ASPECTS OF FOOD FRAUD - Part 1 of 3

I have been investigating the issue of food fraud and find that it is much more complicated than I thought.  My impression was, food fraud dealt with people/companies selling unsafe product or substitution of raw materials.  I am right but only partially.  It is true that fraud can impact food safety and can be caused by people/companies selling unsafe product or by switching your raw ingredients.  However - this is a very limited view of food fraud.

What is Food Fraud?

1. Counterfeit products
• Same as counterfeit hand bags. 
• An unsuspecting customer can buy this when there is a problem, call you
• Against Intellectual Property laws (misappropriation of logo, patent, trademark, etc.)
2. Adulterated ingredients
• Supplier sells you an item that is not as contracted - Honey mixed with corn syrup when you purchased 100% Honey
• Your Company running out of an ingredient and substituting an alternate
3. Smuggling of product to alternate markets
• Country x labelling the product as country z to avoid high Tariffs or to increase the price
• Big in the coffee industry where coffee can cross a border and now have a price that is 5 cents or more higher.  This does not seam like much but each container of 40,000 lbs will net $2,000 US dollars extra if at the 5 cent increase level.
• Also big with Honey - a country will avoid custom tariffs by cross docking and changing paperwork to look like it came from a different country (one with lower custom tariffs)
4. Manufacturing of near identical copies
• Similar to number 1. but with a slightly altered colour, wording, etc.
5. Production overrun
• Your co-packer or one of your facilities makes 100 units as per your order request.  The facility makes 200 - 100 units for you and 100 units for themselves.
6. Tampered product
• Changed Best by dating or lot code information.
• repackaging of a damaged product known to be comprised
7. Mislabelled product
• Wrong Allergen information
• Fraudulent nutritional information
• Wrong net weight or unit count
8. Known contamination
• Food has been tested and is unfit for consumption or is a lower grade than advertised.  It is still sold or remixed into good product to dilute the contaminant
9. Theft of product
• Your product is stolen from the truck, warehouse, store, or production facility and it finds a way into consumers hands.
10. Diversion of product
• Product is sold to a market where it was not to have been sold.

Very long list.  The common string in all the above is, they are crimes of opportunity.  Also the "fraudster" is always looking to get around a country's and your company's laws, rules, practices, and procedures.

Next - I will look why this Fraud is dangerous to consumers.

Internal Audits - Part 1 of 2

Introduction

I have been blogging about SPC (statistical Process Control), which is a great way to accumulate data on your process.  Another method of data collection is auditing.  Auditing will tell you much more than just numerical information and will bring a more visible presence of the programs to the employees.  As part of a GFSI (Global Food Safety Initiative) program - you will find the relevant sections under 2.5.7 in SQF edition 7 and section 3.4 in BRC edition 6.

What needs to be audited

In short - all activities that are being done on the production floor need to be audited.  Specifically mentioned in the BRC and SQF codes are all prerequisite programs, HAACP program, programs in place to maintain/achieve standards (example - maintenance program and warehousing program), food quality program (SQF level 3), and all government regulatory requirements.  In the two codes, only BRC states that hygiene, equipment, and building audits must be at least once per month.  All other audits are at least annually or based on risk analysis.  Please note, the higher the risk to product safety, the higher the frequency of audits. Personally, I typically see hygiene audits once per shift.  Of course the frequency of audits will be higher when a new or improved program is begun and the frequency should decrease as people understand what is required from the program (less non-conformances).

Non-Conformances

An interesting notion on non-conformances is, if the auditor can correct the non-conformance during the audit, then it is a responsibility of the auditor to make the correction.  All corrections and non-conformances need to be written and actioned.  Actioning a non-conformance means that a corrective action is created with a timeline for completion.  Without the completion timeline, I find the correction gets placed in the "I will do this tomorrow" box and tomorrow is always tomorrow never today.  If the non-conformance has a potential risk to food safety it may need to be communicated to upper management.  This communication is to ensure that other company managers know that the non-conformance needs to be corrected and if funds or external help is required - then the appropriate help and funds are made available.

Summary

All audits will need to be placed on an audit schedule with the who, what, where, and when items defined.  Who, keeps this schedule?  The lead auditor keeps the schedule - usually the QA Manager.  This lead auditor will also need to keep upper management informed about non-conformances and corrective actions.  A great way to summarize the information is by construction of a Pareto diagram (see statistics blog part 2).  Also to chart the information as a KPI (Key Process Indicator) where the number of non-conformances or overall audit scores are mapped monthly.  An overall score would need the development of a numerical system on each audit form to make it possible to create a KPI around the audit.

Part 2 will look at what elements are being looked at and what makes a good auditor.  I will also look at the various audit areas and what should be "in" the audit.

How You can use Statistics in the Food Industry - Part 5

In this last part of the series I wanted to go through 2 items one being 6 Sigma and the other putting together an SPC program with the methods that I have blogged about.

