Category Archives: Automation

UNIVERSITY PARK, Pa. The rise of automation and artificial intelligence, and their impacts on certain industries and the economy, will be discussed by Penn State President Eric Barron and a pair of University experts during the next episode of WPSUs Digging Deeper on Sunday, Jan. 26.

Vasant Honavar, director of the Artificial Intelligence Research Laboratory and Edward Frymoyer Endowed Professor of Information Sciences and Technology, and Barry Ickes, professor and head of the Department of Economics, will join Barron for the show.

Digging Deeper will air at 10:30 a.m. and 6 p.m. on WPSU-TV.

Penn State President Eric Barron and a pair of University experts will discuss the impacts of automation and artificial intelligence during the next episode of WPSU Penn States Digging Deeper on Sunday, Jan. 26.

Honavar said the types of jobs being replaced by automation has shifted.

The difference is historically through the Industrial Revolution and until fairly recently, the part of the work that was being automated was physical labor and often work that was dangerous, what people didn't really want to do, he said. But I think what's different now is that we're talking about what used to be considered cognitive work, knowledge work.

There will be tasks in almost every job that are amenable to automation, according to Honavar, and nearly every job will change because of automation and AI.

Ickes said the effect will create a mismatch of skills causing job displacement, and there needs to be a way to address the problem.

Usually the winners outnumber the losers but the losers feel that pain, and it's up to policy to set up mechanisms to help with the people who lose from these changes, Ickes said. Especially if society is going to really gain from automation and AI, where there's big productivity gains and big wealth gains and big GDP gains. That would afford the resources to enable us to deal with the people who are hurt by it.

Visit the WPSU website for more information on central Pennsylvanias public media station. WPSU is an outreach service of Penn State.

Last Updated January 17, 2020

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The impacts of automation and AI on the next WPSU 'Digging Deeper' - Penn State News

ByMike Franz, Founder,ManufacturingPower

Only when manufacturers stop basing critical business decisions on inadequate spreadsheets can the purchasing department succeed. Automated access to summary cost and margin information for each SKU delivers light SaaS MRO/tail-spend price transparency, returning a minimum 11-14% COGS (cost of goods sold) reduction to the bottom line.

Every manufacturer must pay attention to costs and competitors pricing. For better or worse, purchasing professionals are rewarded for capturing the lowest price; few have the time to scour the internet for ever RFQ to see if the bid offered is indeed competitive. Measuring value with price experiments is not realistic.

Full-time competitive pricing professionals could work 24/7 to generate statistically valid and accurate data. For the small and mid-sized manufacturer, the solution is to automate price comparison functionality. Until recently this was prohibitively expensive and often too complex. Automating costs and competitive prices can be achieved in a $5000 per year SaaS solution.

Outsourcing data collection is mandated because of rapidly changing prices. Whether due to tariffs, international competition, spikes in oil causing much higher delivery costs, the monitoring of competitors costs simply takes too much time and effort without automation.

ManufacturingPower, for example, collects competitive prices from sites and marketplaces like Amazon, Google Shopping, Grainger, and eBay in real-time.

Realizing the need for automated price-setting, developers began marketing price scraping software and setting consumer cost for products. Scraping programs automatically retrieve competitors pricing and product information. The scraped data can be posted to an e-commerce site in real-time.

By automating this process, scraping must be tailored to provide the critical analysis and manual adjustments required. Automated solutions introduce a scientific technology combining big data and cloud technology to set price points for even the smallest manufacturers.

Analytic tools can help forecast optimum pricing in advance. Small manufacturers set the best price; this may not be necessarily the lowest price for each customer. Dynamic pricing can look to big data to provide information that affects purchasing strategies.

Beyond competitors prices, market trends affect sales including geographic location and supply chain issues.

Automating product costing is central to the critical decisions made daily by small and mid-sized manufacturers. Decisions about the products manufactured, the prices charged, and the tactics implemented to improve the processes, make or break the companys profitability.

With a cloud-based application designed to quickly calculate, analyze, share, and maintain detailed, accurate, and actionable product costs, users may be able to eliminate spreadsheets as the methodology for price comparisons.

About the Author

Mike Franz is the founder and creator of the WorkCenter from ManufacturingPower, a cloud-based market intelligence solution designed to help small to mid-sized companies streamline and achieve real-time visibility into Industrial Supply spend

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Automating Competitive Pricing for Small and Medium Manufacturers - Automation.com

If you want to know the future of networking, follow the money right to the edge.

Applications are expected to move from data centers to edge facilities in record numbers, opening up a huge new market opportunity. The edge computing market is expected to grow at a compound annual growth rate of 36.3 percent between now and 2022, fueled by rapid adoption of the internet of things, autonomous vehicles, high-speed trading, content streaming and multiplayer games.

What these applications have in common is a need for near zero-latency data transfer, usually defined as less than five milliseconds, although even that figure is far too high for many emerging technologies.

The specific factors driving the need for low latency vary. In IoT applications, sensors and other devices capture enormous quantities of data, the value of which degrades by the millisecond. Autonomous vehicles require information in real-time to navigate effectively and avoid collisions. The best way to support such latency-sensitive applications is to move applications and data as close as possible to the data ingestion point, therefore reducing the overall round-trip time. Financial transactions now occur at sub-millisecond cycle times, leading one brokerage firm to invest more than $100 million to overhaul its stock trading platform in a quest for faster and faster trades.

