Six ways you can Improve Productivity in Manufacturing

Six ways you can Improve Productivity in Manufacturing

Productivity is an ultimate goal of the manufacturing industry. Especially, high production on a short span of time without any bad quality is a big dream of an Industry. However, to achieve this, the manufacturing industry should think of some key factors like people, process, digital maturity, and good management. Productivity Loss is an addressable hotspot of success. The key issue which every industry faces is the productivity loss which results due to variation of estimation time to execution time, bad quality products, worst raw material, improper Visibility, no schedule, no measurement metrics, unoptimized process, & cost inefficient.

By using the 6 ways mentioned below, you can definitely improve productivity. Let us have a deeper look in it.

Analyse and Understand Availability Loss

The first & foremost is the analysing and understanding about failure which will almost help you choose the successful way. Similarly improving productivity where productivity loss actually occurs is a major point. Losses are activities that absorb resources without creating bringing any value to the organization. The difference between Planned Production Time to runtime is called availability Loss. Mostly Equipment failure & Setup Adjustments are key factors that comes under an Availability Loss. A simple solution is the measurement of OEE which helps to rectify the problem.

Isolate Slow Performing Machines

The Slow and worst performance of the machines will have a heavy impact on productivity loss. It is necessary to analyse and measure every machine in a production line with the performance metrics. Now, compare its performance with various machines & detect slow performing machines. Then isolate such slow performing machines and so it won’t affect the workflow of the production line. Finally, diagnose & find out the reason behind the slow performance of machines and rectify it.

Start Micro Planning

Pre-planning the goal of production is an important one. Micro planning determines the detailed implementation of the identified process. On the whole production process should be segmented as a small process with proper planning. This method has a high level chance of improving productivity.

Monitor WIP (Work in progress)

WIP (Work in Progress) is a concept used to describe the flow of manufacturing costs from one area of production to the next, once the raw material arrives. Monitoring of WIP will effectively improve productivity. This is because, it is easy to rectify if there is a delay in progress.

Up skill your workforce

Collecting brains makes an effective solution that every team members like production unit members, quality members, maintenance members, superior, financial team members, selective members from various shifts, technology team and management will be gathered to conduct an effective meeting about productivity loss, Market status, financial Management, Digital Transformation and futuristic Plans. The outcome of the meeting will definitely decide Industrial growth.

Optimize the Process

Once you have retrospect, workflow and output of meeting will start identifying areas where technology and process need some update or change. Now optimize the process and redefine fine-tune architecture with the latest technologies and follow the optimized process definitively to improve productivity.

To know more….Talk to our experts to discuss about your requirements at your shop floor to improve your manufacturing productivity.

IoT Platform Comparison – Bevywise vs IBM vs AWS vs Azure

IoT Platform Comparison – Bevywise vs IBM vs AWS vs Azure

The need for IoT (Internet of Things) in the market is progressing at a speedy rate. The main objective behind this rapid growth is an immense claim for IoT platforms, IoT devices and other components. However, if you start building your own IoT framework, you will end up wasting more money & time. Hence, you need a complete IoT platform which brings down your development risk, speeds up your product’s time-to-market and reduces cost. There are a massive quantity of IoT platforms available in the market. Prior to beginning with an IoT project, you ought to sort a list of choices for picking an IoT platform that suits your criteria. To help you with this selection, we have drafted this IoT Platform comparison article with some of the most popular IoT platforms like Bevywise IoT Platform, IBM Watson, AWS IoT Platform, Microsoft Azure IoT Platform and the essentials of using them in your project.

Choosing an Enterprise-ready IoT Platform

Building an IoT framework / infrastructure is a huge task which requires high mastery across various domains. It can cost an arm & a leg. Hence, an enterprise ready platform constituting of protocols, tools & SDKs supporting a wide range of IoT solutions should be used to build IoT applications easily. This will remove the risks associated with adoption, reduce costs, accelerate time-to-market, and maintain quality standards.

