Advanced Workflow Management App For Major Electronics Manufacturer, Vishay Intertechnology

Vishay Mobile App | Sunflower Lab

Vishay Intertechnology, Inc., the world’s largest manufacturer of semiconductors and electronics testing organization, has partnered with Sunflower Lab to build their innovative workflow management mobile app and software system. Vishay maintains extensive testing laboratories at its facilities in order to meet qualifications to a wide range of specifications vital to the automotive, commercial, defense, medical, and aerospace markets.

Full-Scale Digital Transformation

Vishay engaged with Sunflower Lab to design, develop, and deliver their workflow management application. The software is used to optimize tasks that were previously completed manually, such as: creation and approval of purchase orders (PO), inventory management, report creation pre- and post-testing of a component, as well as tracking the test time and test completion time of a component by their customers etc.

Key features of the application include:

  • Purchase Orders are created on the fly automatically upon identification of testing requirements.
  • Inventory levels are continuously monitored, with push notification alerts of low inventory items.
  • Inventory usage is closely tracked and recorded—e.g. technician name, purpose, date and time.
  • Robust real-time reporting capabilities for tracking readings, testing time, test results, equipment used, and other key factors in the testing process.
  • User management and permissions are enforced to manage approval process and streamline workflow.

Customer-Centric Results

By implementing these features, Vishay hopes to improve the accuracy of the reports while reducing the chances of human error. They also hope to improve overall efficiency by eliminating various manual processes. And, ultimately, exceed their customers’ expectations by delivering a quality product, every time.

As more and more companies are modernizing their workflow and processes, they’re turning to software-based solutions. This was the case for Vishay. Out of the box wasn’t going to cut it. They needed something specifically designed and built to suit their needs. It’s impressive to see a giant in the industry continue to innovate the way Vishay has.—Ronak Patel, CEO, The Sunflower Lab

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Smart Connected Assets

The Essential Guide To Anomaly Detection In Smart Connected Assets For Smart Manufacturing

Yash Patel

Yash Patel is a co-founder and CTO of Sunflower Lab. A dynamic entrepreneur well versed with the full spectrum of proven business practices and leading-edge technologies, he likes to spend quality time with his family and have fun with friends and colleagues – When he’s free!

Unique problem-solving capabilities, he has extensive knowledge and sound experience across multiple business domains and project management disciplines such as resource management, strategy and planning, solution design, product design and go to market strategies. Yash Patel is a seasoned solution architect and specializes in cloud, IOT and digital product designing and development.

At Sunflower Lab, apart from leading the solution architect team he focuses on IT strategy and execution, team building and client relationship.

“Anomaly detection in Smart Connected Assets is an important aspect of Smart Manufacturing. If the performance of the machine or an asset is deviating from the set standards, it will affect the overall operations. Thus, detecting outliers or faults or anomalies before it actually occurs and, taking corrective steps is important during all the phases of the manufacturing process.”

Anomaly detection in Smart Manufacturing

What are smart connected assets?

Smart Connected Assets are assets used by a business for manufacturing or delivery of goods and services. These assets can predict and respond to the environment by gathering the data from various other linked assets of the business. With the help of data, Smart Connected Assets can sense or predict any future failures in machines, environmental impact, supplier performance, customer requirements etc.

At the core of Smart Connected Assets is Industrial Internet of Things (IIOT). With IIoT connecting or linking the assets, and with the help of data gathered from them, it becomes possible to integrate, analyze or predict faults, failures or future demands. Smart Connected Assets can bring in better Asset Performance Management (APM) practices. With IIoT as an emerging trend, Industrial automation is also on the rise, thus resulting in rise of Smart Manufacturing and Smart Enterprise.

Smart Connected Assets and Smart Manufacturing

Smart Manufacturing has started gaining importance amongst various industries. In the USA it’s known as Smart Manufacturing whereas in China it’s called Made in China 2025 and in Europe, it’s known as Industry 4.0.