As edge computing grows, so do the operational challenges for telecommunications service provider such as Verizon Communications Inc., AT&T Corp. and T-Mobile USA Inc. For one thing, moving to the edge essentially disaggregates the traditional data center. Instead of massive numbers of servers located in a few centralized data centers, the provider edge infrastructure consists of thousands of small sites, most with just a handful of servers. All of those sites require support to ensure peak performance, which strains the resources of the typical information technology group to the breaking point and sometimes beyond.

Another complicating factor is network functions moving toward cloud-native applications deployed on virtualized, shared and elastic infrastructure, a trend that has been accelerating in recent years. In a virtualized environment, each physical server hosts dozens of virtual machines and/or containers that are constantly being created and destroyed at rates far faster than humans can effectively manage. Orchestration tools automatically manage the dynamic virtual environment in normal operation, but when it comes to troubleshooting, humans are still in the drivers seat.

And its a hot seat to be in. Poor performance and service disruptions hurt the service providers business, so the organization puts enormous pressure on the IT staff to resolve problems quickly and effectively. The information needed to identify root causes is usually there. In fact, navigating the sheer volume of telemetry data from hardware and software components is one of the challenges facing network operators today.

A data-rich, highly dynamic, dispersed infrastructure is the perfect environment for artificial intelligence, specifically machine learning. The great strength of machine learning is the ability to find meaningful patterns in massive amounts of data that far outstrip the capabilities of network operators. Machine learning-based tools can self-learn from experience, adapt to new information and perform humanlike analyses with superhuman speed and accuracy.

To realize the full power of machine learning, insights must be translated into action a significant challenge in the dynamic, disaggregated world of edge computing. Thats where automation comes in.

Using the information gained by machine learning and real-time monitoring, automated tools can provision, instantiate and configure physical and virtual network functions far faster and more accurately than a human operator. The combination of machine learning and automation saves considerable staff time, which can be redirected to more strategic initiatives that create additional operational efficiencies and speed release cycles, ultimately driving additional revenue.

Until recently, the software development process for a typical telco consisted of a lengthy sequence of discrete stages that moved from department to department and took months or even years to complete. Cloud-native development has largely made obsolete this so-called waterfall methodology in favor of a high-velocity, integrated approach based on leading-edge technologies such as microservices, containers, agile development, continuous integration/continuous deployment and DevOps. As a result, telecom providers roll out services at unheard-of velocities, often multiple releases per week.

The move to the edge poses challenges for scaling cloud-native applications. When the environment consists of a few centralized data centers, human operators can manually determine the optimum configuration needed to ensure the proper performance for the virtual network functions or VNFs that make up the application.

However, as the environment disaggregates into thousands of small sites, each with slightly different operational characteristics, machine learning is required. Unsupervised learning algorithms can run all the individual components through a pre-production cycle to evaluate how they will behave in a production site. Operations staff can use this approach to develop a high level of confidence that the VNF being tested is going to come up in the desired operational state at the edge.

AI and automation can also add significant value in troubleshooting within cloud-native environments. Take the case of a service provider running 10 instances of a voice call processing application as a cloud-native application at an edge location. A remote operator notices that one VNF is performing significantly below the other nine.

The first question is, Do we really have a problem? Some variation in performance between application instances is not unusual, so answering the question requires a determination of the normal range of VNF performance values in actual operation. A human operator could take readings of a large number of instances of the VNF over a specified time period and then calculate the acceptable key performance indicator values a time-consuming and error-prone process that must repeated frequently to account for software upgrades, component replacements, traffic pattern variations and other parameters that affect performance.

In contrast, AI can determine KPIs in a fraction of the time and adjust the KPI values as needed when parameters change, all with no outside intervention. Once AI determines the KPI values, automation takes over. An automated tool can continuously monitor performance, compare the actual value to the AI-determined KPI and identify underperforming VNFs.

That information can then be forwarded to the orchestrator for remedial action such as spinning up a new VNF or moving the VNF to a new physical server. The combination of AI and automation helps ensure compliance with service-level agreements and removes the need for human intervention a welcome change for operators weary of late-night troubleshooting sessions.

As service providers accelerate their adoption of edge-oriented architectures, IT groups must find new ways to optimize network operations, troubleshoot underperforming VNFs and ensure SLA compliance at scale. Artificial intelligence technologies such as machine learning, combined with automation, can help them do that.

In particular, there have been a number of advancements over the last few years to enable this AI-driven future. They include systems and devices to provide high-fidelity, high-frequency telemetry that can be analyzed, highly scalable message buses such as Kafka and Redis that can capture and process that telemetry, and compute capacity and AI frameworks such as TensorFlow and PyTorch to create models from the raw telemetry streams. Taken together, they can determine in real time if operations of production systems are in conformance with standards and find problems when there are disruptions in operations.

All that has the potential to streamline operations and give service providers a competitive edge at the edge.

