Simplifying Industry 4.0 Advancements for Legacy Manufacturers with Tech Mahindra Factory Information System
By Atul Mehta, Head, Strategic Initiatives – Tech Mahindra
By Sathish Arumugam, Partner Solutions Architect – AWS
By Thomas Cummins, PhD, Sr. Partner Solutions Architect, IoT – AWS
By Shonil Kulkarni, Sr. Manager, Solutions Architect – AWS
Renowned author and business strategist Henrik von Scheel is credited with popularizing the concept of the Fourth Industrial Revolution, which he describes as “the biggest structural change of the past 250 years—a transformation of scale, scope, and complexity unlike anything humankind has experienced before.”
Commonly referred to as Industry 4.0, this revolution involves the integration of advanced production techniques with cutting-edge digital technologies such as artificial intelligence (AI), advanced robotics and cognitive automation, advanced analytics, and the Internet of Things (IoT).
Implementing Industry 4.0, including its various technologies, can pose challenges for existing factories—also known as brownfield factories—as they may face limitations imposed by their existing infrastructure. Unlike greenfield factories that have the flexibility to incorporate newer technologies into plant designs from the outset, brownfield factories may be constrained by pre-existing infrastructure limitations.
To address this challenge, Tech Mahindra Factory Information System (FIS) is designed to help manufacturers overcome these limitations and embrace Industry 4.0 technologies.
In this post, you will learn how manufacturers can benefit from the transformative potential of Industry 4.0, optimizing their operations and unlocking new opportunities for enhanced productivity, efficiency, and competitiveness in the modern business landscape by using Tech Mahindra FIS.
Factory Information System Overview
A factory information system is a value-creation methodology in the manufacturing space intended to increase, improve, and accelerate manufacturing. Deployable in countless use cases, FIS can improve yields and quality, optimize energy use, and institute digital maintenance of machinery.
Specifically, FIS solutions are expected to:
- Provide near real-time visualization of plant floor machinery operations to enable the rapid identification and resolution of process problems and bottlenecks.
- Detail shift and daily production trend data to assist in day-to-day production scheduling decisions.
- Report long-term machine efficiency indicators, allowing plant operators to measure and eliminate process bottlenecks.
- Support a diverse set of users including skilled operators, line supervisors, engineers, plant managers, and regional managers.
Tech Mahindra FIS improves factory operations visibility which in turn leads to key performance indicator (KPI) improvements by integrating data from the shop floor with enterprise level data.
By implementing visualization and reporting capabilities that help both shop floor leaders and business executives gain and share relevant insights, Tech Mahindra FIS enables customers to predict machinery status through asset model simulations and ultimately reduce repair costs. Tech Mahindra provides consulting and managed service offering for the factory information system solutions.
Tech Mahindra FIS enhances workplace well-being by leveraging digital technologies such as AI, data analytics, and augmented reality (AR) to promote worker health, productivity, resilience, and overall experience. By integrating these innovative solutions, Tech Mahindra FIS strives to create a safer and more supportive work environment that prioritizes the well-being of its workers while also enhancing their performance and job satisfaction.
Challenges of FIS Implementation at Brownfield Facilities
When FIS is neglected for too long, a facility can find itself dependent on decades-old on-premises technical architecture that’s not easy to scale, takes months to provision, and requires considerable spending to refresh. These environments are typically siloed and do not support a comprehensive operations view of the plant or across the enterprise.
Furthermore, if Open Platform Communications (OPC) standards are used for data collection, only Programmable Logic Controllers (PLCs) are accessible as data sources. OPC does not support standalone sensors and modern protocols like Messaging Queue Telemetry Transport (MQTT) or large data payloads for image, video, and waveform data.
The limited data that’s collated using OPC also needs manual configuration and time-consuming data validation.
The data system has certain limitations when it comes to aggregating data, as it only supports a limited number of grouping options and cannot create data buckets smaller than 10 minutes due to storage constraints. This could potentially lead to inaccuracies in metrics.
In addition, legacy applications do not support the use of AI/ML models on collected data, while on-premises applications may lack support for mobile devices and offer limited charting and graphical analysis tools.
