Global Testing, Inspection and Certification (TIC) company Kiwa has acquired St. Louis-based ASI. ASI has provided farm-to-fork food safety solutions since the 1940s. The company offers a full suite of safety and quality services to the food and beverage, dietary supplements and cannabis industries. The merger will strengthen Kiwa’s U.S. footprint in providing Food, Feed & Farm certifications.
“Kiwa and ASI share similar customer-first business values and follow the same business model when it comes to testing, inspection and certification. By joining the Kiwa family, we’re combining their wide portfolio of accreditations and services (BRC, IFS, FSSC, PrimusGFS, GLOBALG.A.P., Rainforest Alliance, MSC/ASC, Organic/USDA and many others), global business network and expertise with our client network in farm-to-fork food safety to assist our growing client base in North America even better,” said Charray Williams, CEO of ASI.
On January 1, 2023, 29-year-old Tyler Williams, CTO of ASI, will take the reigns as CEO of ASI. He is the youngest CEO in the history of the company. He will succeed current CEO Charray Williams and lead the expansion of Kiwa and ASI in the Food, Feed & Farm sector in North America. Richard Stolk, Kiwa’s global Director for the Food, Feed & Farm sector will serve as President of the Board of ASI and will be directly involved in the further growth of ASI.
“Kiwa already has a strong footprint in the global Food, Feed & Farm sector. With ASI, we significantly expand our reach, expertise and footprint, particularly in the U.S. but certainly with a global perspective. Now that we have welcomed ASI to the Kiwa family, we can better provide our customers with a one-stop shop for food- and feed-related certification services on all continents,” said Stolk.
New Jersey-based Lakeside Refrigerated Services is recalling about 120,872 pounds of ground beef products that may be contaminated with E. coli O103. The issue was uncovered during routine FSIS testing of imported products.
The recall affects ground beef products that were produced between February 1, 2022 and April 8, 2022, and have the establishment number EST. 46841” inside the USDA mark of inspection (FSIS has provided a full list of products and product codes as well as product labels). The products were distributed to retail locations nationwide.
Thus far there are no confirmed reports of illness or adverse reactions related to products affected by this recall. “Many clinical laboratories do not test for non-O157 Shiga toxin-producing E. coli (STEC) such as O103 because it is harder to identify than STEC O157:H7. People can become ill from STECs 2–8 days (average of 3–4 days) after consuming the organism,” FSIS stated in an announcement. The agency has advised that consumers throw out or return the recalled products to the place of purchase.
After finding evidence of rodent infestation during an inspection of a Family Dollar distribution facility in Arkansas, the FDA warned the public of usage and consumption of products purchased at certain stores from January 1 through present time. The affected products, which include food, were distributed to Family Dollar stores in Alabama, Arkansas, Louisiana, Mississippi, Missouri and Tennessee.
“Families rely on stores like Family Dollar for products such as food and medicine. They deserve products that are safe,” said Associate Commissioner for Regulatory Affairs, FDA, Judith McMeekin, Pharm.D. in an agency press release. “No one should be subjected to products stored in the kind of unacceptable conditions that we found in this Family Dollar distribution facility. These conditions appear to be violations of federal law that could put families’ health at risk. We will continue to work to protect consumers.”
The FDA inspection followed a consumer complaint and found both live and dead rodents, rodent feces and urine, and evidence of rodent presence, along with dead birds and bird droppings, throughout the facility in West Memphis, Arkansas. After fumigating the facility, 1100 dead rodents were recovered. FDA’s review of company records also revealed a history of infestation, with more than 2300 rodents collected between March 29 and September 17, 2021.
Among the range of hazards associated with rodents include Salmonella.
–UPDATE–March 14, 2022 — In an agency update, the FDA stated that it has removed the Salmonella Newport illness that was previously noted in the investigation. “In the early stages of this investigation, FDA included all consumer complaints of illness with exposure to products from the Sturgis, MI, facility. After further investigation, the FDA has determined that there is not enough information to definitively link this illness to powdered infant formula. CDC confirmed that this single Salmonella illness is not linked to an outbreak. The FDA and CDC are continuing to monitor for Salmonella cases and consumer complaints that may be related to this incident,” the FDA stated.