Sumeet Singh is vice president of engineering at Juniper Networks Inc., which provides telcos AI and automation capabilities to streamline network operations and helps them use automation capabilities to take advantage of business potential at the edge. He wrote this piece for SiliconANGLE.

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How machine learning and automation can modernize the network edge - SiliconANGLE

Infor Cloud Solutions to Connect Siloed Applications and Reduce Manual Processes

Infor, a global leader in business cloud software specialized by industry, announced that Pechanga Resort Casinoentered into an agreement with Infinium Software Inc., an Infor company, expanding their long-term technology partnership. Pechanga, the largest resort/casino on the West Coast, chose Infor CloudSuite solutions to further integrate core business processes into a centralized location connected to the reservation department. The new, integrated system will allow the resort/casino to make more informed business decisions from anywhere, anytime, on any device. Infors innovative cloud technologies, built to work hand in hand with the world-class capabilities of Amazon Web Services, will provide Pechanga with more adept abilities to query data within seconds. Pechanga has been an Infinium customer since 2001, and an Infor HMS and Infor EzRMS customer since 2016.

Pechanga will implement Infor CloudSuite solutions created specifically for the hospitality industry to better support financials, supply management, human capital management, analytics, and workforce management. These flexible applications can provide business leaders with new insights and real-time data to make decisions quickly that may improve bottom-line results. Specifically, workforce management software and human capital management applications will help support better labor optimization, planning, and time and attendance. An integrated finance and supply management software solution suite will couple modern financial functionality with tools to track supplies and streamline order processes.

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For years, Pechanga has found success with Infor HMS, which has provided it a complete view of all guest value information in one system. This has enabled Pechanga to provide tailored recognition of known players and new guests who are seeking the broader resort experience Pechanga offers. In addition, Infor EzRMShas helped Pechanga automatically calculate demand forecasts for each future use of their hotel rooms and determine the appropriate selling strategies, such as open/close rates, stay controls, open/close room categories, and overbooking levels. Its deep learning AI algorithms recognize patterns dynamically to help ensure accurate business forecasts and optimal pricing recommendations.

Infor Hospitality has been a partner to our organization for a long time, and expanding our technology partnership with them gives us a more streamlined approach and flexibility, said John Kenefick, chief information officer, Pechanga Resort Casino. Infors cloud infrastructure, network services and industry-specific application design will give us more reliability, security, and scalability.

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With Infor solutions, teams at Pechanga will benefit from a simple and predictable path to upgrade from on-premise applications to the cloud. The organization will immediately benefit from quick user interaction experiences and deeper industry functionality that helps retire customizations to provide more thorough analytics and simpler integration.

We are able to provide our customers with industry-first cloud technology, unmatched depth of functionality, and low risk implementations, said Jason Floyd, general manager, Infor Hospitality. The gaming industry continues to become more crowded, so partnering with a technology provider that understands specific pain points in the business and what successful bottom-line results should look like is a competitive advantage in todays industry. Infors hospitality-specific cloud applications quickly automate timeworn processes, decrease costs, and ultimately improve the guest experience.

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Pechanga Resort Casino Drives Automation and Visibility with Infor CloudSuite - MarTech Series

The argument for supporting women to raise their skills in order to thrive in the labour markets of the future is compelling. Employers should pay particular attention to the needs of women who face new pressures from automation, on top of the perennial difficulties they already face in the world of work. McKinsey Global Institute predicts as many as 160 million may need to change jobs in the age of automation nearly one-quarter of all women employed today.

The challenge is not so much who gets hit hardest by automation the impact is roughly the same order of magnitude for women and men but how well individuals are prepared to adapt. This is where women need targeted support.

If women can make the necessary transitions, they could be on a path to more productive, higher-paid work. If the opportunity is not available to them, the gender pay gap may widen, and many women may even leave the labour force as demand for lower-skilled jobs declines. There has been progress, but it has been slow. Companies are promoting diversity but on the current trajectory it will take more than 20 years to reach parity in executive positions, according to the McKinsey Diversity Matters database. This progress could be derailed if women are not helped to make the transitions they need in the face of automation.

More women need occupational transitions by 2030 to remain employed.

Image: McKinsey Global Institute

We know this matters. McKinseys research has found companies in the top quartile for diversity are 15-24% more likely to outperform their peers on earnings before interest and taxation (EBIT) margins than are companies in the bottom quartile. In UK and US data, we found companies with more than 30% women on their executive teams were almost 40% more likely to outperform on EBIT margins than those with 10- 30% women executives.

More than ever, men and women need to develop the skills that will be in demand, the mobility needed to negotiate labour-market transitions, and the access to, and knowledge of, the technology required to work with automated systems. Today, women face relative challenges across all three areas. We are arguing for tailored support from companies, supported by government policy, to enable women to overcome these barriers.

Lets look at the three keys to the future of work for women:

The key arbiter of success or failure in making these transitions will be different and higher skills. In five of the six mature economies studied, we expect net demand for labour to be positive only for jobs requiring a college or advanced degree. In three of the four emerging economies studied, net labour demand for occupations requiring secondary education could rise sharply. MGI research in 2018 found that demand for basic cognitive, physical and manual skills will decline, but that jobs could require up to 55% more time using technical skills and 24% more hours using social and emotional skills by 2030. These substantial shifts in labour demand will require many women to make radical changes to their working lives.

