three stages of quality proofing

Technology continues to advance at breakneck speeds. Whatever your industry, there are definite advantages to riding the wave, even if only to keep pace with everyone else. That includes the often-forgotten quality-control sector, which can be very competitive.

That’s not just in reference to external competition among fellow solutions providers, but internally as well. There’s always going to be the pressure to improve upon set processes for greater error-finding efficiency. It only makes sense then to embrace automation, which has proven incredibly beneficial in that respect, especially when it comes to proofreading solutions.

Here are a few of the top benefits:

Prevent Avoidable Errors

One misconception revolving around automated solutions is they only serve to replace workers. That’s not necessarily true relative to digital proofreading solutions.

In fact, generally speaking, automation can actually help employees. With specific regard to proofreading, it makes employees’ jobs easier, preventing proofing fatigue and avoidable errors from slipping through the cracks. It’s been proven that manual proofreading is simply less effective and shifting gears to an automated solution is an obvious choice. With fewer errors, less pressure is put on employees to consistently perform “above their heads.”

Truth be told, a company is financially healthier and in a better position to keep more workers the fewer errors that result in costly recalls. Automation lets companies allocate human resources to other departments in greater need. And, contrary to popular belief, there will always be a need for technicians to physically check automated inspection results, even if they’re not the ones performing the actual inspections. Put another way, workers control the helping hand digital solutions lend to the overall operation, at least in this case.

Instant Detection and Verification

Companies can shave time off their manual proofreading processes to a greater extent through the sheer convenience of an all-in-one solution. High-quality digital proofreading platforms don’t just look for typos. However expensive one small misplaced decimal point can end up being for the company in question, the right application can pay for itself in other ways too.

A system like GlobalVision offers inspection modes for artwork, color, barcodes, and Braille (in addition to text). Regarding barcodes and Braille regions, the system instantly recognizes each one to be graded or translated/ compared. There’s no need to select anything except the desired mode unless it’s to conduct a partial inspection on a specific portion of the loaded files.

In the case of graphics, you can similarly choose a shape for extraction on the loaded Master instead of opting to inspect the entire file. This just serves to instruct the application to automatically locate all the repeats on a sample press sheet (for example). The benefit here far outweighs any additional steps that need to be taken.

Ultimately, inspections go much smoother and faster, with up to five different types able to be run at once in a matter of seconds or minutes instead of hours or days… a fraction of the time it takes to proof the same materials manually… more effectively too.

Get to Market Faster

Not only does a digital proofing platform translate into error-free packaging on store shelves and artwork files in general. Fewer mistakes also mean fewer revision cycles, which translates into an accelerated time to market and bigger bottom line. That’s in addition to the money saved implementing a quality-control system that’s more efficient and the recalls that will be avoided as a result. Going digital pays for itself over time.

If it’s not the improved speed at which you get to market, it’s the improved speed at which inspections are run and errors get detected. Adding an automated element, such as a digital proofreading platform, to your quality-control process means keeping pace with technological change and competitors alike. That’s undeniable, but it’s the internal performance improvements that make automation a trend that should not and cannot be avoided. Much like the errors the right system so effortlessly and inevitably detects.

Monitor with data integrity checklist

Consistency is a trademark of both validation and data integrity. In fact, without validation and the consistency for which it strives, forget data integrity. It’s like there is no data at all.

What Is Validation?

To be clear, validation is defined as, “evidence that provides a high degree of assurance that a specific process will consistently produce a product meeting its pre-determined specifications and quality attributes.” The “consistently” is key. There is no good use for a system that cannot do what it promises every single time.

Think along the lines of a phone that only dials the right number four out of five times. Eighty percent is a good mark on a test, but not so much in case of an emergency when you need to call 9-1-1 and you get the local pizza place instead. That’s in large part why system validation is so thorough, with Installation, Operational, and Performance Qualification processes entering into the equation.

For its part, data must be consistent throughout its entire lifecycle for it to have integrity. It plays into the need for data to also be accurate, which is one of the Food and Drug Administration’s expectations for Good Manufacturing Practices. Altogether, data is expected to be “Attributable,” “Legible,” “Contemporaneous,” “Original,” and, of course, “Accurate,” or “ALCOA” for short.

