Introduction – Summary of Part 1
In Part 1 of this two-part series, we began by discussing how a perfect storm for inefficiencies and risk has been brewing – especially for automotive suppliers. We identified the quoting phase and its related processes and activities as a key source of this risk as visualized by Figure 1. Part 1 further drilled into these risks and distilled the challenges into three root causes:
- disconnected data and their associated applications
- manual lifecycle processes
- lack of real-time visibility
This was highlighted by the Data Cohesion and Process Repeatability Quadrant graphic below in Figure 2.
To overcome these challenges, we suggested that a product lifecycle management (PLM) platform be used. In the second part of this series, we’ll explore the value a PLM platform can provide and the specific operational activities that PLM facilitates.
What Value Does PLM Deliver?
As was noted in Part 1, the strengths of a PLM solution are its ability to establish a single access point for data, facilitate various processes and assemble data to produce insights and visibility. By leveraging these strengths, suppliers will be able to drive efficiencies, optimize reuse and provide real-time insights to critical decision making.
The diagram below (Figure 3) highlights the primary lifecycle of activities that an automotive supplier might engage in.
Inherent in this process is the connectivity of data, support for defined processes and the ability to re-purpose data into meaningful insights and decision-making score cards. Each of these is addressed in more detail below.
Data Connectivity is the Critical First Step
The diagram below illustrates this concept of data connectivity or cohesion. That is, data elements are connected and related, promoting reuse and ease of maintenance.
Given that these data sources are connected and related, users now can traverse and access data that corresponds to a program or quote. The diagram below further explains this concept as it relates to the information related to a part.
In this example, a part is attached to numerous attributes such as costs, purchase costs, and labor. As suggested, these attributes are single instances and can be accessed by other items through their connected relationship. Thus, various calculations and analyses can readily be achieved such as the impact of change on a quote, labor requirements, etc.
Data Reuse Benefits
The very nature of the Advanced Product Quality Planning or (APQP) process is to collect, analyze and assemble a broad range of data. The diagram below illustrates the various documents and data elements that are produced during this process.
Content developed and applied for one stage in a phase can often be reused in subsequent phases. Hence, maintaining selected data items in one place that is then repurposed to other stages, drives significant efficiencies and supports easier maintenance.
Intervention avoidance is another way of emphasizing the importance of process repeatability. Illustrated in the diagram below is the representation of a workflow process developed within the Aras Innovator environment.
The workflow modeling tool can be used to represent an unlimited number of processes. In addition to the graphical elements denoting tasks and routings, there are numerous options to articulate specific tasks assignments, exit options and escalations.
These processes can be thought of as “carriers” to collect, edit and finalize the data that would potentially be reused and repurposed throughout the various stages of the APQP cycle.
Visibility is about feedback and driving insights to support decision making. Within the context of automotive suppliers, this would include key score cards such as program P&Ls, status of program schedules and various key performance indicators (KPIs). Given the connectivity of data and the processes used to ensure accuracy, visibility can be derived on a real-time and on-demand basis.
Mapping PLM to the Automotive Supplier Process
The sales quotation is the headwaters of a program. Errors here will undoubtedly get magnified downstream … especially if products are produced in significant volume. These errors or leakages in profitability notably showing up as increased COGs (scrap, rework, overtime) and operating expenses (reduced efficiencies).
The nature of producing a quotation is to translate customer requirements into proposed product, schedule and cost attributes. Moreover, this “feasibility” exercise determines if it even makes sense to pursue an opportunity. And that feasibility is ultimately assessed in some form of a program P&L. We’ll call it a proforma P&L.
The screen shot below highlights what initiating a quotation might look like.
In addition to capturing key pieces of information, the various “relationships” are shown as tabs in the lower portion of the form. In this example, users have access to quoting history, the proforma P&L, requirements, the parts and bills and so on.
