Artificial intelligence (AI) continues to attract buzz and fascination in the business world, but its fast becoming an essential tool is making sense and use of the masses of data we accumulate.

In simple terms, AI is machines executing tasks based on smart algorithms; computers learning and acting from rich datasets without being explicitly programmed to do so.

As consumers, or just people, we encounter this technology frequently in the form of predictive modelling, or machine learning, where models are built for making future decisions based on new data points. That takes form in the products and services we use daily, whether its Netflix recommending what you want to watch next, Google Maps knowing youll probably be going home at 6pm, or your Revolut account flagging an anomalous payment.

Its the draw of smart products like these that have led to 93% of UK and US organizations considering AI to be a business priority according to a recent Vanson Bourne study commissioned by SnapLogic.

Too often, though, the power of AI and machine learning is enjoyed by the product user in the form of benefits to UX, and not necessarily by the product-makers, who may still be reliant on legacy solutions, among everyone else in the backend who have their own needs and requirements for the convenience that data-crunching algorithms can offer. Or for business-wide systems that could make enterprises more secure and resilient.

AI has serious applications in optimizing the backend workings of the business, and enabling organizations to predict and respond to trends, events and even threats, faster. Splunks AI and Machine Learning in Your Organization ebook sheds light on where AI is being used for full impact behind the scenes.

While an end-user product may be refined, the developers who made it may still be dealing with complex IT structures, thousands of alerts and an increasingly opaque environment.

Now, there are software systems that can autonomously improve and replace IT operations.

Artificial intelligence for IT operations, or AIOps, is the marriage of big data and machine learning in IT. Coined by Gartner in 2017, AIOps is now a growing trend in IT. It leverages historical data to boost productivity by allocating resources to low value, repetitive tasks, and enables the faster remediation of issues using a combination of predictive analytics and automated incident response.

As all businesses become tech-dependent and IT teams continue to grow, AIOps has become a burgeoning industry with no shortage of vendors and specialists emerging focused on performance monitoring, event correlation and analysis, IT service management and automation.

The end result? Time and money saved for the business and more productive (and happier) engineers. Automation continues to be the most important end-goal for IT operations teams who are swimming in data and routine tasks, OpsRamp SVP, Bhanu Singh, previously told TechHQ.

Gone are the days when businesses could hide in the herd; cybercriminals techniques are so far spread that just connecting to the internet opens the door to threats, including compromised websites, phishing emails, and distributed denial of service attacks.

Unfortunately, businesses are unprepared to fully prevent, detect, and respond to the growing number and sophistication of threats.

Consider that ransomware attacks occur every 14 seconds, according to a Cyber Security Ventures Official Annual Cybercrime Report. Given so many attacks, businesses are turning to AI and machine learning capabilities to help shore up a scarcity of cybersecurity experts.

In cybersecurity, machine learning has applications in advanced threat detection and stopping insider threats, which require a more nuanced approach to monitoring and response. Sophisticated attacks that move laterally within a network, or breaches caused by unwitting access to sensitive information can be tackled by automated and intelligent anomaly detection.

AI and machine learning can enable analysts and security teams to paw through masses of log and event data from applications, endpoints and network devices to conduct rapid investigations and uncover patterns to determine the root cause of incidents.

As the threat landscape evolves, and the cost of a cybersecurity breach becomes increasingly catastrophic for small and large businesses alike, AI and machine learning is handing organizations improvements in detection speed, impact analysis and response.

Data, and lots of it, is core to the success of any AI or machine learning initiative. To leverage the benefits of these intelligent systems within the organization, businesses must be prepared to conduct the manual effort and resource required to refine large volumes of data that give AI models the fuel they need to learn and burn.

At IBM a company with a better view than most of the emerging technologies market data-related struggles are a top reason the companys clients have ceased or cancelled AI projects, according to the firms SVP of Cloud and Cognitive Software, Arvind Krishna.

