machine-generated data to investigate and solve their production line problems. But finding meaningful insights entails applying sophisticated machine learning technologies to a carefully engineered big data repository - a process beyond the technical and financial reach of most manufacturers.

Seebo’s new process-based AI addresses these issues by incorporating the production line process flows, together with OT and IT data, into machine learning algorithms and digital twin visualization for more accurate and accessible intelligence about line disruptions.

Production teams can then use Seebo’s predictive quality and maintenance solutions to mitigate future disruptions, without mastering data science.

“For the first time, AI-powered Industrial IoT is accessible to any process manufacturer with costly downtime and quality problems, “said Lior Akavia, Seebo CEO. “An effective Industry 4.0 solution must include a vast amount of contextual information - specific production process flows, batch recipes, and quality test results, to name a few. Translating that data into predictive insights that users can trust entails lengthy, expensive and high risk projects which businesses today can’t tolerate.

“With our new process-based  AI, Seebo solutions can now be implemented to deliver value in weeks, not months or years. It’s Industrial AI, democratized."

Manufacturers in the food & beverage, chemicals, energy, paper milling, and other process manufacturing verticals use Seebo’s process-based AI for production line intelligence: setting production KPIs, tracking them on a hierarchical digital twin of the production line, and navigating through the digital twin to determine root causes of issues.  

Seebo’s process-based AI automatically detects “golden batch” recipes and machine settings in the production line, and alerts on meaningful deviations from the golden batches. Correlated events and predictive insights provide supportive data into imminent disruptions. Production line teams - from process engineers to floor operators - can then implement automated recommendations that increase production uptime and prevent costly quality defects.

The company has clients already using the new capabilities, and expects to leverage its process-based AI to increase growth through its existing solutions.

About Seebo

Seebo utilizes an Industry 4.0 SaaS Platform to provide predictive quality, predictive maintenance and production line intelligence solutions for industrial manufacturers.

Customers infuse their production line processes and knowhow, together with data from OT and IT systems, into machine learning - without requiring the customer to master data science.  The result: Predictive Quality, Predictive Maintenance, and Condition Monitoring solutions with unmatched accuracy and simplicity.

The Seebo Industrial IoT Platform combines visual, code-free tools for Process and Data Modeling, Automated Root Cause Analysis, Predictive Analytics and Digital Twin Visualization. These tools enable the company to tailor solutions to clients’ specific needs, and to easily adapt the solutions post deployment.

Manufacturers across industries – including Grundfos, Stanley, Procter & Gamble, Ralph Lauren, and many more – use Seebo to increase overall equipment effectiveness (OEE), minimize maintenance costs, and continually improve quality.

Founded in 2012, the company has raised over $22M from top VC firms, and was named a Gartner Cool Vendor in the Internet of Things for 2017.


A digital twin use case for a packaging line

Digital Twin Use Cases for Industry 4.0

The Internet of Things (IoT), machine learning, and other forms of industrial AI are converging and being leveraged to effectively collect, analyze and take action upon production line data. Nowhere is this truer than in industrial digital twin use cases that are driving tangible ROI for the business.


Digital twin use cases and examples
A digital twin example in a food production line showing sensor readouts. The Seebo Platform.


Digital twin technology has actually been around for some time. NASA developed an early digital twin to simulate conditions on Apollo 13, and today utilizes a digital twin to monitor the entire Space Center.

While manufacturing can’t boast digital twin examples as dramatic as rescuing stranded astronauts, applying digital twin technology to both products and the manufacturing process itself is solving painful business problems and yielding quantifiable improvements for the industrial sector.


What is a Digital Twin?

By 2021, half of large industrial companies will use digital twins.

     - Gartner

A digital twin is a virtual representation of a physical system that simultaneously stands as an entity on its own. This digital copy is a ‘twin’ of information embedded in the physical system, as much as the physical parts.

With the advent of IoT, a digital twin can collect data continuously from sensors and mutually pass information with the physical counterpart throughout the system’s life-cycle.  Everything from manufacturing processes to sensor input, to external management software can be fed into, and organized inside, the digital twin.

Industrial Digital Twin Examples - Seebo
Digital twins aggregate data, making it easy to identify patterns in historical events, spot root causes, and optimize line processes.


This capacity for aggregating data from and around a physical product or system, as well as functioning as a digital stand-in, opens the way for a broad range of digital twin use cases.