Data itself can be validated too. For example, to achieve compliance with the FDA (21 CFR Part 11, specifically) in pharma and life sciences, companies must ensure integrity is maintained with regard to drug safety data as it is collected, stored, and transmitted. This is done through preliminary planning, risk identification, and testing. After the fact, everything is regularly verified as working as expected, while regular audits and reports are made to improve processes after the fact.

What Must Be Validated?

In essence, while validation is an admittedly and justifiably complex process, it boils down to that same simple premise: It all has to be verified as working as expected. Anything less and resulting data can’t be trusted. It would be useless and just as good as if it were non-existent.

Now, confusion may admittedly arise over what exactly is being validated. In a word almost “everything.” For example, in pharma, every piece of equipment that touches or impacts the development of a drug during the manufacturing process must be validated.

While equipment validation is nothing specific to pharma and is important in other industries, IT systems and processes also qualify as requiring validation here. Even proofreading software like GlobalVision, which helps to verify packaging as being accurate but may not actually come into physical contact with the product itself, would fall into this category.

Audit trails and the like within applications are generally designed to keep excellent records. And, if they are validated as being reliable without fail, it’s safe to operate under the assumption the data they keep is as well. And, as mentioned earlier, the data must be validated too.

Data Integrity vs. Data Validation

Data integrity and data validation are two separate concepts, but they effectively have the same end goal. Data validation covers the testing and processes that lead to data complying with regulations put in place by the FDA (for example). Data integrity is what you get once it has been deemed to be secure as a result, along with several other qualifications.

In other words, much like data security is a basic tenet of data integrity and not vice versa, the same goes for data validation. If your data has been validated and/ or you have proper security precautions in place, the threat of a breach or malicious attack has been mitigated. There has been no such breach with regard to data that has integrity. It has to be kept that way.

It’s similar to how a ship’s hull has integrity until it hits an iceberg. Steps are taken to avoid contact or keep water from breaching. Since there are measures in place and the ship is afloat to begin with, it means the ship is secure. Meanwhile, validation determines how effective those measures are. If they aren’t with any consistency, the ship shouldn’t have even made it out of the shipyard.

In that sense, the ship is like any other manufactured product. Only its best possible, viable version should be made available. Validation helps ensure that is the case. Consistently.

Error detection of quality control software ensuring data integrity

There are many ways to ensure data integrity. As automation becomes increasingly prevalent in the 21st century, software, especially on the back-end, is as important as ever. Not only does it serve as the driving force behind technology in all of its incarnations, it can also act as a fail-safe. As the term implies, “error-detection software” is one viable way to catch and stop errors that risk corrupting data in their tracks.

How to ensure data integrity? 

This is how to Ensures Data Integrity:

1. Enhances Security

While data security is different than data integrity, the two go hand in hand. Like data quality, data security is a single facet of data integrity (but not vice versa). Nevertheless, without the proper degree of security, data can become compromised due to breaches, among other threats. In other words, for data to have integrity, it must first be secure.

As a result, error-detection software can be considered a key component of any complement of tools designed and implemented to enhance the security of data. Errors are simply outliers or anomalies, which are defined as observations that lie outside of norms. Error-detection software can build baselines of systems, their users, and the data they create, leading to the easy detection of behavioral deviations, whether there is malicious intent or not.

2. Reduces Human Error

There’s an inherent risk whenever you rely on human resources. There are some things a machine will likely never be able to do as well, but analyzing data is not one of them. It’s similar to the situation with manual proofreading, where, the longer the process is, the less likely errors are to get caught. Fatigue sets in eventually and the effectiveness of proofreaders declines over time.

In much the same way, the automated analysis of unstructured data saves time, thereby improving the overall efficiency of the process. Employees wouldn’t be replaced, either. There would still be a need to oversee the analysis. The right error-detection software would all the while keep all relevant parties apprised of how the data behaves. As described in Point 1 above, that’s critical.