According to a blog by PTC, “Asia-Pacific is a leader in smart factories—due to essentially a large number of manufacturing industries in the region—and is expected to continue its growth between 2015 and 2020.”

With IIoT and Smart Connected Assets, it becomes possible for managers to remotely access, operate and manage these devices, i.e. asset tracking becomes easy for them from any location worldwide. Also, data gathering from these assets becomes vital for future processes and is done through industrial cloud computing.  With the availability of this real-time data, decision making becomes easy for managers. Further, tasks like predictive analysis, predictive maintenance, and anomaly detection also get simplified. Smart Manufacturing aims at optimizing the manufacturing process to achieve operational efficiency and meet customer demands on time.

Today, most of the organizations are trying to achieve operational efficiency to stay ahead of their competitors. For this, the organization needs to leverage the collected data for various purposes ranging from anomaly detection to asset tracking and management to meeting customer demands, increasing profits as well as achieving operational efficiency.

Anomaly detection in Smart Manufacturing

Anomaly detection is of great importance when it comes to asset performance management in smart manufacturing and, with the help of gathered data and predictive analysis, anomaly detection can be predicted long before it is detected by standard operational systems. However, early anomaly detection will not help you figure out what is wrong with the asset. To know where the problem lies, tests will have to be scheduled along with a detailed study of the data. The data is gathered by concluding various test cases with questions like “what needs to be done”, “how should it be done”, “what if analysis” etc.

When such data is gathered over a period of time, it builds a knowledge bank which helps brings solutions to anomalies arising in future. So, whenever such signs of anomaly appear in the asset, the data from knowledge banks can be leveraged to know where the fault lies and a solution can be worked out even before there is actual downtime of the asset.

Let’s first define an Anomaly:

“Anomaly means, a deviation in the asset’s normal or standard performance.”

“Anomaly can also be defined as a malfunction or error in operational performance.”


Anomaly detection involves the process of identifying the abnormal behavior of the device/ asset/program/machine. To detect an anomaly, knowledge of normal behavior is imperative. A fault is a type of “Anomaly” but all anomalies are not faults. For e.g. overheating of an electric motor can be considered as an anomaly, but if the temperature is within permissible limits, it might not be considered as a fault. However, if the temperature rises above the permissible limits, thus interrupting the motor’s operation, it can be considered as faulty.

So we can conclude that the outcome or result of an anomaly or fault is independent of its initial event, thus being intentional or accidental and will still impact the production in the same way rather than different ways.

Anomalies are usually categorized as cyber or physical with respect to their components which are networks or machines. But since the systems i.e. networks and machines are always integrated and not separate, this domain-specific approach divides anomalies unnaturally which is not logical. With evolving systems and technologies, anomalies will also evolve and transform or cause different types of anomalies. The types of anomalies depend on aspects like temporality, domain, and multiplicity.

The “temporality” aspect defines whether the anomalies occur at once or over a period of time.

The “domain” suggests the anomaly with respect to cross-domain interactions and so, “Domain” aspect is useful in studying cross-domain interactions of Smart Manufacturing systems.

The “multiplicity” aspect differentiates between the anomalies occurring in a single component or in multiple components at once.

Types of anomalies and its detection mechanisms:

1. Snapshot: anomalies that are immediately observed without the consideration of temporal behavior

2. Dynamic anomalies: are those that have a temporal attribute and evolve over a period of time as in a trend. Such anomalies may also manifest in snapshot anomalies.

3. Physical domain: assets supported by cyber components and controlled or monitored by computer-based algorithms

4. Cyber domain: Functions like communication, controls, and diagnostics supported by network components in Smart Manufacturing

5. Single component: anomalies manifesting only in the single component of a system viz., robot, sensor or a controller

6. Multiple components: anomalies manifesting in sub-set or multiple components. This anomaly might have originated in a single component, but its effect would not be evident until multiple components are affected. Alternately, the anomaly may manifest in two or more components which might be independent units, but due to combined interactions it results in anomalous behavior (e.g. a machine’s program may be modified without accurate changes in its tools and this might result in individually correct behaviors, but the resultant product may be wrong or faulty).

