Keys for Unlocking Business Benefits from Micro-scheduling: Use Case 

The Purpose of Micro-Scheduling of Manufacturing with AI Apps 

Production scheduling, in practice, comprises tasks such as production planning, monitoring activities, and controlling processes. The planning and scheduling team works with the manufacturing goals in mind. Unplanned process deviations or sudden changes in demand can affect the production performance or disrupt a schedule even. The shop floor realities and organizational decisions influence a scheduler at work and, consequently, the output. Micro-scheduling with AI provides a new definition for this vital part of manufacturing. It facilitates significant flexibility, precision, and efficiency in the manufacturing processes, which is difficult to achieve through manual scheduling. 

Here is a quick peek into how a European pharmaceutical company, a world giant providing Active Pharmaceutical Ingredients (API) used AI to optimize processes and thus, maximized productivity while utilizing the existing resource pool with increased efficiency.  

Bodhee® Integrated Micro Scheduling AI App Solved the Business Problems: 

The challenge was to generate an annual production schedule flexible enough to accommodate any sudden change in demand, such as prioritizing certain products in a particular month or adjusting production in response to events on the shop floor. Several products used a combination of equipment lines and areas in different buildings for final processing. The company needed to scale the site for both process and resource optimization.  

Bodhee® Integrated Micro Scheduling AI App enabled an integrated approach for real-time event-based production planning.   

  • Enhanced their existing macro planning deployed for the long-term by closely aligning with day-to-day production events.  
  • Micro inputs on LIVE production status and predictive data analytics helped prevent unplanned asset downtime or process degradation.  
  • Timely alerts and actionable insights or recommendations enabled the production teams to define new objectives, adjusting parameters and constraints as and when required.  

Thus, by providing an end-to-end view of the upstream and downstream activities, Bodhee® Integrated Micro Scheduling AI App facilitated the creation of customized production plans. Simulation of not only one but multiple versions of the plan allowed production planners to publish the version that promised the best results for optimum capacity utilization.  

Bodhee® Integrated Micro-Scheduling AI App helped their large-scale commercial production facility plan, develop, and implement efficient production scheduling. 

What Business Benefits Did the AI App Unlock? 

Post deployment of Neewee’s specialized ML algorithms and AI apps for synchronized production planning, there was a significant positive impact on the productivity within 8-12 weeks  

  • AI-enabled Real-time batch monitoring and comparison visualized actual process efficiencies, not theoretical or hypothetical scenarios. 
  • Minimized WIP 
  • Reduced working capital and cost-effective production
  • Reduced batch cycle time by the removal of hidden buffers. 
  • Flexible production scheduling helped meet manufacturing goals.  
  • Optimum capacity utilization increased productivity. 

You can also download the detailed Case Study here!  

Pro Tip: 

Whether it is improved production performance or maximized productivity that your organization wishes to unlock, the use of AI apps in manufacturing is the Master Key you are looking for.  

Contact us to get the best technological solution customized to your business goals and needs.

Let’s optimize!

Data Visualization: The Lynchpin of AI/ML and Business Analytics

The human brain values visuals over any other type of information. MIT neuroscientists have confirmed that our brain can process an image in just 13 milliseconds60,000 times faster than the speed at which it consumes text. [1]

Data visualization has thus emerged as key to integrated data analytics and gaining valuable business intelligence. Data visualization is no less than art supporting data science, where tabular or non-tabular data, binary codes, and text-heavy data get collected and transformed into simplified graphical representations. The graphs, maps, and charts that are visually appealing make the information easier to understand, analyze, and derive business intelligence. Data visualization can help reveal patterns and acquire insights that would not have been easily detectable in text format.

Data Visualization Techniques:

There are many different techniques for data visualization. Some of the more popular types are:

  • Charts (Line/Pie/Bar)
  • Plots
  • Maps
  • Diagrams and Matrices

The different data visualization techniques are used interchangeably to represent information and reveal valuable hidden patterns and insights. Since each has its strengths and shortfalls, choosing a data visualization technique depends on the data and the goal of the analysis. For example, a business analyst may use a simple bar chart to compare the results of two different Machine Learning models and their performance. However, a more granular visualization technique might help a production planner drill down into the process parameter details to discover anomalies in the production.