Digital Twin Use Cases: Production Processes and Product Performance

Imagine the following example:

A chemical manufacturing plant produces half a billion dollars worth of finished goods a year. Each line contains highly specialized and expensive machines, along with carefully detailed ‘recipes’ of raw materials and machine settings used to produce the end product.

Now imagine a digital copy of the production lines in the factory, including sensor data collected from production line machines (typically stored in a data historian); ERP data of the raw materials, production orders, and recipes; and quality management system data, among others.

The manufacturer needs to continually optimize production yield by reducing unplanned machine downtime, reducing the amount of ‘scrap’ produced in each production run, and minimizing costly production quality faults.

Normally, process engineers collect and analyze mountains of data, slowly narrowing down which information can help them. Only after an exhaustive research would they - perhaps - start noticing correlations that can help them optimize this complex system of assets and instructions.

Now add the Digital Twin:

Effective digital twin software will ingest data from relevant IT and OT sources and display it on a virtual copy of the plant line. Process engineers, QA teams and others can understand the data in the context of the machines, the raw materials, and the entire production line environment.

If the digital twin system is coupled with root cause analysis tools, it will even point out which variables need urgent attention, speeding up root cause investigations and optimization processes.

Monitoring production line and individual machine health

Digital twins are first and foremost the EKG monitors of manufacturing - they visualize and track your production line’s pulse.

Digital twins are the EKG monitors of manufacturing - they visualize and track your production line’s pulse. Click To Tweet

Skilled operators can notice something wrong with a machine just by touching its surface or listening to its vibrations. The digital twin takes that intuition a step further by showing how the problem is reflected through data, helping investigation teams identify problems in assets more quickly and even mitigate the same problem in the future.

Related: Achieving manufacturing excellence with predictive maintenance

In addition, all of this insight is available remotely. Engineers can troubleshoot equipment remotely via the digital twin, reducing incident resolution times more quickly and accurately than through viewing the data on historians, MES or quality management software alone.

Why? Because of the importance of context.

Using digital twins to understand data in context

Production line data is stored in different systems: some data is saved in data historians, some in ERP, MES and quality systems; some is stored manually, and some is processed automatically.

When data isn’t aggregated and organized in a digestible way, it’s useless for reaching accurate and actionable insights.

Take the example of a production line which yields 10,000 bricks per batch, with 1.5% scrap at the end of the process. Over time, each batch produces more waste and smaller yields.

To identify the source of the issue, an engineer first needs to pinpoint the step of the process where they are losing throughput by monitoring machine downtime.

But reviewing downtime data from different sources is time-consuming and makes it difficult to spot correlating indicators. A digital twin gives a more integrated view of environmental factors, individual machines, and how they interact to affect the operational quality and asset performance.

That engineer, viewing downtime on a digital twin, can quickly and accurately pinpoint the root cause of the problem (e.g. low temperature in an oven) and the chain of events that lead to scrap.

Digital Twin Use Cases for OEMs: Control and Visibility

A digital twin of individual assets - for example, a packaging machine - drives business benefits that span the entire product life cycle:

  • Product Development - Engineers, designers, and developers create digital prototypes, then run simulations to test the product’s usability. OEMs can cut development and production costs by reducing bugs before the product hits the shelf.
  • Setup via digital twin: Products can be activated from remote service centers, reducing service costs for consumers.
  • Post-production troubleshooting  - When all data about a live product’s behavior is captured in a digital twin, it’s easy for engineers to troubleshoot reported errors in the product.

The Future of Digital Twins

Digital Twin use cases are as varied as manufacturers are innovative. As AI technologies advance, digital twins will be adapted to deliver even more exciting applications for industrial manufacturing.


Why IoT and Predictive Maintenance is a Game Changer for Manufacturers

Maintenance is a serious concern when developing and manufacturing a product - and for good reason.

For machine operators and factory managers, preventative maintenance and asset repairs consume unnecessary resources, eat deeply into operational costs, and present a serious impediment to efficient operations. A single hour of downtime alone can cost a large enterprise over $100,000 in lost productivity, and can be a hard hit to customer satisfaction.

In fact, a third of all maintenance activities are carried out too frequently - and, according to IBM, nearly half are ineffective.

Similarly, homeowners and consumer electronics users can find their purchase cost double or triple over the years by having to call in professionals to fix or replace faulty electronic products.

