Manufacturing industry has always been asset-centric. This is true for both process manufacturing as well as discrete product manufacturing segments.

From material handling equipment like forklifts and cranes, material processing (compressors, filters, pumps) and engineering assets (assembly lines) to measurement and control equipment (meters, gauges, valves, and actuators), every asset is a critical cog in the manufacturing value chain – sourcing the raw material in the consumable form to the factory floor, processing the same with the specified procedure and in controlled environments and churning out finished products to be inventoried or sold.

Transforming every conventional line asset, whether on plant floor or field to a digitally enabled asset is a strategic imperative every manufacturing organization is facing and is planning for.

Understanding the dynamics of data and applications in manufacturing value chain

Manufacturing companies use ‘Automation Stack’ for production process control and automation to maximize throughput. It uses a bidirectional flow of information across a heterogeneous system architecture of purpose-built control systems applications. It covers systems for

1. Partly/ fully programmed, sequential process steps and routines
2. Supervisory control based on programmed instructions and real-time embedded sensor feedback for the machine state
3. Scheduled jobs and work orders for finished products, after-market component spares
4. Dynamic inventory levels across product/ component value chain

While automation stack is primarily used for automating and controlling the production process, it is not designed to optimally run the ad-hoc analytical workloads for end-user applications like diagnosing asset performance trends and predicting failure or maintenance events. These use cases often feature

1. Complex, multi-cast data integrations among edge, cloud and on-premise workloads
   • Unified namespace for all data regarding device and process state – it works on ‘pub-sub’ mechanism with every value chain component serving as a node
   • Lean, low payload multicast data communication using open protocols like MQTT
2. Extensive use of current, historical data to generate hot, warm, and cold analytics
3. Applications featuring complex modeling algorithms, multiple configuration parameters

For such applications, a parallel IIoT technology stack with connected field devices makes much more sense. These devices need to have the following characteristics.

1. Gather sensor data streams and generate threshold based alerts using their edge compute resources by processing a wide range of state variables in real-time
   • Asset performance – motion, temperature, dynamic, and structural stability
   • Operating environment conditions – ambient humidity, temperature, air quality, and flow rate
2. Provide operating environment and resources for process-specific applications used to execute data, analytical and control workloads using aggregated or time-series device data streams
3. Ensure hardware rooted secure access and partitioning for data streaming from devices
   • External actors with malicious intent do not take control of device data and subsequent data linked actions.
   • Influence the rest of the connected asset network or enterprise application landscape to act detrimental to the overall value chain objectives and throughput.

Apart from above technical requirements, these edge devices will have to be inexpensive so that their deployment at scale generates sufficient RoI for the enterprise in order to justify the investments in adopting such digital infrastructure on the manufacturing value chain assets.

Benefits of connected digitalized assets include

1. Asset have significantly higher reliability in terms of uptime or availability, longer mean time between failure (MTBF), and improved predictability of steady state performance. At the enterprise level, this results in improved throughput.
2. Assets are more energy efficient and have a wider operating range
3. Worker safety is improved with connected safety gear and camera surveillance systems for adherence to safety measures

Bridging the manufacturing asset digitization chasm

Building secure connectivity infrastructure in assets for M2M/ cloud integration and data based, orchestrated actions is trickier in manufacturing than most other industries. Manufacturing assets are mostly custom engineered, making open standards based, and secure communication cumbersome with the resources on-board.

Building the resource infrastructure is the way forward, however, this works primarily for planned assets or the ones in development. Ones in already commissioned and online need native functionality extension. In some cases of legacy assets, the extension of native functionality isn’t an option. In such cases, pluggable connectivity modules i.e. guardian modules are most suitable for bridging the technology chasm and achieve digital enablement.

Exploring technology options in ‘the new normal’ post COVID 19

As post-pandemic realities set in, most manufacturing organizations are at crossroads in their technology roadmaps with

1. Shortened window for strategically imperative ‘asset led’ digital transformation
2. Major investments in such technology initiatives facing extensive stakeholder scrutiny on projected RoI and measurable impact on business growth considering weak demand projections across consumer and industrial segments

An end-to-end, low cost, highly reliable, and seamlessly scalable edge solution for asset connectivity and insights would help address all the aforementioned constraints. Major enterprise infrastructure and platform technology providers have offerings across the device to the cloud spectrum.

For instance, Microsoft has offerings from the secure and connected edge (Azure Sphere, Azure Edge IoT) to data management, analytics on cloud (Azure IoT Hub, Azure Digital Twin). These are available in custom, flexible configurations, and deployment options that fit with every organization’s stage in the connected solution lifecycle.

Expert partner organizations like ours are helping manufacturing companies in assessing this lifecycle stage and technology landscape maturity and subsequently engineering networks of connected assets using the aforementioned market-leading technologies.

eInfochips, an Arrow company, is a Microsoft IoT Partner with experience in engineering digitally enabled asset networks for customers across industry segments and sizes. Contact us for commencing your asset digitization journey.