The PIMM™ CTM program is the first monitoring program of its kind that allows distributors to share specific temperature performance data with their customer’s / brand owners without sharing every detail of their operations.

Our system provides instant feedback and alerts to allow distributors to properly manage their air temperatures across their refrigerated fleet. When an excessive temperature violation occurs the system can project the damage on the underlying products with the use of the PIMM™ PPT simulated product temperature.


The most common cause of conflict between distributors and customers are temperature violations rules that are based solely on air temperature monitoring. In the past, before simulated product temperatures were available, this was the only logical metric for customers to use to ensure products were delivered within their quality specification/guidelines.

The issue is that air temperatures can quickly spike in and out of the designated thresholds without causing much impact to the product integrity and quality. Many times, an external air temperature sensor chart from a route can look downright scary to the customer and could cause unwarranted rejections or claims. This is the dilemma for the current distributor: “at what risk do we put onto our company by offering greater visibility to our customers?”.

Take the chart below as an example. On a five stop route, the cooler zone of a refrigerated trailer showed spikes in air temperature above the allowed upper limit (40F) for every stop that was delivered. At Stop #3, there was a prolonged duration when the air temperature rose above 60F for nearly 30 minutes. When we projected the impact on the product temperature, using PIMM™ PPT, it was not until the beginning of the delivery at Stop #5 where we projected the product temperature rose above 40F.

By monitoring both air temperature and simulated product temperature (PPT), the PIMM™ system is the perfect compromise for customers to gain more visibility into their cold chain, while offering distributors a fair system to measure temperature control and delivery performance.



The underlying algorithm / calculation we utilized to produce our PIMM™ Projected Product Temperature (PPT) is Mean Kinetic Temperature (MKT).

Image: sourced from Vaisala "MKT Application Note - Vaisala.pdf" white paper.
Image: sourced from Vaisala “MKT Application Note – Vaisala.pdf” white paper.


MKT is an expression of cumulative thermal stress experienced by a product at varying temperatures during storage and distribution. MKT is not a simple weighted average. The calculation of MKT gives the higher temperatures a greater weight when computing the average than would a simple numerical average or an arithmetic mean.


The Pharmaceutical industry has widely used the calculation for decades and is deemed compliant by the FDA as an indicator to verify if a particular perishable has exceeded storage conditions.


MKT utilizes the default activation energy of 83.14472 kJ/mol for its calculation. This can be modified for a specific products profile, but any value between 60 and 100 kJ/mol (which covers most foods/liquids) will have only a small effect on the final value.


Extreme and sudden heat (ex: cooking product) will damage the product before the MKT simulated calculation would show a violation. The problem is not highlighted in food distribution where food is considered “tightly controlled” during the supply chain.


The key benefit of our PIMM™ PPT simulated temperature is that it is regenerated with each new air temperature reading we receive from the telematics hardware device. Our default setup is to receive and recalculate PPT every five minutes. This allows our system to be predictive and warn our distributor customers of a potential temperature incident before it becomes a food quality or safety risk.


While the underlying calculation behind our PIMM™ PPT is based on the mean kinetic temperature calculation, through field testing and thousands of simulations against route data in our database, Procuro has enhanced the algorithm to give even more accurate simulated temperatures through customized settings and variable inputs.

Here are some of the variables and customizations we can deploy to give our customers more accurate product simulated temperatures:

  2. Starting Temperature:

    Starting Temperature can be set to a default temperature (ex: 36F for lettuce), or we can use a probed temperature of a designated product during loading. This allows for custom starting temperatures per route to reflect more accurate product simulated temperatures.

    Storage Duration:

    The calculation assumes that the product was stored at the starting temperature for X amount of hours (ex: 12-24 hours) prior to the trailer loading. The storage duration gives the calculation some “weight” to offset initial warm spikes that can be seen during loading/staging, where product temperature is normally not impacted.

