The Internet of Things (IoT) promised to provide untold connectivity. Initially, the IoT spotlight was on creating a myriad of products that could be networked and the network that could handle the load. Advances in microprocessors, sensor fusion, embedded software, and the concept of product as a service changed how we looked at product creation.
More recently, however, it became clear that a digital thread would be necessary to provide a seamless flow of data across the value chain, linking every phase of the product life cycle; from design, sourcing, testing, and production to distribution, point of sale, and even throughout its use. The fully digital representation would look at products and all downstream functions, enabling connected manufacturing and service, product performance feedback and traceability, automated simulation, artifact creation and collaboration, all designed to function in real-world operating conditions.
In a recent webinar titled iRobot’s Journey to Product Nirvana, Kevin Wrenn, divisional vice president and general manager of the PLM segment at PTC, and iRobot’s Steve Drzewiczewski, senior manager of collaborative applications, discussed breaking down barriers between engineering and operations. In the process of implementing a product lifecycle management (PLM) program, time and cost are substantially improved, while engineering and operations roles became increasingly simplified and even more enjoyable.
Three Stages of Transformation
The IoT realm requires the collection, analysis, and capitalization on the data collected that is generated by the product itself, as well as suppliers and customers. Logic alone will dictate that with the expectation of 30 billion connected “things” by 2020 (IDC), the move to product as a service makes perfect sense.
PTC’s solution is to create digital representations of things that will be used downstream—mechanical parts, software parts, electrical parts, elements of traceability including functional models and requirements. PTC defines guidelines as to how to think about digital engineering as three stages of transformation that include:
Understand—Taking available data and making better use of it to improve products as well as the end-to-end decision-making process. By enabling in-context access to all relevant data, the result is better engineering decisions and digital designs that satisfy requirements for all stakeholders. The process delivers greater awareness to all within the enterprise, not just the design engineer.
Advance—This stage involves thinking about designing for the connected world and considering what this product means to the IoT. One outcome is incorporating traceability, the design of custom data streams to bring back data into an environment for analysis and advancing the state of a product to modify it, even when it is in the hands of the customers.
Outperform—The third stage involves constant analysis, predictive performance improvement, the creation of a digital twin, use of outcome-based design concepts, and implementing a distributed AR/VR product review.
How adopting a PLM program works through the three stages can be readily seen in the following case study.
The iRobot Case Study
Founded in 1990, iRobot has more than 500 employees, $616.8 million in revenue, and over 25 years of advanced robotics experience. More than 14 million robots are installed and cleaning the inside and outside of homes globally.
When iRobot received a large government contract, they knew it was time to implement a product lifecycle management (PLM) program, establishing a sophisticated, cross-organizational data management strategy.
iRobot selected PTC’s Windchill PLM software. Initially, it was used as a glorified CAD vault. Unlocking its power would take several steps. As the company transitioned from a CAD-centric world to a part-centric world, Windchill part analysis became central to the effort. After migrating its legacy systems, Windchill became mandatory for each project. At first, the new system represented more work, as the company began to go through greater implementation. iRobot wanted to give its engineers more efficiency and make their jobs easier, but first needed to understand what challenges existed that impeded their work.
iRobot used a steering committee of 10 managers and directors from engineering, finance, and operation, but found they were too far removed from day-to-day operations to provide valuable input. Next, an advisory board was created, dubbed the Windchill Advisory Board (WAB) that reported on recent, existing, and future projects, allowing engineers to vote on the topics that would be discussed. Very quickly, iRobot saw results as stakeholders switched from what was most important to them, to what was most important to the company.
From an advisory board iRobot continued its evolution by creating departmental meetings, allowing for the collection of feedback and the identification of several common pain points. This move provided quick wins and where possible, eliminated steps and procedures that were ineffective and painful for the company’s engineers.
iRobot implemented a way to fix situations, but did so by providing less than a 100% solution initially. For example, a 50% solution after two months, two months later an 80% solution, and then a month later the final solution. By decomposing projects in this way, the value was seen by different groups within the organization at a much faster rate. Engineering and operations felt that their challenges were heard and all felt that they had more ownership of the PLM implementation.
The PLM journey spanned from 2004 through the addition of ever-greater functionality, solving inefficiencies in how the engineers worked, how designs were released, and adding automation for more rapid processes. The use of Windchill by iRobot provided:
Structure (eBOM), visualization, change management—What was obvious to iRobot immediately was that its engineers were excited. The company’s data was previously stored in many places, making it very difficult to find out where a component was truly used across the enterprise. BOMs were managed in Excel, so that the same copy of BOM might be on five different computers with the same exact name but different content. By automating the ability to create PDFs, engineers were freed up to build the next generation of iRobots. The engineering change process was also streamlined.
With the new system, staff had one system to access to find the information they needed. When there was a change, the operations team came into the process earlier so that they could help identify the potential impact of the change. The company also extended the Windchill environment to contract manufacturers who received notifications when changes were completed. Rather than waiting for notification from the operations team, they received it directly from the system itself.
Component and supplier management—As part of the process, the company brought its electrical engineering component library up to snuff, creating part-level detail of part characteristics and implementing the ability to effectively search. By putting an electrical components library integration together, any time a person created an electrical component, it was automatically sent over to the library. Engineers could stay directly in their CAD tool for significantly longer periods of time and focus on designing next-generation products.
iRobot could also, for the first time, negotiate directly with component suppliers to get better rates than it had received via their contract manufacturers. The operations team now flags parts to let its contract manufacturers know that this is a do-not-use part or it is a preferred component.
Packaging: getting the fully defined BOM out of Windchill—A process called vendor quote packaging (VQP) was in place at iRobot and although effective, it was very time consuming and detailed. An engineer could spend up to three weeks gathering the data out of Windchill, verifying that it was correct, and handing it off to the operation team. Instead, they expanded Windchill capabilities so that the packaging process went from weeks to minutes.
Product cost analytics—Before the PLM, one person was responsible for managing all cost quotes from contract manufacturers and data ended up in several different Excel files. When a new design was started, it took several weeks to have a cost breakdown of that new design.
When the company implemented the PTC Windchill Product Analytics (WPA), it finally had a tool that provided one location for all cost data. Engineers could access data that was previously locked up by operations and did not waste their time on designs that weren’t feasible based on cost. Instead, they focused on parts that were in their price range from the beginning of the design. Operations was now able to create detailed cost reports in a matter of minutes, allowing the company to make better decisions.
PLM Solutions for Today
Research firm IDC states that 40% of the top 100 discrete manufacturers plan to provide product as a service (PaaS) platforms. Implementation of this business model requires digital everything, including digital engineering. IDC also reports a 50% increase in investment to enable digitally executed manufacturing by 2017, and that 70% of global discrete manufacturers will offer connected products, increasing the need for embedded software, systems engineering and a product innovation platform that makes sense.
How companies implement PLM programs is based on many variables. What is apparent, however, is that although the journey may be challenging initially, engineers are clear beneficiaries of the substantial benefits it provides.