Earlier this week Abbott issued a recall of infant powdered formulas (including Similac, Alimentum and EleCare) that were manufactured at the company’s Sturgis, Michigan plant. The company received consumer complaints in infants who had consumed powdered infant formula manufactured in this facility—specifically, three reports of Cronobacter sakazakii and one report of Salmonella Newport. All cases resulted in hospitalization, and one death was reported.
FDA began onsite inspection at the facility and thus far has found several positive Cronobacter results from environmental samples and reported adverse inspectional observations. “A review of the firm’s internal records also indicate environmental contamination with Cronobacter sakazakii and the firm’s destruction of product due to the presence of Cronobacter,” FDA stated in a CFSAN update.
The recalled Similac, Alimentum and EleCare products can be identified by their 7-to-9 digit code and expiration date:
First two digits of the code are 22 through 37 and
Code on the container contains K8, SH, or Z2, and
Expiration date of 4-1-2022 (APR 2022) or later.
In a company announcement published on FDA’s website, Abbott stated, during testing in our Sturgis, Mich., facility, we found evidence of Cronobacter sakazakii in the plant in non-product contact areas. We found no evidence of Salmonella Newport. This investigation is ongoing.” It added that “no distributed product has tested positive for the presence of either of these bacteria” but that the company will continue to conduct testing.
Parents and caregivers can find out whether the product they have is included in the recall by visiting the Similac recall website.
For the 23rd quarter in a row, undeclared allergens were the top cause of food recalls and accounted for 45% of them in Q3 2021, according to Sedgwick’s latest Recall Index report. Within allergens, undeclared milk was the leading cause and prepared foods remained the leading category.
“Companies need to concentrate on the basics through the second half of 2021 and final emergence from the COVID-19 pandemic,” the report states. “Amid supply chain pressures, high consumer demand and worker health and safety concerns arising from the coronavirus, food businesses are rightfully focused on their ability to maintain and conduct their core operations in safe manner while delivering quality, safe products to customers.”
FDA Recalls: Notable Numbers (Q2 2021)
106 recalls affecting 7.9 million units
5.8 million units (nearly 69%) impacted by recalls were due to one nut recall
19 recalls were a result of quality issues
18 recalls were a result of foreign material contamination
11 recalls were a result of bacterial contamination—6 from Listeria; 4 Salmonella; and 1 E. coli
USDA Recalls: Notable Numbers (Q2 2021)
Recalls increased from 10 (Q1) to 12, but numbers still low compared to 2019 quarterly averages
Units impacted dramatically dropped nearly 83% to 207,322 units
Undeclared allergens were top cause of recalls, accounting for nearly 42%
Soy milk and eggs were main allergens, but first recall of food products due to sesame also occurred
Other recall reasons were quality (2), lack of inspection (2), bacterial contamination (2) and foreign material contamination (1)
Beef products (93,551 pounds) most impacted category, followed by fish (46,804 pounds)
The report also pointed out that heavy metal regulation will have increased emphasis, as FDA has made it a priority as a result of a report released by Congress earlier this year indicating the presence of dangerous toxic heavy metals found in baby foods.
Packaging is an essential component of our modern, global food supply. While it helps us preserve and protect food and creates instant brand recognition for consumers, packaging also inserts an additional level of necessary risk mitigation into the manufacturing process. Liability stemming from packaging is a serious concern for food manufacturers of all sizes, with millions of dollars and brand-damaging lawsuits on the line. Processed foods need packaging to arrive in the hands of consumers, and processed foods are necessary to feed the world’s population. According to a survey conducted by the United States National Library of Medicine, “60% of calories consumed were from ultra-processed foods.”1 This shows the prevalence of processed foods and the significant impact packaging, a ubiquitous component of processed foods, plays in the food industry.1 However, if not handled properly, food packaging can be a significant liability.