Although the gender gap in education is narrowing, fewer women are graduating in fields that will grow and be vital for future employment. In the United Kingdom, only 37% of first-year full-time female students study science subjects, compared to 48% of men.

The private sector should invest more in reskilling employees, or partner with academic and other institutions. One study found that in 2018, 54% of employers were providing additional training and development opportunities to their existing workforce to fill skills gaps, compared with only 20% in 2014 that share needs to rise further. Public and private investment in digital learning platforms would open up another avenue for women. In the United States, Disneys Code: Rosie initiative recruits and trains women in non-technical positions for software engineering roles, offering 12-month apprenticeships and mentoring schemes.

How government, industry, education and NGO leaders can support job transitions for women

Image: McKinsey

Women are less mobile than men because they disproportionately undertake unpaid care work in the home, compromising their scope for training and paid employment. Technology could give women new flexibility to work remotely in the gig economy or in e-commerce but companies need to expand the range of flexible working options. One 2018 survey of employers found that flexible or remote working options were only offered by 23% of employers. More access to professional networks would help women bolster their chances of moving into higher-paid occupations. For instance, Hilton has created Team Member Resource Groups networks for women and other under-represented groups of employees.

Technology could be the breakthrough that women need, enabling them to work more flexibly in the gig economy. Yet women lag behind men in access to tech, skills and leadership. Globally, men are 33% more likely than women to have access to the internet, and women only account for 35% of STEM students in higher education. Fewer than 20% of tech workers are female in many mature economies. Only 1.4% of female workers have jobs developing, maintaining or operating ICT systems, compared with 5.5% of male workers, according to the OECD. Companies have a role to play, working with educational institutions to develop a broader pipeline of women going into tech fields. In Singapore, many firms have started sending staff members to SkillsFutures Digital Workplace programme. Germany-based software company SAP has set a target and is measuring progress toward it of having 30% of leadership positions filled by women by 2022.

The World Economic Forum has been measuring gender gaps since 2006 in the annual Global Gender Gap Report.

The Global Gender Gap Report tracks progress towards closing gender gaps on a national level. To turn these insights into concrete action and national progress, we have developed the Closing the Gender Gap Accelerators model for public private collaboration.

These accelerators have been convened in Argentina, Chile, Colombia, Costa Rica, Dominican Republic, Panama and Peru in partnership with the InterAmerican Development Bank.

In 2019 Egypt became the first country in the Middle East and Africa to launch a Closing the Gender Gap Accelerator. While more women than men are now enrolled in university, women represent only a little over a third of professional and technical workers in Egypt. Women who are in the workforce are also less likely to be paid the same as their male colleagues for equivalent work or to reach senior management roles.

France has become the first G20 country to launch a Gender Gap Accelerator, signalling that developed economies are also playing an important role in spearheading this approach to closing the gender gap.

In these countries CEOs and ministers are working together in a three-year time frame on policies that help to further close the economic gender gaps in their countries. This includes extended parental leave, subsidized childcare and removing unconscious bias in recruitment, retention and promotion practices.

If you are a business in one of the Closing the Gender Gap Task Force countries you can join the local membership base.

If you are a business or government in a country where we currently do not have a Closing the Gender Gap Task Force you can reach out to us to explore opportunities for setting one up.

Armed with knowledge of the transitions women will need to make, now is the time to step up efforts and help women overcome new challenges and old.

The public, private and third sectors need to work together to support women to make the transitions they need to thrive in the automation age. There are concrete, practical ways that companies can play their part: bolstering the pool of talent and fulfilling their commitment to diversity in their ranks. Better to invest now than lose progress made in diversity for companies, for society, and, of course, for women themselves as the automation age takes hold.

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3 ways to reskill women for the automation age - World Economic Forum

Digital transformation continues to be an exciting andhigh-profilebusiness initiative, but as with everybusiness revolution of the last few decades, its not a silver bullet.

In particular, many businesses have embraced automation as a technology to shift their organization to a new way ofworking, butfailed to understand both the limits of the toolset and the support it requires.

Its vital that companies grasp the potential of automation but realise its limitations too. By helping your staff come to terms with what these technologies offer,yourteamscan advocate formoreintelligent and effective implementation across the business,and ensure the maximum benefit is derived from each tool.

Automation works

Theres no denying that business process automation pays dividends. It can remove or reduce laborious, repetitive work and bring greater accuracy and efficiency to processes and procedures in the workplace.

However, if an organization wants to see more than short term gains and truly realize the potential ROI of the technology, automation needs to be employed intelligently and witha supporting structure that looks at more than just new software.

Scaffolding for success

Thefirstfactor in successful process automation is the process. Without capturing and refining your business processes, even the most effectiveautomation solutions can only achieve so much.

Its another instance of the old programming mantra, garbage in garbage out. If the process itself is inefficient, lacks clarity or is poorly understood, then adding automation may in fact amplify the problems rather than reduce them.