  4. All product simulated temperatures are based off of an air temperature reading from the trailer. Procuro supports a customizable “primary” sensor for each specific asset (trailer / truck). The two most common primary sensors are:

    1. Reefer return air (for trailers that have integration to reefer data)
    2. External air sensor

    Note: The reefer supplied data (ex: reefer return air) is often not available when reefers are shut off, which causes large data gaps during deliveries (when product is exposed to the worst thermal conditions).

  6. For longer transits, the starting storage duration can introduce too much influence on the product PPT calculation. We have the ability to modify our algorithm to only include temperature readings from the last X hours (ex: 4 hours) of the current transit to provide a more accurate current temperature of the product.

    This is especially helpful for products that are more susceptible to thermal abuse in a shortened period/duration (ex: lettuce).


While Procuro recommends running a generic product profile for frozen and chilled goods, since a distribution route consists of multiple products (SKUs) with different temperature profiles, we can support running PPT analysis on specific product profiles. To achieve that we would look to modify the following:

  1. Heat Activation Variable (∆H): food supplier/producer would provide the exact value to utilize (can range between 60-100 kJ per mol).
  2. Starting Temperature & Storage Duration: investigate what the product normally starts at when loading and for how long it is held.
  3. Rolling monitor window hours: during validation test we will analyze the proper monitor window hours to apply for specific products.


Procuro would run a 1-2 week product temperature study where we would monitor three temperature components side-by-side during live routes at one specified DC site.

  1. Primary air temperature (reefer return air / ambient wall sensor)
  2. Data-logger or probed logger placed inside product (to monitor actual product temperature)
  3. PIMM™ PPT

The PIMM™ PPT will be simulated and calculated by the Primary Air Sensor. Procuro will run the PPT calculation through thousands of simulations adjusting the variable fields in our calculation. The goal is to demonstrate a successful PPT “mimic temperature” compared to the product temperature collected on the data-logger (within 0.5 degrees Celsius / 1.0 degree Fahrenheit).


  1. Heat Activation Variable (∆H)
  2. Starting Temperature
  3. Storage Duration
  4. Rolling monitor window hours

All results will be shared with our customer to make final decision on the variables to implement for their customized calculation.



AI is the latest “buzz word” in the technology space but it’s been around forever. Artificial Intelligence is more accepted today simply because we have better tools to collect and analyze data.

IBM’s Watson is probably one of the most famous AI platforms. Invented in the 1980’s to “predict” outcomes based upon data collected for a variety of sources. In 1997, IBM Watson (Deep Blue) defeated Garry Kasparov in chess, followed by its highly-publicized win in the TV show Jeopardy in 2011. Today, IBM Watson is widely uses in the medical industry to diagnose diseases in seconds because its AI engine has access to every medical journal, lab test results and patient records and more.

Shelf Life predictions are relatively straight forward and have predictable outcomes based upon access to product production, storage and transit information.


PIMM™ Cold Chain Management (PIMM™ CCM) solutions provide our customers with “End to End Visibility” or “Farm to Fork” traceability. We track and trace the life cycle of our customer’s products throughout the distribution process.

The quality of the products is always determined by its environmental conditions. PIMM™ CCM provides visibility of these environmental conditions and issues the necessary corrective actions throughout the cold chain process using AI technology.


Temperature, time and sanitary conditions are critically important in determining the “Shelf Life” of a product. Data collection is derived for a variety of sources throughout the cold chain distribution process.

  1. Cold Storage Temperatures (Plants, DC’s, 3PL’s, Stores).
  2. Transit Conditions (Temperatures, Sanitary Conditions, Time Delays).
  3. Product Specifications and Profiles (Optimum Conditions, Thresholds, Sell by Date, etc.).
  4. Traceability Information (Lot #, Mfg. date, GTIN #, BOL #, CO #, PO #, etc.).


Our PIMM™ Analytics (AI engine) system has access to all of the product data as described in the previous section.

