Month: December 2021

How Predictive Insights Can Help You Reduce Operational Waste

Posted on by SuperAdmin

Predictive analytics refers to a category of advanced analytics that can predict future outcomes with the help of historical data as well as data mining techniques, statistical modeling, and machine learning. Businesses use predictive analytics to detect patterns in their data to recognize opportunities or threats.

As interesting as this might sound, there is a vast ocean of unchartered territories and unclaimed knowledge that takes possibilities of predictive insights beyond tomorrow. Learn more about how companies can benefit from streamlining their operations to reduce waste with the help of this ingenious technology.

Ready to dive in? Let’s get right into it!

What is meant by Predictive Insights?

Predictive analytics is generally correlated with data science and big data. Businesses at present have heaps of data across equipment log files, transactional databases, video, media images, sensors, or various sources of data. To achieve actionable insights using this data, analysts and scientists take the aid of machine learning and deep learning algorithms to determine patterns for predictions regarding future trends and events.

These can include nonlinear and linear regression, support vector systems, neural networks, and decision trees. The findings obtained using predictive analytics can then be applied to prescriptive analytics with the goal of driving operations depending upon these predictive insights.

The History and Present Milestones of Predictive Analytics

Even though predictive analytics has been used by businesses for decades, the time to exploit it correctly has just begun. An increasing number of companies are adopting the use of predictive analytics as a way to boost their bottom line and reap the benefits of a competitive edge.

The question that arises is – why is it starting to peak now?

There are several answers to this question:

  1.  Increasing quantity and types of data, or maybe, an increasing interest to utilize data to extract actionable insights.
  2. Quicker and more economical computers are available today with huge computing power.
  3. User-friendly software.
  4. More challenging market scenarios present the requirement for competitive segregation of products and services.

With the rise of interactive and user-friendly software in all markets and industries, predictive analytics is not just a domain for mathematicians or statisticians anymore. This niche is now free of complexities and can be used by business analysts or business experts with ease.

What Makes Predictive Analytics Vital to Reducing Operational Waste?

Companies are rapidly adopting predictive analytics to sort out their existing problems and reveal better opportunities. The common benefits of predictive insights to operations include:

– Fraud Detection: Gathering various methods of analytics can boost the detection of trends and patterns to prevent criminal conduct. With cybersecurity being at the forefront of businesses’ main concerns, the help of high-quality behavioral analytics reviews all the transactions and processes undertaken with suppliers and quality control in real-time. This helps them to find any irregularities that could point towards fraud, advanced threats, or zero-day vulnerabilities.

–  Improving Production Strategies: Predictive analytics are employed to understand the demand and supply chain of their production process. By monitoring their suppliers’ inflow of raw material, they can place orders well in advance, avoiding idle time or resorting to last-minute supplies that compromise quality.

– Efficient Production Operations: Many businesses employ predictive models to project inventory and control their resources. Predictive analytics helps businesses to maximize their operational efficiency and reduce rejects or sub-standard production by figuring out discrepancies in processes or equipment much before these breakdowns occur.

– Risk Reduction: The integrity and competency of the shop-floor staff matter a lot in manufacturing. With predictive analytics and insights, one can look up the reasons for the continual reject causes in their production jobs and batches. If human error is responsible for these wastes, they can be promptly addressed. If the insights rule out human error, it also gives manufacturers the chance to improve accountability, thereby lowering the risk of equipment malfunction or external factors like raw material quality.

How are Predictive Insights Used in the Manufacturing Operations

In the manufacturing industry, it’s crucial to find the factors that cause poor quality and failure in production processes. It is also important to optimize elements, services, and logistics. One of the most successful manufacturers that use predictive analytics to improve their grasp of warranty claims is Lenovo; their step forward led them to reduce 10 to 15 percent in warranty charges. Another benefit of using predictive analytics in manufacturing is that monitoring the equipment performance and yield quality can help track inconsistencies much before the quality checks fail.

Optimizing complex production networks

Predictive maintenance studies are devised to boost the operations and profitability of each production machine and process. Additionally, PPH maximization can streamline the interaction of the processes and machines used. Including all the processes, be it from the purchase of raw materials to the production process and sales, this superior technique of modeling exponentially improves the revenue in complex production operations and supply chains.

Unlike manual planning, advanced analytics generally considers about a thousand different variables and 10,000 constraints to assist the manufacturers to understand what to buy, what to manufacture, and how to manufacture in order to generate great profits throughout the year.

Achieving abundant profits with less

Just like predictive maintenance helps to enhance the uptime of any given asset, predictive analysis can improve its productivity and OEE. Even minute improvement in the overall operational efficiency (OEE) can dramatically boost profits before interest and tax (PBIT). This is achieved by enhancing the production output, material costs, and throughput to boost the profitability of every process involved.

Reduction in downtime in a busy environment

With the help of advanced analytics, manufacturers can boil down the causes of their equipment breaking down and control the input metrics so that they can fix an issue before the breakdown occurs or be prepared to quickly fix issues in order to minimize total downtime. Predictive maintenance can help to increase the equipment lifespan by 20 to 40 percent and reduce downtime by 30 to 50 percent.

