Quality Management in Supply Chains: Performance and Conformance
By Guillaume Roels
With the increase in outsourcing practice since the 1990s, supply chains have become very complex, involving many intermediaries. This figure, produced by Sourcemap.com, illustrates the complexity of today’s laptop supply chains:
Clearly, with so many layers, it is difficult for companies to have full visibility on their operations. And this lack of transparency inevitably leads to some management challenges. For instance, Boeing experienced huge delays in developing the Dreamliner, partly because they had very little visibility on their tier-2 and tier-3 suppliers. As another example, Mattel suffered from a massive recall of toys in 2007 because one of their contractor’s subcontractor’s employees substituted the paint purchased by Mattel with toxic lead paint. More recently, Samsung’s struggles with its Galaxy were attributed to two battery suppliers, the first one because of a design flaw and a lack of insulation tape, and the second because of a separate manufacturing defect.
Managing quality in such fragmented supply chains is indeed extremely challenging. When referring to quality, one often makes the distinction between performance, i.e., the mean quality of a product, and conformance, i.e., the lack of variation around that mean performance. To illustrate these concepts, consider the following output grade of a particular product over time:
Although the mean output grade is 100, the customer may prefer an output grade of 150, so one could increase quality by increasing the mean output grade, or performance, to 150, so as to be more in line with the customer’s desired quality, as depicted by the green line:
And once this is done, one can further improve quality by improving the conformance of the product by reducing the variation around the mean, as depicted by the orange line:
In multi-layer supply chains, issues can arise on both performance and conformance dimensions of quality.
Distorted Performance Standards
More often than not, quality standards are defined only in very loose terms and suppliers are not held accountable for the quality they deliver. Suppose that a supplier is being asked to produce a product with a quality grade of 150 ± 60. If the supplier produces an output quality of 100 on average, the contract terms may have well been filled, but the buyer may experience significant production problems because its input (which is the supplier’s output) may be below its optimal level. For instance, the buyer’s processes may have been optimized for an input quality grade of 150, and its yield may drop significantly when the input grade is lower than 110. If the supplier could improve the grade of its output by a small amount, e.g., from 100 to 120, the buyer’s yield could significantly improve.
In this particular case, what the supplier should certainly not do is to improve the conformance of its own processes. If the supplier were indeed to reduce the variation of its processes to deliver a quality grade that is consistently around 100, the buyer would never have the chance to improve its yield!
Most of these issues of distorted performance standards arise from the fact that the supplier has poor understanding of the buyer’s use of the input. In particular, do suppliers always understand how their output quality affect the buyer’s yield? Or how it interact with the quality of other suppliers’ key inputs?
Suppliers should not necessarily be the ones to be blamed. In fact, their lack of understanding of the buyer’s needs could be because of the buyer’s poor understanding of its own processes. Or it could be because the quality standards are hard to define due to their multi-dimensional nature.
Ripple Effect of Process Variations
Even if the performance standards are properly set across the supply chain, quality issues may arise in multi-layer supply chains due to lack of conformance in the different intermediaries’ processes. And this leads to a ripple effect, which, similar to the bullwhip effect, amplifies with the number of supply chain intermediaries.
To illustrate the impact of poor conformance, suppose that each company in the process chain achieves 99% conformance, i.e., produces 1% defects, conditional on receiving non-defective inputs. To start with, suppose also that the supply chain consists of two such companies and that defects are only detected when they reach the final market. In that case, the supplier produces 1% defects, which get processed by the buyer, therefore wasting valuable capacity. More importantly, the entire supply chain as a whole will produce only 99% x 99% = 98% conforming products, i.e., 2% defects.
Unlike the bullwhip effect, which propagates demand variability backwards as we move upstream in the supply chain, poor quality conformance propagates forward, as we move upstream in the supply chain. Moreover, the more intermediaries, the more defects, even if each company’s process achieves near-perfect conformance! For instance, if a supply chain consists of n intermediaries, each with a 99% process conformance, then the supply chain as a whole may produce as many as 1 – (99%)n defects, which is equal to 5% when the supply chain has n=5 intermediaries, and equal to 10% when the supply chain has n=10 intermediaries. The more intermediaries, the worse the amplification!
Time for Solutions
To reduce the occurrence of these distorted performance metrics and the ripple effect of process variations, supply chain partners need to develop a better understanding of the cost of quality as “defects” propagate downstream and interact with other suppliers’ products. Developing closer buyer-supplier collaboration, improving transparency, identify the causes of performance distortion and process variation, and aligning incentives is the only way these quality issues could be mitigated in the supply chain. Working together, supply chain partners have the opportunity to increase the overall size of the pie and create more value for the chain as a whole, by improving both the performance and the conformance of their inputs.