Automating everyday lab tasks can free up your lab workers for other, more complex undertakings while improving data quality and reproducibility. From single-function instruments to complete workstations, automation and robotics can perform many different functions in your lab. When purchasing these instruments, make sure to consider your future plans and whether the platform can be expanded to meet those needs.
In this eBook, you'll learn about:
- Questions to ask when buying automation and robotics
- Why add automation to your lab?
- Trends in lab automation
- The lab of the future: big data enables a big role for AI
- Assays and automation in robotic workstations
- Automation technology: a solution to labor shortages in food testing labs
47401_LM_Lab Automation_eBOOK_JL (c475171e-0102-4d1e-97a2-661adc4b63e4)
? Questions to Ask When Buying Automation and Robotics
? Why Add Automation to Your Lab?
? Trends in Lab Automation
? The Lab of the Future: Big Data Enables a Big Role for AI
? Assays and Automation in Robotic Workstations
? Automation Technology:
A Solution to Labor Shortages in Food Testing Labs
LAB AUTOMATION
RESOURCE GUIDE
Questions to Ask When Buying Automation and Robotics
When purchasing these instruments for the lab, make sure to consider your future plans and whether the platform can be expanded to meet those needs
by Lab Manager
Automating everyday lab tasks can free up your lab workers for other, more complex undertakings while improving data quality and reproducibility. From single-function instruments to complete workstations, automation and robotics can per- form many different functions in your lab. When purchasing these instruments, make sure to consider your future plans and whether the platform can be expanded to meet those needs. For a list of automation and robotics manufacturers, see our online directory: LabManager.com/auto-robot-
6 Questions to Ask When Buying Automation and Robotics
Would a standalone robot or an automated workstation be more suitable for your application?
How much space do you have for equipment?
Is the workflow completely automated or will staff need to intervene at some point?
What is the cost-to-benefit ratio of the needed equipment?
What software comes with the instrument and how us- er-friendly is it?
How will you implement the equipment in your lab? (Consider not only the physical installation but how you’ll notify your staff of the changes and train them on the new instrumentation.)
The Big Picture
Whether to automate your lab can be a very important deci- sion you make as a lab manager. The choice can be as simple as choosing the automated version of a benchtop instrument or as complicated as adding an entire automated system.
Trends
Automation technology is playing a role in the evolution of bioprocessing. In the next few years, single-use bioprocessing will be more automated and common, with closed systems supporting continuous manufacturing for upstream and downstream activities. Currently, fully automated systems can deliver flowrates and pressure rates to process a 2,000 L bioreactor. These systems will also have Raman spectros- copy technology added in-line to provide real-time Raman analysis. With the appropriate software, these systems can
be connected and controlled while analyzing process data in-person or remotely.
Lab Automation Resource Guide
Why Add Automation to Your Lab?
An overview of laboratory automation choices, the key benefits, and considerations when automating your facility
by Rachel Muenz
Whether to automate your lab can be a very important deci- sion you make as a lab manager. The choice can be as simple as choosing the automated version of a benchtop instrument or as complicated as adding an entire automated system consisting of multiple robots, microplate stackers, and so on. If your lab is performing a lot of the same sorts of tasks over and over, and staff are either getting bored or finding it hard to keep up without making mistakes, it’s probably a good time to think about adding more automation. But how do you decide on the level of automation for your facility?
This article offers an overview of what lab automation is, the benefits it offers, the key technologies available, how to decide when to automate your facility, and how to successfully im- plement automated technologies into your lab.
What do we mean by the term “lab automation”?
“Lab automation” refers to any lab equipment or instruments that can perform tasks in the lab with very little hands-on action from staff. Because of the huge number of tests and experiments they need to complete on a regular basis, large-
scale automation featuring robotic workstations is most com- mon in clinical, pharmaceutical, and life sciences labs.
