Implementing PLM solutions involves joining the dots across a number of concurrent perspectives: from people to data, processes, platforms, apps, enterprise interfaces with ERP, MES and other downstream digital tools. They also involve joining the the dots between multiple disciplines, from strategy definition to execution, from the top-floor to the shopfloor, developing relationships between the business (the users), IT and other enabling functions, such as HR, procurement, finance, operations, etc.
In this post, I highlight a number of experience-driven guidelines and recommendations when implementing PLM solutions, independently on whether these are called ‘best practices’ or else.
Like with any IT-enabled enterprise change initiatives, PLM projects need a business case to set expectations, confirm strategic alignment, justify and secure funding for the required investment. This will mean different levels of commitment across the organization:
- COO will look at operational enablement, flexibility and scalability of the solutions.
- CFO and financial analysts will look at return-on-invrestissment, payback period, strategic alignment across strategic initiatives.
- CIO (and IT) will looks at enterprise data governance, ease of implementation and integration, business requirement alignment, infrastructure management simplification, etc.
- Engineering and manufacturing engineering directors will look at enabling processes and tools to deliver growth, meet product development deadlines, onboard the required users and suppliers, track delivery against quality targets.
- Sales and marketing will look at how to improve product modularity, requirement change management, customer experience and market penetration.
- HR will look at employee experience and the required learning curve.
- Procurement will look at how to enable and improve supply chain engagement.
- Service and quality directors will look at how to improve data traceability throughout, compliance and alignment to quality and safety regulation, warranty cost reduction, customer satisfaction improvement, and opportunities for improving maintenance and building new data-driven business models.
Each and every stakeholder will look for specific ‘best practices’ or guidelines in relation to their field of expertise and expectations. The following entry-level 25-point guidelines are non-exhaustive; they typically apply to PLM and other digital platform implementations:
- Have a robust, yet simple quantifiable business case; this is not only a pre-requisite to getting investment approval, but also an ‘insurance policy’ to review strategic alignment throughout the implementation.
- Build a detailed stakeholder map and internal governance to ensure ongoing alignment and expectation setting.
- Identify the required storyboards and use cases, map them against the project scope and key stakeholder functions as early as possible.
- Understand technical implication and pre-requisites, from PLM, ERP and other software.
- Select the required tools and technologies, being mindful of cultural and organizational fit, through a credible and balanced RFP process.
- Similarly, select a strong implementation partner, considering a solution integrator based on the project size and complexity; assess also their fit in working with the organization, their ability to assign credible and reliable resources, communicate efficiently, demonstrate leadership in managing complexity, become trusted advisors (and not only because they called themselves like so).
- Manage early both supporter and detractors across the business stakeholders to set the relevant expectations and ensure a robust RFQ process execution.
- Define a single, yet holistic delivery governance, both top-down and bottom-up to ensure all aspects of the technical implementation and business change are covered.
- Appoint the minimum set of key-users, with at least one representation from each function.
- Select the implementation approach based on the delivery roadmap, business, technical and data dependencies.
- Assess early the integration and data migration requirements and implications from a master data and data quality perspective.
- Estimate business involvement requirements, including in solution design and testing, data cleansing, education, support floorwalkers and transition support.
- Define master data, configuration and customization principles and governance.
- Engage early with HR to understand how training requirements might need to be adjusted to follow implementation guidelines.
- Define and drive a simple communication plan as early as possible.
- Assess early data quality and define clear data migration rules with data archiving and legacy application decommissioning strategies.
- Appoint a robust program director which experience in PLM, project and stakeholder management.
- Align and coordinate early engagement of IT for all enabling infrastructure and integration elements; this also apply for cloud-based platforms in order to understand and manage budget and associated sizing rationale.
- Appoint a credible solution architect and functional lead to interface with the business.
- Understand the modus operandi between system integrator and vendor, and build a simple, yet effective RACI and governance model across all parties.
- Understand how issues / defect / enhancement request will be handled with the vendor, confirm roles and ownership.
- Establish a meaningful executive governance and build relationships with sales and leadership teams, across vendor and its system integrator.
- Assess how much change the business can absorb at once, and what are possible organizational gaps.
- Define the required organizational changes in order to ensure effective deployment, transition and adoption of the solution.
- Run the required delivery and technical assurance, either via the vendor or by contracting an independent assessor (with no conflict of interest—i.e., not to be engaged in fixing the issues that they might unveil).
What are your thoughts?
This post was originally published on Momentum-PLM on 16 February 2021.
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