This article compares enterprise-ready IoT platforms such as Bevywise IoT Platform, IBM Watson, AWS IoT Platform, & Microsoft Azure IoT Platform with respect to their basic & unique functionalities.

Device management

The first & foremost thing every application developer expects from the IoT Platform is the device management functionality. Device management is one of the most important features of the IoT platform. Application developers bank on the IoT platform to manage a list of devices connected to it and trace their performance status. It should be able to pick up device configuration and lay out device-level error reporting and error handling. Finally, in the end, end users should be able to obtain individual device level status. In that case all IoT platforms such as Bevywise IoT Platform, IBM Watson, AWS IoT Platform, Microsoft Azure IoT Platform provides justice for end users in terms of device management functionality by enabling them to manage IoT devices at scale.

Integration with other Applications

Integration with other necessary applications is another important aspect likely to have from an IoT platform. Most IoT platforms support API integration that provides access to the necessary functions and data that needs to be displayed from the IoT platform. Generally REST APIs will be used to perform this. Platforms such as AWS IoT Platform & Microsoft Azure IoT Platform supports REST API, IBM Watson supports both REST & Real-time APIs. Bevywise IoT Platform exclusively supports Data push over websocket to push the rule or alert message as notification & FCM to push data as  notification to android / ios app along with the REST APIs.

IoT Platform Security

The security actions required to work on with an IoTplatform are much higher as huge number of devices are connected to it. There will be a high risk of security issues & vulnerabilities when the devices are connected to the internet. Generally, the connection between the IoT devices and the IoT platform and the data transfer needs to be encrypted with a strong encryption mechanism to prevent security threats. But, we can’t expect devices involved in the IoT framework to support advanced access control functionalities. Hence, the IoT platform itself should provide some built-in security options to make secured device connection & data communication.

And when it comes to security, all IoT platforms support TLS & authentication features. But in addition to it, Bevywise IoT platform supports inbuilt device identity & authorization, TLS/SSL authentication, Device level authentication and custom authentication to provide customized security functionalities that suit specific needs of users. In addition to this, Bevywise IoT platform supports integration of the IOT Device IAM for the enterprise to leverage and have more control on their devices instantly.

Data Collection Protocols  

The protocols used for data communication & device connectivity need more attention as it is another important feature. This is because the IoT platform needs to be scaled to millions and it requires a lightweight communication protocol to enable low energy use & low network bandwidth functionality.

 Most of the platforms use MQTT as their communication protocol as it is an extremely lightweight messaging transport with minimal network bandwidth. Similarly, IBM Watson, AWS IoT platform supports both MQTT & HTTPS, Bevywise supports MQTT & websockets, and Microsoft Azure supports MQTT, Websockets,  HTTPs, AMQP.

 The above mentioned functionalities are the basic things which every IoT platform should be able to provide.

The IoT Platform Comparison table below provides the more detailed comparison of each & every features of Bevywise IoT Platform, IBM Watson, AWS IoT Platform & Microsoft Azure Platform.

IoT Platform comparison


Features Bevywise IoT Platform IBM Watson AWS IoT Platform Microsoft Azure IoT Platform
Protocol (for Device Connectivity) MQTT, Websockets MQTT, HTTPs MQTT, HTTP MQTT, Websockets,  HTTPs, AMQP
Integration (for Application building ) REST API, Data Push over Websocket, FCM – Mobile Push REST API, Real-Time API REST API REST API
Visualize Data Yes. Dynamic Graph, Comparison graph and Historical graph Yes (Need an additional Service) Yes (Need an additional Service) Yes (Need an additional Service)
Device Security TLS/SSL
Authentication, IAM integration, Custom Authentication
TLS, Authentication LDAP TLS, Authentication TLS, Authentication
Multi Tenancy Yes. Separate console for each user Create individual accounts. Single account is for single user implementation. Create individual accounts. Single account is for single user implementation. Create individual accounts. Single account is for single user implementation.
Admin Console Yes No No No
Mobile SDK Android and IOS SDK Android SDK Android SDK Android SDK
Voice Integration Amazon Alexa and Google assistance No Only for Alexa, but configure through a separate tool No
Extend & Customize Yes. Write code for a common implementation across all your users Yes, You must use the UI to do it No. Have to use the UI to do it No. Have to use the UI to do it
Rebrand Yes No No No
Data Ownership You own the data and you can host it yourself No No No
Host behind firewall Yes No No No
Register command (to add sub device below main device) Yes No No No
Rule engine Yes No Yes No
Widget YES. Line graph, Bar graph, text, Color, Gauge, donut graph, vertical and horizontal scale, LED indicator, Switch, and Table. No No No
Register command to add a sub device below the parent device. Yes No No No
Multi tenant for Users Yes. Allow users to add their customers with predefined roles No No No
Device Grouping Yes No No No
Notification Via Websocket, FCM, Email and to Device No No No