Main benefits of automating your lab
When implemented correctly, automation can greatly speed up processes in your lab and remove tedious tasks from your staff, ensuring they can focus on more stimulating work. This in turn boosts employee satisfaction and retention. Some key benefits of automating your lab include:
Saves staff time for more engaging work
Increases efficiency in the lab
Removes human error and enables more reproducible, consistent results
Improves lab safety by reducing the risk of repetitive strain injuries
A few examples of lab automation benefits include increasing sample throughput and enabling researchers to run many experiments at once or test many samples at once using high throughput experimentation.
Things to consider when deciding if you should automate your lab
No matter what type of lab you work in or whether your in- dustry is regulated or not, there are a few basic questions you should ask to help you decide whether increasing automation is right for you:
Which tasks in the lab are the most repetitive and routine?
How often are you performing these tasks on a daily, weekly, or monthly basis?
Lab Automation Resource Guide
Lab Automation Resource Guide
Which processes need to be mistake-proofed?
How many steps do these processes include?
How much space do you have for equipment?
What equipment is absolutely essential to auto- mate your lab?
How will you implement the equipment in your lab? (Consider not only the physical installation but how you’ll notify your staff of the changes and train them on the new instrumentation)
What is the cost-to-benefit ratio of the needed equipment?
What software/information technology (IT) infrastruc- ture is required?
Key lab automation technologies
While automated systems for the lab can get quite expensive, companies are offering more budget-friendly options to labs that need to automate a few processes, but don’t require fully automated workstations to get the job done. There are also automated versions of single lab instruments if managers don’t need to do a full overhaul of their labs, but just require a single process to be faster and more efficient. Some of the most common types of lab automation instruments/equip- ment include:
Incubators
Titrators
Cell imaging
Liquid handling
Software
Autosamplers
Robotic workstations
How to successfully automate your lab?
You’ve given it a lot of consideration and decided that auto- mating your lab is the right choice, but how do you ensure it’s done right? From staff buy-in to choosing the right equip- ment, there are a few things to consider before a lab automa- tion project gets underway.
Clear and frequent communication
Involve your staff from the very beginning of the lab auto- mation process. Reassure them that they aren’t going to be replaced by robots and highlight the positives of the project. Since they are likely the ones most familiar with the work- flows you’re looking to automate, getting their input and ad- vice on what equipment to buy and where it should be placed is critical. Just keeping staff informed about each step in the process and ensuring they are properly trained on the new equipment are also important steps to a smooth transition.
Automate all at once or in stages?
Depending on budget and time, you may not be able to auto- mate everything at once. Today’s lab automation instruments are modular and you can add equipment or expand over time. Think about which processes are the most important to auto- mate today, tomorrow, and further down the line.
Choose the right processes to automate
Almost any process in your lab can be automated, but that doesn’t mean it should be. A good rule of thumb is to au- tomate tasks staff do most often and that are the most time consuming and repetitive. A task may be tedious, but if you only need to do it a few times per month, it may not be worth automating.
Have clearly defined goals
What is your main purpose in automating your lab and what do you hope to gain? Whether it’s cutting waste in the lab
or improving staff retention by ensuring lab workers’ days are spent on thoughtful and engaging tasks, having a clearly defined goal will help you stay on target and get staff buy-in when discussing your intention to automate the lab.
Software
One thing that often gets overlooked in lab automation proj- ects is the software that will be running the equipment. You can choose the most sophisticated instruments in the world, but if they don’t integrate well with your choice of software or the software doesn’t integrate well with your overall IT infra- structure, your project could be a disaster. How user-friendly the software is can also make or break a lab automation imple- mentation. Make sure you explore software options carefully and always request demos to test out in advance to ensure the software will do what you need it to. This will help you and your team identify any software issues before making a final selection.
Lab Automation Resource Guide
Even when done well, automating your lab means huge changes for you, your staff, and the facility as a whole. How- ever, with careful planning, thoughtful consideration of all
the options available, and clear communication with your team, automating your lab can be the best decision you make as a lab manager.
