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Machine Learning and Creativity: Advertising and Marketing Sectors to Undergo a Paradigm Shift

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Machine Learning and Creativity: Advertising and Marketing Sectors to Undergo a Paradigm Shift

Machine learning has the potential to revolutionize the marketing industry by automating processes and making campaigns more effective. In recent years, such advanced algorithms have also become more adept at creative processes. This has helped integration with and creation of novel advertisement strategies that help brands stand out in the market. 

Artificial intelligence known as “machine learning” enables software applications to gain knowledge and execute processes on their own. This allows the software to function without being explicitly programmed for certain tasks. 

Machine learning analyses massive volumes of data in digital marketing to gather insights, spot trends, and provide forecasts.

Machine learning (ML) has the power to improve audience targeting, increase personalization, and optimize customer engagement. It is a potent technology that makes use of data analytics to forecast customer behavior and enhance marketing initiatives. 

Spotify is a well-known music platform that creates tailored playlists for users. Amazon suggesting items to consumers and Netflix customizing content recommendations are all processes underscored by the extensive use of ML.

Predicting consumer behavior, such as figuring out which clients are most likely to complete a purchase, is one important application field. Upon examining client information, and relevant data like surfing habits, machine learning algorithms can help customize and target brand messaging.

As per Future Market Insights (FMI), the global machine learning as a service market is likely to benefit from supervised learning and surging demand from the retail sector.

Power of Predictive Analytics

Predictive analytics, a subset of machine learning, has the ability to analyze large amounts of data and predict future outcomes with high accuracy. In creative industries, this technology can be used to identify consumer behavior patterns and high-value markets for the best growth opportunities.  

Companies can now create more relevant, personalized content that resonates with their target audience, resulting in high engagement rates and increased conversions. 

For instance, Netflix is using predictive analytics for its online marketing. Netflix analyses user data, including viewing history and ratings, using machine learning algorithms to forecast which movies and TV episodes a user would like. As a result, they can tailor suggestions for each user, which boosts customer retention.

Nascent Consciousness vs. Algorithms – Can Machines Really Be Creative?

As machine learning algorithms become increasingly sophisticated, there is growing interest in their potential to be creative. Some experts believe that with enough data, machines can not only identify patterns but generate novel ideas and solutions that human minds might overlook. 

Others argue that true creativity requires the human touch and that machines can only produce what they have been programmed to do. However, the reality is that machine learning algorithms are already being used to create impactful campaigns in marketing and advertising. 

Several companies are already using machine learning algorithms to develop entire marketing campaigns, from concept to execution. These algorithms can create ads, analyze consumer behavior, and optimize campaign performance in real time.

While machines might not yet be able to match the nuances of human creativity, they can certainly supplement it. Its ability to process vast amounts of data rapidly and accurately is the key differentiator from other tools used in marketing. 

This has rendered machine learning an indispensable tool for campaign managers looking to make an impact on customers.

End of Interruption Marketing – How Personalization Changing the Game

Personalization is transforming marketing and advertising by allowing brands to tailor messages and experiences to individual customers. This signals an end to the traditional approach of interruption marketing. ‘One size fits all’ strategies have been abandoned for a shift towards relevant and targeted messaging that resonates with customers. 

Machine learning is at the heart of this transformation. It allows marketers to gather and analyze vast amounts of customer data to gain insights into their spending and internet habits. This data allows for the delivery of highly personalized content across a range of channels, from email and social media to in-store experiences.

The benefits of personalization are clear. According to research by MarTech, tailored promotional emails increase sales by six times more for each instance than non-personalized emails.

Personalization isn’t a new concept. However, the level of sophistication and scale allowed by machine learning has improved vastly. By using algorithms to analyze customer data in real time, marketers can tailor messages and experiences on the fly. This makes for a highly personalized journey for each individual customer.

Consequently, marketing becomes less about selling and more about creating meaningful connections with customers. Brands that can build these connections are likely to thrive in an age where customers are increasingly sceptical of traditional advertising and sales practices.

 Machine Learning Algorithm to Analyze Consumer Behaviour 

With the vast amount of data generated from online activity, machine learning algorithms are able to analyze consumer behavior and provide insights that were previously impossible to obtain. By tracking consumer preferences, interests, and behavior patterns, marketing and advertising strategies. 

Companies have optimized to reach the right audience at the right time with personalized messaging. Machine learning is allowing marketers to better understand their target audience and make data-driven decisions to drive business growth.

The Future of Machines: Learning & Creating

The future of machine learning in creative industries is exciting and full of potential. With advancements in technology, we can expect to see even more personalized and targeted advertising campaigns that cater to individual needs and preferences. 

For instance, Pecan AI stated in February 2023 that its portfolio of automated, low-code predictive analytics tools now includes marketing mix modeling (MMM).

Machine learning algorithms will continue to provide invaluable insights into consumer behavior over the coming years. It enables companies to optimize their marketing strategies to out-sell competition. Machine learning is advancing at a rapid rate and is set to transform the way people think about creativity and innovation.

