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4 Future Trends That Can Bring the Semiconductor Industry Back into the Spotlight

The semiconductor industry has been growing exponentially every year and offers a plethora of opportunities. But this year, the semiconductor industry is set to face a host of new challenges that will make it difficult for semiconductor manufacturers to grow at the same rate they have gotten used to over the past couple of years. Challenges pertaining to inventory management and the rising pressure to improve device architectures, reduce costs, and develop STEM skills in the workforce will inhibit the growth of companies to a certain degree. 

The inability of companies to keep up with the ongoing pace of IoT development is another factor that stops semiconductor manufacturing companies from growing in the industry. With the cloud economy becoming mainstream in the IoT era, semiconductor companies are facing the need to continuously innovate and drive connectivity across the IoT value chain. In such an era, they can gain more traction by offering comprehensive solutions beyond semiconductor solutions, which includes hardware design, and software and systems integration for applications and products.

At Infiniti, we understand the impact that innovative technologies and future trends can have on your business. And to help semiconductor manufacturBrochure CTAing companies excel in such a competitive landscape, our team of experts has highlighted the four most important future trends that can bring the semiconductor industry back into the spotlight.

Future Trends in the Semiconductor Industry

Future Trend #1: Artificial Intelligence

The rising demand for AI (artificial intelligence) based applications across various industries will create new growth opportunities for semiconductor manufacturing companies. AI will also bring improvements in the semiconductor market, by speeding up the manufacturing process, boosting the performance of the chip, reducing the cost of production, and increasing output. Furthermore, with the growing market for drone technology, 2019 may see the commercial rollout of AI-powered drones, globally. This is one of the future trends that can help the semiconductor industry boost its growth prospects.

Are changing market dynamics and growing demand keeping your business plans at a standstill? Request a free proposal below and access Request Proposalour full portfolio of market assessment solutions for the semiconductor industry. 

Future Trend #2: Autonomous vehicles

One of the significant future trends in the semiconductor industry is the onset of level-three autonomous vehicles on the road. The rapidly growing automotive market presents a huge opportunity for automotive semiconductors to support battery performance in EVs, enhanced sensors, increased connectivity, and other technologies.

Future Trend #3: Internet of Things (IoT)

The semiconductor industry is driving the growth of technologies like the Internet of Things (IoT). The IoT revolution has not only multiplied the demand for semiconductor chips but also shifted the value capture to software and solutions. To capitalize on this new market opportunity, the semiconductor industry needs to change application engineering, sales, marketing, and product development approaches and re-define monetization and go-to-market strategies. Such future trends can help semiconductor manufacturing companies to boost their profitability, marginally.

Future Trend #4: Digital supply networks

Today, digitalization is modifying supply chains in almost every industry. This modification is a shift from a linear, structured system to an interconnected, open, often cloud-based system that is able to combine information from many different sources. In order to improve the value of digitalization, there is a need for the semiconductor industry to take a look at emerging digital supply network technologies. This is one of the future trends that can help semiconductor manufacturing companies in solving traditional problems stemming from limited information transparency across the manufacturing and supply chain.

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The Road to Transformation: AI in the Transportation Industry

Often, the use of advanced technologies in the transportation industry is affected by factors that are difficult to predict. This includes things like traffic, human errors, or accidents. However, artificial intelligence has taken off well in the transportation industry. AI uses observed data to make or even predict decisions appropriately. Furthermore, the use of artificial intelligence results in a significantly lower cost of labor in the transportation industry. Companies in this sector can also solve the issue of employee’s long driving hours and breaks between drives with fully automated fleets. Also, the emergence of industry-wide standards including blind-spot alert, adaptive cruise control (ACC), and advanced driver assistance systems (ADAS) would further fuel the growth of artificial intelligence in transportation.