Manufacturers and asset managers are looking for a better approach to maintenance.  The answer lies in integrating the Internet of Things (IoT) with predictive analytics, to deliver a predictive maintenance solution.

Given this, it’s no surprise that the market for Internet of Things (IoT) predictive maintenance applications is growing rapidly, predicted by one report to hit $10.9B by 2022.

Read on to learn how IoT predictive maintenance replaces both calendar-based and reactive maintenance for better operational efficiency and more robust assets.

Why predictive maintenance is a game changer

IoT predictive maintenance replaces this type of preventative maintenance.
Reducing the need for manual inspections saves time, resources and money.

Imagine if you received an alert from a mobile app ahead of any fault occurring. Instead of having to guesstimate when the part will be obsolete based on past observation, or hope to catch it through condition monitoring, predictive analytics and sensor-triggered alerts tell you when to replace the part, reducing even planned downtime and keeping the product running for an optimum amount of time.

Predictive maintenance also eliminates repair costs, a large unknown for both manufacturer and end user. When an electronic component in a product fails, identifying the problem may take 5 minutes - or 5 hours. The same holds true for replacing broken or worn-down parts.

Major breakdowns are expensive, both because of this lost operating time as well as secondary financial losses - for example, if a commercial or home refrigerator breaks down, the loss of goods can run into the thousands. And the larger or more complex the machinery, the greater impact maintenance has on production and runtime costs. Even a small flaw in the system, if not caught early, can lead to unexpected and costly downtime.

With IoT-driven predictive analytics, you can accurately forecast when when assets will need an overhaul. You can even have alerts sent to a mobile app or web dashboard whenever a part needs to be replaced, ahead of the entire system failing.  This holds true whether the product is an expensive blender, a dialysis machine or a production line.

How it works

During the IoT design phase, manufacturers model their production processes and assets, to create an IoT Model - a blueprint of the connected system of data-collecting and transmitting sensors, applications, cloud, gateway and other system parts. They can configure a set of ‘rules’  that will identify maintenance issues and send alerts when machinery will need to be repaired or replaced.

Once the system is deployed, machine learning algorithms applied to the IoT system's data will analyze relevant historic event information and compare it to the IoT Model, the reference of ‘what should be’, in order to predict an event failure. A predictive analytics dashboard also summarizes the operational data, enabling the user to see how the system is operating at all times.

Every asset, or ‘thing’,  generates data and communicates its status back to a cloud or external system. This creates a closed loop of insights that run back into the manufacturing process.

These insights are the heart of predictive maintenance - and they do far more than reducing downtime.

Benefits of utilizing predictive maintenance from IoT

Analyzing asset and process  data not only minimizes downtime, but it can also impact your company’s top line:

  • Extend asset life - IoT predictive analytics enables manufacturers to perform root cause analysis and find the issues before they can spiral or prevent a machine or factory floor from operating.
  • Monetize Predictive Maintenance - When OEMs can prove they have increased uptime and lowered maintenance costs, they can deliver a measure of predictability to their customers that can increase purchase price and be leveraged as a strategic competitive edge. The opportunity to introduce digital services to customers based on data analytics can also generate a recurring revenue stream and breakthrough growth for the company.
Charge displaying the benefits of utilizing predictive maintenance from IoT
Respondents in a PWC survey expected to reduce operational costs by 3.6% annually through implementing Industry 4.0 initiatives. source : PWC
  • Reduce downtime and improve production yield - Reduce unplanned downtime by catching issues before they can make a whole system fail. And reducing planned manual inspections also boosts productivity and production yield.
  • Improve customer satisfaction - Automated alerts that remind customers when it’s time to replace parts and recommend maintenance services at specific times will both differentiate your product from others in market and keep customers happy.  
  • Reduce room for human error - Servicing mechanical equipment requires an in-depth understanding of its machinery, engineering and operations. Add to that an entire system, including connectivity to a cloud, apps, software and firmware, and you have a host of things that need to be maintained. Predictive maintenance identifies ‘fault lines’ in IoT systems before they become major issues - and reduces the chances for human error.

Predictive maintenance can never completely replace manual supervision, and there will always be a need for some level of human intervention. But the reduction of downtime, and the subsequent reduction in operating costs and increase in product robustness and customer satisfaction, are prime reasons why manufacturers from every industry sector are turning to IoT.