Operations are linked closely with the metrics of production, reject (waste), and downtime, which can now be monitored and controlled better with the aid of predictive analytics. By projecting the future demands, manufacturers can be prepared for market shifts, optimize their processes and improve the overall efficiency of their sites using this sophisticated technology. We hope that this blog gave you valuable insight into the world of predictive intelligence and its link to operational efficiency.

What Is AI Vision and Why Is It Here to Stay?

Posted on by SuperAdmin

Artificial Intelligence is one of the most misunderstood and overused buzzwords of our generation. We hear so many organizations bragging about their use of AI without really understanding what they are doing with it. In this blog post, our experts share how Artificial Intelligence is used in Cameras to improve business operations. Also, learn why it will become the future of how we do business.

Computer Vision is the application of Artificial Intelligence (“AI”) through a visual medium, like a live camera feed or an image. It essentially aims to replicate human perception and visual cognition. You must have already encountered computer vision without even realizing it, for example, the face-lock on your phone screen, to some level, is a simple application of computer vision or the Instagram or Snapchat filters that alter your hairstyle or turn your face into an animal on the screen, are all made possible with Computer Vision.

Any device that can comprehend a visual feed like a human being is a computer vision system, however, Computers have the ability to process more than humans can. Think about yourself reading these words. It’s likely that you’re focusing on one word at a time while you are reading, and recognizing the words around these words without fully processing them. Using Computer Vision, a computer would be able to see and read all the words on the page at the same time, drawing immediate meaning from the entire page

This principle also applies in a CCTV control room. Normally you would have people monitoring the live camera feeds on multiple screens, but how many live feeds can one person accurately monitor at a time? That’s where Computer Vision shines. It’s able to continuously and accurately monitor the feeds from all cameras simultaneously.

Automation integrated with AI Vision enables the workforce to overcome typical challenges of humans, like fatigue. It provides unseen insights that humans find difficult to access or comprehend in real-time. While human perception has its own share of limitations, a camera can gather every second of visual data from your premises that can be further analyzed to identify areas typically missed by humans.

Computer vision is the most natural next step in machine evolution. The purpose of AI is not to replace humans but to assist them. For that to happen, it needs to command similar cognitive abilities with enhanced capabilities. However, the underlying science behind AI Vision is not as complex or overwhelming as it might appear. Here’s a brief understanding of how AI Vision functions:

Capture Metadata from Camera Feeds

Content feed from a camera is analyzed by trained models to recognize objects, their actions, specific characteristics, and interactions in space and time. Typically, in a retail store, a camera equipped with Cogniphi’s AI Vision can detect shoppers, analyze footfall, recognize customers with face recognition, and evaluate customer expressions like anger with emotion recognition. Similarly in manufacturing, AI Vision can do myriad tasks such as identifying quality for inspection, counting warehouse inventory, etc.

Pattern Recognition and Anomaly Detection

Computer Vision derives relevant insights from unstructured data through contexts and occurrences of patterns along with their co-relations. In a retail store, AI Vision can analyze shopper dwell time, shopper interaction with products, alert anomalies such as misplaced objects in the wrong area, water spillage, and identifying reasons for inventory shrinkage, etc.

Recommendations and Predictive Analytics

Smart Vision equipped with actionable insights provides recommendations in the form of real-time alerts, analytics, and also integrates with existing business systems.

Cogniphi’s AI Vision is a pioneer in providing hyper-local computer vision solutions to retailers and manufacturers. Different components of the solution cater to different aspects of visual data and analytics to

– detect objects and their interaction in space and time.

– detect human body parts, their movement, interactions with real-world objects, their transformations, and other associated patterns.

– detect human faces and recognize people in 1:1 (Verification) and 1:N (Identification) mode.

– detect attention, emotions, and expressions on the faces

– identify and detect textures of objects.

– extract data from several documents and categorize data to train the model to interpret and analyze this data.

AI Vision is Here to Stay

Vision Intelligence technology is a cost-effective upgrade to existing data feeding cameras. On top of being a non-disruptive installation, Cogniphi’s AI Vision can help an enterprise reduce human error and experience overall increased productivity.

The Grand View Research states the growing valuation of the global computer vision market is expected to go up from $11 billion in 2020 to reach $19 billion by 2027. Manufacturing, Energy, Retail, Transportation, and Healthcare are the industries identified as best positioned to capitalize on this technology in the coming years.

The 2020 McKinsey Global Survey on AI has concluded that 50% of companies have adopted AI in one or other business functions and that the majority of use cases are aimed at optimizing operations or at product development or at customer service improvement. As companies increasingly look at AI to solve real-world challenges that depend a great deal on visual inputs, computer vision will have a huge role to play in achieving these objectives.

About Cogniphi

Cogniphi is a technology company that enables customers to achieve transformational outcomes through cognitive digital solutions. It believes in a 360-degree problem-solving approach, building solutions that can scale and adapt to changing business demands for continuous improvement.