Leading transportation services providers have started incorporating autonomous tracking and artificial intelligence into their vehicles to track potential real-time data. Want to identify lucrative technologies and processes to capitalize on to yield huge profits? Request a FREE proposal today!Request Proposal

AI in the Transportation Industry: Benefits

Public safety

Artificial intelligence helps companies in the transportation industry to ensure the safety of the public when using their services. Safety of citizens using public transport in urban areas can be improved by tracking crime data in real-time. This will also facilitate the police to increase their efficiency by patrolling and keeping the citizens safe.

Autonomous vehicles

Self-driving cars have been the new buzzword in the transportation sector in recent times. They use artificial intelligence to function and make calculated decisions on the road. Self- driving vehicles prove to be a great opportunity to reduce the number of accidents on highways and increase productivity.

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Better planning and decision-making

The road freight transport system can utilize accurate prediction techniques to forecast their volume using artificial intelligence, which simplifies the transportation company’s planning process. Additionally, several decision-making tools for transport can be designed and run using artificial intelligence. This will affect investments made by companies in the future in a productive way.

Pedestrian safety

Using artificial intelligence, the path of pedestrians or cyclists can be easily predicted, which would help in decreasing instances of traffic accidents and injuries. This will allow for more diverse transportation usage and an overall reduction in emissions.

Control traffic flow

Traffic flow has a great impact on transport. If this data is adapted for traffic management via artificial intelligence, it will result in streamlined traffic patterns and a significant reduction in congestion.  Also, real-time tracking and smarter traffic light algorithms can control higher and lower traffic patterns effectively. This technique is also effective in public transport for optimal scheduling and routing.

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AI in Food Processing: What Are the Perks?

The food industry has always been slower compared to other sectors in adopting modern technologies. But, spurred by innovative startups, artificial intelligence (AI) may prove to be the exception. For the most part, the sector is a very high volume, low margin industry. Identifying innovative and new ways to gain even the slightest increase in efficiency can make the difference between a facility turning a profit or a loss. Therefore, some of the largest food processing companies are turning to artificial Intelligence technologies in an attempt to improve numerous aspects of the process. Food processing basically involves sorting a large quantity of feedstock and careful inspection of the final product. It frequently requires the constant maintenance of equipment, storage, and workspaces in very specific conditions. Advanced technologies like artificial intelligence can revolutionize the way these operations are carried out. Let’s take a look at how artificial intelligence can revolutionize food processing:Request Proposal

Better sorting

Sorting is one of the most time-consuming processes for most food processing companies. Also, it is one of the most important processes. For instance, every orange, carrot, tomato, potato, apple, etc. is slightly different. A plant might need to sort millions of vegetables based on size, shape, and/or color. Earlier, a majority of the sorting had been done manually, but now, most food processing companies have embraced automation for the processes. Furthermore, with the help of advanced technologies like AI that use cameras, Near Infra-Red (NIR) spectroscopy, x-rays, and lasers, it has become easier to measure and quickly analyze every aspect of the vegetable as it moves along. While older automatic sorting systems were just focused on sorting the bad from the good, machine learning and artificial intelligence created the capability to sort foods for their optimized use.

 Improved food safety compliance

In a food plant just like in a kitchen, good personal hygiene is necessary to ensure food is safe — and the facility is compliant. Cameras installed in the kitchen or food facility ensure that employees are wearing masks or hair protection as required by safety regulations. Violations can be caught and corrected in near real time. For example, KanKan, a subsidiary of Remark Holding recently announced a seven-figure contract with one of the largest state-owned enterprises in China to provide Shanghai’s municipal health agency with facial and object recognition. Their AI technology is currently being used in 200 restaurants and is soon expected to expand to 2,000 facilities. The system, which can be used in restaurants as well as manufacturing facilities, uses cameras to monitor workers and then employs facial-recognition and object-recognition software to determine whether workers are wearing hats and masks as required by the food safety law.

