Avoid the trap of large-scale PLM IT transformations in Industrial Goods

Discover effective methodologies to manage PLM systems and overcome challenges.

Avoid the trap of large-scale PLM IT transformations in Industrial Goods

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Product Lifecycle Management (PLM) refers to the handling of products as they move through all stages of the lifecycle, from requirements management and product development to manufacturing, marketing support, service, and decommissioning.

PLM is at the heart of every industrial goods company as it supports many, if not most, of the core processes.

Because of this, having your PLM working efficiently is usually critical to success.

Many companies suffer from issues in their PLM IT systems and processes. Increasing customer expectations and competition has resulted in increased product complexity in many industrial goods segments, including the electrification of vehicles as an example.

Traditional hardware-oriented companies are also increasingly introducing software components as part of their products. Companies need to understand the integration between the hardware and software world in order to support the development, testing and maintenance of their products. New regulations, such as the UNECE WP-29 for the automotive segment, further drives the urgency for industrial goods companies to be in control of both hardware, software and their interdependencies. The systems supporting PLM today are often old, difficult to change and cannot support the new demands, regulations and ambitions. The systems and processes are often designed in a fragmented way, leading to manual and often error-prone handovers. Data is commonly of low quality and locked within legacy core systems, making digital use cases such as analytics, cost transparency, and automation extremely challenging to realise.

Considering these challenges, industrial goods companies are frequently looking to invest in upgrading their PLM infrastructure and many companies are tempted to completely replace and rework their entire PLM infrastructure. The rationale is often to ‘do it all in one go’ and build it right, ensuring that the processes and systems are built well and integrated from the start.

While ripping out and replacing PLM infrastructure may be an attractive idea for some organisations, BCG’s experience shows that most companies underestimate the complexity of large-scale ERP transformations. Because of this, many fail in their transformation efforts

Client example : Asian automotive OEM

The OEM had the ambition to rework the PLM infrastructure for a single category across the entire lifecycle. While the initial assessment indicated a relatively simple transformation, hidden complexities soon emerged, and the scope and complexity grew larger and larger. Ultimately the situation became unmanageable, and the project was stopped and rescoped to focus on only selected parts of the value chain

Client example : Specialized vehicle manufacturer

This organization started a journey to implement a single PLM system across the value chain and across various geographies. A multi-month feasibility study was launched to assess the impact on systems and processes. Once complete, it became apparent that the complexity of implementing a single system and harmonizing the processes across categories and vehicles became too great to manage. The project was never launched with its originally intended scope.

Client example : Tier 1 automotive supplier

In this case, the company launched a program to harmonise the PLM system across multiple business units. Challenges around complex data interdependencies between business units surfaced quickly hindering the progress of the program. The program was eventually descoped to focus solely on a single step of the value chain where the data complexity could be managed.

Why are large-scale PLM transformations likely to fail?

Large-scale PLM transformations covering large parts of the value chain often come with many hidden complexities. This is usually the case due to failure to anticipate them during the analysis and planning phases of the transformation. Common complexities include:

  • Differences in standards, processes, and regulations between business units, geographies and product categories
  • Challenges establishing a business case across the organization, secure budget for cross-organizational efforts, and track realised value
  • Increased complexity of interfacing with and/or transforming multiple legacy systems at once
  • Differing products and product structures across geographies and product categories
  • Complexities to govern transformation programs across geographies, business units, and categories

The danger of adopting a centralised product structure

The challenges above are often exacerbated by a desire to transform the PLM setup from a divisional structure to a centralized product structure. While theoretically desirable, a centralised product structure poses even stricter demands on harmonisation of technology and processes across the various parts of the business, and with that, often causes infeasible levels of complexity.

Minimize the complexity of PLM transformations

Learning from recent large-scale transformation programs in the industry, we see that PLM complexity is often underestimated and that large-scale transformations have a high likelihood of failing.

BCG’s recommendation is to approach it step-by-step in low-complexity increments. By taking an incremental approach, the risk and investment needed is greatly reduced and the high-value opportunities can be worked on first, helping the organization to focus on and jointly drive the change needed to systems, processes and data.

Use a data layer to abstract core systems

Instead of leveraging single or multiple systems to support PLM across the lifecycle, PLM transformations can partly be delivered using a data and smart business layer. This is the core of BCG’s Data and Digital Platform approach (DDP).By implementing the data layer, costly changes to the core system can be minimised and be implemented independently as a service instead, leveraging the data from the core PLM/PDM systems. This approach is particularly applicable to analytical and digital use-cases that use data across the value chain, including cost analytics, recall queries and Over-the-Air support.

Support and sponsorship of top level management is critical for success

PLM is a particularly complex space for industrial goods companies as it spans multiple business units and product categories. In our experience, we have identified the following criteria as being critical for success in the PLM transformation space:

  • Sponsorship from top management with influence to drive projects across business units
  • Agreement on adopting a value-driven approach, such as incrementally defining and realising specific business objectives
  • Active engagement from both business and IT teams to define the target processes, IT architecture and to drive end-to-end change management.
  • Investment in foundational technology platforms including data platforms, APIs and platforms for creating and operating business services to deliver use cases.
  • A cross-organisational team that can implement a governance structure to support fast decision making and planning.

Done right, PLM transformations can realize significant value for industrial goods companies

While being a challenging space to transform, PLM transformation can drive significant value when carried out in the right way. For added context, here are a few select examples of business benefits realised by effective PLM transformations in industrial goods

  • Improved R&D efficiency: 3 to 5%
  • Lead time reduced by 3 to 6 months
  • Materials costs reduced by 2 to 3%
  • Warranty costs reduced by 3 to 5%

In conclusion, companies should avoid large-scale PLM transformations due to their inherent complexity and likelihood of failure. Instead, companies should minimise complexity and deliver value incrementally, prioritised by business value. The Data and Digital approach can help in this journey by reducing the need for complex core system replacements and enabling use cases to be built one by one.

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