Speaking at Wall Street Journals Future of Everything Festival last year, Krishna said that companies are finding themselves underprepared for the work and cost of acquiring and preparing that data work comprising about 80% of an AI project.

[] you run out of patience along the way, because you spend your first year just collecting and cleansing the data, said Krishna. Companies can become impatient and disillusioned with the work, he explained, and kind of bail on it.

However, the more data you have, the better, and once the grunt work and heavy-lifting is out the way, effective AI and machine learning means organizations are no longer bogged down by data; they are elevated by it. The challenge is getting there, but the benefits speak (and work) for themselves.

Read this article:

AIOps and cybersecurity the power of AI in the backend - TechHQ

Worldwide: Anshuman Singh, vice president and head of consulting [Europe] at information technology and outsourcing firm, Mindtree, addresses how companies in the short-term rental space can harness artificial intelligence [ AI ] to navigate the new normal as countries emerge from their lockdowns.

The Covid-19 pandemic has had an unprecedented impact across all industries, but the travel sectorhas been particularly hard hit.

While many travel organisations had crisis management and businesscontinuity plans in place prior to the crisis, few could have predicted the extent of the Covid-19 impact.Travel agencies, tour operators, hotel groups, short-term rentals and airlines, plus many moreareas of the industry, have faced a significant drop in revenue, with the sectorseeing its turnoverreduce by 56 per cent since last year, making it one of the worst hit sectors currently.

Aslockdown restrictions begin to loosenin the UK, with hospitality businesses reopening and airlines having taken to the air again at the start of June, short-term rental businesses mustbegin to prepare for thenew normal.By using this period as an opportunity to leverage digitaltransformation, businesses can ensure technologies are in place in order to achieve economicrecovery and sustainable success.

The short-term rental sector certainly has the ability to bounceback in the coming months and revolutionise, but particularly if businesses choose to harness the fullpotential of data and artificial intelligence [AI].

The Covid-19 pandemic served a stark reminder to businesses in the travel sector that they need tofully embrace digital transformation: it has never been more important. Though it remains unclearwhat normality will be for the travel industry, it is widely accepted that the use of technology particularly greater AI capabilities will be imperative for the future of short-term rental providers, bothduring and post-pandemic.

A recent survey onAI readiness, which surveyed 650 IT leaders in the UK and US, revealed that 51 per centdo not yet understand the data infrastructure needed to implement AI at scale. The data showed that27 per cent of travel and transport providers across the UK and US have not trialled any type of AI technology,and 21 per cent have not fully deployed AI in their business. This demonstrates there is a valuable opportunityfor increased operational efficiency within the travel industry if it can embrace AI more readily, inpreparation for a post-pandemic future.

Not only can AI technologies increase demand, adapt at speeds quicker than human counterpartsand drive revenue, it can also be deployed to save business considerable financial losses byproviding solutions to problems which would otherwise result in full refunds.

Take disruption management as one example within the travel management and services vertical.Some travellers have experienced trip cancellations or been unable to travel home due toimposed lockdowns. If Covid-19 re-emerges as a second peak, leaving travellers stranded again,travel businesses could use AI to analyse the impact on changes in booking and reservationmanagement, and dynamically provide alternate safe and local accommodation solutions. This wouldprovide customers with a safe space to say for a short period while they rearrange their travel plans.

Disruption management is only one example where AI can shape the future of the travel industry toimprove the customer experience and ensure business success.Businesses that utilise technology tohelp customers through times of need will help to retain them which is crucial at a time of financialdifficulty.

With the modern traveller expecting a seamless journey from start to finish, demanding high qualitydigital and mobile experiences and real-time responses, it is important for short-term rentalbusinesses to recognisehow data and AI can shape the customer journey and the overall experience.

Customer experience in the travel industry is not a new concept; travel brands have long providedexperiences to differentiate them from competitors, largely standing at the forefront of this wave.Digital transformation has enabled this, with businesses implementing technologies to ease onlinepurchasing and searching services, realising results through higher precision in segmentation and audience targeting, and optimising customer experiences through personalisation.