As machine learning and artificial intelligence continue to evolve, the future of creativity is looking increasingly automated. 

Although machines might never fully replace human ingenuity, they will undoubtedly play a significant role in shaping market ploys. Marketing and advertising content is on track to undergo a paradigm shift in how it is delivered to larger audiences. The key will be to find the right balance between human creativity and the power of machine computing.

Author Bio

Mohit Shrivastava has more than 10 years of experience in market research and intelligence in developing and delivering more than 100+ Syndicate and Consulting engagements across ICT, Electronics and Semiconductor industries. His core expertise is in consulting engagements and custom projects, especially in the domains of Cybersecurity, Big Data & Analytics, Artificial Intelligence, and Cloud. He is an avid business data analyst with a keen eye on business modeling and helping in intelligence-driven decision-making for clients.

Mohit holds an MBA in Marketing and Finance. He is also a Graduate in Engineering in Electronics & Communication.


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How Machine Learning Has Improved Production Factories’ Robotics

Machine learning, robotics, and manufacturing automation have the potential to disrupt and transform our global economy in the upcoming years. The increased use of robots that are powered by machine learning and artificial intelligence in manufacturing and warehousing means there is a massive rise in efficiency and productivity. Machine learning is quickly improving the capability and competency of robots in production and automated manufacturing. Flexible and large training datasets have led to a marked improvement in several areas. Let’s take a close look at some of them.

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  1. Safety: The use of machine learning in robotics is gradually improving the safety standards of automated workspaces. 2D and 3D image datasets are being used for enhancing the environmental perceptions of all industrial robots. You can get reliable and fast object detection for making sure that powerful machines can avoid human beings and obstacles.
  2. Quality: Better image labeling in the field of robotics, is improving the capability of machines to identify faults and other defects in products that are coming fresh out of assembly lines. Computer vision-enabled cameras in robotics are capable of spotting defects that are not visible to the human eyes. Apart from that, AI-powered inspections may be carried out frequently without dropping the fault detection rates.
  3. Longevity: Machine learning-enabled systems are also being deployed that can be used to carry out the maintenance of other structures and machines. There is a regular use of visual datasets featuring pictures that are properly labeled with examples of wear for training models. These training models are used to spot possible defects in machinery or mechanical problems before there is a catastrophic failure. This type of preventive ML surveillance can improve the lifespan of several vital pieces of equipment.
  4. Product development: One of the more common uses of machine learning is product development. Both things viz. design of new products and the improvement of existing ones require the use of extensive data analysis to achieve the best results. ML solutions help collect and analyze a large amount of product data for understanding consumer demand and uncover hidden flaws to identify newer business opportunities. 

This not only helps in identifying existing product designs but can also develop superior quality products that can develop newer revenue streams for your business. Software developing has played a great deal of a role when it comes to product development, many companies have reached the top through analyzing and fitting software usage to their needs, there are many companies that can do a software business plan and help you achieve the things that your firm is striving for product wise.

  1. Cybersecurity: The solutions using machine learning depend on data, network, and tech platforms for both cloud and on-premise functions effectively. Security of these kinds of data and systems is crucial and machine learning plays a vital role in better regulation of important digital platforms and other info. Machine learning is capable of streamlining the way users will access sensitive data and the type of application they can use. It can also streamline the way you can connect with it. It is extremely useful for businesses to protect their digital assets by detecting anomalies fast and immediately triggering corrective action.

Use of machine learning for robotics in production factories

The various advancements made possible by artificial intelligence and machine learning for robotics have been used in several industries. There are many production factories out there that use AI-driven machinery for production. Some robotics arms that are trained with visual datasets can act as pickers for distribution warehouses. This raises the speed at which items can get moved away from a place. ML-powered robots are being used in automobile factories while using bounding boxes, for identifying vehicles, while they are moving in an assembly line. It allows the cars to avoid possible collisions in a crowded production environment.


The use of machine learning in production and other related processes can provide a significant rise in the efficiency of your manufacturing. This also leads to the development of newer business opportunities. Nowadays manufacturers wish to know how machine learning is useful for resolving specific business issues such as tracing production defects back to some specific steps taken undertaken in the production process. You can also achieve lesser waste with better identification of the presence of faulty components in the earlier stages of the production process. However, newer generations of machine learning must have access to a better quality of training data at a scale desired.




Innovative robotics and automation technology are helping organizations get more done, in less time and with limited facility space. 

Warehousing, distribution centers and logistics companies are some of the organizations that are seeing big benefits with robotics.

According to the 2020 MHI Annual Industry Report, 67 percent of survey respondents said they believed robotics had the power to disrupt their industry and offer a competitive advantage for their organization.

Therefore, it’s no surprise that 39 percent of surveyed companies said they’ve adopted robotics and automation. An additional 73 percent of those surveyed said they plan to add more robotics or start implementing robotics in the next five years.

Benefits of robotics and automation

There’s no doubt that robotics and automation can help organizations meet their mounting needs to standardize production and overcome challenges related to high staff turnover rates. With robotics, you can increase your facility’s outputs without expanding your physical footprint or facility size.