Liability from packaging commonly presents in two ways: First, as foreign material contamination. Broken, damaged or defective packaging material can end up in food products, which increases the risk of a consumer attempting to consume contaminated goods. Second, packaging can be a hurdle to effective remediation of foreign material contamination, as goods can often not be efficiently or effectively inspected back through in-plant critical control points. The resulting disposal of product can contribute to food and environmental waste, as well as lost profits.
The harsh truth is that if manufacturers lack processes to identify, prevent or mitigate foreign material contamination when it occurs in packaged food, packaging can be a significant liability at every stage from the manufacturing facility to the store shelf. Following strict food safety plans can save countless hours, resources and dollars in the long run.2
Foreign Material Contamination: Where It Comes From
Foreign material contamination comes from multiple sources in the production cycle. It can come from raw materials, like animal bones ending up in boneless meat products. It can happen during the production process when a screw or seal detaches from a machine and gets mixed into a pie. It can be biological, like an insect ending up in a bag of chips. Or it can come from packaging: A shard of glass winding up in a jar of salsa or a plastic wrapper finding itself in a muffin. All of these foreign material contaminants are risks and dangers for which manufacturers can be held liable.
Packaging-related contamination is a high priority for manufacturers. Foreign material contamination due to packaging occurs when contaminants like metal, plastic, styrofoam and other objects end up where they do not belong, disrupting the integrity and quality of the product. Packaging materials can break down into tiny pieces that inline inspection machines may not be able to identify. Inline machines are calibrated for a certain size and certain types of foreign material contamination, which may not include packaging materials. Additionally, inline machines are often used at critical points during the manufacturing process but are not commonly used to inspect completed packaged products.
Break It Down: Liabilities Within Food Packaging
The party most affected by missed foreign material contamination is the consumer. Current consumer trends point to greater ingredient awareness, education and research into the companies from which consumers purchase products. The consumer mindset of environmentally friendly products and socially responsible purchases are impacting the food industry directly. Consumers seek transparency from brands about the products they’re ingesting. When a consumer discovers foreign material contamination inside a product, it creates frustration and eliminates trust. In addition to negatively impacted brand reputation, a foreign object from packaging can be incredibly costly. Recalls, especially those that require a local or national public warning, are detrimental to a brand’s reputation.3 Consumer trust in a brand is priceless and can take years to repair when broken.
In the age of social media, consumer reports of foreign material contamination can spread like wildfire across multiple platforms, reaching countless consumers across the world. One tweet about a contaminated product can go viral, costing corporations their reputations or worse–– lawsuits. An accidental miss somewhere along the production line can result in public outrage and cost the manufacturer millions of dollars in wasted product and crisis management. Suppose a consumer accidentally consumes a foreign contaminant from product packaging which results in injury. In that case, the manufacturer could be held liable for the medical bills and even long-term care if the injury is debilitating. These court cases can be highly costly monetarily and in terms of public perception.
In addition to legal liability from consumers, the loss of product for foreign material contamination is typically very costly when labor, storage, time, materials and lost revenue are taken into account. A producer who makes the moral and ethical decision to dispose of product that may be contaminated loses money doing so. They also risk harming their reputation with consumers by contributing to the problem of food waste.
In the 21st century, shoppers are increasingly looking “beyond the label,” and are concerned with the impact their purchase behaviors have on the environment.4 Consumers in younger demographics are significantly more likely to have a purchase decision influenced by a company’s impact on and concern for the environment. Packaging is a major concern for food manufacturers as they seek to lessen their environmental impact to meet market demands. This impacts foreign material contamination in two important ways. First, as packaging materials move towards the use of biodegradable materials, the capability of technology to detect the difference between packaging and food material must increase. Second, environmentally-friendly packaging is still relatively new compared to traditional materials, and the risks of foreign material contamination associated with these materials are still relatively unknown.