See the original post:

How the rental sector can use AI to succeed in the 'new normal' - Short Term Rentalz

ST. LOUIS--(BUSINESS WIRE)--World Wide Technology (WWT) today announced that it has been selected by the NVIDIA Partner Network (NPN) as the 2019 Deep Learning AI Partner of the Year for the Americas. This is the third year that WWT has been honored in this category.

The NPN selected WWT for its ongoing AI research and development program. To help customers develop AI leadership, WWT published six white papers about leveraging the compute power of NVIDIA DGX systems to develop Machine Learning and Deep Learning models for real-time edge video analytics, network optimization, and performance comparisons of multiple reference architectures for ML model development. The WWT research into ML and Deep Learning is tied to real-world business outcomes, and improvements in mining safety, utilities grid optimization, and resource management for manufacturing.

In addition, the WWT Advanced Technology Center (ATC) offers Lab-as-a-service environments for AI development, MLOps, Deep Learning, and testing of storage and networking with GPU-accelerated compute. WWT [also] engineered and deployed some of the largest clusters of DGX-2 servers in North America and China for production of Natural Language Processing applications at massive scale.

Its due to the strength of our engineering and data science partnership with NVIDIA that WWTs customers are today realizing strategic value from Deep Learning and ML solutions that WWT has deployed, said Tim Brooks, Managing Director of AI Solutions for World Wide Technology. Our customers are leveraging Natural Language Processing, computer vision, robotics, and geospatial analysis for intelligent agents, autonomous vehicles, retail loss prevention, mining safety, and manufacturing QA.

NVIDIA has long worked with WWT to deliver AI solutions for data center and cloud-hosted environments across numerous industries, said Craig Weinstein, Vice President of the Americas Partner Organization at NVIDIA. Together with NVIDIA and our OEM partners, WWT provides customers with AI solutions that leverage the power of NVIDIA GPUs and 30 years of engineering and global deployment reliability of WWT.

The NPN honors its top North American partners who have shown growth in their GPU business through the growth, leadership, and investments made throughout the year.

About World Wide Technology

World Wide Technology (WWT) is a technology solution provider with $12 billion in annual revenue that provides digital strategy, innovative technology and supply chain solutions to large public and private organizations around the globe. While most companies talk about delivering business and technology outcomes, WWT does it. Based in St. Louis, WWT employs more than 6,000 people and operates approximately 4 million square feet of warehousing and integration space in more than 20 facilities throughout the world.

For more information about World Wide Technology, visit http://www.wwt.com.

Connect with WWT: Twitter | Instagram | Facebook | LinkedIn

Read the rest here:

WWT Named Partner of the Year for Deep Learning AI by NVIDIA - Business Wire

July 17, 2020 Catalonia is the first State Epidemiological Observatory that will use Big Data and artificial intelligence techniques to generate a new collection of innovative epidemiological models for public health institutions that help them prevent, detect early, and mitigate the spread of epidemics.

This public-private initiative, which is part of the Catalonia.AI strategy, joins the efforts of the Catalan Government, medical and health institutions (Germans Trias i Pujol Hospital and Fundacin Lucha contra el Sida), leading technological research centres (BSC, CIDA, Eurecat, URV and CSIC), mobile phone operators (Telefnica, Orange and GSMA) and Mobile World Capital Barcelona.

The Barcelona Supercomputing Center (BSC) is one of the participating centres and its task will be to collaborate in the development of a pandemic model for future prevention, including all data sources, and also in the storage of data, computing, health data management and meteorological data computing

In the BSC Life Science department, an integrated geographic information system is being developed that includes COVID-19 case data, hospital situations, population data, weather data, and mobility patterns between regions.

Given the heterogeneity and complexity of the data, tools are being developed for the analysis and visualization of information based on the analysis of complex networks and time series. In a complementary way and in collaboration with the group led by Dr. Prof. Alex Arenas of the Rovira i Virgili University, the information system is being used to calibrate and validate predictive epidemiological models.