Develop new products

Food processing companies can offer an endless variety of options to customers given the different varieties of spices, flavors, and ingredients that exist. Recipes can be tweaked in an unimaginable number of ways. To decipher what exactly the customer wants is a big challenge in itself, and companies are turning to capabilities such as AI to help with that process. Food processing companies can leverage artificial intelligence to formulate new products. Wonder how? Machine learning and predictive algorithms can be used by food processing companies to model consumer flavor preferences and predict how well they respond to new tastes. The data can be segmented into demographic groups to help companies develop new products that match the preferences of their target audience. This would help companies to predict the success rate of a new product well in advance of it being launched.

Grow better food

AI could help farmers actually grow better food by creating optimal growing conditions and monitor the effects of variables like UV light, salinity, heat, and water stress on basil. With the data, they’re developing “recipes” for the perfect crops. At the farming level, AI is also being used to detect plant diseases and pests, improve soil health, and more.


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Why Is Artificial Intelligence the Need to the Hour in the Manufacturing Industry?

“Artificial intelligence isn’t the scary future. It’s the amazing present.”-David Gelernter

Thanks to the ample number of subsites available in the market, modern customers are more ‘choosy’ than ever before. There is a rise in demand for products that are customized, personalized and unique over standardized products. Moreover, customers also expect the best products at the lowest prices. However, poor demand forecasting and capacity planning, unexpected equipment failures and downtimes, supply chain bottlenecks, and inefficient or unsafe workplace processes are some of the critical hindrances in the manufacturing sector. To meet the changing demands and overcome these challenges, manufacturing companies must rely on automation, machine learning, computer vision, and other fields of artificial intelligence.  Here is hRequest Free Proposalow artificial intelligence is going to transform manufacturing companies in the years to come :

Artificial intelligence in emerging markets

One of the main problems faced by manufacturing companies is high capital investments and thin profit margins, resulting in outsourcing of manufacturing activities to low-wage countries. However, rising standards of living in these countries and the need for modernization and automation makes the implementation of artificial intelligence a more feasible option. Although several workers in manufacturing companies will lose their jobs due to automation, companies can retrain those workers to perform higher-level design, programming, or maintenance tasks. The real driver, however, will be to develop applications for artificial intelligence that will not just automate tasks but will make entirely new business processes feasible, like the custom configuration of products to individual customer requirement.

Safer workplace

In a manufacturing setup, several minute details are not visible to the human eye or often go unnoticed. Advanced technologies like machine learning and artificial intelligence help to find microscopic defects in products such as circuit boards at resolutions well beyond human vision. Also, the use of collaborative robots by manufacturing companies is becoming increasingly popular. These robots can work productively with human colleagues and can take instructions from humans including new instructions that are not anticipated in the robot’s original programming. Hence, better machine senses will result in a safer workplace in the long run.

Supply chain efficiency

Artificial intelligence is also expected to have a great deal of impact on areas of manufacturing that do not have any connection with robotics. The use of AI technology in the supply chain of manufacturing companies can forecast patterns of demand for products across time, geographic markets, and socioeconomic segments while accounting for macroeconomic cycles, political developments, and even weather patterns using different algorithms. Artificial intelligence is also highly beneficial in carrying out predictive maintenance for equipment, with sensors tracking operating conditions and performance of factory tooling, learning to predict breakdowns and malfunctions, and taking or recommending corrective actions.

Automated quality control

Artificial intelligence can aid in faster feedback loops, helping manufacturing companies to tackle unplanned downtimes, low yield (percent of units that pass quality control), and low productivity (time it takes to make a product). AI can help to speed up processes and ensure accuracy rather than relying on humans for in-process inspection and quality control which is time-consuming and also there are chances of failure to detect errors.


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Why is AI the Need of The Hour for FMCG and CPG Brands?

The recent years have seen artificial intelligence (AI) taking great strides towards conquering the world.  With self-driving cars becoming a reality, our digital moves being tracked by machine learning algorithms, and digital assistants like Siri, Google, Alexa and Cortana finding their ways into millions of homes worldwide, let us address the elephant in the room – AI has become an inevitable part of our daily lives.  If you have been following the latest retail trends, you might be well aware that AI is slowly penetrating into the retail industry in the form of self-checkout counters, customer loyalty programs, RFID to track inventory, etc.IR_Brochure

Why is AI Important for FMCG And CPG Brands?

