Abstract

Ongoing real-time monitoring of the condition of food during transportation processes is currently lacking through many operations in spite of the development of currently available technologies such as RFID, ZibBee; identification, location and condition and GPS traceability, sanitation and others capable of providing increased revenue and return on investment. Container, case and pallet level measurement strategies provide much needed management information that can not only improve food safety and process quality but may also improve shelf life and delivery controls.

The use of these technologies has been shown to be highly informative and successful in a number of local and international pilot and other projects focused on improving food transportation processes that begin at the farm and end at the retail outlet or restaurant from which consumers purchase and consume ill-controlled products.

Keywords

RFID (radio frequency identification); ROI (return on investment); shelf life; ZigBee; GPS; real time controls; ATP testing; Bin sanitation; allergens; logistics

Return on Investment and Financial Benefits for Emerging Transportation Monitors

Assuming that food transportation is a definable, measurable and manageable process subject to quality control requirements, cost analysis and quality control principles may be applied in support of enhanced traceability, temperature control and sanitation standards. Figure 2.1 illustrates process improvement cost reductions and increased revenue streams.

On the left hand side of the figure, Cost Reductions, we see the potential for better controls in the form of reduced inspections, culls, recalls, rejects, disposal, returns, lost customers, reduced insurance costs and manpower reductions, while the potential to increase throughput yields increases.

There are ways to analyze the impact of these cost reduction steps using standard quality cost models. Figure 2.2 is read from left to right and shows that for 100 pounds of harvested tomatoes it is not unusual to suffer a 30% or 30-pound loss due to culls and sorting (size, color, shape, etc.). If the entire 100 pounds had been salvaged, the sale price at $1/pound would have yielded the farmer $100. In the case above, the 30-pound loss yielded the farmer $70. Add to that loss, inspection costs of $2 per hour, overhead expenses of $.05 per pound (equals a loss of $3.50) and transportation losses at $.15 per pound at $10.50: the total loss at the farm per 100 pounds harvested is now $46.00.

Figure 2.3 illustrates the same analysis at the distributor. The distributor sells his resulting yield (another 7-pound loss to the incoming 70 pounds) and with inspection, overhead and transportation costs wasted ends up with a loss of $25.10. The loss figures have increased proportionally because the loss to the distributor is no longer calculated at $.95/pound, but at the distributor’s selling price of $1.50/pound.

Similar calculations are carried out for this shipment through the processor (Figure 2.4), with the loss now calculated at $2.95 per pound based on 63 pounds incoming with a 15% loss totaled at $40.59.

Finally the retailer above receives a reduced load of 53.55 pounds after paying $2.95 per pound at incoming. Outgoing loss is for the retailer at $3.50 per pound totals $39.22 (Figure 2.5).

By totaling losses through handler steps 1–4 (farm – distributor – processor – retailer) and adding up the yield, inspection, overhead and transportation losses, we can estimate a total loss of $150.90 for quality costs. But, when we go back to the originating farm and calculate the final sales potential to the customer, we can see that only 45.52 pounds were on the shelf and these were sold at $3.50 per pound. If all 100 pounds of the original harvest had gone straight to the retailer with no yield, inspection, overhead or transportation losses, the sales potential for the process calculates out at $895.00 and shows a process dollar loss at 16.86%.

If we take these calculations from Figure 2.6 and base them on a farm shipment potential of 1 000 000 pounds of tomatoes per week for a relatively small farm, the supply chain is losing $1.5 million per week (Figure 2.7).

Basic Traceability and Monitoring Models

Regardless of food safety regulations or the financial losses that might benefit from some type of traceability system, traceability and monitoring of the location, type and condition of food in transit should be part of every supply chain quality and food safety system. Figure 2.8 shows a simple farm-level beginning for case-level traceability. Cases are tagged with pre-printed barcode labels. In some cases the labels are printed at a distribution center and sent to the farm. This procedure ensures accuracy over the printing process and helps to keep product, farm and case identification numbers accurate and in sync with the distribution system. Once the barcode labels are applied to the boxes and the boxes are loaded onto pallets, the pallets may be tagged. The pallet tag could be a temperature monitoring tag that can be reused. Barcoded case tags and pallet monitoring tags are then read using (in this case) a handheld RFID reader. System software then forms a parent–child association between the pallet and all individual cases. The system knows that a particular pallet was loaded with specific boxes of produce. Data are stored in a local (farm) database.

The pallet is then loaded into a truck. If the truck has a unique identification number and has been sanitized, the truck could also have its own reader system to record pallets of food that were loaded into the trailer, the time and date of the load, pallet temperatures, and, if GPS is involved, the location of the loading process. In this manner, a record is established that combines container (pallet to truck) traceability, load condition (temperature), location and time/date stamps. This system also allows system users to track the shipment, receive temperature out-of-control alerts and view temperature trends.

Figure 2.9 shows the transit or truck time (tags, temperatures, etc. may also be read during transit). The pallet is then unloaded into the distribution center, where it passes through a door portal system that activates the tags and causes them to download all data into the distribution inventory server. When the pallet is moved into a cooler matching the product temperature requirements, the time/date/temperature information is again downloaded into the server, thus completing a farm-to-truck-to-distribution-center-to-cooler record.

Outgoing pallets are typically built from cases brought into the cooler. Pallets moving out to a retail store, for example, may have a variety of products loaded, rather than a single product. In such cases each box barcode is read using a handheld reader and the pallet tag is again associated with the barcode information. This record then shows that individual cases were transferred to this pallet, were moved from the distribution center at a particular date and time, travelled in a particular truck at a specified temperature, and were delivered to a specific retail outlet.

Figure 2.10 shows a variety of pallets containing barcoded cases being delivered to a distribution center. All...