Engage the people who know the process best in the practice of capturing the process, then provide avenues for feedback and continuous improvement. Once the process is clear, compliant and functioning as intended, there are opportunities to identify where automation can bring greater efficiency and effectiveness.

Identifying the systems and structures already in place can help indicate where automation will be beneficial. The users most familiar with the activities can link their steps to the platforms already in play, and this can provide a map of dependencies and connections. For Lean and Six Sigma advocates, utilizing tags or highlights can pinpoint elements that are ripe for optimization.

Reporting these markers across the processes is an effective way of spotting automation opportunities within the business.

Knowing the tools

There are various forms of automation available, and each has its own arena of effectiveness. Like any toolkit, you cant expect any one solution to fit every problem and trying to apply blanket fixes can in fact create more problems.

Rather than grasp the latest trend, carefully considerwhat your needs are. This is where empowering your business teams helps; since theyre the everyday users of the processes, they know them best. By giving them an understanding of the various technologies, they will be able to identify use cases that best suit the tools.

Workflows

Workflows are the connectors between people, processes,andthe systems they use. Workflows take many forms, but theyre ideal for processes where collaboration and creativity are required. What sets workflows apart is their capacity to keep the human element active, while removing monotonous and repetitive tasks.

A workflow will route data between twoagents, oradd and extract records from integrated systems like a CRM, while still engaging your staff in decision making along the way. When empowered with AI tools like sentiment analysis, a workflow can make rudimentary decisions about how to route emails or documentation, who to engage and what level of response to make.

Intelligent Forms

Digital documents like forms are used to reduce both the physical clutter of paper-based records, and the unnecessary repetition of handling the data they contain. Rather than completing a form, then entering that data into digital records, then accessing and employing the information, intelligent formstake care of those steps automatically. By directing clients, staff or other users to intelligent forms, the time taken up by manually processing that data is saved and the potential for errors is reduced significantly.

The other side of this is document generation. Rather than compiling reports, agreements or other documentation manually, a document generation system will access the relevant records and create a custom document, complete with branding and formatting, without needing user input. This aspect of workflow automation eliminates the delay between compiling the data required and producing the documentation it supports. Coupled with digital signature technology, such forms can turn contract cycles into days or hours rather than weeks.

RPA

Robotic process automation (RPA)utilises virtual bots to undertake repetitive manual processes that follow the same path every time without deviation or decision making. They perform manual steps, interacting with legacy systems and other tools like spreadsheets or web interfaces exactly as a human agent would, but faster, more accurately,and tirelessly.

The important distinction here is that these are rote tasks, and the bot is trained to undertake them in the same way a human user would, but without exercising judgement along the way. That makes them very efficient, exponentially faster than a manual user would be, but unable to tackle variations or decision making.

Power to the people

Some of the fear around automation is that it will replace human employees, but as these examples highlight, the tools are best used to free your teams from monotonous tasks so they can focus on value-adding work and bringing creativity to their roles.

By understandingthe strengths and distinctions of process automation solutions, staff can explore how those tools can enhance what they do and increase their efficiency in everyday tasks. By coupling that knowledge with well captured and managed processes, you have a foundation for effective digital transformation that will make the best use of the resources and benefit everyone involved.

Once these approaches are established, they become a self-strengthening cycle of managing, automating and optimizing processes, always evolving both the procedures and the tools supporting them for the very best outcomes.

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Garbage in, garbage out: save your automation efforts from certain failure - Process Excellence Network

Church Brothers Farms in California changed its planting configurations and spacing to accommodate the width of the automated broccoli harvester.Photo by Richard Smith

A critical labor shortage threatens the ability of leafy green and other cool-season vegetable growers to produce and harvest their crops. What can an operation do to gain some control over the crisis? After all, migrant labor availability is subject to political forces outside of the power of the agricultural community.

However, growers are taking proactive steps to address this issue. Theyre teaming with technology companies to develop new automated harvesting technologies and more.

In the Salinas Valley, for example, there has been swift adoption of machines that thin and weed lettuce and other crops. New technologies have also been developed for transplanting (e.g., PlantTape) and irrigating crops (e.g., use of permanent set sprinkler pipe and single-use drip tape).

If the industry could find a way to develop automated harvesting to the point that most growers can use it, it will be a big step forward.

Carrots and potato operations have mechanically harvested for many years. Thats because they can withstand rougher handling and still maintain quality.

Crops like leafy and other cool-season vegetables, however, present challenges for mechanized harvesting because of the delicate nature of the product. As a result, most picking and packing is still being done by hand in order to maintain quality.

Lets take a look at where we are in developing mechanical harvesting and which issues you should weigh before adopting the technology yourself.

The decision to move to mechanical harvest is not straight forward. Here are some practical decision points that interplay and complicate the decision to move toward mechanical harvest:

An early example was the mechanization of baby lettuce, spring mix, and spinach in the early 2000s. Those early machines used a band saw to cut across the width of a high-density, 80-inch-wide bed and lifted the product to a platform. From there, the machine placed the lettuce into totes used to transport the product to the packing facility.