The objective of the initiative is to develop and provide an integrated information system that allows, on the one hand, to generate periodic reports to monitor the health situation. And, on the other hand, it is oriented to the development and application of epidemiological models as a tool to assist decision-making by health authorities.

Big Data for the prevention of epidemics

The creation of the Epidemiological Observatory consists of two phases (corresponding to the years 2020 and 2021, respectively), the first of which will consist of creating and analyzing a mathematical model to compare and predict specific patterns of epidemics, from influenza and COVID-19. This is the objective of the Observatorys first research project, Big Data for the prevention of epidemics, which will apply Big Data technology and artificial intelligence to clinical data, mobile phone data, census data and meteorological data. The treatment of all the data will be done at all times in anonymized form and in no case will it imply traceability of the users. The first results of this phase can be seen in the fall.

This project aims, on the one hand, a massive improvement in the model of pandemic spread thanks to the inclusion of clinical, mobile, census and climatological data, and on the other hand, to provide public health organizations with a support system in decision-making based on innovative epidemiological models that allow them to anticipate and draw up a plan to face epidemics, as well as improve the management of public resources in areas such as the health system, mobility, education, etc., adapting them to real needs.

Treatment of data and privacy of people

One of the priorities of this Observatory is to define and implement a data processing model that fully guarantees the privacy of individuals. In this sense, in order to study the transmission of the virus, we will work with aggregate mobility data, and not individual data, following the recommendations and best practices of the European Commission regarding the use of mobile data to combat COVID -19.

The budget associated with the Observatory is 600,000 for the two planned years of the project, to which must be added the costs of transferring data from mobile operators in phase 2 (in phase 1 they are transferred free of charge). The Catalan Government will finance 50% of the project and the rest will be provided by participating partners and external funds through competitive calls.

The Observatory will be located at the Germans Trias i Pujol Hospital (HGTiP) and will have the scientific coordination of Dr. Bonaventura Clotet, head of the HGTiP Infectious Diseases Service and president of the Fight Against AIDS Foundation (FLS).

About BSC

Barcelona Supercomputing Center-Centro Nacional de Supercomputacin (BSC-CNS) is the national supercomputing centre in Spain. The center is specialised in high performance computing (HPC) and manage MareNostrum, one of the most powerful supercomputers in Europe, located in the Torre Girona chapel. BSC is involved in a number of projects to design and develop energy efficient and high performance chips, based on open architectures like RISC-V, for use within future exascale supercomputers and other high performance domains. The centre leads the pillar of the European Processor Project (EPI), creating a high performance accelerator based on RISC-V. More information:www.bsc.es

Source: Barcelona Supercomputing Center

See more here:

BSC Participates in State Observatory Initiative Leveraging Big Data and AI That Aims to Detect, Prevent Epidemics - HPCwire

IBM and Verizon Business are collaborating on a global scale to bring AI capabilities at Enterprise Edge for 5G networking. There has been a tectonic shift in the Enterprise Edge industry leveraging AI and the Internet of Things capabilities to push the bar higher for 5G networking. With Verizon Business, IBM intends to bring AI and IoT capabilities at the center of every Cloud-driven enterprise digital transformation.

Read More:AiThority Interview With Bob Lord, SVP Of Cognitive Applications At IBM

5G technology is the mother of all emerging technologies.

We are already witnessing a rise in the number of Edge devices used in fintech, healthcare and manufacturing services, and smart city architecture around the 5G networking platforms. These are all part of the larger global picture we call it the Future of Industry 4.0. AI, alone, couldnt have helped companies achieve all the promises that were made in the first two decades of the century. Its a different industry altogether with the exploding Big Data pushing hard on AI, Machine Learning, Telecom and Internet connectivity.

IBMs partnership with Verizon will bring A, IoT and 5G technologies to co-invent Multi-access Edge Compute (MEC) capabilities for a wide range of applications. These are tested and analyzed for efficiency, accuracy, quality, and availability at an industrial level.