The Power of LIVE Data Visualization with AI/ML:

Data visualization is like the lynchpin between big data analytics and machine learning or AI apps. It helps production planners and domain experts interpret and understand what our Bodhee AI Apps have uncovered from the complex production data. When our Digital Twin creates a LIVE simulation of the shop floor realities, vivid data visualization provides 360-degree visibility of processes. That empowers the decision-makers to develop more accurate and efficient production models.

If our machine learning algorithms are fast at detecting outliers, accurate data visualization expedites the communication of actionable insights. Identifying patterns and correlations between process parameters and gaining insights into shop-floor realities would have been impossible without the simplified data visualization methods. Our AI apps also utilize well-articulated data visualization for real-time communication of results reflected in production throughput, batch quality, yield, etc.

When all production data gets integrated and visualized, organizations can also clearly communicate the findings to stakeholders. Additionally, data visualization can help present complex ideas to non-experts who need to understand the use, impact, and efficacy of AI apps for manufacturing optimization.

Drive optimized production performance with strong data visualization and AI for advanced business analytics!


[1] Trafton, A. (2014, January 16). In the blink of an eye. MIT News.

[2] Peterman, M. (n.d.). 15 Statistics That Prove the Power of Data Visualization.

The 7 Sins of Manufacturing: Why Smart Projects Fail

In this era of sophisticated and rapidly advancing IR4 technologies, enterprises should not be experiencing failure at all!

Flawed decision-making about input process parameters leads to one of the most commonly experienced technical production failures as it directly influences the quality of the product output. On the other hand, organizational failure occurs when the internal communication of data remains siloed, thus preventing transparency in decision-making, which results in activities not aligned with the manufacturing goals. Many such small and big mistakes land manufacturers with results that are a far cry from their intended goals. Unfortunately, they realize the cause of those mistakes a little too late in the day when they have already lost customers and revenue.

Digital transformation is the touted solution for such manufacturing problems. The timely adoption of AI initiatives could salvage your manufacturing from the trickiest situations. The keyword here is “timely.” Also, it is essential that the right IR4 technologies get implemented in the most appropriate manner for your Smart factory to be a sure-shot success.

 So, we thought of listing the 7 common mistakes that lead your Smart manufacturing projects toward failure. We have also shared tips that manufacturers could use to achieve success.

The 7 Sins of Smart Manufacturing That Lead to Failure:

  1. Inadequate IIoT connectivity between machines, business processes, and the entire value chain.
  2. Overlooking the importance of data quality, harmonization, and integrated data analytics.
  3. Lack of clarity regarding the objectives leading to misunderstanding of the project requirements.
  4. Lack of 360 degrees visibility of the entire manufacturing value chain.
  5. Ignoring the need for Live process mapping and real-time comparison of Key Performance Indicators (KPIs).
  6. Forgetting to consider all human resources, availability, and skill gaps.

and finally, the seventh and biggest of all sins would be…

   7. Ignoring the capabilities of Artificial Intelligence (AI) apps in helping you rid your manufacturing of the big 6 sins or mistakes.

AI apps work with advanced algorithms that learn from past actions, preventing the repetition of mistakes. Only AI apps can help solve the great puzzle of achieving complete process control for flawless production planning!

Bodhee® AI apps awaken you

to the knowledge of the past, challenges of the future &

the opportunities hidden in the present,

with their end-to-end capabilities of streamlining the entire manufacturing.”

-Harsimrat Bhasin, Co-founder & CEO of Neewee

AI-powered integrated data-analytics can break down the silos between business intelligence and production planning and scheduling. IT and IIoT convergence is the ultimate way to creating successful Smart factory projects. Flexible operations that adapt to changing priorities will provide the desired value from the manufacturing processes. When all production processes are completely visible, transparent, efficient, and autonomous you can rest assured that your manufacturing has truly evolved into a Smart factory of the future.