7. Instantaneous anomalies: are the ones that are observed in the system without any prior indication. Such anomalies could manifest in either one or multiple variables and, it is not necessary that such anomalies will be detected in real time. They might be detected at the end of a production phase or stage. For e.g. two variables when observed individually might be acting normally, but, when viewed as one, might carry an anomalous behavior.

8. Evolving anomalies: with the evolution of process observations of physical or cyber domains, such anomalies might manifest in single or multiple sources. In such cases, the individual sources might be within the bounds but, their multivariate dynamics would conflict. One variable might increase, whereas the other might decrease resulting in anomalous behavior of the machine or asset.

9. Communication anomalies: are the ones observed in communication network viz., unexpected traffic, faulty data packets, lack of communication, breach of firewalls etc.

10. Event-based anomalies: occurring during an unexpected occurrence of an event or due to missing of an expected event.

11. Integration anomalies: manifest when the system and its elements function normally but, the result is anomalous. For e.g. when there are bugs in upgrades or part of a system is changed but, the algorithm is not adjusted according to the changes.


The detection mechanisms are based on approaches or dimensions and predictive capabilities of the system and data availability. The anomaly detection mechanisms are either used individually or in groups to resolve the problems. There are supervised, semi-supervised and unsupervised detection methods for anomalies. Various types of mechanisms for anomaly detection include:

1. Feature extraction with limit checking: this is a frequently used basic method for anomaly detection which includes checking whether the features of the numeric signal traces are deviating from the user-defined region of normal operation or not. This method is adopted for trend analysis where limits are defined explicitly to capture the drift values before the fault arises.

2. Signal models: it includes anomaly detection for measured process signals which show harmonic or stochastic oscillations. (E.g. rotating machinery’s measurements). Deviations from a normal behavior are identified using mathematical models which use dynamic observations for feature calculations. Signal models are used for reactive and predictive modes’ – domain analysis based on cyber-physical models to predict a remaining and useful life of a product.

3. Knowledge-based methods: in this method, static or dynamic measurements are compared to an existing set of rules or patterns to find deviations or faults. Some examples of knowledge-based methods are expert systems, rule-based, ontology-based, logic-based, and state-transition analysis. Using an expressive logic structure is recommended when a specific network or process faults are included in this model.

4. State estimation: this includes both supervised and semi-supervised anomaly detection wherein both the model parameters and structure are known. Based on the available parameters of input and output variables and previous values, the set of state variables are sequentially estimated.

5. Regression models: This method includes identifying whether predictors and dependent variables are related or not for anomaly detection. Generalized Linear Models (GLM), Partial Least Squares (PLS), Support Vector Regression (SVR), Gaussian Process Regression (GPR), Artificial Neural Networks (ANN), decision trees, and ensemble methods are some of the widely used methods for regression. Regression can work with dynamic or static data depending on the nature of predictors.

6. Classification: Classification methods help in recognizing as to which set of a predefined group a new observation belongs to on the basis of training data. One of the major benefits of using classification methods is that fault detection and diagnosis are performed at the same step

7. Clustering: process observations similar in a user-defined metric are assigned to the same cluster for anomaly detection. This method works with snapshot data in reactive mode and is often used in supervised, unsupervised or semi-supervised approaches. Based on density, distribution, connectivity, multiple forms of clustering are available.


Want to gear up your organization for Smart Manufacturing and take over your competitors? Contact us for further details and we’ll tell you how to do it efficiently.

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Asset Tracking in Smart Connected Operations

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Asset Tracking in Smart connected operations

Asset Tracking in Smart Connected Operations

Asset tracking is when you manage and keep a track of your company’s physical assets and its information either by scanning barcodes, RFID (Radio Frequency Identification) or GPS. These physical assets can include tools, IT devices, equipment (small & large), vehicles etc. and many more assets depending upon the type of business.

