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Data Acquisition, DAQ

Better data management for manufacturers during COVID-19

Will COVID-19 compel manufacturing companies to streamline connected data management more than previously?

By Sanjay Barnwal July 7, 2020
Courtesy: L&T Technology Services

 

Learning Objectives

  • Money for robotics and automation continue to flow in the pandemic, but data management funds have slowed for manufacturers.
  • Productivity for Industry 4.0, IIOT digital transformation requires streamlined data integration for manufacturers.
  • Lack of data connections result in disjointed systems, significant manual interventions with time to market impact, poor quality, high cost of ownership and poor customer satisfaction.

Decision-makers and executives understand the value and need for model-based design (MBD), model-based engineering (MBE), and model-based systems engineering (MBSE). These concepts have existed for decades and have enabled thousands of high quality and extremely complex product design, development and support activities. Many projects are unable to apply these concepts for the full product lifecycle right from design, development and manufacturing to support engineering activities. This results in continuation of disjointed systems, significant manual interventions with time to market impact, poor quality, high cost of ownership and most importantly poor customer satisfaction.

Product lifecycle management (PLM) systems, for instance, are still widely used as document management systems across enterprises for storing engineering artifacts.

On the other hand, quality aspects in the product design stage are managed in separate systems, processes, and often through Microsoft Excel and Word documents linked to PLM or other engineering systems.

Perceptions vs. reality of engineering transformation

Even before COVID-19 disrupted industries globally, engineering transformation programs did not guarantee readymade success for most organizations. Blame it on the perception vs. reality factor.

  1. Real life vs. perceived values: Most of the new engineering programs start with the intent to follow the great design principles and methodologies with an understanding of great value. However, as the project timeline, cost and efforts start escalating and the pressure builds up, teams start going back to the old ways of manual interventions. The new ways of working take up too much of time, budget and efforts and are at times risky as well.
  2. Live for today or sacrifice some for tomorrow: Leaders and executives have businesses to run. Disruptions with newer tools and technologies require upfront planning, communication and commitments. This is why, except for some pockets (pure R&D experimentation, proof of concepts [POCs], digital technology groups), MBD, MBE and MBSE remain in evolution stages within engineering organizations. Because of that evolution, it’s no wonder they do not move to manufacturing, maintenance and repair operations (MRO) and aftermarket areas.
Connected data management graphic explains implementation challenges, root-cause analysis, key drivers for change, and the pathway for improvement. Courtesy: L&T Technology Services

Connected data management graphic explains implementation challenges, root-cause analysis, key drivers for change, and the pathway for improvement. Courtesy: L&T Technology Services

Getting to the root of the digital transformation problem

Engineering and aftermarket leaders understand the value that comes with connecting engineering data to aftermarket for spare parts leaflets, associated technical documents, training materials, videos, effective field performance analysis and improvements. In most cases, the concepts just stay on paper and seldom are implemented. Similar initiatives on making factories paperless, and generating a manufacturing bill of material (mBOM) from an engineering bill of material (eBOM) in a connected way, and performing virtual tooling, manufacturing and simulation activities to detect and fix issues early on remains a wish-list for many operation leaders. Key decision-makers of business units are optimistic and frustrated with the pace of transformation and outcomes these initiatives are generating.

On the other side of the spectrum, manufacturing plants have large-scale capital investments in robotics and automation. There are use cases where a worker with a $25-per-hour wage is getting replaced by multi-million-dollar machines. And, these robotics and automation projects are getting instant approvals.

While critical data management projects that promise long-term value have been shelved off, automation and robotics have gained steady acceptance. Is automation a priority because a business case for headcount reductions is easy to create and understand across the board? Is it difficult to create a business case for productivity, cost-avoidance opportunities and investments for linking engineering data models to manufacturing?

The answer to these questions lies in addressing the fundamental challenges associated with how current teams are structured to meet cross-functional transformation needs.

Driving an end-to-end digital transformation

In a recent visit to a leading MRO facilities for a tier 1 automotive customer, it was surprising to see the engineering team in the MRO plant were unaware of how to access 3D models of their parts in the PLM system. Engineers were working with an outdated drawing and instructions given by engineering years ago. If a manufacturing plant works with Industry 4.0 technologies, it must do so with deep involvement. Even with the best intentions, engineers who are busy with their activities only can provide part-time support to manufacturing plants.

To address the bottlenecks and implement this long-due transformation, mainstream companies need to overhaul their organizational structures to facilitate cross-functional collaboration.

IT/OT convergence helps digital transformation

Standalone functions such as engineering, manufacturing, aftermarket, MRO, digital and even information technology (IT)/operations technology (OT) can be consolidated under one engineering division. This new organizational structure can have better chances of success because:

  1. Engineering, manufacturing, MRO, and aftermarket as standalone functions are siloed. Merging each function can facilitate a seamless flow of data between disparate and disjointed processes.The teams can replicate standard operating procedures (SOPs) and leverage best practices and technology in tandem to drive long-term value.
  2. If a manufacturing plant has to work with 3D models for work orders, virtual tooling, testing then the manual process changes in the factory can’t happen without deep engineering involvement.
  3. The same is true for MRO and aftermarket functions, which need constant support from engineering to drive seamless data management.

Role of leadership and consultants for digital transformation

For this restructured organization, the strategic and decision-making functions need to happen at the top, and then respective teams can figure ways to move forward. The company also will have to incur additional costs of maintaining multiple technologies, tools and platforms in this case. With thorough planning, companies can reduce valuable time lost in creating mundane POCs figuring out which path to go in their connected data management journeys.

It’s a fact that no matter which tool, platforms and solutions are chosen, there will always be soft corners and shortcomings in meeting and satisfying all needs. This is where executives need to use the 80/20 rule, make clear decisions and save teams from spending their valuable time and efforts in mundane POCs, experimentation and debates about which path to take in their connected data management journey.

When radical organizational changes are to be implemented, management consultants come into the picture quite frequently. This is not because they know more than the CEO of the company or corporate leadership. Management consultants bring an outside-in view while working on a given timeline with set objectives. They are flexible to work with and don’t come with preconceived notions of what’s possible and what isn’t. This is why organizations should explore collaborations with consulting organizations for data management initiatives. With a support system for requirement gathering to design, develop, and test, management consultants can ensure the success of a data management project end-to-end.

Looking beyond COVID-19

Capital funds for transformation projects have dried up, and the situation is unlikely to get better immediately. Knee-jerk reactions are natural where key supplier resources and employees are getting laid off and only essential things are getting done. This is where supplier, non-core activities consolidations can free-up funds, and key resources can be funneled for technical data management transformations. These levers were not a must earlier, but enterprises will not have much choice going forward relying on integrated data management to plan and structure organizational blueprints.

Data is a strategic weapon and backbone of the Industry 4.0. Whether it’s systems of records or human interactions or connected systems, everything depends on the data generated, its seamless exchange and effective usage.

The onus is on organizations to capitalize on the current crisis and consider it as a stress test for transforming technical data management foundations and capabilities.

Sanjay Barnwal is vice president of transportation and aerospace at L&T Technology Services, a CFE Media content partner. Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media, mhoske@cfemedia.com.

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KEYWORDS: COVID-19, manufacturing, data management

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Sanjay Barnwal
Author Bio: Sanjay Barnwal is vice president of transportation and aerospace at L&T Technology Services.