Bodhee AI apps support a long-term digitization strategy that can keep your manufacturing on the path to success in a sustainable manner.

Pro Tip:

Whether it is AI apps, ML algorithms, predictive data analytics, edge computing, digital twins, or any other Industry 4 technology you wish to explore, leave it to the experts! Get recommendations for the best technological solution customized to your needs.

Quit committing more mistakes (no less than sins) as you know its direct impact on the outcomes. Contact us. Let’s optimize!

What Is the Right Approach to Resolving Operational Pain Points?

Customer Experience (CX) is the only thing that keeps you in business. And that is something we cannot emphasize enough!

The quality of CX you forward will vary depending upon how efficiently you manage your manufacturing. To improve customer experience, we must start by addressing the manufacturing pain points.

A manufacturing pain point is a problem—with the production process, resources, or supply chain—that will consequently impact the customer expectations as much as it disturbs your business operations.

It is a common belief that all manufacturers are alike, and their pain points are mostly always related to their production operations, which may be categorized as:

  • Productivity pain points
  • Financial pain points
  • Process pain points

However, it is essential for us to understand that the root causes of these pain points are diverse yet unique to every enterprise. Also, many manufacturers, unfortunately, remain oblivious to the flaws in their production system until they suffer monetary losses or lose valuable customers. Adopting and implementing AI/ML technology to discover the anomalies in your production system would work like a safety net!

“I like to listen. I have learned a great deal from listening carefully.

Most people never listen.”

– Ernest Hemingway

Like an empathetic friend, Neewee can lend you a supportive shoulder and a patient listening ear. It is a collaborative approach to understanding your manufacturing goals before deploying customized solutions. Our AI apps are designed to identify and get to the root cause of the pain points you are experiencing and help eliminate them.

Capitalizing on IR4 Technologies to Improve CX

Advanced manufacturing data analytics powered by Artificial Intelligence (AI) apps / Machine Learning (ML) algorithms is the number-one growth driver. Live production data from the entire manufacturing value chain gets analyzed to acquire business intelligence. Diagnostic analytics of the big data can help identify the process parameters or factors influencing the production process, causing deviations in workflows and variations in quality output. Predictive analytics facilitate forecasting of upstream events and can also help prevent unexpected machine downtime, bottlenecks, and such.  Our advanced ML algorithms monitor processes, and our AI apps provide production planners with actionable insights for process corrections in real-time. With technologies that enable 360 degrees of visibility and LIVE shop floor monitoring production planners get superlative intelligence on the realities as well as hidden pain points.

Decision-makers are provided with prescriptive recommendations, which can help them resolve the problems.

When you are achieving all your manufacturing goals, you are pushing all the right buttons that will funnel the benefits down to your customer. You will be meeting all the customer expectations, thus forwarding a favorable customer experience.

Enhancing CX can fetch a 5% increase in customer retention, the increase in profits can be between 25% and 95%. [3]

So, in summary, operations optimization and customer experience optimization go hand in hand. It is, therefore, crucial to prioritize the adoption of AI/ML technologies for identifying and resolving your operational pain points.

Realize your manufacturing utopia of agile, cost-effective processes delivering optimized productivity.

Find out which Bodhee®AI app will suit your organization best. Ask us now!


[1] IDC InfoBrief. (2022, January 21). 5 Critical Statistics to Improve Manufacturing Customer Experience in 2022.

[2] Team Ameyo. (2017, July 28). 193 Customer Experience Quotes to Make You Think Differently about CX. Ameyo.

[3] Georgiev, D. (2020, November 12). 55 Customer Experience Statistics You Need to Know in 2022. Review42.

How to Unlock Connectivity for Holistic Digital Transformation of Manufacturing

How Neewee Streamlines Manufacturing Data Acquisition (DAQ)

“Connectivity” is the password for unlocking the holistic digital transformation of your manufacturing. There must be connectivity, not only between all the assets but also for all the processes in the entire manufacturing value chain. Neewee has always aimed at simplifying “connectivity” to streamline data acquisition and enhance customer experiences.

