Process Disciplines Reduce Variance and Increase Quality
Manufacturing industries have realized enormous improvements in quality over the past decades.
In the late seventies, US automobiles averaged over 700 defects per 100 cars. By the late nineties, defect rates were down to about 100 defects per 100 cars and have continued to decline since then to less than one tenth the level of defects in the seventies. In contrast, the US food industry has seen about a 50% increase in food waste from the mid‐seventies to now. While there are of course several major differences between these industries, there are key lessons and approaches the food industry can learn and adopt from manufacturing sectors to improve quality and reduce waste.
The achievement of these dramatic improvements in automotive manufacturing quality was largely a result of process disciplines such as statistical process control and kaizen (i.e. continuous improvement). Process control provides early detection and correction of problems by monitoring the process and making adjustments as needed. One of the goals is to reduce process variations to make product of a highly consistent quality. In the case of the produce supply chain, quality consistency can be maintained end‐to‐end, so that the produce received at retail has a consistent, reliable shelf life, with much less waste at each stage across the chain. Ultimately this helps maximize the percent of harvest that makes it to consumers’ tables in time and condition to be consumed fresh.
For a produce supply chain, continuous end‐to‐end time and temperature monitoring can drive better decisions in the various processes from cut‐to‐cool, to pre‐cool, loading and unloading, transport, and distribution. For example, pallets being shipped from a farm are grouped into shipments without accounting for temperature exposure variations, resulting in shipments with widely varying remaining shelf life. Rather than assuming all pallets are equal, the time and temperature each pallet has been exposed to since harvest can be used to group together pallets with similar levels of freshness/remaining shelf life, thereby creating loads that have a relatively uniform remaining shelf life. A simple algorithm measuring time exposed to specific temperature ranges can be used to group products and decide where to send them.