Possibilities Abound for
Laboratory automation is not one-size-fits-all.
There are many automation technologies to choose from, capable of improving the consistency and reproducibility of results for higher quality data
Explore the advantages of laboratory automation
Lab Manager 5
Laboratory automation is not one-size-fits-all. There are many automation technologies to choose from, capable of improving the consistency and reproducibility of results for higher quality data.
Download the full infographic compliments of Lab Manager
Product Spotlight
microPro 300: The world’s smallest 96/384 channel semi-automated benchtop pipettor
This innovative compact instrument packs an enormous feature set that offers unprecedented capability and value. Multi-Function pipetting increases flexibility with functions to meet
any user’s needs; from simple aspirates and dispenses to complex custom programs. An intuitive touchscreen user interface and a large, high-resolution screen provide for a friendly experience with all the features and functions right at your fingertips. A ring lock tip system allows for “easy touch” tip changes, without the need for unwieldy clamp handles. PDR (Pipetting depth recall) provides the user the means to set, store and utilize a virtually unlimited number of
containers with completely customizable depth settings. Touchless tip ejection increases safety and reduces the risk of contamination. Saved programs are conveniently stored in the Favorites section and organized by name, type, and creation date. With a 10x Speed Control for all aspirate and dispense operations, the microPro 300 is accurate, affordable, efficient, reliable, and easy to use.
Trends in Lab Automation
Life science labs embrace AI and cloud computing
by Gail Dutton and Ajay Manuel, PhD
Lab automation has moved beyond liquid handling robotics to include a variety of technologies that help lab technicians do more and do so more effectively. Smart instruments are behind this, incorporating multiple features, storing data in the cloud, and sometimes sharing that data with other instru- ments downstream. Often, cloud-based analysis is performed automatically so that lab professionals see results and their interpretation simultaneously.
These capabilities are iterative. As labs accept cloud com- puting, the Internet of Things (IoT) becomes more useful and more prevalent. As more data is collected and stored in widely-accessible compute clouds, artificial intelligence (AI) and its subset, machine learning (ML), become increasingly practical. In fact, AI and ML are leading trends within life science labs today.
Automation reinforces existing instruments, allows for faster data aggregation, and provides deeper analytical insights that may otherwise have not been noticed. By using automated de- vices, including the IoT which enables sensors at the edge of your network, labs can be empowered to solve problems they couldn’t otherwise solve. A representative example would
be using the lab information management system (LIMS) to track patient data, pool the data in the cloud, and with ma- chine learning, detect patterns to obtain greater insights in a matter of a few hours as opposed to weeks of manual work.
The case for automation and AI
It is important to recognize the distinction between AI and automation, especially in the context of laboratory analysis, as neither is dependent on the other. Automation is for efficiency. It is used to streamline processes or to transfer data and make it more widely available, either from multiple locations or devices for your own team or to collaborating teams. In con- trast, AI uses the data collected from instruments to identify patterns, such as analyzing video to understand exactly how flies beat their wings during flight or to count neuromuscular junctures in mice. ML takes this a step further by making predictions (such as predicting a disease prognosis based upon certain conditions). Singly or combined, these technologies can eliminate a lot of tedious, manual work.
Despite these advantages, lab managers remain reluctant in the incorporation of AI to their labs. This can be attributed to a general outlook that AI has a high learning curve, particu- larly for small and medium labs, where there is a struggle to gauge whether the cost and time savings are enough to justify the means.
Yet, automation and AI can add a lot to labs that need it. The development of fully-automated next-generation sequencing during the COVID-19 pandemic is one example. Automating genome sequencing streamlined the process, and sending the testing results to the cloud made it easy for labs to notify patients the next day if they tested positive
for COVID-19, and to track known and emerging viral variants quickly and thus better contain transmission of the virus. Similar success with AI-enabled diagnostics has also been found for applications involving breath testing. A patient breathes into a bag, the contents are analyzed using mass spectrometry, and AI interprets the results in less than a minute with polymerase chain reaction (PCR)-like accuracy. Unlike traditional and manual approaches that may require several days of prep work and analysis, AI can
Lab Automation Resource Guide
Lab Automation Resource Guide
streamline existing process flows and provide faster analysis of results.
