A Shifting Design Environment: Evaluating And Managing Risk Throughout The Product Life Cycle

As global economies slowly reopen in the midst of Covid-19,  new data we obtained has exposed systemic bottlenecks and vulnerabilities governing manufacturing and supply chain processes. Market uncertainty has long required changes in the most well-vetted sourcing strategies. However, with Covid-19’s “black swan” impact, it’s time for the once-siloed discipline of risk assessment to be incorporated into every sourcing professional’s toolkit.

Risk intelligence and mitigation, once the sole purview of C-suite executives overseeing financial issues such as insurance liability or credit, have a key role to play in the design phase of products relying on the global electronics value chain. We found that 80% of the lifetime risk and cost of a typical hardware product is “locked in” during the product’s initial design.

Understandably, many executive leaders view procurement’s role as the guardians of cost efficiencies in a global economy roiled by geopolitical trade risks and uncertain tariff agreements. Sourcing professionals must also cope with increasing complexity as manufacturers demand electronic subcomponents in an ever-wider range of innovative applications.

Under these conditions, the best way to inject resilience into an already stressed system is by incorporating cross-functional risk assessments at the earliest stage of new product introduction (NPI). That means rethinking the collaborative tasks and intelligence that product operations and hardware engineers undertake to create the Bill of Materials (BOM). This unique document is a linchpin of downstream impacts, from directing enterprise investment and a product’s time to market to its profitability.

Major Gaps In Enterprise Resource Planning

Traditional methods guiding enterprise resource planning (ERP) are inflexible and inefficient, as are the legacy enterprise systems governing product life cycle management (PLM). In the new post-Covid-19 environment, digital processes need to support cross-functional efforts to avoid problematic product rollouts.

We recently commissioned Dimensional Research to conduct a  survey of sourcing decision-makers and found that 81% of respondents said their sourcing applications failed to identify end-of-life (EOL) risk components, forcing them to make spot buys at premium prices after product launch. Another 57% of sourcing managers reported their new product launches were either delayed or canceled, while 39% said new product component selection frequently requires manual interventions to revise their BOM document.

How To Avoid Locking In Bad Business Outcomes

To inject reliance into the NPI process and achieve a risk-balanced sourcing strategy, supply chain executives should prequalify alternative suppliers and component parts according to form-fit-function criteria when designing the BOM. True, this requires additional engineering review to accept those options upfront. However, it can be far more efficient to empower procurement teams facing inventory shortages with an authorized road map that allows them to quickly swap out alternate parts or qualified suppliers as needed.

Engineers at NASA’s Jet Propulsion Laboratory leveraged this strategy successfully this spring. The team — facing an urgent need to make ventilators for hospitals battling the pandemic — had 37 days to design and produce a prototype that could be mass-produced for the market and meet FDA approval standards. This prototype and related design BOM were then offered as an open-source design for any organization seeking to rapidly source and assemble ventilators to address critical shortages.

The Need For AI-Enabled Decision-Making

Conducting supplier-based risk assessments and analyzing red flags and other signals require sourcing professionals to process a tsunami-sized volume of data points and relevant insights. No person can absorb that amount of information. To aid its decision-making, NASA used our design-to-source intelligence platform to optimize and finalize its BOM in a matter of hours.

Transformation leaders focused on supply chain optimization should explore systems of insight such as sourcing intelligence platforms, purpose-built using applied AI methods and large data sources. In this market environment, executives do not want sourcing professionals to rely on Excel spreadsheets, limited supplier-provided insights or manual overrides during the new product introduction process.

Keys To Successful Intelligence Solutions Sourcing

Most traditional enterprise systems used to support the direct materials sourcing process at most global manufacturers are incomplete, often characterized by aggregating spend history from multiple ERP systems and capturing netted forecasts on only a quarterly basis. Many commodity managers and sourcing professionals must then build their own unique spreadsheets to marry enterprise spend history with external market intelligence that is limited in terms of sources of insight.

Organizations beginning to develop a digital transformation road map for strategic sourcing and new product introduction should focus on new systems of intelligence that can synthesize multiple sources of insights across large data analytics into the context of key decisions along the strategic sourcing cycle.

The first step is to develop a shared transformation vision for improved agility, supply chain resiliency and sustainable value creation across supply chain, finance and product operations leadership. Second, solutions should be evaluated based on an “outside-in” mindset, focusing on how to scale the intelligence available to sourcing teams from external sources of insight. Third, a phased approach to implementation, adoption and value creation should be adopted rather than a “big bang” approach.

As Dr. Elouise Epstein, vice president and digital futurist at Kearney, wrote in Disruptive Procurement: Winning in a Digital World: “Traditional procurement technology is wholly inadequate to support the digital revolution. Today, there are more potential signals than any human can monitor or consume.” AI-enabled sourcing platforms with deeply targeted business intelligence have proved to make quick work of the research and validation needed to risk-proof the BOM, reducing unwelcome cost increases, shortages or delays in later stages of a product’s life cycle.

Root Out Bias And Hidden Assumptions

To navigate the new normal of supply chain disruptions, cross-functional teams should eliminate the deeply embedded bias of simply reusing components or suppliers. That is no longer a safe choice in today’s environment, but with digitized access to part parametric information, lead times and availability, these can be easy risks to avoid.

Risk intelligence methodologies will never be foolproof in a changing world. It is time to infuse the NPI process with increased vigilance, transparency and flexibility before the BOM is finalized. That is our fastest way to achieve supply chain resilience in a post-Covid-19 world where the only guarantee is more uncertainty.