Industrial customers would benefit from Verizons 5G MEC expertise and IBMs Cloud and AI capabilities. Next-gen high-speed internet connectivity with low latency underlines the future of Industrial Revolution 4.0.

Many industrial enterprises are today seeking ways to use edge computing to accelerate access to near real-time, actionable insights into operations to improve productivity and reduce costs.

Edge Computing ensures low latency with trust-worthy computing and storage capabilities. Edge Virtualization is one of the fastest-growing data center infrastructure management trends that have the power to completely take out traditional IT challenges. With AI and Sensors, these data centers can be managed better, with real-time analytics and predictive intelligence becoming the new gold-standards of future IT businesses.

Edge computings decentralized architecture brings technology resources closer to where data is generated i.e., where devicesare located inan industrial site.

Edge computing provides these benefits:

We see the role of 5G networking in Edge Computing very clearly. 5G is the most advanced standard in global wireless mobile networking. 5G holds the key to connecting every device mobile, car, objects, devices, (some say, even digital pacemakers and nano-brains) with a secured mobile network. To understand each of these interactions with the Edge device on 5G in real-time, we would need AI and the power of automation.

With 5Gs low latency, high download speeds and capacity, any user can increase the number of devices that can be supported within the same geographic area. IoT devices on the 5G network could amplify the pace at which organizations interact with these devices in near real-time, with computing power in the proximity of the device. The proximity factor of data storage and computing is what makes Edge and 5G combination so powerful.

This could mean that innovative new applications such asremote controlrobotics, near real-time cognitive video analysis and plant automation may now be possible.

IBM and Verizon Business are already eyeing their market with advanced Mobile Asset Tracking and management solutions. The mobile asset tracking solutions would help enterprises improve operations, optimize production quality, and help clients enhance worker safety from any location. This would be an advantage that could play out very well in any situation that arises from a COVID-19-like pandemic or catastrophes.

At the time of this announcement, Tami Erwin, CEO,VerizonBusiness said,

This collaboration (with IBM) is all about enabling the future of industry in the Fourth Industrial Revolution. Combining the high speed and low latency ofVerizons 5G UWB Network and MEC capabilities with IBMs expertise in enterprise-grade AI and production automation can provide industrial innovation on a massive scale and can help companies increase automation, minimize waste, lower costs, and offer their own clients a better response time and customer experience.

Virtually Mobile All the Way: The AI + IoT Roadmap for a 5G Future

IBM would leverage Verizons wireless networks, including Verizons 5G Ultra-Wideband (UWB) network, and Multi-access Edge Computing (MEC) capabilities. Verizon would also provide its ThingSpace IoT Platform and Critical Asset Sensor solution (CAS) to jointly develop new 5G-based IoT Edge networks with IBM.

These will be jointly offered with IBMs market-leading Maximo Monitor with IBM Watson and advanced analytics. The combined solutions could help clients detect, locate, diagnose and respond to system anomalies, monitor asset health, and help predict failures in near real-time.

IBM andVerizonare also working on potential combined solutions for 5G and MEC-enabled use cases such as near real-time cognitive automation for the industrial environment.

Bob Lord, Senior Vice President, Cognitive Applications, Blockchain and Ecosystems, IBM says

The industrial sector is undergoing an unprecedented transformation as companies begin to return to full-scale operations, aided by new technology to help reduce costs and increase productivity. Through this collaboration, we plan to build upon our longstanding relationship with Verizon to help industrial enterprises capitalize on joint solutions that are designed to be multi-Cloud ready, secured and scalable, from the data center all the way out to the enterprise Edge.

Verizonand IBM also plan to collaborate on potential joint solutions to address worker safety, predictive maintenance, product quality and production automation.

(To share your views on the role of AI, IoT and 5G on the Future of Enterprise IT, please write to us at sghosh@martechseries.com)

Share and Enjoy !

Visit link:

5G, AI & IoT : IBM and Verizon Business Close to Edge of "Virtually Mobile" - AiThority

A few simple principles can help businesses avoid the potentially disastrous implications of unleashing artificial-intelligence-based agents that develop their own unconscious bias.