3. Prevents Issues from Recurring

It isn’t just the errors software might catch, but the ones in the future that would otherwise slip through the cracks. Consider digital proofreading software as an example. A form of error-detection software, GlobalVision features an audit trail for compliance with FDA 21 CFR Part 11.

So, the platform doesn’t just go over the document pixel by pixel or character by character to detect graphics and text differences (among other types). The application tracks parameter changes and log-ins, so data becomes “attributable” (which is one of the five principles of data integrity). The others are “Legible,” “Contemporaneous,” “Original,” and “Accurate” (spelling ALCOA).

The end result? Detected differences between master and sample files from the printer can be tied to individual departments and testers. The exact origin of any error can be easily discovered and addressed. Similar errors can be prevented in the future. In that way, the number of potential mistakes gets dwindled down. Proper company quality standards get corrected and set moving forward.

As another example, a Corrective And Preventive Action (CAPA) system prevents the recurrence of product and quality problems. In manufacturing, it can become a vicious cycle of sorts. If high-quality products aren’t routinely manufactured, there is pressure to falsify data so that it passes. That leads to a lack of data integrity. So, it can be argued, a lack of data integrity is a sign of a lack of quality.

In contrast, verifying all possible data sources for the root cause of errors keeps the chances of them recurring low. From a data integrity perspective, that means fewer lapses. Product quality and customer satisfaction, whatever the industry in question, can only improve as a result.


Learn how GlobalVision can help ensure data integrity using Automated Proofreading.

Request a free trial for GlobalVision Digital Inspection Solution


Learn More about How to Ensure a Successful Printer-Pharma Relationship


Printing for Pharma in the Age of Automation

Do you print for pharma?

Adding a digital element to and automating the proofreading process is considered a major efficiency advantage in most industries. When it comes to pharma, it’s practically a necessity.

The Automation Advantage

Digital proofreading solutions have been proven to help eliminate errors on packaging by catching them early in the pre-press stage and even during the print run itself. Manual proofers are susceptible to factors like fatigue and human error, which leads to actual errors.

So, by going digital, printers can keep themselves from unnecessarily wasting resources and time. If they were to only discover mistakes after the job is done, they’d be forced to start from scratch instead of moving forward by sending the shipment back to the customer.

In one of several worst-case scenarios, they may even send it out without discovering the mistakes at all. That would strain the relationship between the two. Worse yet, the error(s) may end up in stores, hurting the print-shop customer from a brand-equity standpoint… and financially with a recall. It also potentially hurts their customer, the end user, from a physical standpoint.

Recall-Proofing Your Packaging Workflow

Recalls, especially due to packaging errors, are kryptonite to what might otherwise be an invincible brand. After all, they can single handedly stain a reputation.

In pharma, where recalls were up strongly to start 2018, that goes double. Text has to be 100% accurate considering the importance of dosage and ingredient information on packaging. Something as small as a misplaced or missing period can cause serious damage and negatively impact the health of consumers who could have just been following instructions as they read them. In fact, as of the fourth quarter of 2017, mislabeling was the second-leading cause of pharmaceutical recalls in the United States at 17.4%.

Featuring unique requirements like serialization and other brand-protection features, printing for the pharmaceutical industry and automation simply go hand in hand. There’s no reason a printer shouldn’t take advantage of automation for the purposes of packaging quality control too, as they are clearly benefiting at other stages of the packaging development process already.

More than a Trend

The benefits of automation in pharma have been well-documented. In fact, its role within the industry is growing. To illustrate to just what degree, it was predicted in a 2014 study by PMMI, The Association for Packaging and Processing Technologies, that robots would handle 34% of primary pharmaceutical packaging operations in North America by 2018.

Unfortunately, now that we’re in 2019, an update on the accuracy of that specific prediction is unavailable and probably will be for some time. However, as recently as late 2016, another report from PMMI revealed three out of five (so, the majority of) American pharma companies are using robotics for “palletizing, primary packaging, product handling, and cartoning.”

It’s obviously not a direct comparison, but it does reveal the industry is at least continuing to ramp up the automated aspects of its supply chain, generally speaking. As automation has already left its mark on labeling and packaging, proofreading is the next logical foothold in the industry to take.