Hope this article will help you choose the right platform for your IoT deployments.

You can sign up to Bevywise IOT Platform at our FREE trial set up.

Top 5 Technologies manufacturing should adopt before 2025

Top 5 Technologies manufacturing should adopt before 2025

Adopting new technology is a growing symptom of all Industry and similarly manufacturing Industry is not an exception. Industrial revolutions have made many improvements in manufacturing and service systems. Because of remarkable and rapid changes appeared in manufacturing and information technology, industries realized the importance of adapting to new technology. These advancements are conduced to increase productivity both in service systems and manufacturing environment. In recent years, manufacturing companies have faced substantial challenges due to the necessity in the coordination and connection of disruptive concepts such as communication and networking, embedded systems, adaptive robotics, cyber security, data analytics and artificial intelligence and additive manufacturing. These advancements have paved the way for the extension of the developments in manufacturing and information technology. It is a necessary one for the manufacturing industry to maintain current trends and standard for industrial growth. Let us have a look on top 5 manufacturing technologies that every manufacturing industries should adopt.

Why to implement in before 2025?

“It is not the strongest or the most intelligent who will survive but those who can best manage change  – Charles Darwin”.

For every 5 years, technology as well as dependent Industry is continuously evolving and shaping up the future strategies. Organizations continually try to adopt new technologies to overhaul their Industry infrastructures and to keep up with the pace of changing market dynamics. Industry’s futuristic vision and mission can be accomplished only by futuristic technologies. In next 5 years especially, manufacturing industry will face dramatic changes. So it is mandatory to adopt this simple formula to succeed.

The smart way to overcome all challenges & problems in the manufacturing Industry is the optimistic use of technology.

Here is a list of essential manufacturing technologies :

    • IoT (Internet of Things)

    • Automation and Robotics

    • Predictive Analysis

    • Artificial Intelligence

    • Mobile applications

IoT (Internet of Things)

The Internet of things plays a vital role in the manufacturing industry. In today’s world, there is a heavy demand for customization with the increasing customer expectations, the complexity and many other challenges which encourage manufacturers to find new more innovative ways to remain competitive. IoT is a way to digital transformation in manufacturing. Industrial IoT employs a network of sensors to collect critical production data and it uses valuable manufacturing units to maximize productivity by maintaining production up times, reducing costs and eliminating unwanted time. IoT will help manufacturing industries improve their manufacturing efficiency.

Automation and Robotics

Automation plays a major role in manufacturing field. It is simply, without human intervention but a smart human intelligence way to get the manufacturing process done. It provides world-class quality, without any delay, while reducing errors & waste and increasing productivity.

 Automation and Robotics avoid,

  • Improper data handling
  • Skilled Labour
  • Bad Quality
  • Skilled Labour Shortage
  • Unpredictable Time Estimation
  • Approximation and Inconsistency
  • Cost ineffective

Predictive Analysis

Manufacturing industry needs effective data for decision-making and a proper way to increase productivity. Predictive analysis is definitely the right way to guide the entire manufacturing industry. It is used to make predictions about unknown future events. It uses many techniques from data mining, statistics modelling to analyse current data to make predictions about the future. It combines the power of historical data with AI and Machine learning technology to understand, monitor and identify trends, predict potential problems and it provides recommendations to improve the process and maximize performance. It avoids unwanted navigation in decision-making situations.