Get the complete free guide - Why predictive maintenance is driving industry 4.0

Why predictive maintenance is driving industry 4.0 - iot resource

Closing the loop: creating a cyclic innovation process for Industrial IoT

This is the second article in a two-part series on adapting the Stage-Gate product innovation methodology to Industry 4.0

Manufacturers today need agile, lean innovation methodologies to reach their business objectives. And while product innovation processes like Stage-Gate work for many industries, they require adaptation to stay relevant for developing connected systems.

As discussed in this earlier blog post, companies can mitigate risk and keep their teams agile by using digital simulation to create virtual prototypes of the connected product’s use cases.  

But adapting Stage-Gate to the needs of Industry 4.0 also means addressing the primary business objectives that lead companies to Industrial IoT - including reduced downtime and the opportunity to create new revenue streams by turning products into services.

Linear vs. Cyclic Stage-Gate

Here’s a model of the Stage-Gate innovation methodology in its common form:

Stage-Gate IoT innovation process

This recognizable process must be modified for Industrial IoT. Analyzing products post-launch (i.e., in production) is one of the most important steps in product innovation and should result in product insight that can be applied to the first stage of the next release of the product.

The problem: The traditional Stage-Gate methodology often relies on anecdotal customer feedback at best, and gut feel at worst - and that’s not enough.

In a cyclic IoT innovation process, behavior analytics supplied from the in-market product feed directly back to the beginning of product design. Product teams apply data from an IoT product - product performance, customer usage, and even issues in the connected system -  back to the IoT product blueprint or Model.

This direct feedback empowers teams to improve the product in subsequent releases - systematically improving product adoption and lowering product costs.

The StageGate IoT innovation process made cyclic

Take user experience, for example. If analytics from an IoT system show that certain features aren’t being utilized by customers, the product team can review the features on the model level and consider adapting or removing them entirely to save on costs.

In the same scenario, without objective data, product teams would have to interview customer services, talk with key customers, and spend hours figuring out why customers aren’t using the feature. Even then, their conclusions would be drawn from subjective research, which is significantly less reliable than behavior analytics.

Alternatively, think about parts failure. Imagine that a behavior or ‘instruction’ between two parts of the system isn’t being triggered properly - for example, an IoT system is built to display an alert on a web dashboard if a product part is about to break down, but the alert isn’t ever triggered.

If it’s mechanical failure, no problem - but if it’s a problem inherent in the system, that information needs to be applied back to the design process to improve the product. And only objective data will supply that information.

The success or failure of product innovation for Industrial IoT depends on a product or design team’s capacity to adapt the traditional stage-gate process.

The ability to apply data-driven behavior analytics to product design, thereby creating a cyclic loop innovation process, is what creates a truly lean, efficient new product development process for Industry 4.0 systems.

Making Stage-Gate work for Industrial IoT: overcoming the hurdles

This article is the first in a two-part series on optimizing the Stage-Gate Product Development Methodology for the Industrial Internet of Things.

Product development has never been leaner - and for good reason.

According to a report by Cisco, nearly two-thirds of IoT projects don’t make it past the Proof of Concept (PoC) stage. Companies are spending a large chunk of their resources on ideating, developing and launching new products which fail to succeed. And that’s a major drain on a company’s resources.

It’s no wonder that manufacturers are pushing to use the most tried-and-proven product development methodologies.

This year GE will spend $458 million on research into products that don't yet exist...the trick is to keep everybody focused on goals and deadlines.
Fortune Magazine

The Stage-Gate product development process is particularly popular, benefiting as it does both concept development and operations.

While this methodology has proven successful for new product development in many companies, it requires additional tools before it can be effective for the Industrial Internet of Things (IIoT).

Use case simulation for better gating

The Stage-Gate methodology and its structured system of targeted, measured progress, coupled with reviews, has enabled teams to reach long-term goals when taking a new product from concept to launch.

The Stage-Gate Product Development Process | Seebo IoT
Image: Seebo

Product teams perform tasks according to a structured plan, then analyze the results before presenting them for submission to a gate.

There, the stage process is rigorously reviewed - generally through powerpoint or documented specifications -  before passing on to the next stage.

Here’s the problem: The moment you expand the product specification to include an entire IoT system, specifications become increasingly incomprehensible for external teams, and the review process - torturous.