Technology has not just made customers lazy but has also made them greedy for more comfort. But businesses have no choice but to satisfy their customer’s expectations at any cost to survive among the cut-throat competition in the market. FMCG and CPG products are inevitable items of purchase for consumers, and therefore the frequency of purchase of these items is also high. But who likes to go through monotonous grocery shopping routines and stand in long queues at supermarkets every other day right? This is where players in the retail industry can employ technology such as AI that would ensure convenience and make shopping an exciting experience for the customers. Also, the growth and wide-spread use of handheld technology such as smartphones and tablets have made it much easier for retail companies to implement these technologies into their business plan and make it more accessible for their target customers.

How is This Going to Transform the Market for FMCG and CPG Brands?

With advanced technologies like AI in place, customers as well as retailers are equally going to reap the benefits. As far as FMCG and CPG goods are concerned, most of the times customers tend to push their shopping schedules or look for alternatives due to the time and energy lost while physically making a purchase. Advance AI technologies, especially voice-activated assistants and self-checkout counters in the retail sector would mean increased ease of shopping for customers, which would eventually result in more sales and a simplified shopping experience for customers. This would ultimately result in better customer experience and higher profits.

How can FMCG and CPG Brands Use AI Technology to their Best Benefit?

Implementing AI for FMCG and CPG products is a much simpler process unlike that for bigger ticket items like a car or a TV? Wonder why? When it comes to FMCG, most of the consumers are brand loyal and prefer sticking to a single brand of a particular category of a product. Therefore, when a particular customer orders a product through a digital assistant, they just need to order by the name of the product category, e.g.: “Siri order toilet paper” and the system would automatically know from past purchase records the brand that the customer uses. Also, AI technology will make product replenishment and the supply chain more efficient for retailer companies.

 

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Can Machine Learning Make Replenishment Smarter in Food Retail?

If you were a time traveler from the 90’s who has escaped into the 21st century, you would be astonished to see the transformations in the world in terms of technology. Today, most of the things that you once believed could only be done by human being have been done by machines and robots, in a far more efficient manner. From your local grocery store to hospitals and shopping malls -automation is everywhere. Industries such as food and retail, where there is a high level of ‘customer-centric’ attitude are reaping the benefits of new technologies to take “customer delight” to the next level.IR_Brochure

Machine learning algorithms identify patterns that are not visible to humans, and automatically make adjustments to better processes.

One of the key challenges that many players in the food retail are facing is to make the right quantity of fresh food items available at all times to the customer avoiding surplus or shortage in supply. The main reason why this seems like an impossible task is because fresh food is perishable, and the demand is highly variable and uncertain. But do you believe this can be achieved through technologies like machine learning? The European retailer Otto is an example of a food retail company who has leveraged machine learning to hold accurate levels of stock based on customer buying patterns and predictions. It works on the principle of analyzing past sales, prices and stock levels to decide the optimum stock level at all times, ensuring round the clock supply of fresh food, and avoiding wastage due to surplus or shortage concerns.

How Does Machine Learning Ensure Your Shelves Are Rightly Stacked?

With the availability of numerous analysis tools and data collection techniques, it has become a cakewalk for companies to ascertain customer buying patterns and other related data. However, measuring the shelving execution standards could be a much more complicated task in food retail. So how can machine learning overcome these problems by minimizing the chances of error?

  • Earlier players in the food retail entirely relied on historical data, which cannot be considered as a reliable source for accurate predictions as they ignored the parameter ‘unmet demand’ of consumers. Machine learning helps overcome this drawback by formulating algorithms that build demand-probability curves using sales and inventory data to evaluate the risk of waste against the risk of the out-of-stock
  • Machine learning can be used not only to increase revenues but also keep a check on the other KPIs in food retail depending on the strategic goals of the retailer.
  • Through such advanced technologies, players in the food retail can plan their SKUs better by understanding the sale and demand volumes for each category and making alterations in SKU placements and allocations accordingly.

