Recently FDA regulations including HACCP, various CFR code of federal regulations, and the push to eliminate wasted time and money in fixing temperature related product failures has made Temperature and Temperature/Humidity Mapping an integral aspect of any warehouse operation.
Inadequately controlled environments can lead to ineffective medicines, spoiled produce, damaged materials and frequent critical equipment failures. This all ads up to wasted dollars. Fortunately the cost of monitoring large warehouses has dropped significantly over the years as temperature and temperature/humidity sensing technology has become more accurate, easier to use and more affordable.
Whether you are concerned about maintaining a consistent temperature in a 5,000 or 500,000 sq. ft. warehouse, you’ll want to make sure that you are using the right tools for the job and that you’re using them correctly.
In this guide, we will discuss best practices for mapping temperature in large spaces.
Planning Your Steps:
Mapping is not a one-time job, but an ongoing process that takes into account changes in seasons, HVAC/R modifications, warehouse layout modifications and any other significant changes to the warehouse environment. Proper organization and documentation are critical in maintaining compliance and consistency.
Step 1 – Determine Critical Mapping Points
Step 2 – Determine Sample Rate
Step 3 – Establish Data Logger Criteria and Select
Step 4 – Place Data Loggers at Pre Determined Points
Step 5 – Retrieve, download and evaluate Logged Data
Step 6 - Document Processes and Repeat
Step 7 – Data Logger Maintenance and Calibrations
Step 8 – Remediation
Step 1 – Determine Critical Mapping Points
Problem Locations:
Large open spaces present a considerable challenge when working to maintain a consistent temperature or temperature/humidity level. Problem spots include: • Areas near the ceiling or exterior walls may stay warmer or cooler in response to temperatures outside.
- Temperature levels stratify due to the fact that warmer air rises.
- Temperatures will tend to be higher near heaters. If fans are undersized or improperly placed they will be incapable of mixing the heated air effectively.
- Racking, shelving and pallet storage areas may create “hot spots” by obstructing air circulation.
- Doors that are left open will affect temperature conditions.
Additional Locations:
In addition to problem spots logger placement is also critical for the following locations:
- HVAC outputs
- Exits to unconditioned spaces (loading docs and staging areas)
- Outside (to compare outside temperatures to internal temperatures)
- High, medium and low locations in the general storage area
Spacing:
Studies have shown that a spacing of every 100 to 300 feet in an open warehouse plan, without walls to block airflow, is adequate enough to accurately represent readings that are meaningful. A distance of greater than every 300 feet may yield data that does not accurately reflect conditions in the warehouse space, while spacing data loggers closer than every 100 feet will result in extra data that adds no value while creating extra work.
Determination of critical mapping points should include all potential problem spots in addition to the normal storage area. Be sure to space the critical mapping points in an evenly distributed grid using every 100 to 300 feet as your spacing guide.
Step 2 – Determining Sample Frequency
The key to determining sample frequency is to not take too many or too few samples. Too many samples will create too much data making analysis cumbersome and difficult. Too few samples will not adequately represent changes in the warehouse environment. In most warehouses, one temperature or temperature and humidity sample every 15 minutes should adequately evaluate temperature trends.
If you think you need to take readings more frequently, consider the following:
Scenario 1
10 data loggers sampling every 15 minutes for 1week will provide 6,720 sample points.
Scenario 2
10 data loggers sampling every minute for 1 week will provide 100,800 sample points.
Scenario 1 will provide the same general results as Scenario 2 with 1/15th of the data and much less time spent analyzing logged data.
The key here is response time. Most temperature data loggers take at least a minute to respond to changes in temperature, and in a space as large as a 50,000 sq. ft. warehouse, most changes will happen very slowly over several minutes, making frequent sampling unnecessary and wasteful.
Step 3 – Establish Data Logger Criteria and Select
Temperature and Temperature/Humidity Data Loggers come with many features. The goal is to select the data logger that will most effectively monitor your warehouse. Key features you should consider are:
- Data Capacity: Data Capacity determines how many readings or sample points can be taken by a logger before memory is full. The more sample points a logger has, the more readings it can store.
- Sample Rate: The frequency in which samples are taken. The logger should feature user selectable sample rates.
