July, 2019


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knowledge. Tribal knowledge is information about processes, methodologies and more that is stored only in certain employees’ memories. This informa- tion is unwritten, but is often critical to successful- ly implementing a process, creating a product or maintaining quality levels. Employees that change roles or leave the


company will take this information with them, undermining the process or product they once oversaw. This presents a real risk for many compa- nies, harness manufacturers included. Ten thou- sand Baby Boomers retire every day in the U.S. In Canada, from 2011 to 2016, there was a 20


percent increase in the number of Canadians that reached the age of retirement. In the U.K., it is pre- dicted that between 2016 and 2020 the number of people between 16 and 49 will decrease by 700,000. The workforce is shrinking. How can managers


maintain productivity with a younger, less-experi- enced and smaller workforce? In par- ticular, how can they capture the vital information and expertise held by their current employees to prevent a catastrophic loss of tribal knowledge? In a typical, high-level, wire


industry, manufacturing engineering flow, design engineering first releases initial designs or engineering changes for costing and to provide quotes to the customer. Next, the main formboard is designed, followed by production mod- ules and subassemblies, which occa- sionally require their own assembly board. Then, the engineers will design a bill of process (BOP) for the entire harness, allocating wires, splices, twisted wires, and all remaining mate- rial to its designated equipment or workstation. The BOP is then released into


the enterprise resource planning (ERP) system. This is followed by bal- ancing and optimization of the final assembly carousel and then creation of the work instructions.


Introducing Error Errors from data reentry can


occur at any of these stages, each of which requires great skill and experi- ence. Adjustments and corrections made downstream in the flow must be fed upstream manually in order to achieve data coherency. The conventional wire harness


manufacturing methodology is vulner- able to errors from fragmented processes and the loss of tribal knowl- edge as engineers retire or leave their jobs. Other key issues include inconsis- tent or inaccurate costings, suboptimal formboard design or manufacturing process design, as well as misplacing key information on the shop floor. These can lead directly to inefficiency during production. As a result, manu- facturing and overall costs can over- shoot the quote made to the customer and production quality can suffer.


Model-Based Engineering A model-based flow unifies the


previously fragmented domains of design and manufacturing by automating data exchange and pro- viding engineers with access to cross- domain decisions. Tribal knowledge, previously held by experienced engi- neers, is captured through integrated design rules that support automa- tion, guide all engineers consistently and check designs for issues. There are three key aspects to


digitalization and the model-based enterprise in the wire harness indus- try. First are digital models of the wire harness product and the manu- facturing process. The digital models of the harness and production process together create what is known as a


The three key tenets of a model-based


enterprise: digitalization, automation and data reuse.


“digital twin.” Automation is the second key aspect. Today’s


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Model-Based Engineering for Wire Harness Manufacturing Continued from previous page


harness design and manufacturing solutions can consume design rules created by veteran engineers and use them to automate the transformation of the digital harness and process models into BOPs,


work instructions and other output formats. This simultaneously embeds tribal knowledge into the company’s production flow, safeguarding it from employee turnover. The third aspect is data reuse. Instead of


recreating or reengineering data, in a model-based engineering flow, data is created once and reused to the greatest extent possible by all upstream and downstream consumers. In a digital world, companies create a digital


thread in which all of the functions, from architec- tural and functional design through to physical design, manufacturing engineering and after-sales service, can all use the same data. At each stage of the harness lifecycle, each stakeholder can use the same data models and have access to decisions that are made in other domains. Using a digital thread, design cycles are faster and issues can be caught and resolved earlier in the process when


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