Start with the cloud
Cloud-driven scientific applications have only recently gained popularity. Acceptance was accelerated by the work-from- home mandates during the COVID-19 pandemic that closed many labs and slowed many projects.
To integrate AI into your existing lab operations, start by adding cloud computing. This is the foundation that trans- forms data from instrument-based silos into aggregated data- sets that can be accessed, searched, and analyzed by far-flung teams that need to access both historic and current datasets.
Even if the data stays entirely within your lab, you can benefit by enhanced data transfer capabilities. This is beneficial for applications such as single liquid-chromatography mass spec- trometry (LC-MS) where even a single data set can be up to 2 gigabytes (GB) in size from just one run. Transferring even a single file as such is burdensome which makes it worse when the norm may involve transferring 10 to 100 runs per day.
Storing the data in the cloud as it is created eliminates the need for separate transfers. It also enables access to approved users on- and off-premises, as well as the ability to store enormous quantities of structured and unstructured data. For small- and medium-sized labs, another focused alternative exists in Software-as-a-Service (SaaS), an online cloud-based service, which can scale on demand.
Once data is easily accessible, ML may be beneficial. For ML to be most useful, lab managers need to work with their staff to organize data in a specific format and to clean and nor- malize it, so the data can be searched and deliver accurate, comprehensive results. Many of the data formats (such as from the mass spec machine) may be proprietary, and even simple things like dates are described differently. Bringing all the data together into a single, searchable platform can enable labs to use the massive quantities of data already accumulated and, ultimately, may be worth the up-front effort.
Automation makes headway
Automation in the form of AI or ML is entering many life science labs in a variety of ways. Researchers at Virginia Tech, for example, are designing a ML algorithm to pre- dict the mechanics of living cells. The ability to predict shape-shifting objects like cells, which change in relation to their environment, has been particularly challenging. Their work in physics-guided machine learning aims to systemati-
cally integrate the mechanics of cell motion as biological rules and physics-based model outputs to predict the movement
of cells or other shape-shifting objects in dynamic physical environments.
Computational scientists at Carnegie Mellon University developed an ML algorithm to understand the intricacies of genome folding in the cell nucleus and how that affects gene expression. Analyzing microscope slides may be more mundane, but can save labs significant work. The benefit is high throughput and greater accuracy. A similar concept
is applied to reviewing filmed experiments. Researchers at Case Western Reserve University are using ML to track the movement of flies’ wings to determine how the positioning of their halteres—hard mechanosensory organs that flap out of synchronization with the wings—affect flight. The machine learning algorithm measures the positions of the wings, the halteres, and their angles during flight by learning when their perspectives change. This application frees researchers from watching and manually recording differences from tens of thousands of frames of flies in flight.
Automation options are increasing beyond liquid handling to the processing and analysis of massive quantities of data, and AI and ML are increasingly helping scientists extract value that otherwise couldn’t be achieved quickly or, often, at all. Importantly, these trends aren’t just for large, complex labs. Instruments and applications are available now that enable even small- and medium-sized labs to benefit.
The Lab of the Future: Big Data Enables a Big Role for AI
Big data revolutionized the lab manager’s role—AI is about to do it again
by Sridhar Iyengar and Ajay Manuel, PhD
Buried in a working paper scribed in October 1980 by sociol- ogist Charles Tilly was a consequential union—a marriage of two words forged out of necessity to describe a concept not yet named with repercussions not yet imagined. Such was the first recorded mention of “big data”—a phrase that would swiftly enter the common vernacular and become common practice across industries and geographies. To understand how data can and will be used to shape decisions in the lab, one must first understand what decisions lab managers need to make in the first place.
