Insights from Propel Software at Salesforce’s Dreamforce 2023.
Product insights become even more meaningful when associated with financial performance insights. However, connecting enterprise platforms into an effective PLM data model, single source of truth and product value management (PVM) system can be a daunting task for any organization. This challenge arises due to the specialized tools used by different teams within an organization, such as sales’ customer relationship management (CRM) and QA’s quality management systems (QMS). Think of it as assembling a complex puzzle where each piece represents different data elements vital to manage the lifecycle of a product. This includes the required analytics to connect the product portfolio to financial performance.
The complexity of PLM data integration requires cross-functional experts who can navigate this intricate maze and unlock valuable insights. These roles are pivotal in ensuring that data models fit operating processes and empower the business with the knowledge it needs to make informed decisions and stay ahead in the market.
Propel Software offers one of the few PLM tools to be built natively on a CRM platform—namely Salesforce. It can present and consume product information from ecommerce platforms and financial stakeholders—connecting the worlds of PLM and PIM.
To learn more, Dario Ambrosini, Chief Marketing Officer, and Tom Shoemaker, VP of Product Marketing at Propel, responded to questions from engineering.com during The Factory at Dreamforce 2023, an annual live Salesforce event.. This post summarizes the perspectives of Propel’s trailblazers about product value management (PVM).
Improve the ROI of PLM
Imagine your business as a complex machine with multiple moving parts. AI, Data and CRM are the gears that drive it forward. PLM is the essential backbone that ensures everything works harmoniously by converging QMS, CRM and product information management (PIM) under one roof. This is about interlinking product, delivery timelines and financial data across the extended enterprise.
Linking product with business performance is a critical objective of organizations when looking at leveraging the real ROI of PLM. It is about having live insights on the product performance, while linking to ongoing innovation and product development cycles. It is also about staying ahead of the game by integrating feedback loops across product variants and product families.
Question 1: How Does Integrating Innovation With Order-to-cash Boost Product Profitability?
Ambrosini started to answer this question by introducing the fact that “the need to collaborate and make faster decisions is about having the right data visibility” beyond the traditional product engineering quality scope.
Shoemaker expanded on this by highlighting that “it is about moving fast and decisively … with real-time insights from the supplier chain. PLM historically was born from PDM and the CAD world … and there are so many other contributors beyond engineering that have a stake in product success, such as sales and marketing, operations, etc.”
It is true that PLM (and its predecessor product data management) has traditionally been about optimizing around the cost of the product. It focuses on the product development cycles, rather than the sales and marketing cycles. Extending PDM data into the wider PLM scope can be a challenge. Linking PLM, PIM, CRM and ERP data across product, customer and financial insights is even less trivial for industrial OEMs.
Question 2: How Do You Combine PLM and CRM Insights When Introducing Products to the Market?
Shoemaker first answered this question by saying that organizations need to “take product information and present it in a market-facing way to ecommerce channels and distributor networks, to ensure that products are successful in the marketplace. Beyond managing product quality, it is about promoting the product and faster market introduction.”
Ambrosini added, “It is about what companies do with product data once the BOM is complete and once products are introduced to the market.”
In other words, PLM does not finish at the start of production; it remains meaningful and essential throughout market introduction, commercialization, through component and product delisting.
Shoemaker pointed out that “traditional PLM and PIM are very siloed; connecting the two under one umbrella platform is the core differentiator claimed by Propel—PLM powered by the Salesforce platform.”
This all makes sense as PIM requires information about the product, whereas PLM requires feedback about the performance of the product in the market. The two are intrinsically linked.
Question 3: What Metrics are Vital when Monitoring Customer Value and Employee Experience?
While answering this question, Shoemaker admitted that “PLM never really succeeded in democratizing product data beyond technical functions. PLM is important, but no longer sufficient.” It is about making sure that great products are rightly promoted through effective insight-driven revenue generation management. This is what Propel defines as PVM, the next evolution of PLM operating at the both top-line and bottom-line level.
Ambrosini and Shoemaker also discussed the fact that customer experience and employee experience are equally important. The need to provide simplified ways to connect people and systems is growing significantly. According to Shoemaker, this links to the growing servitization of products, shifting from a product-centric to a service-centric business model.
More companies are focusing on experience to reduce the need for complex data transformation between inside-out and outside-in perspectives. Ambrosini noted that several OEMs are transitioning to subscription models with their products, to make this successful customer service needs to be integrated with the PLM and CRM landscape: “Bringing product and customer data together can have a significant impact to both financial performance and customer satisfaction.”
Question 4: What Opportunities Does/Will AI bring to PLM?
AI is a hot topic, and it ranges across multiple technologies serving several business use cases and scenarios. In the context of PLM, Ambrosini pointed out that “a lot of value from products relates to supply chains, and supplier network operational efficiency.” When it comes to new technology, “AI has the potential to better mitigate product introduction risks. For instance, [it helps you] understand the ability of supplier components, the compliance status of these components and the proximity to end-of-life from these components.”
The Propel duo described a few interesting business use cases that can benefit from AI engines in anticipating supply chain issues. Examples included suggesting the best alternate parts or assessing competitive information to derive new product evolutions. These scenarios might already be in the Propel PVM solution roadmap. And AI is likely to bring more actionable and valuable insights across the combined PLM and CRM data scope than what PLM could bring on its own.
This conversation cut to the heart of what drives innovation, profitability and sustainable growth in today’s competitive markets: the need to connect product insights from PLM with financial insights from ERP and customer insights from CRM. This will help organizations navigate the delicate balance between innovation and profit, illuminate strategic decision-making, explore financial foundations, spotlight customer lifetime value and reveal the transformative role of AI technology