Robotics can help organizations with staffing challenges by offering the following:

-High staff turnover rates often mean added expenses in training and keeping a facility running at full capacity. Robotics can help reduce this fluctuation in staffing by offering a consistent and reliable work source.

-As warehouses, logistics companies and distribution centers look to streamline operations, it often means increasing the weight of fulfillment carts. This puts added strain on workers and can lead to workers’ compensation claims and costly time off, lowering production. Robotics help streamlines product picking and packing activities without straining employees physically.

-Robotics can assist staff members with learning efficient routes through warehouses to pick and pack products. With artificial intelligence, robotics can map out a way to efficiently pick and pack products throughout a facility. This can offer heightened job satisfaction for workers that use “cobots” (collaborative robots) to assist them in their daily activities, allowing them to be more efficient.

But robotics offer more than just improved staffing and a reduction in fluctuations from staff turnover. Robotics can also help facilities do more with the same amount of space. Some ways robotics help with stronger outputs despite capacity limits include:

-Better inventory management allows your organization to automate the inventory process so you have to keep less on hand.

-Set aisle sizes based on robotic width and smart technology that tells machines when another robot is in an aisle. That way, you reduce the need for two-way traffic in an aisle so you can shrink the aisle size and make better use of the space.

-Reduction in need for additional workspaces, such as electronic scales, because it’s built into the robot’s system.

Maintenance for robotics and automation

But with robotics comes new requirements for the maintenance team. 

Preventative maintenance becomes increasingly more important as keeping equipment up and running is crucial to your business operations.

If the robots fail regularly, you could experience worse staff turnover rates than you did without the technology as staff members get frustrated and tired of the loss in productivity. Your organization’s agility and ability to respond quickly to requests become more important than ever as you begin to rely more heavily on robotics.

To add robotics to your warehouse, logistics or distribution center operations, you need a maintenance plan that includes:

-Condition monitoring: Prepare a dashboard that shows each robot’s condition and expected date for new parts to prevent breakdowns.

-Work order requests: Allow staff members to make a work order request and have a process for assigning those work orders to your maintenance team for fast service.

-Reporting: Run reports that help your maintenance team see how often each robot requires maintenance so you can project and anticipate that maintenance in the future to avoid costly breakdowns.

Computerized Maintenance Management Systems (CMMS) help warehouses, logistics companies and distribution centers operate efficiently while taking advantage of the competitive advantage robotics can offer. 


For more than 30 years, Eagle Technology Inc. has worked with various industries. The Mequon, Wisconsin-based company offers clients the ability to boost productivity, control costs and maintain compliance, all from its web and mobile-enabled CMMS software, Proteus MMX.


8 Reasons Why The Packaging Process Should Be Automated

The packaging process is an important part of the supply chain. It ensures that products are packaged for shipping to customers and stores. The problem is that it’s time-consuming, costly, and difficult to do right. Automating the process can help solve all three of those problems and more. This article will discuss 8 compelling reasons why you should automate your packaging process.

Automation Saves Money

Automation saves money. Automated packaging means that a company does not need to hire people to do the work, and this can save thousands of dollars in wages each year.

It also avoids hiring mistakes or costly labor turnover costs, which can be expensive for any business. Take case erector manufacturers, for example, they can potentially save up to a million dollars every year by automating the packaging process. So just imagine: with automated packaging, there’s no need to worry about hiring extra personnel; plus all of these savings help lower your overhead expenses and cost you less money!

Automation Reduces Human Error

Any human worker is bound to make errors no matter their resume. One of the key benefits of automation is that it reduces human error. Machines do not have a mind, so they cannot make any mistakes on their own and complete tasks more accurately than humans can.  The time, effort, and money that would be wasted on human error can instead be invested in other parts of the company. Human error is a huge problem in the business world, and it is imperative to eliminate this type of issue.

Packaging machines will always complete their tasks with accuracy because they cannot make any mistakes. This means there are no human errors occurring which speeds up production times drastically.

Since everything has been automated, nothing can go wrong anymore – unless you’re talking about hardware but that’s another conversation entirely.

Automation Is Time Efficient

Machines can take care of things at a faster rate than humans so it takes much less time for them to complete a process that would take hours or even days with human hands alone.

When employees don’t have to do tasks manually, they can focus on more important things- like the next step in the production process.

Automated systems provide a smoother workflow and reduce bottlenecks that could otherwise slow down productivity or cause quality control issues later on.

For example, the right software will reduce errors by simplifying processes through automation and data collection from across your supply chain – saving time, money, and hassle for everyone involved in the packaging process.

Automated Machines Are Easier To Maintain

Automated machinery doesn’t require much maintenance due to its design. Machines are created with a specific purpose in mind, and they don’t have many moving parts or components that break down easily.

Thus, the equipment for automated machines is less likely to break down than the machinery used by humans. Humans need time off and they can’t work 24/hours a day, this is not an issue with robots since their workloads don’t depend on human intervention. Robots also don’t need to take breaks or sleep.