Conveying the use cases and their behaviors within a complex IoT system - including devices, numerous sensors, data connectivity, cloud services, multiple web or mobile applications, data analytics, security, etc. - results in a recipe that is difficult to follow, and which no one wants to try. The result is a delay in passing to another stage, and even costly errors later on in development.

To effectively describe and communicate complex systems, you need use case simulation.

Simulation of an IoT logistics system on the Seebo Platform.
An IoT logistics system simulated on the Seebo Platform, prior to prototyping.

A use case simulator is a digital prototype of the concept while still in design. 

First, you create a model of all the system components and visually describe the system behaviors and dependencies. A virtual simulator would then play back the possible scenarios in a visually clear and user-friendly way.

Think of it as a visual, department-agnostic specification that makes it easy for teams as different as R&D, Marketing and BI to understand exactly how the IoT system is designed, as well as its planned functionality.

By virtually prototyping the product design, you can expedite the gating process and build a stronger business case to present to stakeholders.

Even after a go/no-go gate, use case simulation continues to provide benefits further along the product development - especially for risk reduction.

Virtual prototyping during design -  reducing risks for complex system development

Industrial IoT products are complex systems, and adding complexity brings on new risks. With Industrial IoT, the cost of errors increases exponentially the later they are found:

Industrial IoT systems and the cost of errors in product development

The Stage-Gate process is supposed to reduce errors in product development and the ensuing costs involved in fixing them. For example, gates are intended to prevent teams from moving to development before establishing a firm business case and ensuring technical feasibility.

But the normal methodology leaves testing and validation until a physical prototype has been developed. As demonstrated in the chart above, catching mistakes in the IoT system logic only during development or ‘validation’, while still better than launching a faulty product, will cost a company an order of magnitude more than if they’d noticed mistakes during product definition and design.

Because of the inherent complexities and rising cost of error, Industrial IoT product development necessitates iterative agility in each stage.
This way, when the final gate is reached, there is a strong consensus that every possible risk or issue has been addressed.

A model-based IoT simulator enables agility in the innovation process; it complements the Stage-Gate methodology by testing and validating a connected system before hitting development, reducing costly errors in later stages.

Continue to part two of the series: Stage-Gate for Industry 4.0: creating a cyclic product development process for IoT.

M2M IoT differences in data applications | Seebo IoT

M2M vs IoT: The differences in data and business applications

Machine to Machine (M2M) communication has been around for decades. The industrial sector - including logistics, retail, utilities and manufacturing - have long depended on machines that communicate and share data with one another. But with the increasing discussion around the Internet of Things (IoT) and its benefits for product teams and users, people are beginning to conflate the two terms. This is particularly true of the Industrial IoT.

When most people think about either term, the first thing that comes to mind is remote accessibility: accessing data from a device, or controlling it from afar. While this has been a common feature of the machine-to-machine world for decades, remote device access is the beginning - and end - of the similarities between the two.

The "Why” - Data applications of M2M and IoT solutionsA robotic arm in a factor. M2M systems frequently have relatively simple applications, compared with IoT

One of the most important applications for both fields is improving business performance through data-driven insight. Data from connected devices, combined with analytical systems, provide feedback on product performance, product resilience, and user experience.

Data from machine to machine systems frequently have relatively simple applications. For example, a microcontroller in a machine receives data from sensors which tell it to trigger another action, or activate another sensor.

This type of data frequently serves a single purpose, often relating to maintenance - locating system errors, or reducing maintenance costs by eliminating the need for manual, preventative maintenance.

Though valuable, the functionality of data from these systems cannot compare with the business applications of data from the Internet of Things. The latter systems not only provide predictive maintenance, but can improve operations and even create new business models by providing product-driven data services to customers. 

How communications are relayed

Another difference between the two relates to how data is communicated between difference parts of the system. Machine-to-machine solutions usually depend on point-to-point communications via embedded hardware and wired or cellular networks. Internet of Things solutions, in comparison, rely on IP-based networks to send data from a machine, or device, to a cloud or middleware platform.

The acronyms themselves provide a clue to the different purposes of each. 'IoT' takes machine-to-machine communications and adds the Internet, i.e. web applications and cloud storage that make viewing and sharing data, and controlling devices, vastly more user-friendly.

With the Internet of Things, data and device management are not solely the realm of tech-proficient experts - it is M2M for the masses.

What machines connect to

Machine to machine communication usually refers to a device transferring data to and from a remote computer, or another device. The consumer and industrial Internet of Things take it a step further, by communicating from a machine or device to a thing, or person, or system. Common systems include ERP/CRM/PLM systems, analytics systems, data warehouses, and control systems.