6 Sigma

6 Sigma is a program that is taught for industry and the name comes from the following concept.  At +/- 6 sigma (6 standard deviations) from the mean (average) of a process you will have 3.4 off specification units per million units produced.  Yes, I am use to seeing +/- 3 Sigma as the standard so 6 Sigma seams rather extreme.  However - considering the thousands of parts in a car, having each part at +/- 3 Sigma there would be a lot of defective cars on the road (the off spec parts are cumulative).  I will stick my neck out and comment that in some cases, 6 sigma IS extreme.  Consider the roasting of whole bean coffee.  There is one raw material and only 3 steps.  Roasting, weighing, and bagging.  In such a simple system - +/- 3 sigma is probably OK

In either case - it is the upper and lower specification that determines how much variability can be tolerated in your process.

SPC Program

The following is an example of putting together a SPC program.  This type of program cannot be created overnight - it will take time, training, patience, and support from MANAGEMENT.  Here are the steps of my example (NOTE - this will need to be repeated at each process step that you are doing testing):

1. Brainstorming - figure out (as a group) what you want to accomplish and at which points of the process.  Fishbone diagram is a good technique at this point.
2. Initial run charts - Creation of a run chart and training in the usage of the chart.  This will get you data to analyse.
3. Plot your Histogram and run your Cp and Cpk study to see if you are in control of the process step and have a normal distribution curve.  If you are in control and have a normal distribution then proceed to step 5, if not go to step 4.
4. Not in Control? Run a gauge R&R to find where the variation is coming from.  With the R&R test results you may need to do a root cause analysis (Fishbone, 5 Whys) and correct for the variation.  This correction will likely require retraining of the staff.  Redo step 3.
5. Great, you are in Control! - Redo your run charts if necessary, taking advantage of the lessons that you have learnt to this point.
6. Monitor - Keep the run charts and histograms going.  This is also your verification step.
7. Out of spec product - you can get samples of out of spec product and do a root cause analysis on them.  For me, this gets linked with customer complaints and is a great place to do a Paredo chart.  Based on the root cause analysis you will need to put a "plan of action" together to correct the issue.
8. The second to last step is the creation of a checklist.  The checklist is used at a frequency that is determined by risk and you.  This is also called the Validation step.  The checklist can include such items as;
• Training record check
• Log check
• Are the calibrations being done
• Preventative maintenance cycles completed
• Observation of the people (are they following protocol)
• Check on the written procedures (are they current, legible, visible)
• View the process
• Review the Cp, Cpk, Histograms, and any corrective actions
9. The last step - return to step 2.

Again - this will need to be done at each testing point of your process - which is why I said that doing an SPC program will take a lot of time and patience.

How You can Use Statistics in the Food Industry - Part 4

Gauge R&R

Gauge R&R (Reliability and Reproducibility) is a method to look at personnel, protocols and equipment when studying your quality control checks. 

1. Are your operators doing the checks the same way?
2. Are our protocols / procedures being followed?
3. Is the equipment (gauges) acceptable for the task that is being performed?

The statistics on this are fairly heavy and yes, I use Excel to do the calculations.  There are some free programs that will do the heavy lifting for you.  Here is what you need to do for a gauge R&R study:

1. Make sure that your measurement device is functioning correctly, calibrated, and the same device is used for all tests.
2. Three operators is optimal - you can use two but the results will not be as accurate.
3. Ten samples that each operator will test three times (mix the samples up so the operators will not know which sample is which).  These samples should be representative of your process.
4. Run the test and plug in the results into your program.
5. You will need to capture the average and ranges for each sample on each test as well as the average for each operator on each of the three tests.

What will come out of the program is the variability of the samples vs. the operators in both numerical and graphic terms.  What you want to see is a total variability below 30%.  Let us take our Jelly Donut example where we tested ten donut weights - the information from an Anova test could look like:

% Gauge R&R = 36.8%
-Reproducibility=-0.4%
-Repeatability=13.9%
-Donut to Donut=86.5%
So, the total number of 36.8% is much higher than the needed 30% - OUCH! Reproducibility is the measure of the variation from the operators - Great! if we had a higher variation for the operators we could look at how the operators are performing the weight checks. The repeatability is variation from the equipment - OOPS - at 13.9% this needs to be corrected.  Either the equipment is faulty or maybe not sensitive / accurate enough.  Looks like we need a "more accurate scale".  The Donut to Donut variation is 86.5% which is good.  You want most of the variability to be found in the product.  As the variation in the equipment and operators decreases, more variation will be accredited to the product.

What about graphs?  Everyone likes to see a visual that summarizes and makes the results very clear.  From the above example we could look at the variation between the operators based on the averages of the samples.

From this Operator 1 and 3 are close to each other but operator three has more variation in the average weights of their samples.  Operator two is quite different from the others.  Visualizing this definitely pin points the need of retraining operator 2.

 

 

The last item to discuss is the type of samples you need to perform this analysis.  The above works really well on non-destructive testing.  If the test is destructive, you will need to do one of two things;

1. Find a method to simulate the destructive test non-destructively.
2. Find enough "identical" samples to run the test