Different manufacturing systems require a different approach to collecting relevant production data. Several machines and processes are involved, and readiness of the equipment for connectivity differs.

At level zero, equipment resources such as sensors, actuators, and PLC transmit data. While at the next couple of manufacturing levels, Distributed Control System (DCS), Human Machine Interface (HMI), and the Supervisory Control and Data Acquisition (SCADA) or CNC machines for manufacturing systems act as the data sources.

Companies typically use an ERP (Enterprise Resources Planning) system for planning, supply chain, manufacturing, financials. While on shop floor or in plants manufacturers use information exchange standards such as OPC or MT Connect to streamline machine connectivity. In such cases, data acquisition can be simple by connecting to these systems.

In other cases, our Bodhee AI (Artificial Intelligence) apps are equipped to decipher a wide range of data sources. In addition, our range of IIOT devices called Rishee are used as data aggregators, smart controls, software connectors and gateways for continuous data communication.

“Information is in plenty, but intelligence is limited,

without seamless connectivity between machines and processes through all levels of manufacturing control systems.”

– Gucchu Gul Lalwani, Co-Founder & Chief Product Engineering, Neewee

Creating Seamless Connectivity in Processes and Machines

Harmonization of acquired data, in-depth diagnostic data analytics, and appropriate data visualization are essential for identifying and solving manufacturing problems.

Neewee’s Digital Twin technology makes the best use of the connectivity. Once multiple manufacturing assets and sub-processes data are linked together from end to end, a virtual replica gets created of every asset, component, and process in your entire manufacturing system. Digital Twin then facilitates the simulation of the LIVE production processes. Enhanced operational visibility provides the manufacturer with a clear understanding of the shop floor realities.

Integrated data analytics powered by AI and Machine learning algorithms then help identify the anomalies in the operations and assess improvement opportunities.

Monitor> Analyze> Control> Recommend

AI/ML-powered production monitoring and predictive analytics improve decision-making, enable process control, and provide actionable insights for process improvement.

 By aligning Neewee’s Digital Twin technology to the value you expect from your manufacturing processes, you can optimize production efficiency and enhance quality with a targeted approach.

To read in detail about the transformational impact Neewee’s Digital Twin technology can make on your manufacturing, you can read our previous post here (Back-to-Back Success for Neewee’s Digital Twin Technology. – Neewee)

Digital Transformation That Leads to Quantifiable Improvement

There will be a visible improvement in enterprise-wide performance. You can track and leverage key performance indicators (KPIs) to drive sustainable growth. Your manufacturing transformation is as efficient as the quantifiable factors indicate.

The Business Impact that Leads to $ Translation:

  • Reduced working capital by 8-10%
  • Enhanced product quality by 10-12%
  • Improved process yields by 8-10%
  • Improved production performance by 5-7%

Pick only the best holistic digital transformation strategy that guarantees quantifiable results.

Contact us. Let’s optimize!


[1] Neewee. (n.d.-b). Neewee – Enlighten your Manufacturing. Optimize Manufacturing with AI Apps That Deliver Integrated Analytics.

How Does Industrial Edge Computing Increase Your Competitive Edge?

The world is witnessing exponential growth in the number of IoT-connected devices, and consequently, tremendous volumes of data are created, captured, and analyzed for business intelligence daily. In 2022, the expected amount of data processed worldwide is 97 zettabytes (ZB), and by 2025 this figure may reach 181 ZB. [1]

The data explosion has escalated the need for enhanced data storage, computing, and network capacities. Instead of the clouds, processing data closer to the data source or what you can call the “edge” of the organization’s network has thus become inevitable. Edge computing utilizes multiple edge nodes meaning devices, servers, or gateways with computing capacity to process data from the equipment on the shop floor instead of sending all data to centralized servers.

“Edge services are powering the next wave of digital transformation.

