Multidisciplinary Decision Making: Not Everything You Need to Know Lives in a Data Repository
Product managers, design engineers, procurement officers, supply chain planners and other participants in designing new products and taking them to production face complex tradeoff decisions almost on a daily basis. And, as evident by frequent schedule slips, budget overruns, manufacturability problems, subpar product quality, and others mishaps, they don’t always make the right decisions. Why do organizations fail to make effective decisions?
The ability to make sustainable high fidelity product related decisions is predicated upon having the right information. This is obvious. However, because different decision-makers have different goals and constraints, the decision-making context for individual decision makers isn’t identical even when using the same information.
An example will illustrate the point. Design engineers focus primarily on fit, form and function. They tend to have less of an understanding – and pay little attention to – downstream topics such as procurement, manufacturing, environmental compliance, or service. Consequently, unbeknown to the design engineer, a certain design might prove difficult and expensive to manufacture. In a similar vein, in negotiating lower price with a new supplier, a procurement agent might source lower quality parts that will result in higher service and warranty costs down the road.
As practitioners who work in product and engineering organizations who utilize information stored in PLM and PDM software, we tend to make product related decisions almost exclusively based on information encapsulated in 3D models, BOMs and other engineering data repositories. Under some circumstances, this thinking can prove wrong or, at a minimum, very limited.
Under some circumstances, this thinking can prove wrong or, at a minimum, very limited.
Effective decision making requires balancing and optimizing multiple factors that are often at odds with each other. For example, improving product quality might result in longer development time and higher costs, which, in turn, will result in a smaller market share, unless, of course, higher quality will be enough of a differentiator to attract customers despite the higher cost. Therefore, a decision concerning the investment in improving designed-in and manufacturing quality should not be based exclusively on technical and reliability metrics (unless, of course, we are talking about product safety).
Rather, a higher fidelity decision should be based on a detailed analysis that takes into consideration numerous factors such as time to market, warranty costs, competitive situation, potential change in market share, and so forth.
Balancing short and long-term goals, such as determining the return on investment on enhanced quality and similar tradeoff decisions require careful consideration of rich multidisciplinary formation. Product organizations should be able to analyze and make this type of tradeoff decisions in every phase of the product lifecycle. However, they often lack complete information to make reliable long-term decisions, because multidisciplinary information is scattered around the organization in different forms and information management tools.
A Single Source of Truth?
Any conversation about product design information touches upon a notion that is near and dear to the heart of PLM practitioners: PLM as the keeper of the single source of product truth.
PLM, or perhaps more precisely, PDM, indeed maintains the most up to date version of product information. However, each decision maker views the information through a lens of goals and constraints specific to the task at hand. The focus on “localized” decision optimization may result in incongruent, even conflicting decisions, as demonstrated in the examples above. This situation is analogous to the parable of the blind men and the elephant, where each man creates his own opinion despite the fact that they use the same data.
However, each decision maker views the information through a lens of goals and constraints specific to the task at hand.
And, in fact, even the single “truth” represented by the PDM system may not suffice to create an accurate and effective decision context, as additional attributes of the “truth” reside and are maintained by other enterprise software, most notably ERP. Moreover, consider all the knowledge and experience (and sometimes biases) that is stored between the ears of those experts and was never formulated into any type of structured information.
Effective Multidisciplinary Decision Making
By definition, daisy-chaining highly optimized decisions will result in a suboptimal chain. Product organizations should shift the mindset and decision making process from focusing on highly optimized individual tasks to a comprehensive long-term decision making strategy that seeks to balance technical, economic and business considerations throughout the entire product lifecycle, from R&D to service.
Enterprise PLM processes and PLM software dictate the cadence of product development and as such is a good choice as the hub for engineering information, especially when it comes to product configurations and variant management. At the same time, the geometry-centric view of PDM should be augmented by incorporating other information stored in ERP systems and other formal and informal tools (email correspondence, spreadsheets and task-specific tools, for example).
As the principles of multidisciplinary decision making are practiced and honed, and more diverse information is routinely incorporated into the decision making process, product organizations can broaden the scope of decision making and add new facets such as design for serviceability, “should cost” analyses, complex compliance and supply chain decisions.
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About the author: (Joe Barkai)
Joe Barkai is a senior executive with extensive experience in business development, marketing, and product management across a broad range of industries. He focuses on helping software companies define and execute business and market strategy to drive market adoption, and working with company leaders to make intelligent strategic investments in software tools to improve operations.
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