Scaling Industrial IoT in Manufacturing: Challenges and Guidelines

November 30, 2022

Explore IIoT’s essentials and use cases, along with the best practices to address the most common adoption and scaling challenges.

The Industrial IoT landscape

Over the last century, the factory archetype portrayed by Charlie Chaplin’s “Modern Times” as an unsafe, dehumanizing workplace is turning into a smart, automated, and interconnected ecosystem.

Among the drivers of this shift, especially in the past 20 years, we may mention the growing reliance on the Industrial Internet of Things (IIoT) for data-driven decision-making, real-time process supervision, and workflow optimization. A 2021 study by IBM mentioned IIoT as one of the four key technologies to help manufacturers in their digital transformation journey.

The most important technology initiatives for manufacturers (source: IBM)

According to McKinsey, the global IIoT market will grow from $290 billion in 2020 to $500 billion in 2025. On the other hand, another report by McKinsey highlighted that 70% of manufacturers surveyed could not scale IoT beyond pilots. Furthermore, a 2022 study by Bain & Company found that four-fifths of companies are scaling fewer than 60% of IIoT proofs of concept.

What makes the Industrial Internet of Things in manufacturing so beneficial, but also complex to implement and scale? This article examines IoT’s major scenarios, pros, and adoption barriers in manufacturing, along with some best practices and guidelines to follow.

How does IIoT help in manufacturing?

The Industrial Internet of Things involves the use of smart sensors and devices to collect operational data from machinery, power systems, or other assets. This data can then be turned into actionable insights via data analytics software. For example, manufacturers can rely on this information to monitor industrial equipment performance, identify process bottlenecks, and predict machinery failures. As a result, this helps to define suitable strategies and initiatives to improve safety, productivity, and efficiency.

As pointed out by Microsoft, Industrial IoT systems are often reliant on cloud technologies and built on a multilayered architecture including:

  • A network of IoT sensors gathering data from machines and routing it to a cloud through a gateway device
  • A data analytics system, which may be powered by a machine learning (ML) engine to provide insights or trigger certain actions
  • Visualization modules on tablets, screens, smart glasses, or mobile devices—to present insights in an intuitive format (dashboards, schemes, views, etc.)

An example of a cloud-based IIoT architecture (source: Microsoft)

Top 5 IIoT use cases in manufacturing

At the same time, McKinsey names use case identification as one of the main challenges in implementing and scaling Industrial IoT (February 2021). Therefore, it’s worth looking into some of the best options to take advantage of this technology.

1. Operations optimization

According to Microsoft’s report (August 2022), the primary goals for deploying Industrial IoT are associated with operational improvements. In this regard, 86% of manufacturers consider equipment effectiveness the most important criterion—along with optimization of production output, quality, or timely delivery.

The goals of manufacturers implementing IIoT systems (source: Microsoft)

McKinsey, too, describes the optimization of industrial operations as the use case with the highest potential economic value. To achieve that, companies adopt Industrial IoT to monitor machinery, analyzing KPIs in real time and fine-tuning equipment utilization.

2. Condition-based and predictive maintenance

Industrial IoT sensors enable real-time monitoring of equipment conditions, such as temperature or vibrations. During this process, machine learning can be utilized to detect anomalies and take actions based on the telemetry data.

By utilizing event-driven architectures, the system can trigger an alert, such as recommending maintenance operations, before a failure actually occurs. As a result, this can ensure a safer working environment and lower repair costs. In this regard, Microsoft estimates a 28% increase in condition-based maintenance investments by manufacturers over the next three years. A similar figure (26%) is expected for predictive maintenance.

To facilitate the adoption of IIoT in this specific business function, several service providers offer comprehensive cloud-based solutions for predictive maintenance, including Amazon. (This and this reference cards from Amazon may be helpful.)

An example of an IoT architecture for predictive maintenance (source: Amazon)

3. Energy management

A manufacturer can also monitor the energy consumption of industrial equipment through a wide network of smart meters deployed across the plant. This allows to improve transformers’ efficiency, minimize load losses, or even predict consumption patterns and peak loads—optimizing energy procurement and distribution. McKinsey found (2021) that IoT implementation can help organizations to enhance energy efficiency by up to 50%.

4. Asset tracking

In manufacturing, the Industrial Internet of Things can also be used to monitor the current location of numerous assets distributed across warehouses. To implement this goal, IIoT can engage radio-frequency identification (RFID) tools.