Manufacturers are in a difficult position when dealing with the liabilities stemming from packaging as a foreign material contaminant. Compounding this issue is the role packaging plays in preventing manufacturers from using in-house processes or inline equipment to inspect product back through Critical Control Points. Inline mechanisms for identifying foreign material contamination are not designed to inspect completed, packaged product. If producers dispose of and rework product, they risk harm to sustainability-focused brands, production quotas and bottom lines. If they attempt to identify the contamination themselves, they lose valuable production time and potentially lose perishable product to spoilage. With nearly every solution, another liability arises.
Packaging Contaminants: Prevention, Response and Liability
The FDA-required Hazard Analysis and Critical Control Points (HACCP) plan has seven principles to ensure manufacturers meet food safety goals from production to consumption. Physical, chemical and visual tests are involved to ensure foreign contaminants do not exist in products produced in the manufacturing facilities.5 The more detailed processes are in place, the more protected companies are from recalls and reducing the chance of a lawsuit where the manufacturer is liable. Implementing different programs and processes to prevent and diminish food waste ultimately positively impacts the manufacturer’s bottom line. The Business Case conducted a study called “Reducing Food Loss, and Waste” that found “99% of companies earned a positive investment when they implemented programs to reduce food waste”.6
Many companies engage third-party food inspection partners as an extra measure to ensure that their product is free from foreign material contamination. By electing to utilize third-party inspection services, manufacturers hit a trifecta: They can typically salvage the majority of on-hold product, reduce food waste, and with the right partner, get the data they need to have traceability of foreign material contamination issues at their plant.
Manufacturers should pursue third-party inspection partners that meet a high standard of excellence. The best third-party inspection partners use cutting-edge technology to inspect products in higher detail than inline machines. Their machines should be capable of identifying foreign material contaminants of all types and have a high capacity to turn around large volumes of product efficiently. Their machines should be capable of overcoming the obstacle of packaging as an impediment to inspection using machines with a larger aperture than typical inline tools. Lastly, third-party inspection adds significant value if it has the ability to find and retrieve foreign material contamination so manufacturers can effectively use the resulting data to identify and remediate problems within the plant. An inspection service that does not meet these criteria is not an inspection service, but merely a method for outsourcing the same practices that a manufacturer would conduct in-house.
Liability Questions Answered
So, when are companies liable for packaging issues? Unfortunately, the answer isn’t always black and white. FSMA is in place to help prevent foodborne illness, requiring Food Safety Plans. In addition, the FDA recognizes “that ensuring the safety of the food supply is a shared responsibility among many different points in the global supply chain for both human and animal food,” so manufacturers may not be the only ones liable in many cases.7 The problem arises when manufacturers miss foreign contaminants or if foreign biological contaminants affect the integrity of the packaging.
Even if companies take the necessary steps, incorporate a HACCP plan and are vigilant, contamination can, unfortunately, happen at any time to any manufacturer. Using a third-party partner as an outside resource for foreign material inspection is important. Choosing a third-party partner with the experience, certifications, technology, processes and reputation to protect your brand is critical. Manufacturers can validate their internal processes and data using reports from their third-party inspection partner more quickly than they could internally.
Food packaging and the potential liability involved can be daunting. Still, with proper processes and procedures in place, manufacturers can have confidence that their products are hitting the shelves with a low probability of recall or lawsuit due to a packaging issue. While there is always a chance of foreign material contamination, quality packaging materials, quality assurance processes and quality third-party inspection partners can significantly reduce a company’s potential liability.
A multinational criminal smuggling ring was involved in the import of mislabeled siluriformes fish, including several species of catfish, into the United States. Import of such fish is prohibited to ensure the safety of the food supply in the United States. The smuggled catfish was labeled and listed on the import paperwork as other types of fish, which was discovered during a customs inspection. Subsequent seizures of shipping containers and warehouses led to the discovery of large amounts of mislabeled fish. The defendants face steep prison sentences.