Artificial Intelligence

Artificial intelligence has a great impact on the manufacturing industry. It ultimately empowers manufacturing Industry to continue to be the backbone of the global economy. It is the next level of success in the manufacturing industry. Experts predict that artificial intelligence will be a game changer for the growth of the manufacturing industry.

Mobile applications

Mobile applications provide more flexibility in manufacturing and help increase productivity. It eliminates the use of paper and it helps maintain the fully paper free environment with the access of storing key documents and data on cloud environment. Manufacturers can access such data at anytime and anywhere. It boosts up employee productivity and saves so many resources.

Proactive Leaders deeply think and never stop taking initiatives. It’s your chance now. Get utilized with these manufacturing technologies.

To know more….Talk to our experts to discuss about your requirements at your shop floor to improve your manufacturing productivity.

Digital Transformation for Manufacturing – Challenges & Solutions

Digital Transformation for Manufacturing – Challenges & Solutions

Manufacturing Industry has a major key role in the economic growth and development of countries. Historically, it has been the heart of all developed and developing nations. Also, the economic growth of the country relies only on the increase in productivity growth. Manufacturing Industry plays a key role in the success of the socio-economic system, because of its close relationship with economic welfare and hence it has a major impact on national growth also. But it has been facing huge difficulties and challenges. The internal Manufacturing ecosystems will not be a systemic & proper intellectual system which largely creates a negative impact on industrial productivity. This is because some shifts yield better output with a high productivity rate and some with an average productivity rate and the final overall visibility will be irregular resulting in lack of productivity. Hence, the goal of productivity increase & economic growth is fully dependent on a new chapter called Digital Transformation. Let us have a deeper look on digital transformation in manufacturing industry.

What is digital transformation?

Digital transformation has been the trending term frequently known as the adoption of digital technology to transform services (or) business by replacing non-digital or manual processes with digital processes. It is a journey of strategic planning, organizational change and it starts empowering manufacturing teams with new methods to create a highly responsive productive environment.

Industrial Revolutions

Industrial revolution refers to the simple change and transition in the manufacturing industry. Till now, it  has made a 4 master changes and it will continue to trigger next level of growth in the manufacturing industry.

The First Revolution was the transition of new manufacturing processes which begins in Europe and the United states, during the period of 760 to sometime between 80 and 840.The transition involves moving from hand production methods to machines.

The Second Industrial Revolution was a rapid standardization and the industrialization period which occurs in the late 19th century. This Industrial revolution waves largely impacts the build out of railroads and steel production. The new arrival of electrical steel, boosts mass production.

The Third revolution begins during the period of 1970. It revolutionized the use of electronics and IT into further automation and production. Outcomes of Industry 3.0 introduced a more automated system into the assembly line to easily perform human tasks.

The Fourth Industrial revolution is the new decade of manufacturing industry. It is the recent trend of automation and data exchange in manufacturing technologies. The goal is to enable autonomous decision-making processes & monitor assets and processes in real time.

Industry 4.0  & Digital Transformation

Digitization impacts all the systems of the global world and also on the recent forms of society. There are some distinct ways in which digitization becomes effective which is the Digital transformation and presentation of information. It is not just about technology, it is about re-imagining business.

The global economy relies on the growth of the manufacturing industry and the recent trend totally changes the minds of customers demanding more customized products. The digital world could provide so many valuable solutions to meet the demands of customers. With the help of digital transformation in manufacturing, the entire industry will be under the control of our hands. Moreover, digitization provides so many benefits like paperless environment, easy & instant communication, Proper Production Planning, Downtime and Machine idle time reporting, with an improvement in productivity and profit, enhanced data collection and quality of products.

Why is it needed?

In the Hyper competitive Business world, adopting and adapting is a more important one. The increase in competition in the field of manufacturing Industry puts more pressure on organizations to reduce costs, improve customer experience and increase profitability.