As Jarrod Medeiros of R&D Magazine writes: “Organizations need to use the tech available to them to automate specific processes and ensure security and compliance.” That’s just logical, to benefit from tech that’s readily available. If you don’t, you lag behind.

Furthermore, as compliance with Food and Drug Administration regulations extends to text accuracy, the move to digital proofreading would be dual-purpose. In effect, automating the proofreading process is no more a trend than any other shift in industry standards. It’s not the future as much as it is a reality in the here and now.

The question is, “Are you printing for pharma as efficiently as possible?”

The Art of Securing Customer Data Over Its Entire Lifecycle

The difference between data integrity and security is clear. As far as information technology is concerned, you can’t have one without the other, as the integrity of data relies in large part on it being kept secure throughout its entire lifecycle.

There are of course other factors that dictate the degree to which the integrity of data is maintained. The accessibility and traceability of data play a role, but at the end of the day, its trustworthiness and reliability is front and center, especially when the data belongs to consumers of a SaaS application. In such a context, security errors and malware/ cyber attacks must be addressed as constant, potential threats and doing so effectively is just as much an art as a science.

For starters, you must consider the individual phases in the data lifecycle, as each represents a point at which the data is vulnerable. Each stage also presents firms with unique challenges. The exact number varies, but most agree it has several critical components that can be grouped as follows:

  • Collection/ Processing
  • Analysis/ Usage
  • Archival/ Purge

The Collection and Processing of Data

These can be considered two separate stages, but for the purposes of this simple blog post, we’re grouping them together. After all, data must first be collected and processed before it can be used.

Hypothetically, in this post-GDPR (General Data Protection Regulation) world, it’s generally considered to be a good business practice to protect data collected from consumers and be completely forthcoming with regard to how it will be used. Ethics aside, it makes you look good in the eyes of consumers. At the very least, you’d be keeping pace with competitors by being transparent. Why risk lagging behind by being secretive about it when there’s so much more to gain by getting consent and keeping your consumers’ data as secure as possible?

In any case, at this stage, you would limit your vulnerability from a legal standpoint by being selective about the data you collect. There’s no need to ask the consumer for their life story when only certain bits are relevant. Getting more than you need might enable the data to be repurposed, but in such an instance additional consent may be required anyway.

When it comes to processing the data, the National Institute of Standards and Technology (NIST) has you covered. It established NIST SP 800-53 as a standard, whereby compliance is dependent on, among other things, limited data access only to parties that need to use it. Lax access policies only detract from the point of implementing them in the first place… and attract attackers.

The Analysis and Usage of Data

There’s also the potential for harm if data leaves the organization by way of it being shared or published. Even an invoice sent to the customer can fit the bill (no pun intended), here.

An in-house comprehensive data management policy should be enacted to ensure universality of any agreed-upon practices/ processes. From an IT perspective though, cryptographic key management for the cloud is one option that can protect data as it moves throughout the network. Ironically, with regard to data sharing, there’s still a shared responsibility model at play here.

It’s justifiably the usage stage that worries consumers the most, as they go about submitting their data. It’s a reasonable expectation that it will be used responsibly, but it’s also the consumer’s responsibility to ensure they read any applicable terms and conditions as well as to appropriately set privacy settings. Assuming they do, the firms receiving the data have no choice but to respect their wishes… and do their best to keep their cloud infrastructure as secure as possible.

The Archival or Purge of Data

It might not be something every company considers at first, but after the data is used there is still a need to manage it. Whether the decision is made to retain the data or destroy it, there are still steps that need to be taken to keep it secure.

Archiving, which effectively translates to the removal of data from the active environment into storage, is always attractive to firms. It’s an inexpensive, low-maintenance option for companies who may want to preserve the ability to analyze it. The data still has to be secured and, the more data a company chooses to hold onto, the more data that company has to protect. Even if archiving data is becoming more and more cost-effective, space is still a finite resource.