Apart from this, automated machines are less likely to injure themselves since they have the ability to sense what’s around them and stop before any accidents happen. Human errors can lead to injuries when packaging products such as heavy boxes that could cause a personal injury if dropped on their foot for example. Automated machinery doesn’t make mistakes like these which means “packaging-related” accidents will be greatly reduced with automation; you don’t have to pay for hospital bills.

Automation Is Predictable

A human can’t be expected to do the same job consistently. Consistency is an important part of any process, and it isn’t possible for humans to perform tasks in exactly the same way every time. This difference makes your product more varied, which may not always be a good thing. Automation removes variation from your manufacturing process, giving you consistent results each time.

The automation process will produce a consistent quality of work that humans cannot achieve, such as making sure each box is sealed tightly before moving onto the next one so there are no leaks or spills in transit.

Automation Means Less Product Damage

Less product damage is one of the most important reasons why packaging should be automated. Product damages happen even before the products are shipped to customers due to improper handling or rough transportation. The only way to prevent this from happening in a manual setting would be double-checking and triple-checking every step which takes more time away from production line efficiency but automation would prevent this by doing the checking automatically once and for all.

Automation Means Better Customer Satisfaction

The automated packaging process will dramatically increase customer service and satisfaction. By eliminating the need for human error, the number of times customers have to wait can be drastically reduced. This means that you’ll be able to offer better communication with your prospects since they won’t feel like their needs are being ignored because there is no one available to answer them.

Customers also like their products coming from an automated system because they know that there will be no human error involved in packing them up correctly; this builds trust between customer and company. For manufacturers who want to sell globally, customers outside North America are wary about packaged goods being tampered with if people pack them manually.

Automation Means Lower Environmental Impact

One of the best reasons to consider automating packaging is that it will lead to less environmental impact. This means there are fewer emissions being pushed into the air or water because the machine is using less energy to operate. With an automated system, the operator doesn’t have to do as much physically and thus will be able to use a smaller footprint of space in their facility when they’re not operating it.

Another reason why automating packaging can lead to lower environmental impact is that there are fewer emissions being released into the atmosphere over time because machines don’t need any maintenance or replacement parts like humans would if they were doing this task themselves on a daily basis.

The automation process is predictable, which means you can be sure that your machines will work as intended. This also saves time and money in the long run because automated machinery requires less maintenance than human-driven equipment. Plus, when it comes to customers, they appreciate not having to wait on hold for hours or talk with someone who doesn’t know what they’re talking about! By implementing a degree of automation into your production line, you’ll have happier customers and more efficient operations without sacrificing quality.



A Fortune 500 chemicals company experienced surges in its tariff classification requests and predicted future volume would be even greater. Without support, the risk of misclassifying items was extremely high. Procuring an automated global trade system helped alleviate the strain on resources and mitigate the risk of delays and penalties. It also allowed the company to cut outsourced services, which yielded meaningful P&L savings and helped the organization manage its growth projections efficiently.

It is a timely case study as enterprises that engage in international trade continue to experience increases in tariff classification requests as their import and export shipments surge. With global merchandise volume forecast to grow 7.5% this year and 4.1% in 20221, organizations still using manual processes for product classifications — researching and applying HTS codes — may be misclassifying a variety of their products, including anything from direct materials to back-office supplies.

Misclassifications not only cost organizations shipping delays — sometimes from two to 14 days — increasing the likelihood of an audit, but they also lead to steep penalties. In fact, some companies have had more than 80% of their classifications incorrect for products and have incurred U.S. Customs and Border Protection fines of up to four times the lawful duties, taxes and fees.2

However, there is an overlooked solution. Today’s global trade management systems come equipped with automation and machine learning capabilities to streamline classification requests. They cut classification errors and the cycle time, improve a team’s productivity, and help prevent fines and border delays.

Here are the keys to success for organizations using trade systems to overhaul their tariff classification process:

1. Automate the consistent, repetitive classification requests that take up more than 60% of a resource’s time. Organizations can immediately alleviate the workload for classifiers by leveraging automation and machine learning for repetitive product classifications that have slight deviations. Those items can take hours of a resource’s time, leaving little to no bandwidth for other categories that may require more research. As the system learns more about the minor deviations in product types, it can provide accuracy of close to > 95%. Taking manual processes out of the equation helps guarantee supply assurance to an organization’s customer base while mitigating penalties from errors.

2. Eliminate third parties or outsourced contracts involved in classification overflow assistance. Implementing automation for tariff classifications allows an organization to remove outside brokerage services, equating to an immediate P&L savings impact. Some organizations have seen upwards of 10% savings captured by eliminating these obligations. That, in turn, helps positively impact the overall trade governance budget. Not only are the short-term effects instant, but for the long-term, global trade systems can help identify discounts for various classification codes based on trade agreements between importing and exporting countries. These discounts usually go overlooked by internal resources because of how busy they are with other tasks.

