In a recent webinar hosted by Configit, “How to overcome the barriers to digital transformation in global B2B manufacturing,” Henrik Reif Andersen, Chief Strategy Officer and Co-founder of Configit, addressed the end-to-end digital transformation challenge for manufacturers of complex products. As he points out in the webinar, configurator solutions similar to what we now call Configuration Lifecycle Management (CLM) have long been considered the ideal solution for providing an end-to-end digital process for managing product complexity through the entire lifetime of the product.
Digital transformation is not a new trend. In fact, it is decades old, especially for manufacturers. ERP systems were first introduced in the 90’s while PLM and CRM systems were deployed in the 2000’s. However, these are siloed solutions focused on digitally transforming a specific functional process. PLM and CAD systems focus on engineering design, while ERP focuses on logistics and manufacturing, and CRM focuses on sales. The ultimate vision is to connect all of these siloed systems to enable one end-to-end product lifecycle management process that transcends departmental functional barriers.
This is not a new vision, but one first introduced in the 80’s. In the webinar, Henrik presents an article written in 1982 by John McDermott of Carnegie-Mellon on the RI “rule-based configurator” developed for Digital Equipment Corporation’s manufacturing organization. In fact, the first mention of this solution is in a paper written by McDermott in 1980 that describes the solution [1].
One of the issues with RI noted by McDermott in a follow-up Artificial Intelligence article in 1984 [2] was that RI constantly needed to be extended and grown in order to meet demands for new functionality and insight. The AT&T solution tries to address these challenges by designing the PROSE knowledge base in a purely declarative form, which allows developers to add knowledge quickly and consistently. This enabled the solution to be used for a variety of products and also to be used for both the generation and verification of configurations. This allowed the solution to be used to support sales, engineering and manufacturing.
However, this original dream of end-to-end configuration was challenged by the complexity of the task at hand, which required a great deal of resources and know-how, and was also constrained by the tools available at the time. Originating in the realm of artificial intelligence, the solutions were often difficult for others to understand and apply. This made widespread adoption of end-to-end configuration solutions difficult. Product configurators were mainly applied to specific applications, which were better understood, more readily implemented and justified, such as sales solutions like Configure Price Quote (CPQ) systems, but not for end-to-end configuration. Until now.
In 2015, a group of like-minded companies got together to discuss the growing need for end-to-end integration. There was an identified need to connect the siloed digital solutions already available in manufacturing companies in order to manage the growing complexity of products, improve efficiency and reduce the risk of costly mistakes due to incomplete or erroneous configuration data.
This gave birth to the concept of Configuration Lifecycle Management (CLM). The declaration provides a definition and goals of CLM, stating, “CLM differs from other Enterprise Business Disciplines because it focuses on cross functional use of configurable products.” CLM explicitly addresses the need for product configuration insight in engineering, manufacturing, sales and service through modeling of configuration information. This establishes a single-source-of-truth that can be used and expanded on by all departments enabling cross-functional collaboration.
In this example, engineering design relies on a number of tools, such as PLM systems like PTC Windchill or Siemens TeamCentre, and CAD systems like Solidworks or EPLAN. Similarly, sales relies on legacy sales systems, dealer systems and CRM systems like SalesForce to manage their work. Manufacturing relies on ERP systems like SAP as well as MES systems while service functions need solutions like spare parts systems. Today, all of these functions and systems need to be aligned on the product definition including all the product configuration choices that can be offered to the customer. The CLM system provides the single-source-of-truth on product configuration information that can be shared by all these functions and systems.
When implementing CLM, one could be tempted to follow a “big-bang” strategy by implementing CLM in all functions at once. The alternative is a stepwise implementation where existing processes and systems are not replaced before the new implementation is proven. This provides the implementation path with the least risk, but with tangible benefits at each step. It also allows changes to the plan to be made after each step to accommodate new information and circumstances.
There are many paths to end-to-end CLM. The key concern is not whether one is following the right path, but whether each step is providing value. CLM can start with a focus on sales and improving CPQ, digital product brochures, eCommerce sites or cross-selling initiatives.
Alternatively, CLM can start with a focus on scaling and extending the variant management capabilities of engineering PLM systems. Both paths deliver direct value to the functional department involved, but also lays the groundwork for cross-functional collaboration as in both cases, product configuration information needs to be shared to be effective.
CLM provides an end-to-end product configuration solution that enables more efficient and reliable management of complex products throughout their lifecycle.
However, it also provides a single-source-of-truth that can be made available to all departments enabling cross-functional collaboration on more efficient solutions and processes that are the ultimate goal of digital transformation.
[1] Carnegie-Mellon University, J. McDermott, R1 a Rule-Based Configurer of Computer Systems, April 1980.
[2] The AI Magazine, J. Bachant, J. McDermott, RI Revisited: Four Years in the Trenches, Fall 1984.