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The Dynamics of ODMs and OEMs

I've seen a lot of different thoughts about "original equipment manufacturers" and "original design manufacturers" recently, so I figured I'd offer my observations from my time working in Shenzhen for my IoT company.

Backstory: we’re partnered with Qualcomm to cloud enable bluetooth mesh technology across myriad US, Asian, and European based companies, primarily for lighting and smart home products in consumer/commercial markets. I spent about 6 months in Shenzhen and Hong Kong during 2017 putting together the supply chain partnerships.

From what I’ve experienced, “brand,” i.e. the companies we’re familiar with as consumers, and Original Equipment Manufacturer “OEM” are used interchangeably, while Original Design Manufacturer “ODM” refers to the “factory.”

In most of my interactions, there is a tight albeit painful relationship between the OEM and ODM in consumer electronics because cooperation between multiple vendors is often required to get a product to market, especially in IoT. Typically, the most differentiated intellectual property (IP) is in the hands of the OEM (brand)— industrial design, software, firmware, and it’s in their best interests to obfuscate as much as possible throughout the supply chain to make it harder to replicate the technology, which everyone assumes will happen. And it does. This is especially true during the rise of the IoT, where connectivity challenges plague both sides of the pond, and clever solutions are the 11th hour superpower everyone is fighting to find first to use as leverage in the supply chain. 

There is another class of manufacturers— not sure the technical name, but we call them “module makers” — companies that specialize in the design and production of drop-in PCB modules for various connectivity chipsets to make them easier to productize. An example would be ITON, who provides chips for several of GE’s products to the prime ODM (such as Leedarson or Eastfield) who is responsible for final assembly (note: many ODMs are also module makers— they keep chips in house to maximize control and profits).

Both ODMs and module makers participate in a process of product innovation that presupposes the market. Chipmakers (and other tech vendors) like Qualcomm send their reps out to the factories to demo new silicon technology in the form of a “reference design” in a bid to get the ODM to create a module or product based on that chipset that answers to a trend they’ve noticed from their OEM/brand customers. In this way, the ODM bears the R&D cost as a bet for business, but doing so gives them a chance to retain the right to get a royalty on every module sold. Ask an ODM to hand over any firmware they've made and they’ll tell you with their sweet puppy dog eyes “eat my shorts” because it’s how they keep you from just taking everything to another vendor.

For brands like Home Depot (or more generally companies less interested in designing hardware) these ODMs are essential because they are flexible enough to develop a catalog of partially developed products on speculation— whatever successfully sells up the food chain at Home Depot, they make real (note: the “make real” part is where a lot hits the fan because this stuff is hard to scale).

The OEM-ODM-module maker ecosystem creates a sort of “it takes a village to make a product” atmosphere, but with grumpy uncles, annoying neighbors, and meddling kids abounding. There's a constant sense of quiet espionage on both sides, although that tends to get better if you develop a direct relationship with your mfg partners. Western business has evolved to sustain trust with purely transactional relationships-- this is way less true in places like China. Go to lunch with them and take them to dinner a few times, invite them to Macau, get them drunk and having fun with you. These relationships are insurance policies on getting screwed. Further, having boots on the ground near your manufacturing is practically a requirement nowadays if you want to have any hope of your supply chain operating smoothly. 

In the case of a brand like Apple, who meticulously defines and controls every little detail of their product and supply chain works with an Electronic Manufacturing Services company “EMS” like Foxconn who primarily invest only in building other designs precisely to specification.

So OEM v. EMS: OEM: “build this for me, exactly like this, and don’t ask too many questions, or I’ll eat your children.” 

EMS: ;)

The ODM/OEM relationship is a bit shakier: 

OEM: “build this for me, and pretty please do your best not to use lead paint or explode my users.” 

ODM: ¯\_(ツ)_/¯

All that said, many companies I’ve encountered are chimeric— companies that usually do business as an EMS could also be caught as an ODM if the opportunity is right. I’ve wracked my brain over how to approach meetings with ODMs that also have an OEM/brand side to the company. The ODM side is a potential partner while the OEM side is a potential customer— in the already confusing world of IoT this can be quite the rollercoaster.