It’s the difference between connecting isolated bodies of sensors, and islands of data, versus the ability to connect disparate systems and display them - and the data they convey - in a view that’s user-friendly and easy to manage.

Who benefits from the data

Both manufacturers and customers benefit from these two schools of solutions. However, Internet of Things solutions can cater to a wider variety of customers - due, it part, to its reliance on software rather than hardware.

The difference in benefits to manufacturers are even more marked. Traditional machine to machine solution benefits, such as data about how the machines are running, can save maintenance teams valuable time reduce the need for service management. IoT behavior analytics provide invaluable feedback to product and marketing teams, adding new layers of business applications to remote access and connected devices.

So how can we summarize the difference between the two terms? While they may externally ‘look’ the same - a collection of hardware, machines, devices, computer and other software -  the different methods of communication, and the benefits of the communicated data for multiple groups of people - from product and engineering teams, to marketing, to the end users - distinguishes the potential of the Internet of Things.

The Internet of Things presents inexhaustible opportunity for innovation, product performance, and new value that sets an asset apart.

Interested in learning more about IIoT? Watch the on-demand webinar, "How to prepare for Industry 4.0"

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Seebo 2017 Roundup: Investments, Awards, and Exciting News

For Seebo, 2017 saw everything from integration with the biggest names in IoT platforms, to massive new platform releases, to beautiful new offices, a Gartner Cool Vendor award, and dozens of high-profile users. Not to mention $8 million in new funding.

See what’s happened for Seebo in 2017. We can’t wait to share with you what’s around the corner for next year!

Seebo - A Year in the Life

December 13

Seebo Reels In $8 Million In Series A Funding With Channel Growth In Mind

Seebo Reels In $8 Million In Series A Funding With Channel Growth In Mind


Seebo, which was named a 2017 Cool Vendor in the Internet of Things by Gartner, currently works with a couple of boutique IOT service providers and system integrators that resell Seebo to their clients.

But the company wants to work toward generating at least 25 percent of new sales bookings through indirect channels –  representing triple-digit growth, said Akavia. Read More

December 13

IoT product development platform Seebo banks $16.5M Series A

IoT product development platform Seebo banks $16.5M Series A

Other IoT-platform companies such as Carriots (now acquired by Altair) and Prodea (now part of Arrayent) also provide IoT-enablement features for consumer products and enterprise companies. However, Seebo goes an extra mile by providing an ‘idea-to-market’ ecosystem for product/service developers. It provides integrated features to help generate BOM (bill of materials), estimates the cost of the whole solution…Read More

December 6

‘Stacy on IoT’ – Seebo wants to make product design easier

Internet of Things guru Stacey Higginbotham (Former senior editor at Fortune) sits down with Seebo CEO Lior Akavia to talk about IoT product design, integration, and the challenges of simulating hardware and software. Read more >>


November 30

Seebo Extends Series A Round by $8 Million to $16.5 Million

The investment, which brings the Company’s total investments to $22 million, will support Seebo’s industrial IoT (IIoT) platform, with business solutions addressing product resilience, produce efficiency, and data-driven product innovation. Additionally, the funds will be used to help extend the Company’s recently signed and soon-to-be announced strategic partnerships. Get the full story >>


October 4

Seebo makes CRN’s list of “IoT Startups You Need To Know”

A new breed of vendors are emerging in the networking and Internet of Things market, who are building their practices around keeping up with networking, analytics and security challenges and opportunities as more devices come online.


October 2

Seebo launches IoT Behavior Analytics

Seebo’s new IoT behavior analytics helps companies reduce risk, improve their offering, save on production costs, and create new value for both manufacturer and end user. Read More >>

September 5

Seebo integrates with Jira, hundreds of other apps

This year, Seebo made it even easier to support your team’s existing productivity platforms by integrating with Jira, Slack, Asana, and hundreds of other apps. Check out the dozens of collaboration tools that integrate with seebo for delivering your IoT system.

August 12

Seebo is ISO compliant for information security

Seebo is receiving the International Organization for Standardization Certification for Information Security (ISO 27001:2013).