Think “production line.” From the time a quotation opportunity is initiated, multiple parallel processes are activated to collect the data and information needed to produce a quotation and the associated proforma P&L. In effect, a “quotation bill” is established and carries with it all contributing data, calculations and documentation. And, just as a part comes under change control, so would the quotation bill.
During this process, all needed stakeholders participate to address planning, engineering, purchasing, quality and manufacturing considerations. The processes can be comprised of serial or parallel tasks and activities with various exit criteria ranging from weighted voting, overrides and escalation options. The PLM platform would serve as the central repository with secure access based on user roles and rights.
No doubt, multiple quotations are typically prepared and submitted. Maintaining the “quote bill” from the outset, allows suppliers to understand the impact of change as reflected in the pro forma P&L and other derivative reports.
Upon being awarded the proposal, the quotation bill is converted to a “program bill.” The proforma P&L would continue through the lifecycle processes.
Program management tools are an essential component of the PLM platform. As illustrated in the diagram below the various tasks activities of the APQP cycle are illustrated.
The significance of the program management tool is that it is tied to other data elements of the program. This would include such elements as the parts/BOM, tooling, the numerous documents that are processed during the APQP activities and the Production Part Approval Process (PPAP) deliverables.
These program components can be based on templates to reflect one’s process peculiarities or incorporate OEMs specific requirements. And, of course, operating underneath these are the different workflow capabilities previously mentioned.
The design process inherently relies on multiple applications. The strength of PLM is to tie these applications and the resultant data together to inform the design. These applications can include CAD, configurators, or Configure Price Quote (CPQ) for example.
During this phase, varied content undergoes multiple changes. As part of this solution, visualizing this varied content is critical. In the diagram below, visual collaboration is illustrated, representing the ability for review and comments while varied content is a work in process.
The drum roll leading up to the actual production is the validation phase; the PPAP is the primary output of this phase.
The data produced from the numerous proceeding activities can be applied to the various PPAP documents and gathered as one virtual PPAP package – another perfect illustration of the power of the reuse theme.
However, the PPAP package is a living collection of data that is under change controls. Because any change to equipment, tooling, facilities, etc., would require the package be updated to reflect an accurate and current state of the execution process.
Once all the work has been done during the planning, design and validation phases, the supplier can launch into full production. During this phase, organizations can track configurations to the serial number (if applicable), identify effectivity of parts and capture performance metrics along the way.
While in production, a predecessor to any approved changes is based on the corrective action/preventative action (CAPA) processes. CAPA is a workflow process meant to capture data that can be analyzed and repurposed into a change request and processed into a resultant change order. A sample CAPA screen shot is shown below.
CAPA also plays a key role in understanding the concept of design maturity. The theme of design maturity is to understand the impact of change for improving quality. For example, if a certain manufacturing process labeled version #1 produces ten issues while a subsequent process, labeled version #2, produces six issues, then it could be said that the design process or in this case the design maturity is improving.
Support and Feedback
Given that data has been captured and controlled from the onset of the lifecycle starting with quoting and extending into delivering, organizations can readily develop lessons learned and insights that can be plugged back into new opportunities. This inherently supports a philosophy of continuous improvement that adds to the value of the business.
Automotive suppliers walk a fine line of balancing continuous improvement, risk reduction and pursuing quotation profit margins. PLM with an extended application layer specifically designed for suppliers, provides the needed data cohesiveness, process repeatability and visibility to meet these challenges.
This application layer supports the full lifecycle of activities starting with the quotation and extending out to the lessons learned. Organizations can leverage the out-of-the-box templates designed for the quotation process, APQP, PPAP, and CAPA deliverables.
Automotive suppliers face a combination of market forces that have created a unique business environment. This environment is characterized by high volume production, low margins and rigorous quality requirements. These in turn create a perfect storm for inefficiencies and risk. Companies such a General Motors, Lear, Magna, and Schaeffler have tackled these challenges with Aras Innovator’s platform-based approach to PLM. Via this digital transformation, they have been able to focus on the business of engineering and accelerate their ability to compete and drive improved profitability.