Success is driven by satisfying (or even delighting) customers— which depends on delivering above and beyond expectations. The supply chain can be considered as a system, with the goal of delivering on time, in the right quantities, high quality, highly reliable products at a reasonable price to co-optimize customer satisfaction and profitability expectations of the stakeholders. Process Development of the supply chain system can leverage the predictive engineering methods of DFSS / DRM, integrated with a First-Time Quality and “Quality Begins With Me” philosophy. Goals of on-time delivery, quality/yield, reliability, and financial expectations become the manufacturing metrics flowed down throughout the supply chain system, and are predicted for the supply chain for a new process under development, or for optimization of a current supply chain. The predicted manufacturing metrics are co-optimized to allow for rational decisions involving tradeoffs. First-Time Quality methods are leveraged to anticipate and prevent issues. Results from pilot efforts at Medtronic have been promising.
“For a manufacturer, success is driven by satisfying (or even delighting) customers— which depends on delivering above and beyond expectations”
For a manufacturer, success is driven by satisfying (or even delighting) customers—which depends on delivering above and beyond expectations. The supply chain can be considered as a system, from suppliers through internal manufacturing to delivery to customers (Figure 1)—and as a system, it is only as strong as its weakest link. Consequently, customer satisfaction is achieved if the goals of the supply chain—on time delivery, optimal price, high quality and reliability, and short lead or cycle time— are shared throughout the supply chain.
Supply Chain Process Development methods align perfectly with Lean Principles and the RADIO-V version of DFSS. As illustrated in Table 1, RADIO-V can be divided into discreet, yet interdependent phases of process development that are necessary for a robust supply chain— Requirements, Architecture and Design, Integrate, Optimize and Verify.
Figure 1. Medical Device Supply Chain
The requirements phase provides a methodology to effectively capture manufacturing metrics that identify value to end customer needs as design requirements. The relationships between and among these Manufacturing Metrics are illustrated in Figure 2.
A set of linked relationships as shown in Figure 2 can be used as guidance during the architecture and design phases of Process Development. Predictive engineering and the use of preventative design rules utilize Manufacturing Metrics to detect tradeoffs between the metrics— for example, where a proposed manufacturing cell layout or a possible supplier might have a favorable impact on a metric such as cost but an unfavorable impact on yield or cycle time. Predictive engineering can leverage tools such as Monte Carlo simulation to predict the probability that product technical requirements will be achieved and discrete event simulation to predict the probability that the cycle time, cost and throughput expectations will be met.
Table 1. Supply Chain Process Development alignment with Lean Principles and DFSS
During the Integrate phase of DFSS for Process Development, parts of the supply chain such as manufacturing cells or suppliers and the later assembly processes, are integrated together to establish an overall flow that can be assessed using the same Manufacturing Metrics shown in Figure 2. Through integration, the parts of the supply chain can be designed to work seamlessly together—largely through effective and timely sharing of information.
During the Optimize phase of Supply Chain Process Development, the development team cannot only work on co-optimizing the Manufacturing Metrics; they can also collaborate with the manufacturing operators to work towards ensuring a fault-free manufacturing process using an approach referred to as First Time Quality: at each step, the opportunities for errors, including human error and equipment error, can be assessed, and approaches such as Poka Yoke can be leveraged to reduce the probability and impact of errors. Specifications can be evaluated using Readability indices to ensure that the specifications can be read and understood by the operators, with clarity and easy-to-follow instructions and illustrations that the operators can help develop.
Pilot efforts aligned with this approach have been promising, albeit rather anecdotal. Successes include a $1.5 million savings in one factory and $918K savings in another from reduction of excessive inventory levels to more appropriate levels to buffer supply chain uncertainties, cost avoidances of $326K in one facility and $220K in another facility through prevention and elimination of factory escapes, and process developments associated with new products that were achieved on schedule for successful product launches.