I could be off, but the cash value of the above has navigated me through hella lots of conversations from ivory tower to where the dog food gets made. It is a truly global and complex web of associations, across cultural, language, political, and social boundaries. Read “Poorly Made in China” and “Barbarians at the Gate” to see the differences in East vs. West strategies for business success, which I see as orthogonal values of Replication and Dominance.

If you’re interested, here’s a great article by a Shenzhen based supply chain expert: https://www.linkedin.com/pulse/3-types-partners-product-managers-can-use-development-changtsong-lin/

 

Thanks for reading! Our company is expert at IoT integrations, and we thrive on building ecosystems of partners with positive feedback loops on new services and revenue streams. Kindred spririts, please reach out to me at [email protected] 

 

Best, 

 Preston

COO @ Droplit

https://droplit.io

[email protected]

 

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Cloud computing allows companies to store and manage data over cloud platforms, providing scalability in the delivery of applications and software as a service. Cloud computing also allows data transfer and storage through the internet or with a direct link that enables uninterrupted data transfer between devices, applications, and cloud.

Role of Cloud Computing in IoT:

We know that the Internet of Things (sensors, machines, and devices) generate a huge amount of data per second. Cloud computing helps in the storage and analysis of this data so that enterprise can get the maximum benefit of an IoT infrastructure. IoT solution should connect and allow communication between things, people, and process, and cloud computing plays a very important role in this collaboration to create a high visibility. 

IoT is just not restricted to functions of systems connectivity, data gathering, storage, and analytics alone. It helps in modernizing the operations by connecting the legacy and smart devices, machines to the internet, and reducing the barriers between IT and OT teams with a unified view of the systems and data. With cloud computing, organizations do not have to deploy extensive hardware, configure and manage networks & infrastructure in IoT deployments. Cloud computing also enables enterprises to scale up the infrastructure, depending on their needs, without setting up an additional hardware and infrastructure. This not only helps speed up the development process, but can also cut down on development costs. Enterprises won’t have to spend money to purchase and provision servers and other infrastructure since they only pay for the consumed resources. 

(Case Study: DevOps for AWS, Continuous Testing and Monitoring for an IoT Smart City Solution)

How Cloud Services Benefit an IoT Ecosystem:

There are several cloud services and platforms that play different roles in the IoT ecosystem. Some of the platforms also come with inbuilt capabilities like machine learning, business intelligence tools, and SQL query engines to perform complex analytics. Let us understand how these cloud services and platforms benefit an IoT ecosystem.

Cloud Platform for Device Lifecycle Management:

Enterprises create applications and software through cloud services (SaaS), which can connect devices and enable device registration, on-boarding, remote device updates, and remote device diagnosis in minimal time with a reduction in the operational and support costs. Cloud introduces DevOps within the IoT ecosystem, which helps organizations automate many processes remotely. As more and more devices get connected, the challenges with data security, control, and management become critical. Cloud services enable IoT remote device lifecycle management that plays a key role in enabling a 360-degree data view of the device infrastructure. Certain cloud providers offer multiple IoT device lifecycle tools that can ease the update and setup of firmware and software over the air (FOTA).

Application Enablement Cloud Platform:

Cloud enables application development with portability and interoperability, across the network of different cloud setups. In other words, these are the intercloud benefits that businesses can take advantage of. Intercloud solutions possess SDKs (Software development Kits) on which enterprises can create their application and software without worrying about the backend processes.

Enterprises can run and update applications remotely, for example, Cisco is providing the application enablement platform for application hosting, update, and deployment through the cloud. Enterprises can move their applications between cloud and fog nodes to host the applications and analyze & monitor the data near the critical systems.

Many cloud service providers are focusing on building the cloud environment on the basis of OCF standards so that it can interoperate smoothly with the majority of applications, appliances, and platforms, that will allow D-to-D (device-to-device) M-to-M (machine-to-machine) communicationOpen Connectivity Foundation (OCF) standardization makes sure that the devices can securely connect and communicate in any cloud environment, which brings in the interoperability to the connected world.

