Manufacturers play a vital role in the engineering product lifecycle, transforming design plans and raw materials into functional parts and products in the real world. When there’s a major shift in the design and definition phases, it’s natural that downstream product lifecycle phases would be impacted.

Currently, the engineering world is undergoing a shift from using traditional 2D drawings to 3D model-centric product definition approaches. The natural progression for model-based definition (MBD) is for manufacturing to incorporate model-based approaches into their workflows and day to day operations. However, before you can successfully integrate model-based methodologies into the manufacturing process, it is necessary to optimize your strategy to ensure your supply base can consume and create parts using a 3D dataset.

Supply Chain Strategy Development

A strong, well-developed strategy is the foundational element of an efficient supply chain. When beginning to craft a model-based implementation supply chain strategy, consider factors like the composition and capabilities of your team, the landscape of your supply chain, your current state technical data package, and your reusability.

1. Staffing Your Team

To facilitate successful model-based implementation into the supply chain, your team must be staffed with people who have the necessary skillsets and personality traits to get the job done. Having the right players in place can significantly impact the success of your strategy as 3D model use migrates into manufacturing.

First, an ideal team for maximum impact should be made up of change agents—meaning they are visionary, adaptable, and willing to drive change across the organization. If the focal figures leading the initiative are resistant to change, it will make selling the concept to others drastically more challenging and create roadblocks for the initiative as a whole.

Second, having a team staffed with subject matter experts or individuals who have experience in model-based engineering will help to support the overall transformation process. If you do not have employees internally who have this experience and are not able to hire resources who do, the next best thing is to upskill existing employees to understand modern product definition and manufacturing processes. This entails training your current team to better understand where the pain points are in manufacturing once you start to shift data around, as well as how to find opportunities to feed manufacturing data back into product definition.

2. Assessing the Supply Chain

When developing a supply chain strategy, baselining the current landscape of the supply chain is key. One way to go about this is through conducting supply chain surveys.

Surveying is low investment, but it does require considerable planning, thought, research, and testing prior to deployment to ensure the information you receive is actionable. A common mistake made when assessing the supply chain through surveying is distributing surveys before you fully know what information you need back from suppliers.

To avoid this mistake, take time to thoroughly understand the capability matrix of the supply chain and what the supply chain targets are. Having a clear picture of these key components will support in crafting a survey that asks the right questions based on the information you need back.

3. Evaluating the Technical Data Package

Evaluating your current state technical data package (TDP) is a necessary part of developing a supply chain strategy. Evaluating the TDP gives insights into what data you are working with, how digitally mature the data package and elements are, and if the current state TDP will be suitable for supporting long-term goals.

When first extending model-based definition (MBD) into the supply chain, it’s necessary to include all MBD elements within the TDP. This includes geometry, annotations, characteristics, attributes and parameters, and presentation states.

In addition to inclusion of all MBD elements, the TDP also needs to be able to support a wide range of suppliers and maturity levels. When working with a variety of suppliers, you cannot assume they will all be starting from a similar spot. Realistically, your suppliers will be starting at vastly different places in terms of MBD consumption and creation, with varying levels of experience and capability, and diverse tools. Some suppliers will need more of a runway, whereas others will be further along in progression.

The TDP also needs to be applicable to different interoperability scenarios. When working to support a range of tools and systems, you should first understand where you are going to consume data to know what data needs to be consumed. From there, you can determine how ready each neutral file format is to support each of the product and manufacturing information (PMI) elements. Typically, on the engineering side, consumers do not care which file format is used, as long as it pulls in all the data they need.

4. Assessing Reuse

Creating models with the intent to be reused across the product lifecycle is a crucial part of enabling a traceable digital thread. Before moving model-based methodology into the supply chain, you must look at your MBD and assess its reusability with your current setup.

When considering MBD reuse in the supply chain, think about the areas of the business that consume geometry, annotations, and characteristics. Additionally, consider the landscape of CAD tools and software and where you are going to have interoperability gaps. Then determine how often you find yourself recreating or duplicating data. Once those gaps and inefficiencies are identified, those are your areas of opportunity for improving data reuse.

Supply Chain Strategy Execution

Crafting a strategy is just phase one of integrating model-based engineering practice into the supply chain. Subscribe now to receive the next Digital Blog article, which will discuss considerations and steps toward executing a model-based engineering supply chain strategy.

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