With 60 per cent of UK companies already using or planning to implement artificial intelligence, the debate around its ethical challenges is gaining momentum. Earlier this year, for example, the European Commission drafted a white paper on AI outlining a new approach to excellence and trust a clear indication that Europe is seriously considering stricter measures its use.

According to Gartner, a quarter of employees who already use technology in their work will have an AI-powered digital colleague that they interact with on a daily basis by 2021. While the opportunity to create new efficiencies and offer an elevated level of service to consumers is immense, so are the risks.

For organisations just getting started, there are some key considerations to apply in the process of defining a responsible AI game plan.

First, you need to plan for the unexpected. Remember what happened when Microsoft deployed its customer-facing chatbot, Tay, on Twitter? Within 24 hours, Tay had become a racist and sexist spokesperson by re-posting content learned from her online interactions. This illustrates that while you cannot predict every possible interaction, your implementation strategy must be carefully planned. Common roll-out strategy safety checks should include testing with representative users before roll-out, using human oversight as an extra layer of analysis and safety, and designing the way the digital employee responds to unplanned and out-of-scope interactions.

Companies using intelligent digital employees must also think about how their brand fits in; the style and tone of the dialogue, the level to which the agent can execute tasks or not and the audiences they interact with. Whether an agent is used internally or in customer service, the AI solution inevitably becomes a brand ambassador. This understanding will help define appropriate and consistent guidelines, which will ensure the AI is trained to represent the values, goals, and culture of the business.

The collection and provisioning of adequate training data is another big challenge for AI developers. Amazon, for example, has been scrutinised for the bias of its automated recruitment too, trained with data from CVs submitted over a 10-year period, which reflected the gender imbalance within the tech industry and therefore unfairly disadvantaged female applicants. The impartiality of training data and modelling is only as impartial as the design team. It is of critical importance for companies to reflect on the potential gaps in their data objectivity, use test methods that explore the potential impacts and operate with transparency on the progress of these issues.

To build a diverse team from a list of qualified applicants, consider using techniques that mitigate unconscious bias. Three tactics that can help are: removing names from the applicant review processes, augmenting candidate evaluation with a skills test to evaluate potential, and introducing structure to the interview process in order to focus on the same set of questions and answers for every possible hire. Lastly, consider setting diversity goals for your AI team that emphasise the importance of representation and balance of decision-making power for successful AI projects.

One way to leverage the potential of machine learning without risking a Tay-like disaster is through managed self-learning. This means that a digital employee is first trained via a defined data set and then learns through user interactions a process that is overseen by a supervisory body to check and approve the newly acquired knowledge. This approach ensures that learned behaviours correspond to the organisations functional and ethical vision for its AI implementation, and are based on accurate and unbiased data.

Before embarking on any AI implementation, you need to think hard about what your ideal digital employee would look like. Ensuring its ethical development requires comprehensive and thoughtful planning: reviewing the make-up of the team, the representativeness of datasets, and how it will be managed on an ongoing basis.

Considering the potentially disastrous repercussions of unconscious bias in this field, its clear that organisations need to make an enormous effort to ensure their AI solutions are designed and set up to act impartially. By adhering to the principles described here, companies can not only minimise the risk of reputational damage, but also benefit from better outcomes of their AI investments in the medium and long term.

Esther Mahr is a conversational experience designer with IPsoft. Noelle Langston is the companys director of experience design.

Sign up to the E&T News e-mail to get great stories like this delivered to your inbox every day.

Read more from the original source:

Why impartiality has to be a key part of your AI game plan - E&T Magazine

The coronavirus pandemic has turned so many aspects of businesses upside down; it is changing how companies approach and execute their strategy. The issue touches all aspects of business and operations, and in a brief Q&A with Food Safety Tech, Mike Edgett of Sage touches on just a few areas in which the future of food manufacturing looks different.

