Above image: When it comes to engineering change, looking at the big picture implies planning, measuring, and adjusting processes, tools, and training for each party directly or indirectly involved in driving and delivering data quality—and ultimately product quality. (Image: PEXEL)
Data issues can lead to poor decision-making, higher operational costs and poor engineering change traceability. The resultant mistrust in the available data feeds ineffective governance and degraded process efficiency and effectiveness. Enterprise tools are often blamed for these shortcomings, but is this mistaking symptoms and causes?
The UK Government Data Quality Hub highlighted in 2021 that “poor quality data, including data that is inaccurate, incomplete, or out of date, is data that is not fit for purpose. Poor quality data increases risk and can cost […] time and money.” According to this study, data experts reported that “organizations spend between 10-30% of revenue on handling data quality issues, [combined with] direct and indirect costs associated with poor quality data that are more than just monetary.”
Engineering products typically regroup tens to hundreds of thousands of parts, managed as part of EBOM-MBOM management practices, including customer variants, alternates, and substitutes. Over the lifecycle of complex products, each part might go through tens of successive engineering and manufacturing changes.
Diagnosing and revolving engineering data issues implies leveraging a combination of tacit process and enterprise tool knowledge, understanding how information flows across functions and systems, and how it feeds the business operating governance. Building and maintaining system and process maps contributes to highlighting the need for good information flow and transparent stakeholder communication.
In this article, I elaborate on what it takes to drive successful engineering change management (ECM) and the importance of regular data and process deep dives and holistic continuous improvements.
It is best executed as part of a continuous improvement program, rather than a one-off study to support an issue assessment.
Engineering change processes and governance
PLM, ERP, MRP and similar enterprise systems only perform as well as they are configured, integrated, and used by the relevant operational teams. Processes have input and output; they are typically stitched together by operators (people) and feed into the relevant decision-making and governance (operations). Quality decisions rely on quality information and data. Master data clarity, single integrated data sources, and effective governance are essential driver to effective decision-making.
A good example is how accurately and timely BOM alignments can be performed and managed across engineering and manufacturing BOMs (EBOM and MBOM). These multiple BOM views are the cornerstones of engineering change for both customers and suppliers, and the ability to perform impact analysis, raise, approve, implement, and track changes is a mandatory commodity. Changes to both new and existing products often encompass all levels of innovation from incremental changes to radical innovation, of which there are multiple change types and interdependencies.
Broadly speaking, engineering change governance span across 4 perspectives:
1. Technical perspective: change management systems, PLM/ERP/MRP platforms, CAD / CAM / CAE and PDM systems, with their technical interfaces.
2. Organizational perspective: decision-making structure and governance, modus operandi, approval, and organizational structures.
3. People perspective: skills, competencies, training, certification, learning, lessons learned, etc.
4. Enabling process perspective: portfolio management, resource management, knowledge management, digital automation, performance management, manual processes, etc. across a wide array of business use cases.
Business first, system second
Not everything can or needs to be integrated and/or automated. Digital enterprise platforms support the management of complexity and provide the required operational traceability. Any improvements, small to large, will require a strong focus on how people operate, what they formally and informally do in their day-to-day activities, and how do they collaborate and share data.
In a digital transformation or continuous improvement context, it is often advised to “first change the people: improve existing processes and educate users before considering new systems” (Grealou, 2019). This implies that the ability to change is mostly about the people aspects, their ability to learn and ultimately connect the dots across processes, data, and systems, and considering:
1. The importance of a governed ECM process with stage gates to drive stakeholder alignment.
2. Consistent and controlled approach to provisioning solutions; from business requirements to processes, culminating with system capabilities and features.
3. Robust and reliable system implementation steps (test, deploy, pilot, fix, go-live); typically enabled by a robust DevOps pipeline to streamline how change are deployed and adopted.
4. Real-world pragmatic use cases to test system connectivity, functionalities, and integration layers.
5. Effective user training, and support, to ensure continuous learning and relationship building.
Automation is an enabler, not a business goal or even a uniform requirement across all enterprise IT systems. The systems or their technical interfaces alone are not the answers to everything. Ultimately, technology need to be “humanized” by integrating peoples’ points of view into the wider perspectives, supporting people in distinguishing the useful from the worthless, providing solutions that contribute to enhancing user experience.
What are your thoughts?