The manual process of collecting data and logging it in documents or spreadsheet is an unreliable & long one. Calculating Machine idle, Availability Loss, Performance Loss manually will not be accurate and also manual calculation of overall equipment efficiency will not be fair at all time. This is where digital transformation comes in providing more accurate & reliable measurements. A disconnected industry without digitization is nothing but just an island of data. There will not be any flow between start to end. Every department that is shop floor, Management, sales etc will remain isolated and there won’t be any possibility of exchange of information because there is no connectivity between them. With the digital transformation in manufacturing industry, it is easy to build a connectivity within industry. So that, everything from data acquisition to reporting will be simple & clear. Every emerging industry needs valuable solutions to their problems and most importantly they want it fast. The digital transformation can achieve it providing a good customer experience.

Top 5 functional areas that need digital transformation

Inventory Management

Observing your customer’s needs and making wise decisions on how to manage them though inventory is a challenge in today’s buying environment. Even with the availability of technology many companies still have outdated inventory management systems and manual processes.The demands of customers are constantly changing and now they are expecting distributors to be more flexible with their orders. If there is a limited visibility in inventory management, there won’t be any proper traceability.

Hence they need automated inventory management with real-time status to streamline work operations.

Quality Management

In the business competitive world, product quality is very important to create a brand in the market. Bad quality reflects sales and marketing, consumer relationship & finally will damage your brand value. Defective product will cause rework costs, refunding cost, Lost sales etc.

Hence, a complete production monitoring & management solutions can solve the basic quality issues by avoiding the production of rejects.

Production time and Downtime reporting

Industry expects the best productivity with lower downtime & proper utilization. These are key functional units for the production environment. If you manually measure Production time and downtime reporting it will be unreliable and will cause a tedious problem. This is where production monitoring comes in providing a complete report on downtime. This enables you to act proactively on the occurrence of downtime.


Entire Manufacturing Industry has many departments and various shifts. Hence, the overall workflow and process disconnect will not be clear. However, digital transformation in manufacturing helps in providing overall visibility much easier with the support of mobile applications and various software applications to access and view visibility remotely.

Customer relationship management

Customer relationship management refers to maintaining and creating good relationships with customers. It is not only about business it is also about creating a strong personal bonding with the people. This will definitely lead to a new value creations. If the customer is satisfied they will remain in business forever resulting in an increase in customers. Without digital transformation maintaining customer relationship will be a difficult process.

Top 5 Challenges in implementing Digital Transformation

Feasibility Study

The challenges, industries have been facing should be observed & understood well. Analysing what actually the problem is & what can be the solution is a major one. This is because most of the times, the problems faced and the solutions provided will be a contrast to each other. So the implementation of digitization will become more difficult.

Skilled Labour

Human Resource availability is a major challenge in the manufacturing industry. Skilled Labour shortage have a big impact as it stresses them to complete a job in a short span of time. Due to overtime work, low salary & no growth there is a possibility of quitting the job by Labours.

So as a result, the manufacturing industry will struggle for productivity.

Struggles to adapt

The major challenge is that many industries are not ready to adapt to it. However, the only way to adapt to a constantly changing and rapidly disrupted present is to speed up and operate at pace.

Limited Budgets

Small Scale to Medium-sized manufacturing industry facing financial reasons to adapt a digital transformation. Many manufacturers will hesitate to adapt to digitization thinking as it could cost them an arm & leg. But the thing is digital transformation will simplify work process & resource utilization, improving productivity & ROI.

Employees are ready to implement

It is human nature, that the routines will always make us feel comfortable and we will seem to be grim when our routines are changed and the uncertainty enters our lives. Experiencing a digital transformation is the epitome of discomfort. So it may make employees feel threatened. Hence, changes in employee mindset are major challenges.

Benefits of Digital Transformation

Improved  Profitability

The arrival of digital transformation boosts the measurement of work, performance & availability metrics. This provides the data on overall equipment efficiency helping manufacturers to take decisions on improving overall efficiency. If the efficiency improves there will be a definite improvement in the profit.