3. Use machine learning to help realize a cycle-time reduction for classification requests. Enterprises should leverage global trade services to automate customs rulings updates, ensuring compliance is current for all import/export nations. That leads to a reduction in the time spent by internal resources on researching the data each time a regulatory change occurs. Also, organizations should integrate databases with their global trade management systems to classify past and new unique classifications. Machine learning can leverage past classification mistakes for the future, but for new items, linking information flows from databases can help automate requests as they appear for the first time.

Organizations experiencing growth in their imports and exports must pay attention to global trade systems with automation and machine learning now more than ever to ensure business continuity and future scalability. While digitizing classification processes results in crucial P&L and cost savings, it’s also critical to mitigating the risk of future border delays and steep fines.


Alex Hayes is a consulting manager at GEP, a leading provider of procurement and supply chain solutions to Fortune 500 companies.



maintenance decentralized

Why Mobility is Vital for Maintenance Management

Companies are fortunate enough to be operating during a time when there are several technological advancements regularly occurring in several sectors, including the maintenance sector. From 5g technology to predictive maintenance conducted with the assistance of sensors. The maintenance sector continues to benefit from technological advancements. 

A technological advancement that is sometimes overlooked is the smartphone. We often take for granted the benefits of a mobile device. However, the system can provide many benefits for businesses too. Many companies have already begun integrating mobile applications into their processes and cannot imagine conducting maintenance operations without them. 

While several maintenance applications are operable on a smartphone, from enterprise asset management systems to integrated workplace management systems. The focus will be on computerized maintenance management systems

While a CMMS is often usually operated from a fixed desktop within a workstation, this option has never fully gelled with the responsibilities undertaken by a maintenance manager. Maintenance requires employees who are energetic and proactive. Aways on the move. While administrative duties are unavoidable, they cannot be the core focus of a maintenance manager. With this in mind, mobile devices can be seen as a tool that can leverage the benefits of a CMMS and respond to how maintenance employees operate. It is also important to note that in the current business environment a lot more is expected of employees. When you couple this with an increase in machine production, maintenance employees are expected to be more responsive and attend to the task as soon as possible.

A CMMS that is operable on a smartphone instantly increases flexibility and communication among employees. The system provides flexibility by providing a platform for employees to access information whenever they require it. Having the ability to access information whenever necessary means that maintenance employees are empowered to be more responsive when conducting their tasks. Employees can also communicate more effectively when it comes to maintenance activities as they have a platform to upload and review information. Previously this would have had to be done through email or call, which leaves the employees open to miscommunication. Now employees can access and communicate in a simplified way over the CMMS platform. 

Features of a CMMS

Let’s look at some of the features that make a mobile CMMS application vital for so many companies, or whether mobile capabilities are just a nice-to-have.

Work order assignment and monitoring 

Often the first function that comes to mind when looking at the benefits of a mobile CMMS. With a mobile CMMS, employees can be more agile in the way they conduct their work orders. If an employee spots a problem with one of the assets, they can immediately send a notification via the CMMS. This will result in a work order being created and sent to the relevant technician. 

Another benefit of a mobile CMMS is that the manager that oversees the facility, factory or plant, does not need to present to get feedback. Once the technician finishes their work they can upload the details onto the application for review. 

Access inventory on the go

Out of sight, out of mind. This saying is particularly true for inventory. Within large enterprises inventory, such as spare parts or lubricants, is vital. However, often the teams in charge of these items cannot recall whether they have these items or not and how much of it they do have. With a CMMS inventory, data can easily be uploaded and accessed at any time. This allows for efficient utilization when necessary. The accessibility of the information can also help the supply chain team with data that will reduce the company from making unnecessary purchases. 

Keeping track of spare parts is also much easier with a CMMS. The ability to know what spare parts are needed, and what is available can increase the speed at which maintenance and repairs are completed. 

Develop and access maintenance schedules

Maintenance schedules are vital, and one of the main benefits of implementing a CMMS is to simplify the maintenance scheduling process. Having the ability to access the maintenance schedule of a mobile CMMS is just as important. Schedules are more accessible to a maintenance team through a mobile CMMS app. This means that employees can come into the factory in the morning and know exactly what activities they need to conduct for the day. More importantly, with the insight received from being on the floor, technicians can assist in creating more accurate maintenance schedules. 


After looking at the benefits a mobile CMMS provides to employees. From operational flexibility to simplified access to information. We can see that a CMMS with mobile capabilities is no longer a luxury in today’s work environment. But rather a necessity that enables maintenance employees and technicians to operate at the optimal potential. 


Shayur Maharaj, Marketing Analyst. My interests include researching innovative business ideas that increase the well-being and quality of life for employees. I aim to make an impactful and positive change.

analytics self-storage

Location Analytics Market: Top Key Trends that will Boost the Industry Growth through 2026

The global location analytics market size is poised to expand at substantial CAGR during the forecast period. Location analytics uses advanced technologies like Artificial Intelligence, Natural Language Processing (NLP), and Machine Learning (ML). They are used to find out the location data of customers to provide customized solutions for all their personal and business needs.

Enterprises across the world are increasing their focus on collecting dynamic location data to identify the preferences and tastes of the customers. This piece of information is quite useful for them as it helps to create effective marketing strategies. It even helps in identifying patterns in customer behavior and purchases which eventually assists them in making more informed decisions in the future.

