Achieving Consistent Produce Quality—Part Two

Applying Kaizen and Knowledge-based Systems to Improve Cold Chain Processes


Kaizen methods can help continually improve produce cold chain processes, starting by addressing low-hanging fruit problems and then progressing to ever more advanced practices. Knowledge-based systems can provide situational awareness and help workers make smarter, data-driven decisions in real-time, taking into account changing circumstances on the ground, such as expected field harvesting rate/timing, pre-cooler capacity and queue length, and projected reefer truck arrivals, departures, and capacity.


In Part One of this series, we discussed how to improve the consistency of produce quality and shelf life by adopting modern process disciplines and quality management techniques. Here in Part Two, we look at the application of kaizen (continuous improvement) and knowledge-based systems in the produce cold chain.

Kaizen/Continuous Improvement in the Produce Cold Chain

Applying PDCA to Improve Cold Chain Processes

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Kaizen (Japanese for ‘improvement’) is an approach to continuous improvement. It may incorporate PDCA and other methods. PDCA (plan, do, check, act) is an iterative approach to continuously refining processes and improving performance and quality. Popularized by W. Edwards Deming,1 PDCA has been applied broadly over the decades as part of Total Quality Management (TQM) initiatives. The four ever-repeating steps are:

  1. Plan—Specify updated process steps and their expected outputs (i.e. the parameters by which to measure the results). For example, you may change your cut-to-cool process by imposing a time-limit on how long the product can stay out in the field, rather than always waiting until the truck is full. This time-limit might be different for different types of produce. The expected results might be an improvement in freshness, as measured by a metric designed specifically to calculate the expected remaining shelf life.2
  2. Do—This is where the actual work happens; harvesting the produce, building the pallets, transporting them to pre-cool, building and loading shipments, and so forth. With an intelligent system in control, these steps are now guided by the system for optimal results.
  3. Check—This involves studying the actual results vs. the expected outputs. For example, looking at what the actual remaining freshness is after imposing cut-to-cool time-limits and comparing it to the expected results. Advanced solutions can provide real-time alerts and prioritization that automate the “check” process, and enable management-by-exception.
  4. Act—If the new method results in an improvement in performance and reduction in variability, then this method becomes the new standard for the process; how to act going forward.

The process is repeated continuously as new process improvement ideas are tried out. For example, once a cut-to-cool time-limit has been implemented and shown to improve freshness and is working well, the formula for cut-to-cool time might be improved from a static time-limit to a variable time-limit based on the temperature in the field. The expected output may be a narrowing in the variability of remaining freshness for the product going into the pre-cool, longer average freshness, and a reduction in field-to-pack house transport costs (because on cooler days, the time-limit is relaxed, allowing fuller truckloads to be taken). These calculations can be quite sophisticated if they are being executed by an intelligent computer system controlling the operation. In that case, the system simply alerts the field crew when the optimal time to send the truck to the pre-cooler has been reached … neither the crew nor the supervisor needs to perform any calculations or set any timers.

Cultural Changes and Metrics Required to Achieve Continuous Improvement

Kaizen requires a cultural change where all members of the operations, especially the workers actually doing the work, are empowered and encouraged to look for opportunities for small continual improvements to the flow of product and each process step. By encouraging and incentivizing suggestions for improvements, a continual flow of new ideas can be generated. By constantly measuring process throughput and freshness metrics, positive improvements can be directly recognized and incorporated into the process. As well, this helps workers take pride and ownership of the work and the process.

This also requires that workers and management have immediate and ongoing visibility to the metrics defining success. In the case of a farm, an overriding metric might be something like the average remaining shelf life for pallets shipped from the farm. A system that can accurately calculate remaining freshness, and makes those metrics easily and continually visible to workers and management alike, is thereby critical. There may be other metrics for specific operations on the farm. For example, for the field crew, the metrics might include the average remaining shelf life for pallets arriving at pre-cool as well as the average throughput of the operation (cases or pallets per hour). The key is to make these metrics and current performance continuously and prominently visible to workers and managers.

Knowledge-based Systems

Situational Awareness/Total View of Operations

It is important that the system running the operations incorporates situational awareness, so its algorithms encompass a total view of the operation. For example, the “cut-to-cool” time-limit calculation may need to factor in available pre-cool capacity, as well as knowledge of the queue of other products waiting for pre-cool. Ideally, by knowing the capacity of the pre-cool unit, the expected shipments and rate of loading the trucks, availability of trucks, and expected rates of harvest—in other words, a total picture of the operation—the system makes smarter decisions, rather than blindly implementing simple threshold algorithms.

Intelligent Handling at Peak Operating Capacity

If operating capacity is not properly accounted for, the process and feedback loop breaks down during peak operating times. It may not be possible to precool every pallet to the optimal temperature, in which case workers are forced to start making their own judgments on when and how to keep things moving, and which pallets to cut short at which steps, to avoid ever-increasing backups and congestion. Real-time feedback and situational awareness can be used to identify when the operation is nearing full operating capacity, enabling the system to ‘shift gears,’ modifying its decisions to accommodate the higher product flows in the most intelligent way—maximizing freshness, matched with different customers’ requirements, within the constraints of the operation’s capacity vs. the volume of product being processed. The ability to intelligently handle surges at peak operating capacity is a critical component to successful adoption and utilization.

Incorporating Product, Customer, Supply Chain Knowledge

Source: Pexels, of pixabay

The system should also incorporate knowledge of the product, the customer’s requirements, and the supply chain. For example, different product have different handling requirements and impacts on shelf life. Different customers have different remaining freshness requirements. Different locations have different transport times. All this needs to be put together considering the current demand, i.e. shipments scheduled for the current day, available inventory, fields ready to harvest, pre-cool capacity, and so forth. The system can then recommend which fields to harvest and prioritize pre-cooling sequences to match the current day’s shipment requirements. Similar knowledge can be used in determining the sequence of shipments and which pallets to send where, as in the ‘intelligent distribution’ approach discussed above.

Getting Started

Produce supply chains have not employed nearly the level of process discipline seen in the manufacturing sector. As a result, there are enormous opportunities for process improvements in produce. Improvements from implementing these process disciplines in the cold chain have been proven to cut losses in half for both growers and retailers.

Though these systems are relatively sophisticated, getting started does not have to be difficult. There are cloud-based systems available that do not require deploying any computer systems and that already have much of the built-in knowledge and algorithms we have been discussing. You do not have to reinvent the wheel!

Furthermore, you don’t have to start with overly complex process controls. Experimentation on process improvements can start small and simple, for example with one harvest at one operation with one process. Once the team starts seeing results, they will be encouraged to do more. Change management is critical here—helping supervisors and workers understand why things are being done in a new way, making them part of the planning team for the new approach, giving them ownership and responsibilities for improvements, setting up incentives for offering improvement suggestions, and a culture of rewarding feedback. The results are well worth the effort. Not only will your operation continually improve, but your workers will feel more engaged and take pride in the operations they are helping to improve. Process disciplines can be a win-win for your workers, your firm, your customers, and ultimately the end consumer.

1 Deming was a highly influential pioneer and promoter of modern scientific quality control methods. — Return to article text above

2 Some software systems have built-in knowledge of how exposure to different environmental conditions over time will impact the remaining shelf life and can thereby calculate remaining freshness metrics. — Return to article text above

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