Digital Twins:

Device shadowing or digital twins is another benefit that an enterprise can avail through cloud services. Developers can create a backup of the running applications and devices in the cloud to make the whole IoT system highly available for faults and failure events. Moreover, they can access these applications and device statistics when the system is offline. Organizations can also easily set up the virtual servers, launch a database, and create applications and software to help run their IoT solution.

Types of Cloud Computing Models for IoT Solutions

There are three types of cloud computing models for different types of connected environment that are being commonly offered by cloud service providers. Let’s have a look:

Cloud Computing Models

 

Infrastructure as a Service
  • It offers virtual servers and storage to the enterprises. Basically, it enables the access to the networking components like computers, data storage, network connections, load balancers, and bandwidth.
  • Increasing critical data within the organization lead to the security vulnerabilities and IaaS can help in distributing the critical data at different locations virtually (or can be physical) for improving the security.
Platform as a Service
  • It allows companies to create software and applications from the tools and libraries provided by the cloud service providers.
  • It removes the basic needs of managing hardware and operating systems and allows enterprises to focus more on the deployment and management of the software or applications.
  • It reduces the worry of maintaining the operating system, capacity planning, and any other heavy loads required for running an application.
Software as a Service
  • It provides a complete software or application that is run and maintained only by the cloud service provider.
  • Users just have to worry about the use of the product, they don’t have to bother about the underlying process of development and maintenance. Best examples of SaaS applications are social media platforms and email services.

 

Apart from these, cloud service providers are now offering IoT as a Service (IoTaaS) that has been reducing the hardware and software development efforts in IoT deployment.

Example of implementing cloud computing set-up in a connected-factory:

There are different sensors installed at various locations of an industrial plant, which are continuously gathering the data from machines and devices. This data is important to be analyzed in real time with proper analytics tools so that the faults and failures can be resolved in minimal time, which is the core purpose of an industrial IoT ecosystem. Cloud computing helps by storing all the data from thousands of sensors (IoT) and applying the needed rule engines and analytics algorithms to provide the expected outcomes of those data points.

Now, the query is which cloud computing model is good for industrial plants? The answer cannot be specific, as every cloud computing model has its own applications according to the computing requirement.

Leading Cloud Services for IoT Deployments

Many enterprises prefer to have their own cloud platform, within the premises, for security and faster data access, but this might not be a cost-effective way as there are many cloud service providers who are providing the cloud services on demands, and enterprises just have to pay for the services which they use.

At present, Amazon Web Services (AWS) and Microsoft Azure are the leading cloud service providers. Let’s see the type of cloud platforms and services AWS and Microsoft Azure provide for IoT implementations

AWS IoT Services

AWS has come up with specific IoT services such as AWS Greengrass, AWS lambda, AWS Kinesis, AWS IoT Core, and a few other cloud computing services, which can help in IoT developments.

AWS IoT Core is a managed cloud platform that allows devices to connect easily and securely with cloud and other devices. It can connect to billions of devices, store their data, and transmit messages to edge devices, securely.

AWS Greengrass is the best example of an edge analytics setup. It enables local compute, messaging, data caching, sync, and ML inference capabilities for connected devices in a secure way. Greengrass ensures quick response of IoT devices during local events, which reduces the cost of transmitting IoT data to the cloud.

AWS Kinesis enables data streaming that can continuously capture the data in terabytes per hour.

AWS Lambda is a compute service that lets you run code without provisioning or managing servers. It executes code only when required and scales automatically from a few requests per day to thousands per second.

AWS DynamoDB is a fast, reliable, and flexible NoSQL database service that allows enterprises to have millisecond latency in data processing, enabling quick response from applications. It can scale up automatically due to its throughput capacity, which makes it perfect for gaming, mobile, ad tech, IoT, and many other applications.

AWS Shield is a managed Distributed Denial of Service (DDoS) protection service that safeguards applications running on AWS. It provides automatic inline mitigation and always-on detection that minimize the application downtime and latency. This is why there is no need to engage AWS Support to benefit from DDoS protection. There are two tiers of AWS Shield — Standard and Advanced.