This technology enables manufacturers to label their inventory items with a tag containing essential data. Then, it is possible to track the location or flow of assets via multiple RFID readers connected to an IoT system (like GE did here). According to Microsoft, inventory management and location tracking are among top 3 scenarios for IIoT products—along with monitoring dashboards.

5. Digital twin

As the name suggests, a digital twin acts as a virtual copy of a factory or specific equipment. It is a representation of the key assets and processes used by manufacturers for simulation, supervision, and testing of industrial objects. The role of IIoT here is to connect the plant (or equipment) to its digital counterpart, collecting data on-site and translating it into virtual representations.

This enables manufacturers to remotely monitor machinery condition and workflows in real time. The technology can also be used to share information such as inventory levels among procurement partners and distributors, optimizing the supply chain.

Despite the benefits, Microsoft reports that digital twins are still not so widespread—due to a combination of technical complexities, integration challenges, and skill gaps. However, the study suggests that manufacturers are expected to increase investments in this regard by 26% over the next three years.

The Industrial Internet of Things examples in real life

The successful applications of the Industrial Internet of Things can help to better understand the impact of this technology on manufacturing.


This global provider of consumer goods implemented an IoT platform to create a digital twin of the company’s facilities and improve control over operations. After launching a pilot project at its factory in Valinhos (Brazil), Unilever was able to optimize soap and ice cream production, as well as reduce energy costs by $2.8 million.


Microsoft deployed an Industrial IoT platform for its own plant in Suzhou (China). The system is powered by machine learning, gathering and analyzing inventory data. As a result, the platform facilitated the identification of stocks on the verge of obsolescence and helped to cut inventory costs by $200 million.

A sample IIoT architecture for monitoring equipment condition (source: Microsoft)


This US-based petrochemical company implemented an IIoT system introducing sensors and data analytics to monitor its plant and minimize the risk of human error. After adoption, planned maintenance costs were reduced by 50%.


Tenaris, a global manufacturer of steel pipes, partnered with ABB, a major electrical equipment producer, to build an IoT-based predictive maintenance solution. Deployed at Tenaris’s plant in Italy, the system relies on a network of IoT sensors to monitor 460 electric motors. The goal is to detect vibrations or power anomalies that may be signs of impending failures—therefore, reducing the frequency of incidents.

5 major IIoT adoption challenges and guidelines

Implementing and scaling IIoT in manufacturing is a rewarding, but a challenging journey, encompassing both technology and organizational aspects. Here are some guidelines to avoid the common missteps.

1. Legacy systems and integration

The report by Bain & Company names the complexity of integration the main barrier to scaling IIoT proofs of concept (September 2022). The layered architecture of an IIoT environment inevitably includes different types of software and hardware—hundreds or even thousands of apps and devices. One of the problems here is that each component of an IIoT system may rely on different network protocols and handle specific data formats.

In particular, HiveMQ reports that HTTP (55%), MQTT (48%), Modbus (41%), and OPC Unified Architecture (33%) represent the most common protocols for connecting equipment (October 2022).

The variety of communication protocols in IIoT today (source: HiveMQ)

This issue is further amplified by the presence of legacy systems utilizing outdated technologies. The combination of the factors above prevent the components of an IIoT platform from seamlessly operating as a whole. Consequently, organizations risk creating data silos or facing data consistency issues.

In an aim to bridge the systems, manufacturers try to integrate myriads of software and devices via application programming interfaces (APIs), if the latter are delivered out-of-the-box. If not, companies spend months developing APIs and brokers from scratch or finding other ways to transfer signals and information between equipment, production line, BI/ERP modules, etc.

(For instance, a roof truss manufacturer we worked with needed to integrate its existing CAD system with the robotics management software to identify defective elements. To take them off the production line as early as possible, the company transferred the information about the design of the products to the assembly line in real time via APIs. Once a defective item was detected, the platform sent signals to the robotics software, introducing changes to the assembly process on the go. The company spent several months transforming a rough POC into a production-ready management system.)

To facilitate integration among such components, McKinsey recommends taking into account the following initiatives.