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Top 4 IoT Trends That Drive Smart Manufacturing

Internet of Things (IoT) is set to transform the way the manufacturing industry operates and functions. Smart manufacturing or advanced manufacturing technology is driving the companies to transform the competitive market landscape. Thus, making it inevitable for the companies to adopt smart manufacturing and seize the benefits it has to offer. Smart manufacturing coupled with IoT enabled devices is an aIR_Brochuremalgamation of information, human interface, and technology that helps evolve and develop every aspect of the business. IoT is an interconnected network of devices driven by connectivity and software that has brought the fourth industrial revolution – Industry 4.0.

Trend 1: Industrial Internet of Things (IIoT)

IIoT is the process of using IoT enabled devices and integrating it within the manufacturing processes on the factory floors leading to an increase in production efficiency, product quality, and speed. Purely built on connectivity, IIoT enables people and facilitates business processes to collect vast amounts of data and leverage advanced analytics to gain insights.

Trend 2: Cloud-Computing Technology

With the help of cloud computing coupled with IIoT, companies can reap the benefit of sharing important and critical information in real-time. The data shared from beacons and sensors help organizations to anticipate needs and predict trends, obtain required inventory details, and minimize turnaround time for machines as well as the suppliers and manufacturers.

Trend 3: Artificial Intelligence

Thanks to big data, vast amounts of information is available to the organizations, which can be leveraged to gain consumer insights, predict trends, and foresee market developments. With the help of machine learning, artificial intelligence helps organizations understand patterns, anticipate information, and track incongruities, which helps them to refine processes, enhance productivity, and adopt smart manufacturing techniques.

Trend 4: Smart Sensors and Beacons

The organizations can benefit with the help of IoT enabled sensors that will help them to record, generate, and understand information right from packaging to the core machine elements. Besides these, the IoT enabled sensors help the manufacturing industry to measure and track everything, right from volume to temperature; thereby, creating seamless information flow to facilitate automation and drive smart manufacturing.

 

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Is Open Innovation the Solution to Overcome Roadblocks in Autonomous Vehicle Technology Development?

Autonomous vehicles are believed to be one of the most disruptive innovationAsk an analysts of this generation. Even though autonomous vehicles may still seem like a futuristic technology, rapid advancements in this field have led to such vehicles being frequently tested by established companies. The future of autonomous vehicle industry is driven by decreasing cost of operation and the increased safety and reliability provided by such vehicles. The technology in autonomous driving has in some way surpassed that of human drivers in terms of safety.

Innovations in Autonomous Vehicle Slowing Down

Autonomous vehicle technology is developing at an unprecedented rate. However, companies are hitting the brick wall in some areas as innovation within the company is slowing down. For instance, carmakers are still struggling to keep up the pace of innovation when it comes to driving safely despite unclear lane markings, ability to respond to signals from safety officers, and capacity to operate safely in all-weather conditions.

Possible Solution Alternatives

To tackle such problems and keep up the pace of innovation, autonomous vehicle manufacturers are looking at open innovation to gather ideas from the outside. Major automakers are also considering acquisition, investment, or partnership with other automakers, infotainment systems, cloud computing providers, human-machine interface systems, and connected vehicle and device services. This enables the companies to gain a deeper knowledge of the problem areas and devise a solution with a diverse range of experts.

Example of Open Innovation

Olli has been exemplary in embracing the open innovation. In order to enhance their machine learning algorithms for autonomous vehicles, Olli has embraced IBM Watson’s technology to learn from transportation data collected with the help of more than 30 embedded sensors. This will allow the machine to learn faster and perform better than human drivers under human supervision.

A large sum of money has been invested in research and development to solve complex problems in autonomous vehicle. By adopting open innovation, both costs and time-to-market could be significantly brought down by gathering expertise from different fields.

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