Asset tracking is a pivotal aspect of Smart Connected Operations. Smart Connected Operations (SCO) which can be defined as the integration of the assets, operations and business systems, with the help of IIOT (Industrial Internet of Things) platform. Asset tracking helps save time and money when done accurately and improves operational efficiency. It is also used for Predictive Analysis and Maintenance and Anomaly Detection as it provides valuable data about the usage, maintenance, re-calibration and location of these assets. It also intimates if new assets need to be purchased or if old and used assets need to be discarded.

Various forward-thinking industries are taking a step towards the creation of Smart Enterprise. Smart Enterprise creates an environment where employees are provided with the freedom to do their job with any devices which are readily available or by any mode and have direct access to reliable information from anywhere and anytime. Smart Connected Operations are also used for Industrial Automation and Smart Manufacturing for the creation of a Smart Enterprise. Smart Manufacturing is also known as “Industries 4.0” in Europe and “Made in China 2025” in China.

Smart Enterprises are created with Smart Connected Operations (SCO) with the help of IIOT (Industrial Internet of Things) platform as its base. IIOT is a sub-category of a broader platform called IOT (Internet of Things). IIOT is concerned with increasing operational efficiency and improvement of safety standards. On the other hand, IOT is associated with creating better user experiences. IOT is a set of homogeneous software capabilities which help in improving the asset management decision making, operational visibility and control of these assets within manufacturing facilities, plants and depots.


With IIOT as the base of Smart Connected Operations, operational efficiency becomes inevitable. This platform is devised to support safety, security and technological requirements within operating environments. IIOT platform consists of various technological functions:

  • Asset Management and tracking
  • Device management
  • Integration
  • Data Management
  • Analytics
  • Application management &
  • Security

With the introduction of IIOT & SCO, a whole new dimension of Asset Management and tracking is introduced. Asset management or asset tracking is a vital and continual process for any business. For e.g., in a manufacturing business, the asset’s life-cycle starts from the purchase of an asset and continues until the end of its utility life. Asset management also includes improving operational efficiency. Through asset management, tracking of the assets is done with the help of various systems viz., desktop software, barcode scanners, barcode labels and mobile devices to streamline asset tracking (i.e. tools, equipment etc). Asset tracking can be for a small business or for multiple facilities of a large business spread across various locations in the country.

IIoT-enabled asset tracking can benefit various industries who have adopted Smart Connected Operations. Many industries have successfully done asset tracking and are also continually innovating and adding new applications for sake of improving its accuracy. Some of the industries who were able to successfully adapt asset tracking are:

  • Transportation – managing fleets and consignments
  • Manufacturing – tracking devices, tools, equipment and various other small and large assets
  • Agriculture – tracking live-stock
  • FMCG, Supply chain & Logistics – tracking parcels, freights etc.
  • Power & Utilities – tracking devices, tools, service equipment etc.
  • Government agencies – tracking of human resources and government-provided equipment
  • Automotive – Tracking the automobile’s diagnostics

Though these industries use different tracking solutions, the commonality is that they use wireless technology to obtain reliable data from these assets. When these industries gather unique data from these assets, they can leverage this data and gain a competitive advantage for their business. This helps them in making rapid and efficient business decisions, thus improving business efficiency and providing high customer satisfaction.

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Benefits Of Industrial Automation

What Are The Benefits Of Industrial Automation?

It is becoming increasingly important for manufacturers to adopt industrial automation and smart manufacturing capabilities to streamline production and boost profit margins. Manufacturers can benefit largely through industrial automation processes to reduce downtime and provide customer-centric services.

The manufacturing sector is evolving.

In the past, products were more standardized. The demand signals were consistent and predictable. Underlying supply chains and systems were simple. Existing processes were able to meet the efficiency and quality standards as demanded by customers.

But such is not the case anymore.

Today, the world economy has become interconnected. Consumers have become more choosy in their selection of goods and services. This is forcing demand cycles to reduce their turnaround times and yield more productivity. Dynamic market trends are compelling manufacturers to deliver quality products in lesser time.

Moreover, the market is flooding with new competitors. They’re trying to gain their share of the lucrative industrial market. And they’re ready to use any technology or platform to create a unique selling proposition that can beat their competitors.