With the ability to place infrastructure and applications close to where data is generated and consumed, organizations of all types are looking to edge technology as a method of improving business agility and creating new customer experiences.”

-Dave McCarthy, Research Director, Edge Strategies at International Data Corporation (IDC) [2]

Advantages of Data Processing on the Edge

The Industrial Internet of Things (IIoT) is only effective if communicated data gets speedily processed to deliver business intelligence in real-time. Precious time is lost when massive amounts of industrial data must travel to and from the cloud data center, causing a significant lag in reaching the users waiting to leverage it.

Some of the advantages of Edge Computing are:

  1. It reduces latency and enables rapid data analytics for efficient real-time decision-making.
  2. Decentralized data handling reduces the costs involved in the transportation, storage, and processing of voluminous data to an external server or in the clouds.
  3. It can be deployed on a smaller scale, which means edge computing can process data more quickly.
  4. Distributed data processing at different edge nodes ensures data security and reduces the chances of data loss or disruption of the entire network at once.
  5. Since smaller data packets require less bandwidth, data processing can continue even with low connectivity.
  6. Edge computing of microdata also supports thorough data quality checks. Thus, only relevant big data gets sent to the clouds for centralized processing and storage.

Edge AI: The Future of Smart Manufacturing

Flexible production is the only yardstick of truly Smart manufacturing. The key performance indicator (KPI) here would be the time taken to provide recommendations for changing the process parameters and the rate at which the manufacturing problems get resolved.

The most efficient strategy for optimal leveraging of data is to deploy Artificial Intelligence (AI) applications and Machine Learning (ML) algorithms at the edge of the organization’s network. Edge AI creates visibility of the entire manufacturing value chain for efficient processing of production data. When Edge AI and ML algorithms process LIVE industrial data and train with it through end-to-end connectivity, production planners get a prediction of potential anomalies and actionable insights for process improvement in real-time.

Minimal latency in data processing enables timely recommendations that can help manufacturers achieve:

  • Process Control
  • Quality Assurance
  • Resource Management
  • Production Optimization

Edge AI fuels data-driven agility on the shop floor, propelling your manufacturing to stay ahead of the curve and get the coveted edge over your competition.


[1]  Vailshery, L. S. (2022, February 25). Edge computing market size worldwide 2025. Statista.

[2] I-Scoop. (n.d.). Edge spending 2024: expenditure, drivers, and industries. I-SCOOP. Retrieved April 15, 2022, from

Our 5 Most Popular Algorithms and How They Are Used by Businesses Today

The minute you switch on your computer or smartphone and scroll the internet, you engage with algorithms. Also, as a manufacturer, you have entered the Age of Algorithms that Industry 4.0 has been serenading. Algorithms are not only a ubiquitous part of our everyday life now but are becoming imminent to business as all enterprises capture, integrate, and analyze massive amounts of data. And in the future, algorithms with machine learning are only getting faster and more accurate. The more you deploy algorithms, the more they learn and catalyze business intelligence (BI).

“Once your computer is pretending to be a neural net, you get it to be able to do a particular task by just showing it a whole lot of examples. All you need is lots of data and lots of information about the right answer, and you will be able to train a big neural net to do what you want.”

– Geoffrey Hinton, the “Godfather” of AI.

There could not have been a simpler explanation for machine learning (ML) algorithms. Geoffrey Hinton applied his knowledge of cognitive neuroscience to computing. He proposed that connectionist AI must work just like the brain using biological neural networks. Artificial neural networks (ANNs) use algorithms that can learn from the inputs they receive and adjust, enabling effective non-linear data modeling.

ML algorithms are at the heart of data analytics for turning your business into intelligent manufacturing.

Different algorithms are applied to solve several manufacturing problems. Although there are many ML algorithms (each suited for a specific task), there are 5 closest to our hearts. Here is a high-level overview of each, and we will explain why they are popularly applied to manufacturing process optimization.