A new era of science means new decisions for lab managers
Ten years ago, the titles of those who supported a lab’s oper- ations were similar to today—technicians, lab managers, and information technology (IT) managers. Yet, the responsibil- ities under their purview and the challenges associated with them have changed drastically in the decade since.
Today’s scientists don’t just need their equipment to be operational; they need it to be transformational. Researchers
now expect their tools to act as both collectors and reporters of data. To empower scientists with the data they require, operations professionals are now presumed to be experts in cloud infrastructure, data security, and encryption. As for the assets under their jurisdiction, many were manufactured before the internet was even established. With this paradigm shift, lab managers are now required to understand every piece of equipment in the lab from what it does, why it does it, and what can be done to troubleshoot performance-related problems.
Such growing responsibilities give lab managers a litany of new decisions. How can we draw new insights from old
equipment? How is our data encrypted? How do we get data into the right hands with ease and out of the wrong hands with veracity? As such, the role of a lab manager has become highly technical with frequent communication between techs and vendors of laboratory instrumentation. Luckily, Internet of Things (IoT) technology has enabled the collection of thousands of data points without human involvement. Today, sensors are embedded in new equipment, while legacy assets can be connected to the cloud via inconspicuous and easy-to- install sensors.
Outfitting a lab to collect data for today’s needs alone is short-sighted. It’s imperative that those seeking to leverage data in the lab consider not only the expanded data pipeline of today but the colossal one of tomorrow.
Artificial intelligence requires operational excellence
The questions answered with data today will be asked by artificial intelligence (AI) tomorrow. Yesterday’s executive hypotheses are today’s data-driven plans and tomorrow’s fully automated discoveries. In the not-so-distant future, robotics will handle automation as AI evaluates protocols. Eventual- ly, discovery will require little to no human involvement at
Lab Automation Resource Guide
Lab Automation Resource Guide
all. Some experts fear that robots and AI may lead to a high rate of unemployment for manpower-based jobs. To the con- trary, AI technology has the potential to assist their human counterparts by taking care of daily operations, that may otherwise be boring tasks, while facilitating improvement in various processes. With good design and execution, AI can also help identify process flaws and procedure redundancy as well as catching operational defects and optimization opportunities.
As the tidal wave of data crests, some researchers are still recording measurements on paper, manually transferring their notes to spreadsheets, and individually exporting spreadsheets into databases for processing and storage. If such habits are antiquated today, they’ll certainly be detrimental tomorrow. Any remaining “if it ain’t broke” devotion to paper notebooks will break under the weight of a data-hungry,
AI-shaped future. But eventually, so will manual collection of any kind.
To be truly transformative, AI requires input from mass quantities of data. Its collection must be copious, reliable, and automatic. Such widespread collection requires univer- sal connection of every asset, every metric, and even the lab environment itself. IoT technology was born for such a time as this. A representative example relates to the use of online systems, over manual spreadsheets, to monitor temperature changes of freezers simultaneously on one page.
Some growing pains remain
While most recent laboratory equipment on the market comes with cloud connectivity embedded, vendor-specific solutions solve one problem while creating another. Data is siloed into superfluous and clunky dashboards, rendering it all but useless to those who need it. Experts believe that an estimated 300 million assets aren’t yet connected to anything. Most are fully operational and widely familiar (balances, centrifuges, freezers, etc). But thanks to turnkey IoT sensors and vendor-agnostic cloud solutions, the world’s unconnected assets will live on and live as one. Rather than being side- lined in favor of connected equipment, inconspicuous sensors enable seamless retrofitting in seconds. As such, tomorrow’s connectivity needs can be met while stewarding yesterday’s investments.