The invention of GPS and GIS technologies has taken the world by storm. These are some of the most sought-after technologies to track down any location, no matter how far it may be. Based on the data about the number of times the customer has visited a place and the frequency of taking the same route, marketing companies provide customized suggestions that help customers take informed and timely decisions.

Some of the trends that will positively impact global location analytics market growth are as follows:

Rising use of GPS and GIS technologies in Europe:

Europe’s location analytics market will reach a valuation of more than $7 billion by the year 2026, according to market experts. GPS and GIS technologies are some of the most sought-after and advanced technologies and have seen a period of boom in demand among young consumers in the region in the past decade.

The main reasons for this are that these technologies help in location tracking, transferring real-time information to businesses and monitoring and tracking consumer behavior and buying patterns. Industries across the region like banking, insurance and retail are not essentially location-based industries but do take location tracking into consideration while processing insurance claims during natural calamities. They make use of the precise geographic coordinates to find out the area of disaster and work accordingly.

Scope of location analytics services in Europe:

The location analytics services segment will showcase strong growth in the coming years, according to market reports. Governments in countries like Italy, Germany and the UK are increasingly using location analytics in various industries like defense, construction, transportation and retail. Location-based marketing is on the rise in these countries as customers nowadays prefer to get customized information to make more informed decisions while purchasing their products. The adoption of smartphones by the younger generation is adding to the demand for location analytics services in the region.

Europe BFSI segment will boost demand for location analytics:

Out of all the segments undergoing digital transformation, the BFSI segment will contribute significantly towards the rise in demand for location analytics solutions in Europe. The banking sector is increasingly adopting location intelligence technologies to help in carrying out various activities like increasing safety in monetary transactions, route analysis, record management of customers and ATM network management.

Outdoor positioning location analytics use in North America:

North America’s location analytics market is predicted to be worth $8 billion by the end of the forecast period.

The outdoor positioning segment will play a vital role in the overall advancement of the location analytics market in North America. Companies that operate at different locations have to use geographic analytics to get in-depth insights and make sound business decisions. Advanced analytics are being used by businesses to make plans for outdoor spaces. This kind of positioning even helps companies track objects and customers with the help of real-time locations.

Use of thematic mapping and spatial analysis in Canada:

Canada will play a vital role in encouraging the rise in demand for location analytics solutions in North America. The thematic mapping and spatial analysis segment in the Canadian market will experience substantial growth in the coming years as these tools are being increasingly used in the field of business intelligence.

With the help of thematic maps and spatial analytics, businesses can get a visual representation of their future action plans. In November 2020, the New Brunswick Department of Transportation and Infrastructure (NB DTI) was awarded for its creative use of GIS and location analytics to identify problems in road construction and having long-term plans for changing old culverts.

Google is another example of product innovation as its product, Google Maps, has an AR-enabled Street View mode that helps the user find real-time directions and custom recommendations as well.

Effective supply chain planning application in APAC:

Location analytics market size in Asia Pacific is reported to reach more than $8 billion by 2026. The supply chain planning and optimization segment will contribute significantly towards boosting location analytics services use in the future. There are several obstacles that supply chain organizations have to face while transporting raw materials or finished goods from one place to the other. With the use of location-based analytics, these problems can be effectively sorted out and delays in delivery can be greatly reduced.

For example, the Philippines National Economic and Development Authority, in May 2020, announced the launch of advanced location analytics solutions to identify the disruptions in supply chain management during the COVID-19 pandemic. This greatly helped the officials to manage their supply chain operations and work in a more efficient manner, with the help of real-time data and visibility.

Role of COVID pandemic in APAC location analytics market:

The COVID-19 pandemic greatly affected different businesses across the world with countries like India and China being adversely affected by the virus outbreak. This resulted in tremendous rise in smartphone usage across the region, leading to increased use of location analytics solutions. The Government of India has immensely benefited from the use of this as it has helped the officials in conducting effective contact tracing of people who have come in contact with COVID positive patients.

Some of the key organizations providing location analytics solutions and services across the globe are Cisco Systems Inc., Alteryx Inc., Esri Global Inc., HERE Global, Google LLC, IBM Corporation, SAP SE and many others.

data science

Data Science and Supply Chain: Bringing People and Algorithms Together

In its constant pursuit of efficiency, the Supply Chain sector can now count on new technologies resulting from Big Data to improve the performance of its activities. The abundance and diversity of data generated every day by its various actors have allowed the emergence of a multitude of very attractive applications. But when it comes to artificial intelligence (AI), the key lies in the collaboration between Human and Machine. How is this articulation between human intelligence and algorithms established? What is the place of the human being in the development of a connected supply chain? Answers in this article.


A new era for Supply Chain Management


Driven by academic research and large companies like Walmart and Procter & Gamble, the logistics industry underwent its first major transformation in the 1990s. While some players are still working on implementing best practices, Big Data is now revolutionizing the supply chain again.