Food Safety Tech: How are food manufacturers and meat processors using AI and robotics to mitigate risks posed by COVID-19?

Mike Edgett: Many food manufacturers and meat processors have had to look to new technologies to account for the disruptions caused by the COVID-19 pandemic. While most of these measures have been vital in preventing further spread of the virus (or any virus/disease that may present itself in the future), theyve also given many food manufacturers insight into how these technologies could have a longer-term impact on their operations.

For instance, the mindset that certain jobs needed to be manual have been reconsidered. Companies are embracing automation (e.g., the boning and chopping of meat in a meatpacking plant) to replace historically manual processes. While it may take a while for innovations like this to be incorporated fully, COVID-19 has certainly increased appetite amongst executives who are trying to avoid shutdowns and expedited the potential for future adoption.

FST: What sanitation procedures should be in place to minimize the spread of pathogens and viruses?

Edgett: In the post-COVID-19 era, manufacturers must expand their view of sanitation requirements. It is more than whether the processing equipment is clean. Companies must be diligent and critical of themselves at every junctureespecially when it comes to how staff and equipment are utilized.

While working from home wasnt a common practice in the manufacturing industry prior to March 2020, it will be increasingly popular moving forward. Such a setup will allow for a less congested workplace, as well as more space and time for bolstered sanitation practices to take place. Now and in the future, third-party cleaning crews will be used onsite and for machinery on a daily basis, with many corporations also experimenting with new ways to maintain the highest cleanliness standards.

This includes the potential for UV sterilization (a tactic that is being experimented with across industries), new ways to sterilize airflow (which is particularly important in meatpacking plants, where stagnant air is the enemy) and the inclusion of robotics (which could be used overnight to avoid overlap with human employees). These all have the potential to minimize the spread of pathogens and, ultimately, all viruses that may arise.

FST: How is the food industry adjusting to the remote working environment?

Edgett: While the pandemic has changed the ways businesses and employees work across most industries, F&B manufacturers did face some unique challenges in shifting to a remote working environment.

Manufacturing as a whole has always relied on the work of humans, overseeing systems, machinery and technology to finalize productionbut COVID-19 has changed who and how many people can be present in a plant at once. Naturally, at the start of the pandemic, this meant that schedules and shifts had to be altered, and certain portions of managerial oversight had to be completed virtually.

Of course, with employee and consumer safety of paramount concern, cleaning crews and sanitation practices have taken precedent, and have been woven effectively and efficiently into altered schedules.

While workers that are essential to the manufacturing process have been continuing to work in many facilities, there will likely be expanded and extended work-from-home policies for other functions within the F&B manufacturing industry moving forward. This will result in companies needed to embrace technology that can support this work environment.

FST: Can you briefly explain how traceability is playing an even larger role during the pandemic?

Edgett: The importance of complete traceability for food manufacturers has never been greater. While traceability is by no means a new concept, COVID-19 has not only made it the number one purchasing decision for your customers, but [it is also] a vital public health consideration.

The good news is that much of the industry recognizes this. In fact, according to a survey conducted by Sage and IDC, manufacturing executives said a key goal of theirs is to achieve 100% traceability over production and supply chain, which serves as a large part of their holistic digital mission.

Traceability was already a critical concern for most manufacturersespecially those with a younger customer base. However, the current environment has shone an even greater spotlight on the importance of having a complete picture of not only where our food comes frombut [also] the facilities and machinery used in its production. Major budget allocations will surely be directed toward traceability over the next 510 years.

More than 10,000 workers at meat plants have reportedly been infected or exposed.

The complimentary event will discuss how the food industry can protect their employees and consumers during this crisis.

The Consortium will be held December 24 in Schaumburg, IL, but were prepared to go virtual if necessary.

Held on May 27, the complimentary event will offer practical tips on best practices to mitigate workplace exposure, along with cleaning and sanitation advice.

Original post:

COVID-19 Leads Food Companies and Meat Processors to Explore AI and Robotics, Emphasize Sanitation, and Work from Home - FoodSafetyTech