In the lab, data maturity advances in reverse
In most categories, maturity is a quality that comes effort- lessly to the aged and arduously to the young—not so for data maturity. When it comes to data, today’s startups spring to life already pushing the boundaries of its collection while
harnessing its insights and leaning on AI to make sense of its root causes. Despite their bound booklets titled “Digitization Strategy” and secured rooms labeled “Lab of the Future,” titans of industry are challenged with wriggling their way out of longstanding practices, breaking free of tired infrastruc- ture, and asserting their way to modern data practices over the objections of thousands of internal stakeholders. Iner-
tia is real.
Despite the unequal hurdles presented to startups and indus- try leaders, the importance of achieving data maturity in the lab remains imperative to both. The organizations that will dominate market share tomorrow are those that prioritize data today. Amidst the myriad models and guidelines for data maturity in other sectors, practical handrails for leveraging data in the lab are few and far between. As such, the following offers an outline of the five stages of data sophistication in the lab. Evaluate your organization’s standing using the informa- tion below.
The Five Stages of Laboratory Data Sophistication
Stage 1: Elementary
Asset data is available but siloed
Equipment data populates single-asset interfaces
Some assets remain unconnected
Accuracy is questioned
Access is cumbersome
Stage 2: Irregular
Organizational data strategy plans are forged but confusing
Sensors are deployed for complete lab connectivity
Data is trusted but siloed either by seniority or asset type
Progress is stunted as data strategy is not fully prioritized
Stage 3: Championed
Data strategy and vision are formalized, adopted, and concise
Lab director champions the use of data and analytics
Lab Automation Resource Guide
Algorithms detect and alert of anomalies
A single universal dashboard enables access to all data anytime, anywhere
Primary and secondary data are integrated
Humans remain integral to analysis
Stage 4: Committed
Lab director and company executives fully buy in to orga- nization-wide data and analytics strategies
Data informs business decisions and lab activity alike
Data and analytics are viewed as a key component in driving lab innovation
AI details the root causes of reactions, anomalies, and errors, and predicts those to come
Stage 5: Transformative
Data and AI are central to discovery
Discoveries are fully automated without human involvement
Robotics handle automation and AI evaluates protocols and results automatically
Utilization data informs all purchasing decisions in the lab and across the organization
A chief data officer maintains a seat on the board
For now, achieving a “transformational” level of data maturity may sound like a lofty goal and a clear competitive advantage but it will be essential for survival and thus become the status quo. Thanks to IoT, AI, and organizational prioritization of data maturity, the lab of the future is coming into focus.
Charles Tilly likely had no idea in 1980 that his casual dec- laration of big data would eventually become sacrosanct. Lab managers had little indication of how quickly assets would measure themselves. But for anyone willing to listen, every indication is that AI will fulfill the promises enabled by big data. For legacy scientific and research enterprises, mature handling of data will determine whether their reign continues or ends. For emerging players, data maturity could be their ticket to disruption. Lab managers enacting the automation and optimization of data collection within each will maintain a place in history as the linchpins who enabled discoveries long elusive. The future of the lab is bright.
Product Spotlight
World’s first vertical collaborative and distributed lab
automation platform
Automate your scientific workflows in a compact footprint with the Thermo Scientific™ inSPIRE™ Collaborative Laboratory Automation Platform. The inSPIRE platform’s light, vertical, and collaborative design coupled with innovative smart technologies provides a completely interactive and user-friendly experience. With modularity and flexibility being foundational
to its design, the inSPIRE platform provides a scalable solution for laboratories looking to reduce footprint, and realize the throughput, efficiency, and reproducibility benefits
of automated workflows. Apart from being a modular solution, the inSPIRE platform is intuitive to operate and designed to work collaboratively
alongside users. Its unique SmartHandle interface provides touch-enabled automation control
with haptic feedback, allowing operators to effortlessly share instruments and interact
with the system. In addition, the SmartHandle also provides instrument status at a glance.
Add a Thermo Scientific™ SmartCart™ Solution to your inSPIRE platform for a quick and easy way to move devices or samples between systems or labs.