It is an exciting time to be in the food industry. Consumers are ever more aware of what they are eating and more demanding of quality. And the vital need to reduce global food waste is transforming how we produce and consume food. This is driving innovation all the way along the supply chain, from gate to plate.
One of the biggest areas of opportunity for the industry to increase automation and improve food safety is in the processing plant. The challenges processors have faced in the last 12 months have accelerated the focus on optimizing resources and the drive for more adoption of new technology.
Foreign material contamination is a growing issue in the meat industry and new types of detection systems are emerging to help address this challenge. As Casey Gallimore, director of regulatory and scientific affairs at the North American Meat Institute, highlighted in a recent webinar, 2019 was a record year for the number of recalls related to foreign object contamination, which totaled 27% of all FSIS recalls in that year.
“There are a number of potential reasons why recalls due to foreign object contamination have increased over the years: Greater regulatory focus, more discerning consumers, [and] more automation in plants. But one important reason for this trend is that we have a lot of new technology to help detect more, [but] we are not necessarily using it to its full potential,” said Gallimore. “As an industry, we have a strong track record of working together to provide industry-wide solutions to industry-wide problems. And I believe that education is key to understanding how different detection systems—often used together—can increase the safety and quality of our food.”
Types of Detection Systems
Processors use many different detection systems to find foreign materials in their products. Equipment such as x-rays and metal detectors, which have been used for many years, are not effective against many of today’s contaminants: Plastics, rubber, cardboard and glass. And even the most well trained inspectors are affected by fatigue, distraction, discomfort and many other factors. A multi-hurdle approach is imperative, and new technologies like vision systems need to be considered.
Vision systems, such as cameras, multi-spectral, and hyperspectral imaging systems can find objects, such as low-density plastics, that may have been missed by other detection methods. Yet, depending on the system, their performance and capabilities can vary widely.
Camera-based systems are the most similar to the human eye. These systems are good for distinguishing objects of varying size and shape, albeit in two-dimensions rather than three. But they become less effective in situations with low contrast between the background and the object being detected. Clear plastics are a good example of this.
Multi-spectral systems are able to see more colors, including wavelengths outside of the visible spectrum. However, multispectral systems are set up to use only specific wavelengths, which are selected based on the materials that the system is expected to detect. That means that multispectral systems can identify some chemical as well as visual properties of materials, based on those specific wavelengths. It also means that other materials, which the system has not been designed to find, will likely not be detected by a multispectral system.
Another relatively new type of vision system uses hyperspectral imaging. These systems use chemistry to detect differences in the materials being inspected and therefore recognize a broad range of different contaminants. They are especially good at seeing objects that cameras or human inspectors may miss and at identifying the specific contaminant that’s been detected. The same system can assess quality metrics such as composition and identify product flaws such as woody breast in chicken. Hyperspectral systems also gather tremendous amounts of chemistry data about the products they are monitoring and can use artificial intelligence and machine learning to get a more holistic picture of what is happening in the plant over time, and how to prevent future contamination issues. This might include identifying issues with a specific supplier, training or other process challenges on one line (or in one shift), or machinery in the plant that is causing ongoing contamination problems.
Many processors are considering implementing new inspection systems, and are struggling to understand how to compare the expected performance of different systems. One relatively simple methodology that can be used to evaluate system performance is, despite its simplicity, called a “Confusion Matrix”.
The Confusion Matrix
A confusion matrix is often used in machine learning. It compares the expected outcome of an event with the actual outcome in order to understand the reliability of a test.
Figure 1 shows four possible outcomes for any kind of test.
Actual (True Condition)
True Positives (TP)
False Positives (FP)
False Negatives (FN)
True Negatives (TN)
P = TP + FN
N = FP + TN
Figure 1. Confusion Matrix
But what does a confusion matrix tell us, and how can it help us assess a detection system?
The matrix shows us that a detection system may incorrectly register a positive or negative detection event—known as a ‘False Positive’ or ‘False Negative’.