Hence, the alternative, to destroy it. In fact, depending on the industry in question, there might be a requirement to destroy it, like in finance and healthcare. Nevertheless, it’s a bit of a gray area. Not only are there different disposal methods, but different extents to which the data can be destroyed. For example, a user deleting his account could just mean they would be denied access from that point onward. The company could still keep the account active in case the user changes their mind.

In any case, the way data is classified dictates how it will be deleted. Files can be time-stamped, facilitating the purging process, if there are regulatory timelines and guidelines to follow. Meanwhile, metadata helps to identify obsolete data, which may not be so easy to delete. The redundancies and back-ups that may have once been a godsend in case of mishap have to be addressed too, so as to prevent data from becoming zombified, which would also put your security at risk.

Once security goes out the window, so does your brand equity and then the very customers whose data you once collected. Needless to say, with such a thing as zombie data in play, the data lifecycle takes on a whole new meaning, one whose every single intricacy companies would do well to understand and then master, for the sake of their customers and themselves.

Cartoon computer work on the digital inspection fast and efficient comparing to manual work

Regardless of the industry, all companies have the same goal in mind: provide high-quality products to keep customers loyal and attract new ones. To accomplish this, each product must go through a strict proofreading process to ensure the accuracy of all labeling and packaging. This can be time-consuming. As a result, some early adopters have switched to proofreading software due to time constraints. In fact, sticking with human proofreaders has proven to be the potentially costly mistake… one that may even lead to expensive recalls.

Too Many Errors

Although they do their best, human proofreaders are not able to catch all errors that can be found on a label. In a North Carolina State University article about errors in food manufacturing, 25% to 50% of recalls were due to human error. There are many reasons why these errors were missed; For one, a proofreader could have been tired or simply unfocused.

With new technologies continuously introduced into every aspect of our lives, people can be distracted by an email alert or text message. When an error is detected in packaging, companies must issue a recall on their product, costing them thousands of dollars and leaving them with a ton of unsellable inventory. Fortunately, there are measures to ensure each packaging component is error-free. These measures can be combined into a single solution: an automated proofreading platform.

Check Every Word

Human brains are trained to skip words. So, no matter how fluent they are in a language, they can overlook one that’s misspelled. Most people use spell-checking software such as the spellcheck feature in Microsoft Word or maybe even an online solution like Grammarly. These tools help catch those mistakes, but they do have flaws. For example, some English words are spelled differently, such as “color” and “colour.” One solution would be proofreading software that automatically detects errors in a loaded file.

Every Shade of Color Matters

Despite evidence to the contrary, some may argue human proofreaders do catch misspelled words. Nevertheless, color differences are a different animal. It’s important that the labels of packaging have a consistent color pattern. This helps promote brand awareness and identity. If the color has faded or the hue is slightly different, the product will come off as rushed. Color-detection software is one way to identify any issues before the file gets sent to the printing press.

A+ Barcode Grading

Every company wants to send out Grade “A” barcodes and nothing less, as recalls can be issued if they will not scan. And, yet, despite the very real need, human proofreaders are unable to tell if a barcode will scan properly when it reaches the customer. Leveraging technology is the only option here, and it makes sense to opt for the best solution possible. Having a process in place to digitally check barcodes saves you time and money. This is especially true when it comes to consumer goods.

Checking Labels Even After Printing

There is still a chance some errors or color differences will slip through the cracks even after the job is done and the labels are printed. The best way to review your printed files is by reverifying them, using proofreading software. This way the user can find any differences and decide to reprint if necessary.
It’s important to make sure that, even when the final label is produced, it remains error-free. It would be infinitely preferable to find errors in labels immediately after they’ve been printed instead of having a customer find them while in line at a store.

Making sure your products are high-quality extends to the packaging. Errors are not only costly but embarrassing. People are far from infallible, and companies who rely on human proofreaders risk having their products recalled and losing customers. Making the switch to automated proofreading, like with GlobalVision, can save you time and money by sparing your firm from having to issue recalls. Businesses should consider spending a little more money now on solutions that help maintain their reputation and brand integrity. It’s not about short-term gains, but rather the long game, in which everyone is a winner.