1)    Process Memory Algorithm (PMA)

Neewee’s proprietary magic wand, the process memory algorithm (PMA) is a multivariate supervised learning algorithm for pattern recognition. It not only supports technical step mapping of production batches but also enables the analysis of multiple data variables to recognize patterns and detect hidden irregularities in the production process. Report cards get generatedto evaluate the statistical results. The algorithm compares the various parameter combinations and identifies variables responsible for the anomalies by pattern recognition. Our PMA has displayed accuracy in recommending the most appropriate model of the production plan and enabled process optimization.

2)    Genetic Algorithm

The Genetic algorithm is an evolutionary algorithm of machine learning based on natural selection, the process that drives biological evolution. Multiple models get developed for different combinations of the input parameters as individual solutions. By the “Mutation Rule,” the Genetic algorithm repeatedly modifies a population of solutions. With every iteration, the population “evolves” until an optimal solution gets generated. The Genetic algorithm is deployed for solving both constrained and unconstrained production optimization problems.

3)    Long Short-Term Memory (LSTM)

The long short-term memory (LSTM) algorithm is a more advanced type of Recurrent Neural Network (RNN) used in Deep Learning (DL). LSTM is a supervised learning algorithm capable of retaining information for a longer time and is built for resolving sequential prediction problems. The input time-series signal data gets used for diagnostic analytics like machine health monitoring (MHM), anomaly detection, and predictive analytics for production planning and scheduling. LSTM is trained with industrial Big Data—time series data—applied to specific production scenarios. It then provides real-time recommendations to adjust upstream or downstream process parameters for production schedule and process optimization.

4)   N-BEATS

N-BEATS is a univariate time-series forecasting model with a purely deep neural architecture. Instead of handling forecasting as a sequence-to-sequence problem, theN-BEATS model treats it as a non-linear regression problem. The N-BEATS model uses backward and forward residual links to build a very deep stack of fully connected layers for analyzing past data and predicting future output values. Easy to implement and fast to train, N-BEATS is an algorithm that does not depend only on time series-specific feature engineering or input scaling. Its accuracy in performance translates into significant operational savings. [1]

5)    Reinforcement Learning

Model-based Reinforcement Learning (RL) is an unsupervised ML algorithm. It enables an autonomous system that learns from interactions with the industrial environment through trial and error and does not rely on preprogrammed training. Since experimentation on the shop floor could get expensive, Neewee creates a Digital Twin, an end-to-end virtual model for the entire manufacturing value chain. The algorithm then learns to perform from the real-time simulation of the industrial environment. RL algorithms converge toward optimal process control through continuous monitoring and adjustment of controllable parameters. RL-based approaches have delivered impressive results, impacting the production throughput.

Deep dive into Reinforcement Learning and its business benefits here… Reinforcement Learning, the Effective Solution for Process Control Optimization. – Neewee

Utilizing data-driven ML algorithms for AI-enabled intelligent manufacturing is the best Industry 4.0 strategy.

Derive maximum business benefits; book a demo now!


[1] Oreshkin, B., & Carpov, D. (2021, April 15). The fastest path to building state-of-the-art AI. Element AI.

Hinton, G. (n.d.). Geoffrey Hinton Quotes. BrainyQuote. Retrieved April 8, 2022, from

Legacy Machines, Connectivity, and Smart Manufacturing: Myths Debunked.

Undisputedly, a myth is the archenemy of change! 

More than the circumstances, pre-conceived notions hold you back from taking prompt and crucial steps towards success. Most feel stifled with fear while turning away from traditional beliefs or age-old practices onto new, less explored paths. IR4 is the giant advocate of change, and digital transformation is the battleground where manufacturers must fight persistent myths. 

One widespread myth trapping the manufacturers in its net and restricting them from adopting smart manufacturing technologies must get debunked immediately.

“Legacy Machines Cannot Get Integrated into the Smart Factory.”

Naturally, digital transformation will be met with apprehension when statements like that get callously thrown around.  