As we shall also see in a subsequent example, in order to make these harvesters work effectively, growers had to change production practices. They shaped beds with power mulchers to create smooth, uniform beds that allowed cutter bars to operate efficiently. High-quality, baby leaf vegetables required specific plant spacing to assure ideal and uniform leaf sizes, which, in turn, required optimal plant populations and planters to assure that spacing.

Just as youll need to change cultural practices for mechanical harvesting, youll also need to adjust your variety choices.

The processing tomato harvester in the 1960s is a classic example of an automated harvester meshing with the appropriate variety. This machine required varieties that ripened uniformly, stored well in the field, separated from the plant easily, and could withstand handling.

Once breeders developed varieties that worked well with the new machines, automated harvesting of processing tomatoes quickly became the industry standard.

A similar example of this type of synergy between machine and variety is underway in the Salinas Valley. Josh Ruiz, Vice President of Ag Operations at Church Brothers Farms, has been developing an automated broccoli harvester for the past five years. It uses a cutting bar for a once-over harvest.

To improve harvest efficiency, Josh collaborated with Seminis Seed Company to use varieties from their high-rise portfolio (e.g., Eiffel and Hancock) that have broccoli heads higher on the plant than traditional varieties.

Seminis Seed Company collaborated with Church Brothers Farms Josh Ruiz, Vice President of Ag Operations, on developing broccoli varieties that work well with automated harvesting equipment.Photo by Richard Smith

Josh says having the broccoli heads located higher up on the plant helps the machines harvest. But equally important, the varieties have good uniformity, which allows efficient harvest and good yields.

Josh has found that to make a mechanical harvester work, you must be willing to change production practices. For instance, Church Brothers Farms has changed both planting configurations and spacing to accommodate the width of the harvester. Their work on their automated broccoli harvester is not 100% complete, as they continue to refine it and make improvements.

Early harvesters work best on whole-head bulk lettuce destined for salad plants and bagged salad products. They cut all the lettuce on an 80-inch-wide bed, lift the lettuce onto a platform where it is hand sorted and deposited into bins for transport to the processing plant.

The quality standards for bulk lettuce are not as stringent as for fresh market lettuce, and these machines work well. However, the cost, speed, and capacity of the machines are important considerations for their use. Also, although this harvester eliminates the need for people to cut lettuce, you still need labor to sort and core it.

Quality standards for fresh market lettuce are more demanding. And harvesters for this type of product are trickier to develop.

The Italian company Ortomec has developed a harvester for fresh market romaine. Bob Sutton of Sutton Ag in Salinas, CA, has worked with several vegetable companies evaluating the Ortomec 9700 Lattuga (type Lattuga on YouTube). He is determining how it can save labor and effectively harvest lettuce.

According to Sutton, issues that companies face when evaluating an automated harvester are the cost of the machine, net savings in labor, and its overall harvest capacity and speed.

A key question is how many machines are needed to satisfy harvesting needs and for addressing breakdowns. A non-selective cutting mechanism that cuts all the lettuce on the bed, like the Ortomec 9700, requires more labor on the platform to sort the product prior to packing.

A machine that uses a selective harvest mechanism (e.g., a mechanical arm) might require less labor for packing. However, it would be more complex and move slower through the field.

These factors make decisions about automating lettuce harvest complex. Understandably, there has not yet been widespread movement to mechanical harvest for fresh market lettuce, although significant progress is being made.

There are still some key challenges remaining.

For one, once the harvester lifts the crop to a packing platform, can trimming, bunching, and bagging be automated? Can computer vision improve which product the harvester selects? These are all questions that engineers are working to resolve.

However, beyond these purely technical questions, for automated harvesters to succeed, they must give a return on investment in a reasonable amount of time. Given high development costs for automated harvest machines, there may be opportunities for companies to work together, collaborate, and pool resources.

These technical factors do not exist in a vacuum. Outside forces could also accelerate industry-wide adoption of automated harvesting.

If, for example, the U.S. decided to suspend the H-2A temporary agricultural worker program, the impetus for automated harvesting would increase dramatically. Likewise, if a new machine could make the work easier (especially important to older workers), or increase worker satisfaction and retention, these could be important factors tipping the balance toward mechanization.

Clearly, we need all technical and financial ideas to adjust to the labor crunch. And we keep growers in the business of providing cool-season vegetables to a demanding marketplace.

Richard Smith is a University of California Vegetable Crop and Weed Science Farm Advisor at the Cooperative Extension in Monterey, Santa Cruz, and San Benito counties. See all author stories here.

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Automation in Vegetable Harvesting Picks up the Momentum - Growing Produce

The research study presented in this report offers complete and intelligent analysis of the competition, segmentation, dynamics, and geographical advancement of the Global Oil & Gas Automation Market. The research study has been prepared with the use of in-depth qualitative and quantitative analyses of the global Oil & Gas Automation market. We have also provided absolute dollar opportunity and other types of market analysis on the global Oil & Gas Automation market.

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The authors of the report have segmented the global Oil & Gas Automation market as per product, application, and region. Segments of the global Oil & Gas Automation market are analyzed on the basis of market share, production, consumption, revenue, CAGR, market size, and more factors. The analysts have profiled leading players of the global Oil & Gas Automation market, keeping in view their recent developments, market share, sales, revenue, areas covered, product portfolios, and other aspects.