Man working on compliance with a laptop

In spite of the term Software Development Lifecycle (SDLC), there aren’t concrete steps to follow when coding. Each case is somewhat different, even if there is one universal truth: Certain standards apply across the board. Data integrity is one such standard.

The Ultimate SDLC Goal

It may not be the ultimate goal, as that would be releasing a functional piece of software. However, as a key consideration of any successful SDLC, data integrity is a requirement that must not be ignored. That goes for GlobalVision proofreading software, which follows an SDLC approach, too.

The term “data” is relatively straightforward: information collected and potentially used by the application. “Integrity” is, of course, the end result achieved through the process of keeping that data uncorrupted and unchanged. It goes hand in hand with the concept of data security, which is sought after through the act of taking the necessary measures to protect it.

For example, individual lifecycles have proper security precautions built into and controls added at each step. One precaution would be the act of defining early on the sensitivity of the data a given system will require. As James E. Purcell writes in his paper, “Defining and Understanding Security in the Software Development Life Cycle”, this enables the security needs of the firm to be considered come the time for decisions to be made later on in the process. Failure to do so leads to potentially unforeseen consequences and add-ons and patches after the fact. Almost by definition, those should be last resorts.

The Software Development Process

Taking the development process one meticulous step at a time is a way to avoid them. As alluded to earlier, there are a variety that can be followed, but, generally speaking, as a list, it looks like the one below:

Analyze and Plan, which should include preliminary risk assessment. Relevant regulations that will come into play should be looked at and a plan of action as to how to address them should be devised. This should be reflected in the resulting software-requirement-specification document that comes out of these two phases.

Design, where whatever needs to be in the software gets included in the design-specification document. Depending on the system being designed and compliance considerations, it may end up including failsafes with regard to the database, so it is easily backed up and restored. Furthermore, as a security precaution to protect against data being stolen or corrupted, encryption, if applicable, is identified as a necessity at this stage.

Build the Software, presumably using secure code to eliminate any potential vulnerabilities that may surface.

Test, with regard to all aspects of the software in a thorough, structured fashion. Testing is as universal as it gets and becomes a focal point of a product’s development regardless of the industry. When it comes to software though, aspects like code quality and security testing are just two of many that should be considered here, ideally by a dedicated quality-assurance team.

Unit testing, where the smallest testable parts of an application are verified for bugs and vulnerabilities independently, is also a popular pre-emptive strike against breaches but can be automated. Meanwhile, black-box testing, which places the application under the microscope of a tester who does not look at its internal workings (as if it were in a black box), seeks to test a variety of things, including external database access and the software for data-structure errors.

Deploy or Release, finally. That’s assuming all tests have passed and any security shortcomings that had been documented during the testing phase have been resolved. As part of the final quality checks of the software, further security testing may actually be done.

Maintain and Evaluate, which translates to updating the software via security upgrades and improvements whenever necessary. It turns out, sometimes there are good reasons behind those Apple IPhone updates.

Dispose, as, even in obsolescence, software must be handled with discretion. If software is in the midst of being replaced, caution must be exercised to ensure any sensitive data is archived securely or disposed of in its entirety.

After all, any SDLC seeks to create software that is useful to the organization. Logically, software that can be superseded by a newer version has outlived that usefulness. By the same token, an application without the appropriate level of security is relatively useless, to begin with.

The Importance of Data Integrity

As a result data integrity becomes increasingly paramount, depending on the industry in which the software will be used. Expensive changes may be called for at a later date by auditors, but that’s only if the organization in question is relatively lucky.

In a worst-case scenario, hackers could find a way in, compromising not just the integrity of the data but the brand equity of the company too. That could cut any software development cycle drastically short. It doesn’t have to be that way, as the above steps show.

Capitalized letter, checklist, chart and calculator

It’s easier to think of an audit trail as a collection of breadcrumbs leading out of the woods.

The Bright Side of Being Audited

Admittedly, the word “audit” gets a bad reputation. It’s usually associated with the unpleasant process of the same name that can be initiated by the U.S. Internal Revenue Service. In actuality, audits are an unavoidable part of life for many corporations in the sense that they can take place not once but multiple times of year. With an audit trail, they don’t have to be nearly as grueling as people have come to expect, though.