  • Protocol standardization: On the one hand, early adoption of IIoT devices sharing the same communication protocols can simplify integration. On the other hand, in reality, we will deal with legacy IoT devices sooner or later, making it necessary to create an Enterprise Service Bus (ESB) or its analogue at some point. This architecture represents a centralized middleware to convert different communication protocols, connecting legacy devices via customized adapters. McKinsey also highlights that a focus should be “placed on having correctly labeled data (especially time stamps) to make use of data.
  • API development: McKinsey emphasizes microservices (below) and APIs as “the key to developing a technical platform capable of supporting the level of flexibility and agility needed.” APIs can be created from scratch and may involve the adoption of tools that automate the development. Turning to a trusted partner may also help. The main types of APIs are RPC, SOAP, Websocket, and REST. According to Amazon, the latter is currently the most popular type. For IoT integration, Red Hat recommends REST APIs—due to their lightweight nature and scalability.

The systems IIoT platforms need to integrate with (source: HiveMQ)

  • Microservices: Integrating and scaling complex IIoT systems requires continuous changes and upgrades. However, ongoing maintenance can disrupt the system as well as the processes relying on it. As a response to this challenge, microservices can replace typical monolithic architectures with modular, loosely coupled units that work independently without affecting the other components.One of the ways to amplify the ease of integration and scalability ensured by microservices is implementing the serverless model. Here, a cloud provider takes care of the underlying infrastructure on which a particular microservice/function runs, reducing operational efforts.

2. Data management

Manufacturers scaling IIoT may struggle to handle data due to its volume and complexity. These two parameters refer to the presence of huge data sets with their own native formats and flowing from disparate sources.

To properly store and analyze this information, an IIoT platform will need a suitable data storage. The system selected should be able to easily scale, manage time series data, and support flexible schemas (the way information is organized within the database). Here are a few options mentioned by McKinsey and Amazon to choose from:

  • NoSQL databases. While SQL databases store information based on well-defined schemas, their NoSQL counterparts organize data into more flexible structures. This enables NoSQL databases to store complex formats, such as unstructured data, making them ideal when scaling IIoT across multiple use cases, as pointed out by Microsoft.
  • Databases as a service (DBaaS). It is a licensing and delivery model in which a cloud provider offers highly scalable storage and computing resources on demand. Manufacturers prioritizing scalability for their IIoT solution may find DBaaS a very cost-efficient option.
  • Time-series databases. These are optimized to store and query time series data, which is a very common data type in manufacturing scenarios (for example, temperature trends in industrial machinery).
  • Data warehouses. It is a type of an enterprise system designed to unify the most critical information from multiple sources for data querying, consistency, and other purposes. This architecture involves ETL (extract, transform, and load) and data quality processes, and may become a key component in business intelligence.
  • Data lakes. Unlike data warehouses, these systems have no strict requirements in terms of data formats to ingest. They can store structured data for analytics, but can also be used as cheap repositories to keep unstructured information that might be useful in the future.

When it comes to data management, another issue to consider is the so-called data drift. This phenomenon entails a progressive change in the data collected by IIoT  sensors due to firmware upgrades, device replacements, or feature modifications. The result can be a degradation in the accuracy of data analysis, as it’s based on incoherent inputs.

A solution to data drift comes from machine learning. Scientists have already developed several ML algorithms to facilitate drift detection, and a variety of providers offer ML-powered tools to help in this regard, such as Azure Machine Learning.

An example of an IIoT architecture with multiple integrations needed (source: McKinsey)

3. Security

IIoT platforms can have several points of vulnerability due to the reliance on remote connections and the multitude of integrated devices. Furthermore, legacy systems are usually more prone to risk. To protect sensitive data and assets, a reliable IIoT solution will require solid cybersecurity and monitoring measures. Here are some recommendations from Amazon:

  • Establish centralized security policies and perform regular audits on all the IIoT components—including sensors, edge gateways, and networks—to identify vulnerabilities.
  • Divide your IoT network into smaller groups of components. This approach (named microsegmentation) enables to isolate workloads from each other, reducing the potential scope of a cyberattack.
  • Safeguard your IIoT platform with activity monitoring, risk analytics, security information and event management (SIEM), encrypted data exchange, identity and access management (IAM), as well as network segmentation through virtual private networks.
  • Ensure safer communication between IIoT platform and edge devices through data exchange standards and protocols designed to strengthen security. These encompass the OPC Unified Architecture, along with CIP Security, Modbus Secure, and more.
  • Define a disaster recovery plan and update it based on security incidents. This may include reliance on cloud services to ensure data backup and business continuity.