- Monitoring Range and Accuracy: Be sure to select a data logger with a temperature range that can monitor temperatures even in the most extreme of cases. Don’t pay extra for accuracy you don’t need. +/-2oF and +/-2% RH should be adequate for most warehouse mapping situations. For refrigerated storage areas or locations requiring tighter tolerances, data loggers with an accuracy of +/-.5 oF should be selected.
- Size: Make sure the logger will fit in your selected locations. Some loggers are as small as a quarter making them perfect for tight locations or when you don’t want them to be noticed.
- Networking: Ethernet connectivity lets you view and download logged data and modify logger settings from your PC on any logger connected to your Local Area Network. These are perfect for smaller warehouse locations where critical items are stored and more frequent temperature readings are necessary.
- Battery Life: Make sure the battery life is long enough to last between mapping sessions. Many data loggers feature battery life between one and five years. More than enough to last through several mapping sequences.
- Calibrations: Be sure to purchase your data logger from a manufacturer who is A2LA accredited, NIST traceable, ISO 17025 compliant and can provide calibration services. The data loggers should be calibrated at least every 12 months.
- Software: Make sure the data logger software is easy to use and that you can export data into Excel for easy mapping of Mean Kinetic Temperature
Be sure to purchase at least one data logger for each location.
Step 4 – Place Data Loggers at Pre Determined Points
Be sure to document the location of each data logger and label each data logger to ensure that it is repeatedly placed in the same location.
To ensure consistency practice the following rules:
- Using the data logger software, name each logger by its location.
- Label the outside of each logger by its location
- Label the exact spot where the data logger should be placed by the data logger’s location name.
- Create a physical map with all data loggers marked by name
Step 5 – Retrieve and download Logged Data
Once the loggers have been placed and data has been collected, collect the data loggers and transfer the logged data to your PC.
The logged data can now be exported to Excel where Mean Kinetic Temperature can be calculated.
Mean Kinetic Temperature is a calculated fixed temperature that simulates the effects of temperature variations over a period of time. It expresses the cumulative thermal stress experienced by a product at varying temperatures during storage and distribution.
The formula for Mean Kinetic Temperature is as follows:
In addition to calculating MKT it is also recommended that Min and Max temperatures should be monitored carefully and that the location and the time of day at which they occur should be recorded. Any trends should be investigated.
A free program to calculate Mean Kinetic Temperature is available at: www.stabilitysystem.com
Step 6 - Document Processes and Repeat
Now that you have completed your first mapping, be sure to place the data loggers back in their original locations throughout the warehouse and make sure to document each and every step used.
Step 7 – Data Logger Maintenance and Calibrations
Over time the most robust data loggers can drift causing inconsistencies in recorded data thus requiring regular calibration in order to ensure accurate readings.
It is recommended that each data logger be calibrated at least every 12 months. In addition, it is prudent to request before and after readings when calibrating each data logger so corrections can be made to previously logged and mapped data.
Best practice recommends sending the data logger to a NIST certified calibration facility, and to the original manufacturer for calibration whenever possible.
Step 8 – Remediation
Now that you have your results it is time to fix any trouble spots that show up in your calculations.
Common Fixes:
- Hot Spots: Hot spots are frequently caused by walls or shelving that block airflow and/or inadequately sized fans that are unable to circulate air. Increasing the size or number of fans and removing unnecessary walls or rearranging shelves to promote airflow is helpful.
- Wide Temperature Fluctuations: Frequent changes in temperature in one location can be caused by direct exposure to outside air sources. Doors that are habitually left open, sky-lights, open doc doors and hallways to nonconditioned locations can make maintaining consistent temperatures difficult. Plastic curtains over open hallway or dock entrances will bock much of the hot or cold air from entering the warehouse space.
- Inability to maintain target warehouse temperature. If the overall warehouse temperature can not be maintained, an expert evaluation of the HVAC system is in order.
Conclusion
Temperature mapping can be an extremely powerful tool to aid in regulatory compliance and create possible cost savings via implemented improvements and efficiencies. The key is to carefully analyze the warehouse space to ensure proper placement of data loggers, document logger locations and mapping processes, audit data including having data loggers calibrated on a regular basis and finally making the necessary changes to continuously improve warehouse conditions.