As an example, say we are testing for a disease such as COVID-19. We want to know how often our system will give us a True Positive (detecting COVID when it *IS* present) versus a False Positive (detecting COVID when it *IS NOT* present).
Let’s apply this to processing. If you are using an x-ray to detect foreign objects, a small piece of plastic or wood would pass through unnoticed. This is a False Negative. By contrast, a system that uses hyperspectral imaging would easily identify that same piece of plastic or wood, because it has a different chemical signature from the product you’re processing. This is a True Positive.
A high rate of false negatives—failing to identify existing foreign materials—can mean contaminated product ends up in the hands of consumers.
The other side of the coin is false positives, meaning that the detector believes foreign material to be present when in fact it is not. A high rate of False Positives can lead to significant and unnecessary product wastage, or in time lost investigating an incident that didn’t actually occur (see Figure 2).
The secret to a good detection system lies in carefully balancing the rates of true positives and false positives by adjusting the sensitivity of a system.
This is where testing comes in. By adjusting a system and testing under different conditions, and then plotting these outcomes on the confusion matrix, you get an accurate picture of the system’s performance.
Effectiveness of a Detector
Detection is not just the act of seeing. It is the act of making a decision based on what you have seen, by understanding whether something of importance has occurred. Many factors influence the effectiveness of any detection system.
Resolution. This is the smallest size of object that can possibly be detected. For example, when you look at a photograph, the resolution affects how closely you can zoom in on an image before it becomes blurry.
Signal to noise ratio. This measures the electronic “noise” of the detector and compares it with the “background noise” that may interfere with the signals received by the detector. Too much background noise makes it harder to identify a foreign object.
Speed of acquisition. This measures how fast the detector can process the signals it receives. Motion limits what you can see. As line speeds increase, this impacts what detectors are able to pick up.
Material being detected. The type of material being detected and its properties will have a significant impact on the likelihood of detection. As previously mentioned, for example, x-rays are unlikely to detect low-density materials such as cardboard, resulting in a high number of False Negatives.
Presentation or location of material being detected. Materials that are underneath another object, that are presented on an angle, are too similar to the product being inspected, or are partially obstructed may be more difficult for some detectors to find. This also presents a risk of False Negatives.
Complexity of the product under inspection. Product composition and appearance vary. For example, just like the human eye, finding a small object on a uniformly illuminated and uniform color background like a white kitchen floor is much easier than finding the same small object on a complex background like industrial carpet. Coarsely ground meat might be more difficult to detect than uniform back fat layers, for example.
Environment. Conditions such as temperature and humidity will have a significant effect on detection.
To understand system performance even better, we can use a detection curve. This plots out the likelihood of detection against different variables (e.g., object size) and allows us to objectively compare how these different factors impact the performance of each system.
Figure 3 shows how this looks when plotted as a curve, with object size on the x-axis (horizontal) and the probability of detection (a True Positive from the Confusion Matrix) on the y-axis (vertical). It shows three examples of possible detection curves, depending on the detector being used.
A detection curve tells you both the smallest and largest object that a detector will find and the probability that it will be found.
In the example presented by Figure 3, Detector 3 can see essentially 100% of large and very large objects, as can Detector 2. But Detector 3 is also more likely than the other two systems in the example to see microscopic objects. Based on this detection curve it would likely be the best option if the goal were to detect as many foreign objects as possible, of all sizes.
Of course, the performance of a detector is determined by multiple measures, not just size,
Detection capability can be improved for most detection systems, but typically comes at a significant cost: Increasing sensitivity will increase the number of false positives, resulting in increased product rejection. This is why looking at the detection curve together with the false-positive/false-negative rates for any detection system gives us a clear picture of its performance and is invaluable for food processing plants when selecting a system.
Using the confusion matrix and a detection curve, processors can compare different detection systems on an apples-to-apples basis. They can easily see whether a system can identify small, tiny or microscopic objects and, crucially, how often it will identify them.