Under the name “Supply Chain 4.0″ or “Connected Supply Chain”, these promising advances are the result of teams of Data Scientists exploiting artificial intelligence, blockchain, or even robotics. These technologies aim to make the supply chain more agile, predictable and profitable for organizations. How can they do this? By shortening lead times, fully automating demand forecasting, and improving on-time production and delivery.


The Contributions of Data Science to the Supply Chain sector


Improve anticipation of demand


Capable of exploiting very large and diversified sources of information, Data Science and Machine Learning are particularly interesting for identifying trends in a very large quantity of data.

In the Supply Chain sector, Data Science is used in particular to:

-identify weak signals to be actively monitored in order to elaborate prospective choices;

-integrate data from different sources (web…);

-group products according to different consumption behaviors;

-highlighting action strategies adapted to each situation.

Optimize the management of logistics flows


In terms of warehouse management, data analysis can be correlated with certain external factors (raw material supply problems, goods traffic, weather conditions, etc.) to help companies reduce the risk of disruption.

To facilitate the choice of carriers and optimize the organization of delivery rounds, many factors can be taken into account: costs, type of products to be handled, specific transport standards and conditions, packaging, road traffic…

By optimally distributing tasks according to the warehouse’s own data, AI algorithms also contribute to a better allocation of resources and thus allow for greater efficiency.

Improve customer relations


With Data Science, the relationship established with consumers is also becoming more and more personalized. Unsupervised Machine Learning algorithms allow us to segment our customers very finely in order to target promotional offers and services to each profile.

Combined with the analysis of customer feedback, this segmentation data provides valuable information on the steps to be taken to improve customer satisfaction, which remains a core concern for any supply chain.

Human/machine collaboration: a key issue for Data Science


From data to action


In any artificial intelligence process, the autonomy given to the machine takes place gradually. This Gartner graphic shows how the work entrusted to the systems (in blue) is gradually replacing human intervention (shown in green).

The collaboration between human and machine then takes place in 4 main stages:

1. the analysis of the data by the machine (Analytics);

2. the human intervention necessary to interpret the data (Human input);

3. the resulting decision (Decision);

4. the transformation into concrete action (Action).

As time goes by, the amount of autonomy left to the machine is increased, until we can obtain total confidence in the system. But to make the machine capable of deciding as well as the human, a phase of collaboration is essential during the various stages of development of the algorithm. It is more or less long and advanced according to the degree of autonomy wished.

The different types of algorithms


Depending on the nature and intensity of the collaboration between human and machine, there are three main types of machine learning algorithms: supervised, unsupervised and reinforcement learning.

Supervised learning


In supervised mode, the algorithms work from data chosen by humans for their characteristics and their known impact on the result. For example: the outdoor temperature curve influences beverage sales, or the number of orders to be shipped impacts the picking load in the warehouse. Sales forecasting models use this type of algorithm in particular.

The intelligence is in this case mainly provided by the human. The machine is then mainly used for its calculation capacities on the basis of several series of data.

Unsupervised learning


The objective here is to meet 2 specific objectives:

-to create clusters, meaning groups of individuals with similar behaviors, in order to define management rules that are refined and therefore particularly efficient;

-to discover, thanks to the machine, which data have an impact on the performance of the supply chain: the theoretical approach acquired as a professional is not always sufficient to detect and explain certain phenomena that can affect the efficiency of a warehouse. Capable of identifying even weak signals, in real-time and continuously, the machine then represents a powerful vector for analyzing operations, and therefore for improving processes.

In both cases, the machine is used to establish the diagnosis, while the human being intervenes in the exploitation of the data and the definition of the actions to be implemented as a consequence.

Reinforcement learning


Mainly used by voice or banking assistants and robotics, these algorithms work on cycles of experience and improve their performance at each iteration. This is the most advanced mode of collaboration between human and machine. Through a scoring principle, the human gradually teaches the system to make the best decisions. It transfers its experience to the system and teaches it to adapt to many different situations.

Data Science is a magnificent opportunity for the Supply Chain. It is as much about gaining efficiency, reducing processing times and operational costs, as it is about acquiring a better reactivity in case of hazards, or being able to satisfy the demands of the consumers. However, it is important to keep in mind that Data Science cannot work without humans. Indeed, it is the human being who transmits the intelligence necessary to the development of AI algorithms.

This article originally appeared on Republished with permission.


Benefits of using HMI for Industrial Purposes

HMI stands for Human Machine Interface. We use HMIs to control and monitor machines in any industry. It is mainly used in the manufacturing sector. Process industries massively use HMIs, such as in oil and gas and mining processes in which many operations are managed remotely from a control room. Industries have implemented HMI software where human interference with a machine or automated equipment is needed. This could be in a system, plant, building, or even a vehicle. The level of assimilation and refinement may vary, but we can use HMI for just about any application type.

Let’s come to the point and see the benefits of using hmi for industrial purposes:

Improved Productivity

A human-machine interface, i.e. HMI, improves the performance of the given task. Even if a person can perform that same task, this kind of software/device increases productivity in an enormous amount. Using an HMI facilitates more work in any industry in a short time.