Microsoft Azure IoT Services:

Microsoft has come up with many initiatives in the field of IoT, providing industrial automation solutions, predictive maintenance, and remote device monitoring, etc. It is also providing services like Azure service bus, IoT hub, blob storage, stream analytics, and many more.

Azure Stream Analytics provides real-time analytics on the data generated from the IoT devices with the help of the Azure IoT Hub and Azure IoT Suite. Azure stream analytics is a part of the Azure IoT Edge that allows developers to analyze the data in real-time and closer to devices, to unleash the full value of the device generated data.

Azure IoT Hub establishes bidirectional communication between billions of IoT devices and cloud. It analyzes the device-to-cloud data to understand the state of the device and takes actions accordingly. In cloud-to-device messages, it reliably sends commands and notifications to connected devices and tracks message delivery with acknowledgment receipts. It authenticates devices with individual identities and credentials that help in maintaining the integrity of the system.

Azure Service Bus is a great example of cloud messaging as a service (MaaS). It enables on-premises communication between devices and cloud in the offline conditions also. It establishes a reliable and secure connection to the cloud, and ability to see and monitor activities. Apart from this, it protects applications from temporary spikes of traffic and distributes messages to multiple independent back-end-systems.

Azure Security Centre is a unified security management and threat protection service. It monitors security across on-premises and cloud workload, blocks malicious activities, advanced analytics system to detect threats and attacks, and also can fix vulnerabilities before any damages.

AWS and Microsoft Azure are providing a robust IoT solution to enterprises. An IoT Gateway can collaborate with multiple cloud service providers to maximize the advantages of the cloud solutions for IoT systems.

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Guest post by Toby McClean

In 2016 Microsoft, IBM, and AWS each made concerted efforts to extend their IoT platforms to the edge. The main reasons for this are economics, physics and legal. Using the terminology defined in this paper; the edge is the hubs and devices in the system. In this article, we focus on the analytics capabilities that extend to the edge.

Descriptive Analytics

A descriptive analytics capability will identify what is happening. Descriptive analytics can be as simple as providing an alert if a value exceeds a certain value.

The IBM Watson IoT Platform provides an environment for defining rules that run in the cloud or on a hub at the edge. IBM announced the capability as part of the Cisco partnership and recently made it generally available.

The recent AWS Greengrass announcement allows for AWS Lambda behavior to run on a hub. A descriptive analytic is written in one of the languages supported by AWS Lambda.

The Azure platform mentions edge analytics here, but it does not provide any specific tools or extensions to existing analytics capabilities to run edge analytics.

Diagnostic Analytics

Why is it happening? Diagnostic analytics can help to determine why an alert is triggered and whether it is relevant or not. Often organizations use diagnostic analytics they develop the models for predictive analytics.

None of the three platforms offers the ability to run diagnostic analytics models at the edge. With AWS Greengrass, in theory, a diagnostic model could be developed as a Lambda and run at the edge.

Predictive Analytics

What will happen? The most common use case of predictive analytics is predictive maintenance. More and more use cases are attempting to predict positive outcomes. For example, analyzing parts that come off a production line to predict those parts that do not need further testing.

The three platforms provide cloud-based services to build and execute predictive models. However, none of them provides the ability to provision and run the predictive model at the edge.

ADLINK, IBM, and Intel collaborated on enabling predictive maintenance and quality models to run on a hub at the edge. For more information see,

Analytics provisioning, configuration, and management

Being able to build analytics models is fine. But, there is a need to be able to push those models to the parts of the system where it makes the most sense to execute them. For this article, we are concerned with the ability to provision the gateways or things in the system.

Provisioning of descriptive analytics to the edge can be configured and managed from the Watson IoT Platform. AWS IoT is fully capable of provisioning of Lambdas from the AWS IoT cloud to hubs or things running AWS Greengrass. For Microsoft Azure IoT, the public documentation does not reveal anything on this aspect.

Conclusion

The article has made no attempt to make any specific recommendations about which platform is better. Its goal is to provide the reader with information in order to help them make an informed decision for their specific use case.

Hopefully, you find it useful and please leave comments and suggestions.

This article originally appeared here. Cover Photo: Tomas Havel

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