Market Segmentation

In terms of automation type, the Oil & Gas Automation market can be segmented into asset optimization; cost reduction; Health, Safety and Environment (HSE); and skills gap. In terms of application, the Oil & Gas Automation market can be divided into gas processing, LNG, offshore assets, onshore assets, pipeline & transportation, and refining & petrochemical. In terms of region, the global Oil & Gas Automation market can be segregated into North America, Latin America, Europe, Asia Pacific, and Middle East & Africa.

Oil & Gas Automation Market: Key Players

Key players operating in the global Oil & Gas Automation market include ENI, Rockwell Automation, Siemens AG, and GE.

The report offers a comprehensive evaluation of the market. It does so via in-depth qualitative insights, historical data, and verifiable projections about market size. The projections featured in the report have been derived using proven research methodologies and assumptions. By doing so, the research report serves as a repository of analysis and information for every facet of the market, including but not limited to: Regional markets, technology, types, and applications.

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The report has been compiled through extensive primary research (through interviews, surveys, and observations of seasoned analysts) and secondary research (which entails reputable paid sources, trade journals, and industry body databases). The report also features a complete qualitative and quantitative assessment by analyzing data gathered from industry analysts and market participants across key points in the industrys value chain.

A separate analysis of prevailing trends in the parent market, macro- and micro-economic indicators, and regulations and mandates is included under the purview of the study. By doing so, the report projects the attractiveness of each major segment over the forecast period.

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Oil & Gas Automation Market Size and Forecast

In terms of region, this research report covers almost all the major regions across the globe such as North America, Europe, South America, the Middle East, and Africa and the Asia Pacific. Europe and North America regions are anticipated to show an upward growth in the years to come. While Oil & Gas Automation Market in Asia Pacific regions is likely to show remarkable growth during the forecasted period. Cutting edge technology and innovations are the most important traits of the North America region and thats the reason most of the time the US dominates the global markets. Oil & Gas Automation Market in South, America region is also expected to grow in near future.

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Oil & Gas Automation Market is Anticipated to Register a Value of Million by the end of 2017 2025 Dagoretti News - Dagoretti News

RPA is the primary technology for rapidly automating existing user processes across industries

In contrast, a full specification for making tea would require a tea cup, a volume of water, a thermometer, a timer, a measured quantity of tea, etc. It defines what should happen when the thermometer or timer is broken. It would check that the tea cup is large enough to hold the volume of water and describe the result otherwise. The software engineering life cycle describes the formal process of software development from gathering requirements to coding, testing, documenting and delivery. Even in its agile form with minimal up-front specification, the software development process is necessarily formal to understand and capture the objectives and deliver a verified solution that can be understood, integrated into other solutions, maintained and perhaps reused in the future. These are critical, particularly as the number of users and size of the solution increase.

RPA fills the space between the lightweight macros and heavyweight software engineering to efficiently provide solutions. It sometimes utilizes a recorded script (like Excel) that enables a solution to be developed without programming, but more commonly uses a scripting language to construct a rule-based workflow. RPA adds enough documentation and tests to achieve its goal without being generalizable. Unlike the Excel macros that function only in Excel, RPA operates at the user-computer level so it mimics a user actions. This user-interaction approach can even transform the automation of legacy systems written by former employees with no documentation. Rather than trying to understand the system and programming interface (API) yet alone try to rebuild it, RPA can interact with the legacy system and add functionality with a user-level business process understanding. Furthermore, RPA solutions solve existing challenges of software versioning, deploying the software across enterprise computers, auditing processes, enabling access security, load balancing computationally-intensive tasks across systems and even disaster-recovery.

Can we replace our software infrastructure with RPA?

With most businesses today centered around data, companies aim to effectively leverage their data to make better, timely decisions. Software engineering focuses mostly on the storage and access of data, whereas RPA focuses on the user workflows. In that sense, RPA depends on the formal software engineering management of the data and enhances user activities through automation. RPA only manipulates data through the software systems user interfaces which is slower and less efficient than API access, but RPA can be scaled to overcome these limitations. As companies productionize 100s of RPA scripts, the challenge of maintaining, interconnecting and perhaps reusing the scripts will require additional specifications, testing and documentation that encumbers their software engineering counterparts.

RPA is the primary technology for rapidly automating existing user processes across industries. Accounting and finance organizations have led the way due to their repeated auditable processes. In the pharmaceutical space, Pfizer is using RPA for report processing for the FDA, clinical trial management and product labeling. The range of opportunities seems almost endless.

Although RPA has already added significant value to the workplace, it doesnt currently use artificial intelligence (AI).General purpose AI tools such as OCR enable RPA workflows to scan and recognize texts, and some natural language processing capability has entered the workflows but the current RPA technology is mostly running a set of business rules. Enabling AI to assist with each individuals business decisions will require machine learning systems to be trained for each specific human decision. Such training currently requires data science expertise, but recent AutoML advances show promise in training AI systems to make decisions automatically. This enhancement of smart RPA capabilities will certainly transform the workplace.