4. Digital twin implementation

According to Microsoft’s survey, digital twins share several challenges with other IIoT use cases, including integration, skill gaps, data volume, and data quality. However, the adoption of a digital twin can also be hampered by other building and scaling IoT scaling problems due to its overall complexity.

The challenges of adopting a factory digital twin (source: Microsoft)

That’s because providing a realistic representation of a factory requires a constant stream of real-time data. This information should be collected with a sprawling network of sensors, covering every single manufacturing operation (in an ideal scenario). Furthermore, as you introduce new machinery to scale production, the IIoT network fueling the digital twin should scale, as well. Here’s what you can do to mitigate such issues:

  • When scaling your operations, consider creating multiple, interconnected digital twins (one for each business function, for example) instead of expanding the existing one. According to Dr. Emile Glorieux of the Manufacturing Technology Center, a modular system can help you to address potential failures, facilitate maintenance, and scale IIoT by adding new subparts.
  • McKinsey names edge computing one of the most efficient approaches to help with managing large volumes of real-time IoT data. By minimizing the physical distance between a data source (e.g., machinery) and the computing power, latency reduces, and the digital twin can portray production processes faster. On the other hand, maintaining or updating software on the edge can be quite hard, and may require specific techniques, such as containerization.
  • Connectivity can also be improved by utilizing the 5G network, which provides high-speed, low-latency communication among multiple assets. Real-time process control is one of the manufacturing use cases where 5G is expected to have a huge impact.
  • A 2022 survey by PwC suggests that low-code platforms can act as accelerators for IIoT implementation and scaling. These development environments provide visual tools to facilitate coding, minimizing programming efforts. In this regard, Mendix reported that 41% of manufacturers surveyed in June 2022 want low-code systems to integrate with shop-floor devices and systems, while 39% aim to connect with legacy systems. 43% of the respondents expect low-code platforms to provide manufacturing-oriented app templates.

5. Business and organizational complexities

Manufacturers should consider IIoT implementation as a digital transformation impacting multiple business processes across their organizations. McKinsey highlights that you should be ready to handle these challenging implications:

  • Use case identification. Prioritize IIoT use cases that are likely to ensure the maximum ROI in the shortest period of time. For example, you may target essential business functions suffering from major inefficiencies. Or, start with noncritical processes presenting an opportunity of fast implementation, and then scale as these scenarios succeed. Besides, consider the expertise and resources required for such use cases, along with their scalability and replication potential across multiple locations to foster digital transformation with IoT.
  • Phased rollout. To mitigate IoT scaling problems, McKinsey recommends a phased deployment across multiple waves. The rollout should be carefully planned by setting up a suitable IIoT implementation roadmap to define use cases, timeline, and responsibilities. The first results of the pilot use cases can be observed within 6–8 weeks, enabling to collect feedback from initial deployments and adjust the adoption plan.
  • Upskilling. This aspect of IIoT implementation and scaling should address the lack of expertise required to leverage the technology. The first way to fill skill gaps is through role transitions, facilitated by proper training initiatives. The second is via external partnerships by involving experts in relevant fields, such as data science or IoT software development.
  • Change management. To better deal with the transformations triggered by new IIoT initiatives, manufacturers will need a change management strategy. First, you should establish a center of excellence to coordinate IIoT scaling. It is also recommended to promote cross-team collaboration and involve on-site professionals to ask for their feedback. Another best practice is the identification of the operational changes required to integrate IoT into your workflows.

Cross-team collaboration to streamline IoT scaling (source: McKinsey)

Allies, expertise, and a good plan

Fostered by a progressive reduction in sensor deployment and data storage costs, along with improved connectivity based on Bluetooth, Wi-Fi, and 5G, Industrial IIoT has made great strides in manufacturing and shows no signs of stopping.

Sometimes, however, “the enemy is within the gates,” as Cicero used to say. When it comes to deploying and scaling IoT, this can be a mix of nonstandardized protocols, integration issues, brand-new processes, as well as the lack of resources or expertise.

Still, you can overcome IIoT implementation challenges with a solid adoption strategy, POCs, KPIs, upskilling investments, and support from partners. This will help you to reap the benefits of this powerful technology.

Further reading

The article was written by Andrea Di Stefano, Alex Khizhniak, and Sophia Turol.

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