For the uninitiated, trails are the lists of transactions or events kept track of to help auditors and, in many ways, those being audited too. Of course, at its most fundamental level, a company’s audit trail does contain financial transactions. However, they can be chronological catalogs of so many more types of events. An audit is simply an investigation of accounts and records in general. They aren’t limited to those of the financial variety.

For example, audits can be key to achieving and maintaining regulatory compliance, which is in turn critical to operating in sectors like pharma. For a company that develops software intended to meet ISO compliance, perhaps for use within that same pharmaceutical industry, regular internal and external audits are to be expected.

External vs. Internal Audits

External audits can either be initiated by the relevant regulatory body (for certification purposes) or even a client that relies on the given software. Take a digital proofreading application for example.

Continuing within pharma, each piece of equipment that enters into a drug’s chain of custody has to be “validated” as meeting pre-determined specifications and attributes. Obviously, software qualifies as equipment in that context. It makes sense that a firm with as much at stake from a quality perspective as a pharmaceutical company would want to get assurances that a piece of equipment on which they rely comes as advertised as being compliant. It’s theoretically similar to how consumers depend on medication and accurate information on its packaging.

In contrast, an internal audit serves as an evaluation of the company’s effectiveness, from risk-management, governance, and process standpoints. As data integrity arguably touches on all three areas, its importance in a corporate environment cannot be understated. In fact, audits specifically aimed at examining data integrity are a real thing.

Benefiting from an Audit Trail

Regardless of the focus of an audit, trails are undeniably critical to their success. And success is what all parties should strive for, whether they’re doing the auditing or being audited. No one wins however unnecessarily hard one becomes to complete.

That’s one of the misconceptions regarding audit trails that is generally associated with the earlier IRS example. Obviously an audit isn’t exactly something to look forward to, but it can be made less of a headache if all required records have been kept and are easily accessible for the auditors. Automated trails that are easily searchable make smooth audits more of a reality.

Trails are theoretically included in software as one of many required technical controls that enable users to achieve compliance with 21 CFR Part 11 with the Food and Drug Administration (in the United States; equivalent to Annex 11 in the European Union). Compliance here ensures companies implement good business practices through reliable electronic records, which must be able to be accurately displayed and exported. Here, the audit trail serves to log what changes to application data were made, when, and by whom and be available for review.

Whoever ends up conducting that review, whether it’s an agency or the company itself, the auditor will no doubt thank you as the bigger picture begins to take shape. Identifying the individual trees is key to seeing the forest as a whole, though. Finding your way through can be hard, but an audit trail can clearly reveal the right path to take.

Misconceptions of Packaging Quality Control

For most people, the commercial printer is no different than the office photocopier. You press print to make 100 copies, and out comes 100 perfect, identical copies. The expectation is no different when printing 500,000 Aspirin labels, cookie cartons, or potato chip bags, but is this true? Are commercial printers like photocopiers?

Let’s turn our attention away from the printer and look at the question of liability.

What if the printed components are shipped to the production plant and everything is packaged, filled, and distributed to all the warehouses across the country?

“How did the text copy I wrote change in the printer’s proof?”

A few days later, the brand company calls the printer very upset that the barcode is missing on all the cartons!

Even if the brand company settles with the printer without paying for the printing, that represents only about 5% of the total distributed cost of the product sitting on the store shelf. The loss is huge.

The loss includes the cost of the contents, the production cost, the cost of distribution, the cost of collecting and destroying the defective product, and eventually the cost to rerun the production again.

If the QC department at the printer or the brand company or the production company would have checked the printing, everyone could have saved a lot of money, time, and environmental impact.

Now let’s get the lawyers involved so we can blame someone and recoup the losses. Whose fault is it? Who is in the wrong?

The print job that the brand company thought was as easy as printing on a photocopier has become a huge liability.

So you pay for lawyers and may save the cost of the print job, but you’re still at risk for the loss to your supply chain and customers. What if the consumer gets injured or dies because of the printing error? It won’t be the printer who is liable.