Every detection method—X -ray, metal detection, vision systems, manual inspection—presents a trade-off between actual (correct) detection, rejection of good product (false positive) and missed detections (false negative). This simple way to compare differences means processors can make the right decision for the specific needs of their plant, based on easily gathered information. For all of us data geeks out there, that sounds like the Holy Grail.
Today the FDA announced a new plan to collect samples of romaine lettuce as part of its ongoing surveillance after the spring 2018 multistate outbreak of E. coli O157:H7. The samples, which will be tested for Shiga toxin-producing Escherichia coli (STEC) and Salmonella, will be collected from commercial coolers in Yuma County, Arizona during the current harvest season.
FDA plans to collect and test about 500 samples (each of which will consist of 10 subsamples), beginning in February and continuing through the end of the harvest season. In order to reduce the time between sample collection and reporting results, an independent lab close to the collection sites in Arizona will be testing the samples. FDA expects to receive test results within 24 hours.
“Helping to ensure the safety of leafy greens continues to be a priority of the FDA. This assignment adds to other work underway in collaboration with stakeholders in the Yuma agricultural region to implement actions identified in the Leafy Greens Action Plan, including a multi-year study to assess the environmental factors that impact the presence of foodborne pathogens in this region. Consistent with the action plan, the agency will engage with industry on conducting root cause analyses for any positive samples found during this assignment. Root cause analyses are important in that they seek to identify potential sources and routes of contamination, inform what preventive measures are needed, and help prevent outbreaks of foodborne illness,” FDA stated in a release.
COVID-19 precautions will be taken during the sampling plan. Agency investigators will preannounce visits and wear PPE while conducting the work.
The right preventive maintenance approach can improve food safety while saving money. With the right plan, food processing professionals can prevent serious machine failure, decrease maintenance costs and get a better sense of which machines may be more trouble than they’re worth.
However, not every preventive maintenance plan is guaranteed to help processors cut costs. Investing in the right strategy and tools will be necessary for a business that wants to save money with effective maintenance.
How an Effective Preventive Maintenance Approach Can Save Money
To start, the food safety benefits of a preventive maintenance program can help food processors avoid significant troubles down the line. Contamination and recalls will cost time and money.
They can also damage the professional relationships that businesses have with buyers. Recalls are extraordinarily expensive for food and beverage companies, costing an average of $10 million per recall, according to one joint study from the Food Marketing Institute and the Consumer Brands Association (formerly the Grocery Manufacturers Association).
Preventive maintenance can also extend machines’ life spans, giving a company more time before they’ll need to completely replace or rebuild a piece of equipment. Over time, this will help a business prevent machine failure or injuries resulting from improper machine behavior or function. In some cases, it can also mean cheaper repairs and less downtime.
Improving Records With the Right Plan
An effective preventive maintenance plan also generates a significant and detailed archive of maintenance records.
If a plan is implemented correctly, technicians will create a record every time they inspect, repair or otherwise maintain a particular machine. These records will be an invaluable asset in the event of an in-house or third-party audit, as they can help prove that machines have been properly lubricated, calibrated and otherwise maintained.
If a food processing business needs to resell a particular piece of equipment, they’ll also have a full service record that can help them establish the machine’s value.
Over time, the records will also give a highly accurate sense of how expensive the machines really are across an entire business. If the staff records repairs performed, tools used and resources and time spent, professionals can quickly tabulate each machine’s cost concerning man-hours or resources needed. These logs can help single out machinery that may be more trouble than it’s worth and plan future buying decisions.
With a digital system, like a computerized maintenance management system (CMMS), managers can automate most of the administrative work that goes into a preventive maintenance plan.
Modern CMMS tech also provides a few additional benefits beyond streamlining recordkeeping. For example, if a business is up against a major maintenance backlog or trying to balance limited resources against necessary repairs and checkups, a CMMS can help optimize their use of resources. As a result, they can make the most of the time, money and tools they have.