July 18

Virtually Prototype with Seebo’s IoT Product Simulator

Seebo’s new product simulator allows product managers to verify their chosen smart behaviors, mitigating risks and future costs down the line. Keep reading to learn how these features will help you take your smart concept from modeling to launch better, faster and quicker. Read More >>

June 6

Gartner names Seebo a 2017 Cool Vendor in the Internet of Things

We’re excited to be one of only 5 startups to receive Gartner’s prestigious title of Cool Vendors in the Internet of Things, 2017. The report, published on May 5, 2017, recognizes vendors “that can help CIOs and their stakeholders build IoT solutions via building-block technologies in design, integration, IoT platforms and security.”


April 24

Seebo gets a new home!

We moved to new headquarters in sunny Tel Aviv! You can now find us in a 4-story building full of people having fun and building an amazing platform.

Check out our new home’s transformation from bare concrete walls to the coolest high-tech office in the Startup Nation. See the transformation >>

April 4

Easy IoT modeling: from smart products to IoT systems

Defining and designing IoT systems on Seebo's collaborative IoT Platform

Back in April, we launched an innovative modeling environment that lets users model an entire IoT system – including multiple physical products, cloud, gateway, and web and mobile apps. Read More >>

February 16

Seebo and IBM Collaborate to Simplify IoT Development for Product Companies

Seebo, the development platform for smart products, and IBM announced a collaboration to bring together the Seebo platform and IBM Watson Internet of Things (IoT). Connecting Seebo’s easy-to-use IoT development capabilities with IBM’s Watson IoT Platform and Bluemix empowers manufacturers to accelerate the delivery of smart products and gain insights from user data.   Read More >>

Seebo Extends Series A Round by $8 Million to $16.5 Million

The article below was originally published on PR Newswire on November 29, 2017.

Seebo Extends Series A Round by $8 Million - to a Total of $16.5M - to Help Manufacturers Develop, Deploy and Analyze Connected Products

Funding, including new investors Pritzker Group Venture Capital and Global IoT Technology Ventures, will support company’s rapid growth in the Internet of Things and fuel new strategic partnerships with leading CAD and IoT cloud providers



Tel Aviv, Israel, November 29, 2017 / PRNewswire / -- Seebo, the leading platform for Internet of Things (IoT) planning and delivery, announced today an $8 million extension of its Series A funding, for a total of $16.5 million.

The investment, which brings the Company’s total investments to $22 million, will support Seebo’s industrial IoT (IIoT) platform, with business solutions addressing product resilience, produce efficiency, and data-driven product innovation. Additionally, the funds will be used to help extend the Company’s recently signed and soon-to-be announced strategic partnerships.

“Since its inception, Seebo has been focused on building the leading platform for IoT planning and delivery,” said Seebo CEO and co-founder Lior Akavia. “We’ve succeeded in driving customer adoption and outstanding product value. Today we see a surge in market demand from industrial manufacturers in dozens of verticals - such as mining, packaging machinery, water filtration equipment and industrial pumps - in addition to top-notch B2C brand manufacturers that we continue to serve.”

Lior and Liran Akavia, Seebo Co-Founders, in Seebo's Tel Aviv headquarters.

The emergence of big data analytics, new integration standards, and the coming-of-age of cloud technologies are leading industrial companies to develop and deploy connected products. Developing connected products, however, is a complex and lengthy process which requires new skills and is fraught with risk. Additionally, once the products are in market, product teams must sort through reams of data in order to improve product resilience, efficiency, and safety.

Seebo’s software uniquely combines IoT modeling, simulation and execution tools to quickly and cost-effectively turn existing machinery into smart, connected systems that report their health and usage data. Behavioral analytics are automatically layered on top of this big data to provide insights that drive ongoing product improvement and new business value to customers.

Over the last 6 months, Seebo has significantly enhanced its offering, including IoT Simulation and Behavior Analytics, closing the IoT development loop, and powering data-driven services, user insight, and product resilience.

The new investments, which bring Seebo’s total funding to $22 million, come from existing investors TPY Capital and Viola Ventures, as well as Pritzker Group Venture Capital and Japan’s Global IoT Technology Ventures.

“Seebo’s recently-struck partnerships with giants in the world of CAD and IoT Cloud providers is a tremendous validation of the unique value proposition and complementary toolset that Seebo’s platform brings to enterprise manufacturers,” said Guy Yamen, Managing Partner of TPY Capital and Seebo’s Chairman of the Board. “We look forward to helping the company accelerate its reach with global enterprises.”