Assays and Automation in Robotic Workstations
These technologies improve a lab’s output and save on priceless resources
by Mike May, PhD and Ajay Manuel, PhD
The evolution of robotic workstations resembles that of com- puters. Gargantuan systems that only experts could operate gave way to smaller and more user-friendly systems. Despite the decreasing size and simplified use, today’s robotic work- stations often outdo their predecessors, thanks to ongoing improvements in various technologies.
A decade or so ago, automated liquid handling conjured up images of room-size systems at pharmaceutical companies costing hundreds of thousands of dollars and run by teams of experts for operation and programming. Today, less than
$10,000, enough bench space for a microwave oven-size device, and some taps on a graphical user interface can get any scientist going with automated liquid handling. A huge workstation handles far more samples, but that’s not needed in most basic research labs. In fact, some scientists turn to a do- it-yourself approach to automate processes in a lab.
Although life science and commercial labs primarily use robotic workstations for liquid handling, that’s not the only process that can be automated. These platforms can also heat or cool samples, seal multi-well plates, and more. One team
of scientists turned esterase-based biosensors and a robotic workstation into a pesticide-detection system, reporting that such robotic systems can be easily assimilated into industri- al production lines, and subsequently improve monitoring efficiency and the use of real-time biosensing devices for envi- ronmental detection.
When it comes to the basic reasons to automate a workstation, most scientists know that this technology can improve a lab’s efficiency. Plus, reducing human intervention leads to fewer errors and variability in experiments. Despite those benefits, some labs get more out of this technology than others. This holds true for situations involving invariant workflows, as is the case in clinical, forensic, and analytical service labs, where automation has taken over repetitive tasks involving familiar tests or assays. These labs benefit from the tracking of samples and how they were treated, which are two of the strong points of a robotic workstation. Nonetheless, the capabilities of au- tomated workstations keep growing. As access to this technol- ogy expands to more labs, the applications and modifications will expand as well.
Enhancing the advancement
More than the parts of a workstation matter when it comes to what it can do. In some cases, advances in one area spawn im- provements in another. One such example involves coupling automation with assay technologies where advances in one group results in advances in the other. The results of those advancing steps let scientists explore more complex questions, often in more precise ways.
Such progress is evident in today’s automated liquid handler technology where the focus has been to provide a wide range of volumes, spanning nanoliters through microliters, along with a streamlined workflow and precise execution. The ongoing trend of miniaturizing assays to use less sample re- quires the ability to work accurately with very small volumes.
Lab Automation Resource Guide
Lab Automation Resource Guide
Workstation control has also improved thanks to visual guides that allow users to interact easily with systems while prevent- ing inadvertent process changes.
Exploring the economics
Expense comes to mind when any lab manager thinks about an automated workstation. In the days of gigantic systems, the cost of robotic liquid handlers far surpassed the budgets of most labs. Today, some scientists think that automated sys- tems include an economic incentive, but that’s not necessarily the case. An automation platform would probably require the same amounts of consumables and reagents- if not more-that are used in manual methods. The economic benefit is from reduced retesting, faster sample accessioning, and improved data integration. In this manner, automation becomes eco- nomical over the lifespan of the platform
An automated workstation, though, can also save a lab money in other ways. Beyond the equipment it may host, the greatest
costs accrued by a lab are its own personnel. Increasing personnel to match an increase in sample processing is not cost-effective. To handle more samples, a robotic system—an affordable one—could be a lab’s better choice. Such a system could even save lab money in less obvious ways by reducing or eliminating injuries related to ergonomic risk factors, such as repetitive motion injuries, that may further impede individual or lab progress as a whole.
During the COVID-19 pandemic, labs around the world looked for ways to speed up and ensure accuracy in a range of diagnostic tests. Automated robotic systems were utilized to perform tests to discover spike proteins from SARS-CoV-2. Such a public health example reveals some of the crucial benefits—even life-saving ones—of using automated work- stations, although more assays will have to be run on these systems to expand our knowledge and improve our environ- ment and our health.