The Industrial Internet of Things (IIoT) is connectivity between automated machinery, sensors, and devices. IIoT enables machine-to-machine (M2M) communication for big data analytics and machine learning (ML), driving operational efficiency in the manufacturing industry. However, several factories that are more than a decade old still use machines that do not have the capabilities to communicate. These are known as legacy machines, outdated for IIoT.  

IR4 is about implementing AI/ML technologies for connected manufacturing. However, it is a myth that you would need to throw out your existing legacy machines because they are ill-equipped for connectivity. Completely replacing the manufacturing equipment on the shop floor would mean expenses shooting through the roof. Besides, why should a manufacturer lay waste of resources that have been serving well for a reasonably long time? Such an overhaul for digital transformation would seem challenging, demanding, and impractical. 

A big reason legacy machines must get integrated into the smart factory is the vast amounts of historical data that each can provide. Entirely removing the link could cause data silos. Enabling connectivity between legacy and modern data systems is crucial for business intelligence acquisition and integrated analytics.

More important than new machines are the robust IIoT ecosystem with scaling potential. A recent McKinsey report that surveyed more than 700 global manufacturers published that more than 40% percent considered connectivity deficiencies the main challenge in successfully implementing digital initiatives. [1]

Evolutionist theory suggests that pre-humans made primitive fires using existing resources from their environments, such as sticks and flint. Similarly, today, innovation has led us to discover new methods of turning older equipment communicable with simple connectivity tools. Irrespective of their make or model, it is possible to bring the existing resources—legacy machines—into the IIoT ecosystem, and it is not an expensive affair either.

What Are the Enablers of Legacy System Integration in Manufacturing?

It has been more than 50 years that the industry has struggled with the costs involved in legacy integration. With the emergence of innovative technologies, the process is less expensive now, and many connectivity solutions are available. 

The first step in busting the myth about the perils of legacy integration would be thorough scrutiny of the existing factory site. You would need to evaluate the scope for enabling the old equipment for connectivity. In most cases, you need to introduce middleware as an integration layer to be able to access the data in the legacy systems.

If the legacy machine has a port, you could extract the data using simple ethernet or serial connectors. The data gets translated into modern communication protocols, such as MQTT, OPC UA, MT Connect, etc., and the problem gets resolved. If there is no in-built data collection mechanism, added external sensors with proxy communication parameters get installed.  

As the next step in the solution for integrating legacy equipment, Neewee has developed proprietary edge hardware called Rishee that acts as customized, surface-mount gateway devices for secure connectivity. These have enabled our client sites to achieve better system integration.

Over the past years, IR4 has led many factories to evolve into smart manufacturing systems. Digital transformation is not disruptive but a constructive change. 

Where there is a will, there is a way to address the white elephant on the shop floor.


Lauritzen, M., Lee, D., Lehnich, M., & Liang, K. (2020, June 2). Industrial IoT generates real value–if businesses overcome six myths | McKinsey.

Back-to-Back Success for Neewee’s Digital Twin Technology.

Predicting manufacturing defects and malfunctions in the process.

In quick succession to our story of how Neewee got listed as a Digital Twin Operator in an influential business and technology report, here is yet another. We are elated by the newest feather in our metaphorical hat!

The “Smart Factory Trend Report”, distributed by the Japanese TECHBLITZ, lists eight categories of digitalization services that will shape the future of manufacturing. We are proud to share that Neewee features as a representative in the ‘Design & Simulation’ category for our Digital Twin technology.

An article published online as a summary of the report points out that AI-powered design data visualization delivers accuracy in simulation. It also improves the iterative process efficiency. All crucial for product development.


Transformational Impact Through Connectivity

The industrial internet of things (IIoT) is the ecosystem of devices, people, and processes that are connected and interoperable. To briefly define the Digital Twin, it is a virtual representation of all those physical objects and processes in the entire manufacturing value chain. IIoT enables the data collection from various sensors, and Neewee’s AI app helps process it in real-time. The digital twin creates simulations of every step in the production process, unlocking valuable insights that help improve decision-making. The digital twin technology optimizes process efficiency in different manufacturing areas—product design and production to marketing— impacting business outcomes significantly. Thus, the potential for increased productivity using digital twins is immense as it transforms the entire industrial operations into intelligent manufacturing.