Factors like product quality, customization, after sales service, delivery time and availability have become increasingly important by the day.

IIoT and industrial automation propose to address these problems and provide effective solutions that can increase production levels, reduce operational downtime and make better utilization of manufacturing resources.

What is industrial automation?

Industrial automation can be widely understood as a system comprising of interacting technologies and automated control devices that result into automatic functioning of manufacturing operations and controlling them without significant human intervention. The automation process typically uses devices such as PLCs, PCs, PACs, etc. and various types of industrial communication technologies.

Traditional manufacturing processes and new technologies have evolved with time to give rise to modern industrial automation processes.

IIoT automated systems can work round the clock without any stoppage. So, earlier, the main purpose of automating an industrial plant was to increase productivity levels and reduce waste. Moreover, automated processes replace manual labor. This helps companies to save on labor wages and benefits.

industrial automation
Today manufacturing needs have evolved to include flexibility in the manufacturing process and produce goods of high quality.

For example, in the automobile industry, pistons can be installed in engines manually as well as by automated processes. While doing it manually, the error rates fluctuate between 1% to 1.5%. But the rates are reduced to 0.00001% when done through automated processes. This can help auto manufacturers to save substantially upon replacement costs as certain car models can be expensive to buy.

There are several reasons why companies opt for industrial automation systems:

High productivity

Manufacturers can hire hundreds of workers and have them work around the clock to increase production levels. Even after doing that the plant still has to be closed for maintenance and repairs, and on holidays. So, it’s not possible to have a 100% production up time.

Industrial IoT automation helps to attain close to 100% up time by working 24 by 7 and 356 days a year. It leads to a dramatic increase in production levels. IIoT based predictive maintenance capability further helps to prepare companies deal with breakdowns in a much better way and reduce operational downtime.

High Quality

Automated devices and robots don’t tire or experience fatigue like humans do. They don’t get bored and loose work focus. So, they don’t make many errors.
Industrial automation reduces errors and helps to enhance quality standards.

High flexibility

Humans need training for new processes. Adding a new task in the assembly line results into many training hours for operators. Besides, operators have to get used to new processes which takes time. Companies have to suffer the loss incurred due to reduced production and trainer fees.

Manufacturing robots and devices can be programmed to do varied tasks. New processes and methods can be instantly integrated in the manufacturing process. New production capabilities can be availed at affordable costs. This increases production flexibility.

High information accuracy

Management needs production related data to make informed decisions. Data collection, if done manually, can prove to be costly. Moreover, there could be instances of human errors while collecting data. So manual data has to be verified and checked for reliability before it can be used for any meaningful purposes.

The factory automation process gathers data automatically from sensors and devices. So it’s not required to “sanitize” it. Moreover, data can be obtained frequently as and when required. Managements can acquire the most recent data before undertaking important decisions.

High safety

Certain industrial environments are risky to work in. Many industrial processes use strong chemicals and/or heavy electrical voltage to manufacture goods. Workers and operators safety can be a concern in such cases. Companies have to provide, in some cases, large financial compensation in the event of work related accidents.

Industrial automation systems make the production line safe for operators to work in. Robots handle all hazardous work.

Take away

Companies can reap the benefits of IIoT and smart connected operations to streamline their manufacturing activities and make them cost effective.

In many ways, new technologies are revolutionizing the manufacturing sector.

Predictive maintenance helps manufacturers to anticipate production related pitfalls in advance. Proper contingency plans, prepared in advance, aid in mitigating risks and maintain production up times.

Data generated by IIoT enabled smart connected devices provide important insights which help managements to make informed decisions. Factory automation makes the manufacturing process more agile and customer-centric.

For manufacturers, it is becoming increasingly important to adopt smart manufacturing processes and create a special niche for their products, and to boost their profits.

Ideally, manufacturers should consult industrial automation experts to discover important manufacturing areas which require smart manufacturing capabilities and create a transition plan to adopt industrial automation.