As we see with more significant frequency across many industries, companies that invest in emerging technologies can gain a competitive advantage. Just a few of the many benefits include optimizing productivity and operations, improving employee and customer satisfaction, reducing errors, and mitigating risks. Quite simply, digital transformation enables forward-thinking technology organizations to save time and money.
Manufacturing and transportation companies, in particular, must shift how they do business. Quality control and risk management are key concerns for them, and to achieve meaningful change, they need to adopt advanced and emerging technology solutions to optimize the supply chain from the manufacturing floor to delivery, improve quality control, mitigate risks, and prevent downtime. Technology will also enable them to better prepare for what may come next and respond in real time to current situations.
In this blog, we'll discuss why organizations in the manufacturing and transportation industries need a technology-led focus that includes anomaly detection, predictive maintenance, and visual quality control to:
Anomaly detection finds patterns of interest – or outliers – that deviate from “normal” behaviors within datasets. With this information, organizations can utilize anomaly detection to reduce operational costs and time needed for investigation, prevent failure propagation, improve product quality, and reduce downtime.
For instance, today’s industrial machines and equipment can record data from various sensors. All this data can be utilized to reduce errors by discovering an anomaly in the data stream, like a slipped machine setting or maintenance need. The insights from this type of outlier mean that a manufacturer can preemptively prevent a minor issue from turning into a large problem, resulting in time and money saved.
Other anomaly detection use cases include finding outliers in:
At Grid Dynamics, we build solutions that enable organizations to minimize risks and react quickly to incidents:
Unplanned downtime is expensive. It costs industrial manufacturers approximately $50 billion annually, and for industries across the board, it is one of the largest causes of lost productivity and revenue. The old way of doing business – a reactive process where companies make repairs as equipment fails – is not optimal for certain applications.
To overcome this challenge, companies need to utilize predictive maintenance. Predictive maintenance is not new, but with data science and machine learning, companies can more accurately predict and prevent future equipment failures with more lead time and reduce operational and maintenance costs.
For instance, with predictive maintenance, transportation companies can collect real-time data to optimize maintenance schedules and accelerate response times for roadside assistance. Manufacturers, through industry robotics, can gather sensory data from assembly lines to detect early-stage problems, prevent equipment failures, and optimize robot service life.
Where and how predictive maintenance can be used to help companies reduce costs and improve efficiencies are plenty. A few real-world examples include:
Delivering quality products throughout manufacturing is critical to a company’s success. This can be difficult due to the volume and reliance on manual inspections, which are time-consuming and prone to human error. To help companies overcome these challenges, they need visual quality control - essentially anomaly detection based on visual signals. Visual quality control relies on a comprehensive toolkit of computer vision algorithms for anomaly detection. Visual quality control can detect anomalies, prevent more significant failures and outages, and improve product quality by using a wide range of signals and data sources, such as IoT sensor data, infrared and X-ray imagery, and video streams. This means that a company saves time and money through the consistency of quality assurance.
Today, visual quality control is utilized in several industries, including the manufacturing of automobiles, electronics, and consumer packaged goods. And for the automotive sector, visual quality inspection is a business must-have to ensure consumer safety. Just one minor malfunction in a car’s part could result in a severe and even life-threatening accident that could lead to significant financial loss and damage to a brand’s reputation. Additionally, since one car can have over 30,000 parts, visual quality inspection is the only way to increase efficiency, improve accuracy, and reduce costs.
To understand how visual quality control works, let’s delve into electronics manufacturing and how companies can find defects that are not visible to the human eye. Companies can do this by using deep learning to discover minor anomalies in products like microchips, monitors, and CPUs, and utilize X-ray tomography to generate radiographic projections. The algorithm then translates an image for 3-d representation. It’s important to note that to be effective, manufacturers must narrow the scope of analysis to those parts that are susceptible to defects. Otherwise, it is difficult to train the deep learning network for anomaly detection.
Companies today want and need more actionable insights from data. Cutting-edge technology solutions that enable real-time, actionable insights through anomaly detection, predictive maintenance, and visual quality control give companies the insights needed to pre-emptively resolve issues before they become a problem. And that means they save time and money.
Grid Dynamics builds solutions that increase the speed of insights so that companies realize business value within weeks. For instance, we worked with Jabil Inc., a global manufacturing solutions provider headquartered in St. Petersburg, Florida. Jabil has over 100 plants in 30 countries and 260,000 employees worldwide.
The company worked with us to future-proof its smart manufacturing operations. We developed a modern, mature analytics platform of pipeline orchestration, data quality, and self-service capabilities that enabled Jabil to improve time-to-market, data management, and scalability. The company realized three years of cost savings estimated at half a million dollars. Read the case study here: Analytics Platform for smart manufacturing: A Jabil case study.
We have a proven track record of co-innovating with recognized brands to solve complex problems and optimize business operations so our clients can better serve them. Driven by business impact and agility, we create innovative, end-to-end solutions to help clients grow, from anomaly detection in industrial applications to building smart solutions that reduce operational risks and enabling AI-powered loss prevention and risk mitigation in fuel distribution for our client, who has projected ROI exceeding 650% and $250M in new revenue opportunities.
Our secret sauce? We hire the top 10% of global engineering talent and employ our extensive expertise in emerging technology, lean software development practices, a high-performance product and agile delivery culture, and strategic partnerships with leading technology service providers like Google, Amazon, and Microsoft.
Ready to invest in emerging technologies to gain a competitive advantage and save time and money? Contact us to get started.