So, next time you think there is no need to QC the print job, remember who carries all the risk.

Here is a list of the most common print errors:

  1. The right label on the wrong package
  2. The barcode is missing/wrong
  3. The colors are wrong
  4. One of the ink colors is faded
  5. An older version is printed
  6. The position of print is off
  7. Blurry text
  8. Spelling mistakes
  9. Missing logo
  10. Smears, ink splatter, smudges
  11. Cut-off section
  12. Folded wrong

Put simply, proofreading protects the brand company against errors like these by helping to verify shipments from the printer. The above cautionary tale is an example of what can go wrong when both parties fail to do their due diligence.

It is undeniably a misconception, considering printers as being one step removed from photocopiers. Truth be told, it’s much more complicated than that, and, yet, expectations of pristine packaging on the part of the end user remain a very real thing.

Numerous touchpoints along the workflow are necessary to get the job done, but also increase the risk of conversion errors, while manual proofreading can only catch so many mistakes before fatigue and human error sets in. Digital proofreading software is one logical solution that maximizes cost-effectiveness as it pertains to quality control, resulting in a less-strained relationship between printers, brand companies, and of course their customers.

Cut Costs Not Quality: Proofreading Software Makes it Possible

Labeling errors can cause massive losses in almost any industry. There is an alternative option, though: Avoiding them through automation.

Human proofreaders may still be the go-to for creative and informative long-form content where style, consistent narrative, and grammar are key. When it comes to labels for packaging, there are better options.

Studies and good sense tell us a human being who is tasked with visually scanning similar items for hours on end is guaranteed to make errors. The only ways to decrease the number is to use software and limit the workload of each individual proofreader or to limit the workload of each individual proofreader by multiplying the size of the workforce. Only one of these options makes long-term financial sense.

Learn from Pharma’s Mistakes: Automate Quality Control

Bottles with wrong labels

In 2012, a pharmaceutical company voluntarily recalled a huge deployment of cough syrup because of labeling errors. The Food and Drug Administration identified the product as being mislabeled with incorrect indications as to the amount of the active ingredient being used.

A mistake of this magnitude is serious. The losses are believed to be in the millions, but they would certainly have been even greater had there been any injuries.

There are (a lot of) other cases.

In 2013, Vita Health was forced to recall a whole range of products for labeling inconsistencies. A separate recall of potassium chloride injections took place in 2014. A labeling error there led to the injections potentially having been packaged with shipments of others containing gentamicin sulfate.

The consequences could have been much worse, with each of these firms escaping potential injury and wrongful-death lawsuits by taking action in time. Such a development would have constituted a bigger blow not just to their profit margin, but lasting reputation as well. Taking action even earlier could have spared them from any financial inconvenience altogether, though.

No Risk of Massive Recalls with Proofreading Software

Every company that manufactures and packages large amounts of products and does not automate the proofreading process is needlessly exposing themselves to the possibility of a profit-crushing recall.

Quality Control Software Doing Packages Comparison

Automated proofreading software scans your labels with a very low to non-existent risk of errors. In fact, more than 95% of cases in which packaging software failed to correctly scan a packing label occurred due to the label being improperly presented to the reader mechanism. That means that either human error or a fault in a conveyor or other device was the culprit instead.

These automated packaging systems recognize and extract data from the image on your labels. This data is then compared directly to the original document. If there is any variance relative to the pre-set expected label, the system will alert the employee(s).

The fix may include reprinting or relabeling, but it’s infinitely better to be forced to do it early than when it’s too late. And, in most cases, this simple remedy reduces errors to within a fraction of a percent to zero. Technology like this is a critical component of modern food and drug packaging and will most likely become similar for every major manufacturing and packaging operation by the end of the decade.

Even in the best-case scenario, the alternative means overworked and underpaid proofreaders who labor under the constant fear of making mistakes. Or it means running a group of professional proofreaders who are not overworked but cannot produce a product commensurate with the expense. For any moderate to large-scale operation, this should be unacceptable.

Using software on the other hand alleviates huge amounts of overhead and removes the risk of a disastrous recall. For any operation, this should be cold, hard logic.