 Troubleshooting with old data

The HMI system detects, have the systems inspected based on past data feed. The entire evaluation, isolation, and correction of the alarm took less time to do it manually. HMI’s makes it troubleshoot the problems in early stage.

Report generation

The creation of a perfect report and save it for data analysis is one of the crucial tasks. As a human, we can make many mistakes while recording such things. Maintenance and feeding the data for future use is something that should be done on time.


With the ability to control a device easily, their use in production and ease systems has dramatically improved people’s lives by increasing comfortability. The machine should be accessible from a long distance so that the operator can be at more ease.

Keeping Records

They have high abilities to keep records. By inserting instructions into an HMI, the system to which it is connected can automatically store the data. Such data can be utilized later for other researches, for example, troubleshooting future analysis.

Internet of Things (IoT)

Internet of things refers to a combination of devices that are all coupled to the internet. HMIs can also connect to the internet since they are devices too. This enables remote access to the devices.

Reduce the Cost of Hardware

An HMI reduces the expense that an industry acquires in terms of devices like consoles, connections, and control panels. An HMI can replace them, thus saving on costs.

Asset management

Accuracy in real-time data and reports gives managers the ability to be more efficient and make just the right maintenance plans for businesses. The drilling and fracking industries have to handle some of the most challenging circumstances in managing and observing large numbers of regularly moving assets.

Data availability

Data is an essential input when making decisions. How users utilize these records will discover the value applied to the process. The power is in the availability of data and relying on people’s abilities and skills to get insight and make enhancements. This data is available for other analysis too.

digital transformation

A Digital Transformation Driven by Artificial Intelligence

Digital transformation is one of the most important drivers of how businesses deliver value to their customers in a competitive, fast-changing business environment. The process aims to leverage digital technologies to create or modify user experiences and business processes, thus meeting users’ changing needs and the market.

Artificial Intelligence (AI) is now recognized as one of the main enablers of digital transformation in multiple industries. AI can help companies become more innovative, flexible and adaptive than ever before. Many think of it as a future or visionary technology, but the opposite is true. AI is already being cost-effectively deployed in numerous companies, accelerating productivity and competitiveness, while helping speed digital transformation.

What is Digital Transformation?

Digital transformation is a set of processes, tools, and methodologies used by businesses to optimize their operational activities, such as offering differentiated service, increasing performance and expanding their reach. More than a concept, digital transformation is a dynamic movement; it attracts organizations looking to review processes, innovate and gain competitiveness with the help of technology. Artificial intelligence is a critical strategic factor for businesses to expand their impact.

AI and Digital Transformation

Digital transformation took a big step forward when AI and machine learning became part of business strategy. Aside from boosting productivity, these technologies are essential because they enable better use of the data collected by a company. With useful data, businesses can expand, improve their current products and services, and create innovative strategies.

Here are three examples of how digital transformation coupled with AI can deliver top results:

1. 360-Degree View of Customers

AI helps businesses, especially their marketing sector, understand customer needs and preferences. Every digital transformation success depends on gaining accurate buyer personas and audience discovery. It is crucial to gain a comprehensive understanding of the customer at the beginning of the digital transformation, and AI is ideally suited to this mission.

AI helps reveal consumer behavior trends, purchasing history and digital channel engagement to create a buyer’s detailed view. Today, buyers expect personalized experiences along their journey. AI is essential to quickly unlocking consumer insights to create and reinforce those personalized experiences. This way, companies can gain a competitive advantage through higher customer satisfaction, increased customer loyalty and more effective marketing campaigns.

2. Dynamic Analytics

As already mentioned, collecting useful data is essential to digital transformation strategy. But what powers the success of the strategy is how that data is capitalized. AI processes thousands of data points to gather insights and identify trends in real time. This is how companies can accurately pre-empt consumer needs rather than react to consumer actions. With these insights, companies can create highly personalized experiences for their buyers and make data-driven decisions to develop more effective marketing campaigns. Success is reflected in significant savings across budget allocation and in more cost-effective media and advertising purchases.

3. Growth and Profitability

AI helps businesses profit through their digital transformation by increasing operational efficiency, mitigating risks and accelerating growth and innovation. For business leaders, marketing, sales and customer service are the top three areas where AI can make the most impact. According to Accenture, AI can increase profitability in multiple industries by an average of 38 percent by 2035. By that same year, AI will boost additional revenue by $14 trillion!

AI-Driven Automation for Customer Service 

Digital transformation that incorporates AI is redefining and revolutionizing the end-user experience in countless ways for both B2B and B2C organizations. The new capabilities offered by AI-driven Conversational RPA for Customer Service can auto-resolve 65 percent of user requests and reduce issue resolution time by 90 percent—improving customer satisfaction scores by 80 percent. Sophisticated workflow automation integrates AI and RPA to automate customer workflows across product support, upgrades, troubleshooting, case management and more. The horizon for new revenue streams, cost-savings, and productivity is nearly unlimited.

Redefine your employee and customer experience with the world’s first AI-driven service desk: Visit Aisera to learn more or request a demo.