Improved Customer satisfaction

Advanced level of technology and its growth increases customer satisfaction. Even for the demand of customization, digital transformation can help implement it fast & satisfy customer expectations.

More Reliable

Reading data manually from machines is unreliable. IoT provides M2M communication enabling automotive data acquisition which improve reliability and accuracy.

Improved Productivity

Machine and Operator performance can be evaluated. With proper software along with tools and process that work together can streamline workflow and improve productivity. By automating many manual tasks and integrating data throughout the organization, team members will get empowered to work more efficiently.

How can we help ?

The numerous solutions & software’s are available in market to succeed in Industry 4.0 race. Factovize is one of the best solution provider as it totally optimizes the shop floor, & Connects entire factory, establishing an end to end data visibility and overall traceability providing a fully paper free environment along with Quality Control and Management, Proper production planning,WIP tracking/traceability,Valuable Reporting.


Factovize MES will Optimize your shop-floor to maximize profits. It provides the best solution to identify Loopholes or Low performing departments with a complete view of the production.  Organisation can work proactively & look ahead to make long term decisions and think smart.

To know more….Talk to our experts to discuss about your requirements at your shop floor to improve your manufacturing productivity.

Announcing L4 server for TCP / UDP Digital Transformation

Announcing L4 server for TCP / UDP Digital Transformation

We are happy & more excited to announce the availability of Layer 4 Transmission server (L4 server). The server allows you to collect data from TCP and UDP endpoints.

One of the key challenges every developer facing today in developing a successful IoT application is presenting the raw data in a more meaningful form and processing it to serve it for a particular purpose. The data collected will become useless, unless it is analysed & processed to make decisions. keeping this in mind we have developed a L4 server which allow developers to collect, manage & visualize TCP and UDP data along with the support of AI/ML Integration. The raw TCP / UDP server can connect any device that can send TCP and UDP data in whichever format you have to tell and can be visualized.

Data Processing is Influential

Data is the most important tool for every business application to help businesses make critical decisions. But having piles of data alone is not worth anymore for decision-making. It is all about the way we process data and analyse it to get meaningful information to literally be able to take actionable insights. That’s why, they need to analyse and focus on data collection, cleaning and changing or transforming it in the format which is needed. Hence, L4 server was developed wholly for devices that transmit the Raw TCP and UDP data that is to collect raw data and to visualize & process it to make better decisions.

Collecting Raw TCP and UDP Data

L4Server was developed exclusively for sensors and devices that communicate using the RAW TCP / UDP Packets to enable developers to collect, manage, and visualize data. The TCP/UDP server will listen to data at a specified port, set up TCP and UDP sessions and processes the data. The data will be transferred reliably to the central server by ensuring it through the support of Acknowledgement.

Configuring Data Format

L4Server processes data collected from edge devices based on the data format specified. L4Server accepts plain TEXT and JSON formats as input to TCP / UDP data inorder to collect custom data from remote devices. Mostly the client will publish the data as a Plain text format. On L4Server, a plain text data is acquired from the edge device, and it will be configured to JSON format and rendered in the UI.

Visualizing the data

As mentioned earlier, the data needs to be presented in a way that drives meaningful insights. Hence, We have added a custom hooks to extend the UI module & customize the user interface in the way you need. In addition, L4 server consists of pre-built widgets which helps you create your own dashboard in minutes specific to your application. Just layout the widgets in the dashboard & visualize your data to make better decisions.

AI/ML Integration

For advanced implementation, L4 server supports embedding AI / ML algorithms. But as mentioned earlier the data must be transformed into a usable form so that the ML algorithms can understand it. If the data itself is not appropriate, it is not fair to expect ML algorithms to bring the value that is needed.  Hence, data needs to be aggregated and to make such aggregation easier, L4 server provides scheduler module which helps processing of TCP and UDP data received on a predefined time interval. And, now it is easy to embed AI/ML codes & to get your decisions ready.

L4 server currently supports windows and Linux. We will provide support for the other OS soon.

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