The storage and shipment data is analyzed by our AI engine in PIMM™ PPT – it is then re-analyzed with the product specification and traceability data to determine the number of “Lost Days” of shelf Life. The last calculation is to apply the number of “Lost “Days” to the “Sell by Date” which will determine the new Estimate Shelf Life Date.


The PIMM™ CTM system was designed to give distributors and fleet operators remote control of their refrigerated fleet. Telematics devices allow for real-time updates of the trailers location and temperature, but it’s how a system utilizes that data that is important. Our system analyzes the data to trigger real-time alerts and notifications when temperatures fall outside allowable limits. The aim is to alert our distributor customers to make a corrective action before a temperature event escalates above allowable food safety limits.


Our flag system is based on an escalating alerting system that has three different tiers. The first two tiers (Green & Yellow) are meant for internal alerts / events that would only be visible to the distributor. This will give them ample time to make a corrective action before an event escalates to the highest state (Red Flag), in which the event would be documented and visible to the customer as part of their temperature compliance program.


Distributors can implement a customized temperature reporting system for each key account customer they serve. This allows each distributor to be fairly graded against the KPI metrics that were contractually agreed to for each account they serve. For daily route and temperature management, we recommend to setup alerting at the DC level to the customer settings that have the strictest guidelines for temperature compliance.


Below is an example of a sample program that Procuro would recommend. All settings tied to alert thresholds, duration rules and person(s) to be notified can be customized to fit your temperature compliance program.



For every route monitored in PIMM™ we dynamically generate a Route Detail Report that summarizes the delivery progression of stops, charts the temperature for each refrigerated zone and compiles temperature data grouped by categories of Temperature Analysis and Temperature Events. This report is updated every 5 minutes during a live route and is available for historical review through search.


The CTM Delivery Report is a daily summary report that compiles all of the routes ran at a single Distribution location/center to measure temperature compliance in both the cooler and freezer zones. The analysis includes charting plots to represent when a delivery was made, when a temperature violation occurred and analysis on how many stops were delivered within temperature specifications.

The Event Journal describes the time of each designated event and our system can be expanded to include root cause analysis of the events along with corrective actions inputted from the distributor.

The CTM Delivery Report can be configured to be emailed to a distribution list numerous times throughout the day so managers can stay up-to-date on overall performance and review any violations that have occurred that day.

Below is an example of the CTM Delivery Report for one route in the Cooler zone.


All route and stop data collected in PIMM™ is recompiled into our reporting database that allows managers to review data in different summary views to target gaps and issues in their cold-chain. All historical reports are updated daily, allowing the latest route results to be included in the overall analysis each day.

The CTM Summary Report is a 3-level roll-up that summarizes temperature performance by:

Company Overview:

Compares temperature performance statistics for each DC location for the given time-range.

Site Summary:

Allows temperature data to be sorted and summarized at a single DC location by Asset (trailer/reefer) or Driver, which are the two largest contributors to proper temperature control for a refrigerated delivery.

Asset Details:

Lowest level view allows temperature data to be visualized by a specific Asset (trailer/reefer) or Driver. Each route that was delivered during the report time-frame would be displayed in this view.

All of our reports are interactive (clickable), so if the user clicks into the specific route it will take them directly to the Route Detail Report.


Every product that enters your distribution process can be tracked and traced by Lot # using the International Standard GS1 protocol. During the distribution process, PIMM™ captures the same GS1 data i.e. Lot #, Mfg. Date and GTIN #, that is used for traceability and apply our PIMM™ Analytics and PIMM™ PPT to predict the revised shelf life of a product.

The report below depicts the tracking/traceability of each product from its origin (Plant GLN) through transport, 3rd Party cold storage, retail delivery and store operations. The report also provides the details of the elapse time and project product temperature (PIMM™ PPT) during each phase of the distribution process in order to determine the revised or Estimated Shelf Life.

Leave a Reply