“We’re excited to see the impressive strides that Seebo has made,” said Zvika Orron, Partner of Viola Ventures.

“With Seebo’s strong vision and technological innovation, we believe it could transform the way enterprises and startups ideate, design and build IoT solutions.”

About Seebo

Seebo is the leading platform for manufacturers to plan and deliver superior IoT systems that drive outstanding customer value. Seebo’s cloud-based software uniquely combines tools for IoT Modeling, Simulation, Execution, and Behavior Analytics into pre-packaged business solutions that address new product innovation, product resilience, venue operational excellence, and data-driven services.

The company serves manufacturers from dozens of industries, including Multotec, Oseco, Durabrite, and Stanley Tools. Established in 2012, Seebo has offices in San Francisco, Tel Aviv, and Shenzen. Seebo was named a 2017 Gartner Cool Vendor in the Internet of Things.

Machine Learning Cornerstone

5 ways the Internet of Things (IoT) is changing product management

Product management is all about moving with technology. Agile development processes such as SCRUM dramatically changed the product manager’s processes - and in some case the product manager’s role - in the product lifecycle. With the advent of the Internet of Things, the focus for product managers is shifting from product and technology to client-centricity and business growth.

Why is this change occurring? The combination of connected products and data analytics, is giving PMs more information about the product and user experience. This insight enables everything from new revenue streams, to improved product reliability, enhanced safety, improved user adoption and other strategic business benefits.

Now, product managers are tasked with all these business goals. The product manager simultaneously has a much greater impact to the business, and a much greater responsibility to ensure these business goals are attained.  

To keep up with the emerging digital technologies and drive new business growth, effective product managers must adapt to the technologies and processes implemented in their company.

Dominate the data

Companies today rely on data to make important product and business decisions, and nothing promises more data than smart systems. But there’s a catch: product managers can be swamped with ‘data overload’ from the sheer quantity of information coming through the products. PMs must decide early into product planning precisely which data is the most valuable, and focus on gathering that targeted data.

Collecting data is just the first step - you’ll need to document the data in a way that can be easily tracked, in order to translate it into insights that can increase sales or improve the product in later releases. And traditional spreadsheets fail spectacularly at capturing the complexity of data coming from smart devices.

Fortunately, there are software systems which track analytics for data management and insight. However, because they choose which data matters most, the data must refer back to the original IoT Model - the blueprint of the smart system - to ensure that the data coming through, i.e. with the data about behavior between machines or devices in the smart system.

Take a stronger role in product design

To ensure control over the direction of captured data, and the quantity of it, PMs must be involved early and frequently in the product design process. By active involvement in the design of ‘rulings’, i.e. the rules that govern the collection of data and ‘if/then’ of each smart machine or device, PMs develop a model which can be used as a point of reference once a product is in market and transmitting data back to an analytics platform.

Innovation is King

To stay ahead, product managers need to consistently improve their offering with an eye on technology. Assessing products qualitatively via client surveys and interviews takes up valuable time - and technology, like time, waits for no one. Much like blood tests provide more objective data than a verbal patient interview, analytics platform efficiently provide PMs with real, data-driven insight.

Instead of devoting time to gathering data, product management can focus more on making informed decisions about product development, as well as how to further improve and innovate.

Build products more efficiently

Embarking on an IoT project involves multiple layers within the product:

  1. Hardware
  2. Embedded software
  3. Cloud platform
  4. Applications
  5. Communication

You’ll create specifications for all of these - in an app, for example, you’ll specify everything from what appears on the screen, to how commands are relayed from the apps to the smart product, and the reverse.

Find the tools and hacks to clearly organize multiple specifications for different parts of an iot system and iterate quickly and often.

IoT Communications - PM as translator

Aproduct manager in the digital era is a translator - coordinating crucial communications, including product requirements, between teams that speak different technical languages.

Managing feedback from multiple departments and teams through product design and development iterations is one of the great challenges of IoT product management, and massively influences time to market and a product team’s agility. The key is finding a way to collaborate between departments so that  the product is consistent and everyone is aligned with what the product will be.

The Bottom line

To succeed in the Internet of Things, product managers must understand each layer of the smart system - from business planning to in-market analytics feedback. PMs will need to get hands-on at every stage of the process. The good news is, these required transformations will pave the way for more innovation and a long-term, goal-oriented approach within IoT product management.

Discover the IoT development platform for product managers.