A New FIS Approach
Tech Mahindra FIS addresses these issues for customers in a three-pronged approach:
- Connect: Connect to Computer Numerical Controls (CNC), Programmable Logic Controllers (PLCs), robots, sensors, Distributed Control Systems (DCSs) to collect machine performance and parameters data like cycle times, cycle counts, blocked, and starved machines using Telit DeviceWISE workbench.
- Manage: Create a digital twin of the factory using AWS IoT TwinMaker in conjunction with AWS IoT SiteWise to collect, organize, and analyze industrial IoT data. Create virtual representation of physical assets, process equipment data streams, and compute industrial performance metrics.
- Consume: Visualize data using Grafana dashboards (Tech Mahindra recommends AWS Managed Service for Grafana) to query, visualize, alert on, and understand metrics stored on AWS IoT SiteWise. Create, explore, and share beautiful dashboards to foster a data driven culture.
Figure 1 – Factory information system approach.
Following the above FIS approaches, a high-level architecture of the solution is as follows:
Figure 2 – Tech Mahindra FIS architecture.
Here are the key components of the architecture:
- Telit DeviceWISE: When building and managing an Industrial Internet of Things (IIoT) solution, you need a platform to connect devices and applications with ease. Telit DeviceWISE collects and transforms data and integrates machines and systems. It’s a scalable, integrated IIoT platform that provides visibility and control over all your connected devices and data.
- Amazon S3: Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance. Tech Mahindra’s FIS uses Amazon S3 to store time series and metric data in this solution, and data is archived for any future analytics.
- Amazon Managed Service for Grafana: This is a fully managed service for Grafana, a popular open-source analytics platform that enables you to query, visualize, and alert on your metrics, logs, and traces. It provides an overall equipment effectiveness and downtime analysis dashboard.
KPIs and Metrics Supported by FIS
Tech Mahindra FIS supports various KPIs and metrics. They can be grouped into the following machine performance metrics:
- Overall Equipment Effectiveness (OEE): Measures how well a manufacturing operation is being utilized (facilities, time, and material) compared to its full potential, during the periods when it’s scheduled to run. OEE is equal to availability x performance x quality.
- Availability: Calculated as the ratio of runtime to planned production time.
- Quality rate: Refers to the ratio of good products produced compared to the number of rejects.
- Performance: Considers performance loss, which accounts for anything that causes the manufacturing process to run at less than the maximum possible speed, including both slow cycles and small stops.
- Takt time: A tool for aligning the pace and rhythm of your manufacturing process with customer demand. As a metric, it represents the amount of time budgeted to manufacture each part, such as producing one part every 22 seconds. It helps you to establish flow and save time. For more details, please refer to the International Automotive Manufacturing Conference whitepaper.
- Schedule attainment: Straightforward calculation expressed as a ratio of completed planned work divided by planned work.
- Jobs per hour: Measures the number of jobs produced during the hour. This allows a floor manager to improve and optimize the processes to get greater output from the factory.
- Throughput: One of the simplest yet most important manufacturing KPIs, allowing companies to measure the average number of units produced on a machine, production line, or by a plant over a specified period of time.
- Mean time to repair: Mean time to repair (MTTR) is a basic measure of the maintainability of repairable items. It represents the average time required to repair a failed component or device. Expressed mathematically, it’s the total corrective maintenance time for failures divided by the total number of corrective maintenance actions for failures during a given period.
- Mean time between failure: Mean time between failures (MTBF) describes the expected time between two failures.
This post showcased the benefits of utilizing data-driven manufacturing and how Tech Mahindra Factory Information System (FIS) can help enhance efficiency, provide increased visibility, and support informed decision making.
Ready to take your factory to the next level? Tech Mahindra FIS enables real-time monitoring and data analysis so you can optimize your operations and boost productivity. To learn more, reach out to Tech Mahindra and schedule a demo.
Tech Mahindra – AWS Partner Spotlight
Tech Mahindra is an AWS Premier Tier Services Partner and MSP that specializes in digital transformation, consulting, and business re-engineering solutions.