Common Preventive Maintenance Pitfalls
Typically, an effective preventive maintenance plan starts with a catalog of facility equipment. This catalog includes basic information on every piece of equipment in the facility — such as location, name, serial number and vendor, as well as information on how frequently the machine should be inspected or maintained.
Keeping spotty or incomplete records can make a preventative maintenance plan both less effective and more expensive. For example, a partial service record may give an improper idea of how well-maintained certain equipment is. Missing machine information may also confuse service technicians, making it harder for them to properly inspect or maintain a machine.
Too-frequent maintenance checks can also become a problem over time. Every time a maintenance technician opens up a machine, they can potentially expose sensitive electronics to dust, humidity or facility contaminants, or risk damage to machine components.
A maintenance check also means some downtime, as it’s usually not safe or practical to inspect a running machine.
Using the wrong maintenance methods can also sometimes decrease a machine’s life span. For example, certain cleaning agents can damage door gaskets over time. This can eventually cause equipment like a freeze dryer to be unable to create a proper seal.
The equipment manufacturer and technicians can usually help a company know what kind of maintenance will work best and how often they should inspect or tune up a machine.
Going Beyond Preventive Maintenance
Preventive maintenance is the standard approach in most industries, but it’s no longer the cutting-edge of maintenance practices. New developments in the tech world, like new Industrial Internet of Things (IIoT) sensors and real-time artificial intelligence (AI) analysis, have enabled a new form of maintenance called predictive maintenance.
With predictive maintenance, a food processing plant can outfit their machines with an array of special sensors. These sensors track information like vibration, lubrication levels, temperature and even noise. A digital maintenance system will record that information, establishing baselines and data about normal operating levels.
Once the baseline is established, the predictive technology can use fluctuations or extreme variables to predict improper operation or machine failure. If some machine variable exceeds safe operating thresholds, the predictive maintenance system can alert facility supervisors — or, depending on what kind of control the system has, shut down a machine altogether.
The predictive approach can catch issues that may arise in-between checks in a preventive schedule. This can help reduce the frequency of maintenance checks — possibly preventing further machine damage and saving the business money on technician labor.
The data a predictive maintenance system collects can also help optimize equipment for maximum efficiency.
Implementing a predictive maintenance plan will require a bit of a tech investment, however.
Food Processors Can Save Money With the Right Maintenance Approach
Preventive maintenance isn’t just essential for food safety — done well, it can also be a major cost-saving measure for food processors.
Good recordkeeping, a regular maintenance schedule and new technology can all help a business decrease maintenance and equipment costs. For processors that want to invest more in their maintenance plans, a predictive approach can provide even better results.
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Data generated from cookies and other behavioral tracking technology is not made available to any outside parties, and is only used in the aggregate to make editorial decisions for the websites. Most browsers are initially set up to accept cookies, but you can reset your browser to refuse all cookies or to indicate when a cookie is being sent by visiting this Cookies Policy page. If your cookies are disabled in the browser, neither the tracking cookie nor the preference cookie is set, and you are in effect opted-out.
In other cases, our advertisers request to use third-party tracking to verify our ad delivery, or to remarket their products and/or services to you on other websites. You may opt-out of these tracking pixels by adjusting the Do Not Track settings in your browser, or by visiting the Network Advertising Initiative Opt Out page.
You have control over whether, how, and when cookies and other tracking technologies are installed on your devices. Although each browser is different, most browsers enable their users to access and edit their cookie preferences in their browser settings. The rejection or disabling of some cookies may impact certain features of the site or to cause some of the website’s services not to function properly.
The use of online tracking mechanisms by third parties is subject to those third parties’ own privacy policies, and not this Policy. If you prefer to prevent third parties from setting and accessing cookies on your computer, you may set your browser to block all cookies. Additionally, you may remove yourself from the targeted advertising of companies within the Network Advertising Initiative by opting out here, or of companies participating in the Digital Advertising Alliance program by opting out here.