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The Rise Of Robotic Process Automation - CIO Applications

When it comes to the 2020 Democratic Presidential candidates, theres the boring and then theres the roaring. Perennial frontrunner and former Vice President Joe Biden continues to maintain his sizable lead over opponents and hopes that he can autopilot his way into getting the Democratic nod. Aside from some cringey gaffes, Biden is nowhere near as interesting as former Silicon Valley executive Andrew Yang, who actually has a story to tell about why America is supposedly in a funk and how it can overcome its challenges. Unfortunately, that narrative happens to be dead wrong. But in order to see why, Americans everywhere must understand the seductively simple explanations that the candidate is pedaling.

Yang: Automation is no longer just a problem for those working in manufacturing. Physical labor was replaced by robots; mental labor is going to be replaced by [Artificial Intelligence] and software.

Yang, along with his Yang Gang acolytes, have a story to tell the American people. According to Yang, an increasing wave of automation is shrinking employment, killing labor force participation, and leading scores of depressed and discouraged Americans to get hooked on opioids or worse and eventually take their own lives.

But its important to look at working life in America is today. Most Americans, including Yang, realize that the unemployment rate is the lowest it has been since the 1960s (sub-4 percent at the start of 2020). Ah, but many Americans are discouraged from the job search altogether and these discouraged workers arent counted in the standard definition of the unemployment rate.

To quote our new favorite tech politico (Yang): Labor force participation rate (LFPR) in the United States is 63.2 percent, the same level as Ecuador and Costa Rica. Yang takes the LFPR for granted, and in doing so completely forgets that America is getting older real fast and our balding boomers are retiring en-masse. Fortunately, the U.S. Bureau of Labor Statistics can account for this by looking at the prime labor participation rate, which zeroes in on the 25 to 54 age range. For these eminently employable Americans, the LFPR looks way betterat 82.8 percent as of November 2019. This is (ever so slightly) higher than the rate exactly fifteen years prior, within one percentage point of the rate twenty-five years prior, and two percentage points higher than the rate thirty-five years prior. Theres no evidence whatsoever of a problem with people fitting into the labor force, at least not any more so than in other decades in recent memory.

But life isnt just about statistics; Yang has examples that allegedly back him up. From the candidates website: Over 3 million Americans work as truck driversSelf-driving truck technology is rapidly becoming sophisticated enough to replace these driversSome estimates have the mass production of these vehicles as occurring within the decade. This has potential for serious unrest if not handled properly.

Except, the job of a truck driver cant simply be boiled down to driving for long stretches on the highway. Writing in Harvard Business Review, analysts Maury Gittleman and Kristen Monaco correctly point out that drivers have an array of important roles to play, ranging, from checking vehicles and securing cargo, to maintaining logs and providing customer service. Many of these tasks are nowhere close to being automatable. For example, there is currently no technology available (or being widely tested) to automate the loading or unloading of trucks.

A lot of cool new automation features are slated for mass adaption on-board trucks, such as lane departure protection and automated steering and breaking. Thats a far cry from full automation. Moreover, partial automation simply serves to make truck drivers lives safer and easier. Long driving stretches lead to sleep-deprived truckers, which leads to easily-preventable traffic tragedies. The partial automation actually being tested, rather than the complete automation that lives only within Yang Gang dystopian fantasies, will likely save hundreds of trucker lives rather than lead to mass unemployment.

But what about the deaths of despair allegedly happening in the here-and-now due to automation? Heres Yang again: Americas life expectancy has declined for the last three years in a row, the first time in a hundred years, because of surges in suicides and drug overdoses.

According to the Centers for Disease Control and Prevention, the declining life expectancy can count among its culprits rising overdoses. Yang has focused on the opioid crisis a great deal as a byproduct of declining job opportunities and the rise of robots. But large studies by reputable institutions such as the Federal Reserve Bank of Boston paint a considerably more complicated picture. They study the opioid crisis at the county-level in New England, which lies at the forefront of the overdose and death crisis that has gripped America over the past decade. The researchers examine the relationship between fatal overdoses and prime labor force participation, AND find an interesting and opposite relationship from the one predicted by the Yang Gang.

Thats right: deaths are actually higher in counties with more labor market engagement (albeit not a statistically significant result). Its complicated of course, but its easy to imagine a scenario where job gains are associated with better healthcare and greater access to things like opioids. These pain-relieving medications are a boon to around 99 percent of users, helping make life more livable amidst chronic pain. But widespread access to these revolutionary drugs also leads to the dangerous byproducts of addiction and overdose deaths, which is why its so critical to develop lifesaving counteractive medications such as Naloxone and comprehensive drug counseling programs.

Even in this strong economy, layoffs still happen all the time and job losses can certainly lead to depression, drug use, and possibly even overdoses. Its important to expand opportunities for all Americans, and in particular hard-working people who are having a hard time finding work. But acknowledging that is different from kowtowing to a dystopian narrative based on zero evidence. Yes, America has its fair share of difficulties, but no, that doesnt justify implementing a new open-ended, $2.8 trillion entitlement program that would lead to skyrocketing taxes and less work. Americas economy can continue roaring, but that may require policies and stories a bit more boring than what Andrew Yang has to offer.

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Andrew Yang's Misguided Obsession with Automation | Ross Marchand - Catalyst