Product Spotlight
Get Started with Automation: epMotion® Series of Liquid Handlers
In 1961, Eppendorf launched the first commercial piston-stroke pipette, a tool that has transformed scientific research. Now, Eppendorf’s liquid handling portfolio includes everything from manual and electronic pipettes to fully automated workstations. The epMotion series of liquid handlers make the transition to automation easy with the user-friendly epBlue™ software. For additional support, Eppendorf’s application
team can program workflows specific to you and your lab. Many NGS library prep kits have also already been qualified on the epMotion
with guidance from manufacturers or directly at a customer site. Automate your routine tasks such as normalization, reagent distribution, and serial dilutions on the epMotion 5070.
For more complex applications such as NGS library preparation and nucleic acid purification, the epMotion 5075t features up to 14.5 deck positions with optional thermal modules.
Automation Technology: A Solution to Labor Shortages in Food Testing Labs
Automation helps overcome a variety of challenges and enables labs to be more efficient
by Wilfredo Dominguez
The ongoing COVID-19 pandemic has introduced unique reasons for people to leave their jobs. Workers want more pay, more flexibility, and a career they are more passionate about, especially as more people realize that life is too short to work somewhere they are unsatisfied.
As the world heads into its third year of the pandemic, the food manufacturing industry is not immune to the challenges presented by the resulting labor shortage. Many of these jobs require someone to be in the lab, which means working from home isn’t an option. It also has led to outbreaks in labs, send- ing workers home and sometimes shutting down entire plants.
How do labor shortages affect productivity and efficiency?
Labor shortages in the food manufacturing industry pose a significant challenge to testing timelines. Fewer people to run tests results in delays in the manufacturing process, delays in getting products out the door, and increased costs for storage. Furthermore, lab technicians who are working overtime to keep up with demand face burnout.
Labor shortages are not the only cause of production ineffi- ciencies in labs. In a lab environment where it is critical to en- sure proper testing and training, onboarding new employees can take months. Additionally, ongoing supply chain issues have led to backorders, further delaying food manufacturers from prompt delivery of products. Changes in safety regu- lations can also alter a lab’s testing procedures, sometimes increasing the amount and duration of testing, as well as changing the technology used to conduct the tests.
What solutions exist for food testing labs?
Investing in automation technology is a food testing lab’s best defense to limit the impact of labor shortages while simulta- neously increasing productivity and efficiency. The use of au- tomation technology allows lab technicians to multitask with the ability to step away from tests. Additionally, automated technologies nearly eliminate human error and don’t tire.
In addition to incorporating automation technology into food testing labs, utilizing ready-to-use equipment can reduce the time spent putting testing instruments together. For example, rather than a technician spending multiple hours prepping petri dishes within a lab, utilizing ready-to-use petri films or dishes can help them gain time back to complete more testing. Products like ready-to-use dilution buffers, media, and other reagents are also available to help labs increase efficiency.
Lab Automation Resource Guide
Lab Automation Resource Guide
Often, lab managers hesitate to incorporate automation technology due to the cost. When weighing the investment of automation, there are two questions lab managers should consider:
What volume of tests is the lab conducting? If a lab’s testing volume is low, automation may not be as useful. However, it can also depend on how much testing a lab is looking to complete in one day and the availability of trained technicians.
What are your customers’ expectations? If your customers routinely need results the same day or next day, automa- tion technology can help increase productivity, as well as customer satisfaction.
Understanding your total volume and customer needs is cru- cial, and these questions can be altered to best suit the needs of your lab. For example, even labs with relatively low testing counts might benefit from automation technology if it needs to provide multiple results the same day.
Meeting changing expectations
As food testing labs adjust to evolving customer and employee expectations, investing in new technology and procedures like automation technologies to increase efficiency can help. It will continue to be critical for labs to be willing to adjust how they operate to attract and retain employees, as well as meet the needs of their customers.
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Lab Automation Resource Guide