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 cars1 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 waste2 from the mid-seventies to now. While there are of course several major differences between these industries, there are key lessons and approaches that 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 consistency3 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 In the 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, as illustrated
in Table 1 below.
|Group||Exposure Metric||‘Send To’ Rules|
|Group A: Longer Shelf Life||Pallets with less than three hours|
cut-to-cool exposure at 62°F
|Send these to the furthest away and/or slowest consumption retailers|
|Group B: Medium Shelf Life||Pallets with three to five hours|
cut-to-cool exposure at 62F – 65°F
|Send these to nearer and/or medium consumption retailers|
|Group C: Shorter Shelf Life||Pallets with greater than five hours cut-to-cool exposure OR more than two hours above 65°F||Send to fast-consuming buyers (e.g. food processors, restaurants, etc.) and/or retailers within same-day radius|
Applying Process Disciplines in The Cold Chain
Mapping out perishable foods supply chain processes involves identifying and capturing various elements, including:
- Key decision points—Identify the key decision points in the end-to-end cold chain processes that impact freshness and/or shelf life the most; such as deciding how long to leave produce in field before taking it to pre-cool, or deciding which pallets to put together to form a single shipment.
- Environmental parameters—Identify the input data and process metrics that have the most impact on key decisions; such as temperature exposure history.
- Harvest conditions—Identify key harvest parameters that help determine the total starting shelf life of the produce, such as: maturity level, harvest conditions, irrigation schedule, and so forth.
- Product parameters—Incorporate knowledge about different products’ handling requirements and the impact of environmental conditions on shelf life.
- Supply chain parameters—Incorporate supply chain parameters, such as origin-to-destination pair transport times, storage conditions, different customers’ freshness needs, and so forth.
A Freshness Metric to Drive Key Decisions: In the vast majority of produce supply chains, there is currently no ‘freshness metric’ that can reliably estimate remaining shelf life. Temperature exposure monitoring is typically only done piecemeal during transportation on a leg-by-leg basis. Several different process parameters impact freshness (e.g. product maturity at harvest, harvest climate conditions, temperature exposure over time, and humidity over time) each to a varying degree, with varying impacts on different types of produce. By tracking the appropriate combination of parameters throughout the end-to-end life of a pallet, a reliable remaining freshness metric can be calculated for each pallet. This enables managing and optimizing the end-to-end process and accommodating variations in freshness through intelligent routing.4
This knowledge can then be combined and mapped onto the various end-to-end cold chain processes, from harvest through final delivery, using algorithms to guide each key decision point in the process (such as which pallets to group together in a shipment and where to send each shipment). Knowledge-based rules can also be used to create alerts, measure performance, and drive potential corrective actions.
Identifying Key Decision Points
When mapping out the processes, key decision points are identified. These are where actions are taken that will have a material impact on freshness and/or decisions are made that should take into account the remaining freshness of the product. Examples of key decision points include:
- Harvest-to-pre-cool: In a typical operation, the trucks transporting produce from the field are filled to capacity before being sent to pre-cool. In warmer weather, this creates significant loss of freshness for those pallets that have sat in the warm temperatures the longest. Instead, data should be used to optimize the best time to send harvested produce to the pre-cooler, balancing shelf life vs. the cost of extra trips to the field. Input parameters for this key decision point include temperature exposure over time (cut-to-cool exposure), previous night temperature, and humidity.
- Grower pack house: Shipments from the farm are typically assembled into loads based on the sequence they happen to be put in the pre-cool unit. Instead, this critical decision should take into account remaining freshness of each pallet so that uniform freshness loads can be created (as outlined in Table 1 above). This ideally occurs prior to pre-cooling, but uniform loads can also be constituted just prior to shipment, in cold storage staging.
- DC load building: Similarly, at the distribution center, total temperature exposure history and other parameters can be used to calculate remaining freshness to select and sort cases destined to the store into uniform freshness loads. Customer destination parameters such as total transport time, velocity of consumption, and freshness requirements, can be used to determine which freshness group should go to which destination. This decision-making approach to load-building can be used at the grower’s, wholesaler’s, and retailer’s DCs.
In Part Two of this series, we look at the application of kaizen (continuous improvement) and knowledge-based systems in the produce cold chain.
2 This includes both pre- and post-consumer waste. For more, see The Progressive Increase of Food Waste in America and Its Environmental Impact. — Return to article text above
4 For more on intelligent routing (a.k.a. intelligent distribution), see Pallet-level Monitoring for the Fresh Food Supply Chain – Part Two: Intelligent Distribution. — Return to article text above