Neewee’s Digital Twin Technology for Predictive Analytics and Monitoring Production Performance.

Manufacturing is a cost-intensive and time-sensitive process. Predictive analytics in manufacturing can help companies forecast process variations, reduce waste, and control costs. Predictive analytics can also help manufacturers improve quality and provide more personalized customer service.

Dynamic, data-powered simulation of the physical world with the digital twin technology is a boon for predictive analytics. Getting an organization-wide view of shop-floor realities is key to effective decision-making. Statistical and analytical models help production managers predict and control future events in the process workflow. Predictive analytics uncovers patterns in production data to detect dysfunctional processes, which can cause anomalies in the output. Manufacturers can mitigate such risks if they receive timely actionable insights.

The digital twin also helps forecast deviations or disturbances in the manufacturing process before they occur. For example, if it predicts an issue with the equipment, an alert gets sent out to the production team. That helps them fix the equipment before the issue escalates and becomes an actual problem. The production team gets saved from unexpected downtime, which could have caused delays in batch-cycle time. Thus, a digital twin operator can help manufacturers save money by preventing any small or big interruption of the production line. Aside from monitoring production performance and catalyzing production flexibility, predictive analytics with the digital twin supports maintenance tasks too.

By aligning Neewee’s Digital Twin to the value you expect from your manufacturing processes, you can optimize production efficiency and enhance quality with a targeted approach.

Gartner’s survey revealed Digital Twins are entering mainstream use. Benoit Lheureux, research vice president at Gartner said, “Over two-thirds of companies that have implemented IoT will have deployed at least one digital twin in production.” This rapid growth in adoption is because digital twins are delivering business value and have become part of digitalization strategies. [1]

On the other hand, Thomas Kaiser, SAP Senior Vice President of IoT said, “Digital twins are becoming a business imperative, covering the entire lifecycle of an asset or process, and forming the foundation for connected products and services. Companies that fail to respond will be left behind.” [2]

You do not want to be left behind. Act now! Book a Demo.

For those who missed the detailed enumeration of “What Is a Digital Twin?” and the value it brings to manufacturing, read our previous post here Forrester Lists Neewee Amongst Digital Twin Service Providers in 2022 Report – Neewee


[1] STAMFORD, Conn. (2019, February 9). Gartner Survey Reveals Digital Twins Are Entering Mainstream Use. Gartner.

[2] Marr, B. (2017, March 6). What Is Digital Twin Technology – And Why Is It So Important? Forbes.

AI Assistance Imperative for Net Zero Steel Manufacturing!

Digital Transformation Can Help Save the Planet.

The world has woken up to the fact that “Climate Change,” if left unchecked, can cause serious global problems. Research has predicted that energy-related CO2 emissions will increase by 6%, rising from 33 Gt in 2015 to 35 Gt in 2050. A global energy transition is urgently needed if the average global surface temperature increase must stay below 2° Celsius. Transitioning away from fossil fuels to low-carbon solutions is essential, as energy-related carbon dioxide (CO2) emissions represent two-thirds of all greenhouse gases (GHG) [1]

The increased awareness has caused steel manufacturers to consider adopting more efficient processes that minimize negative environmental impacts. A growing number of organizations are also realizing significant financial benefits in operating sustainably. Technology can play a vital role in mitigating climate change while guiding manufacturers toward economically-sound processes too. However, shifting to sustainable business practices involves completely reimagining how we manufacture goods and use energy.

Net-Zero Manufacturing is the ultimate in sustainability. It aims at bringing greenhouse gas emissions down to zero while manufacturing quality products with the help of digitalization.

In the steel-making process, coal gets first converted to coke and then fed into the blast furnace to supply heat and fuel the chemical reaction between the iron ore and other raw materials. Coke is the primary source of carbon produced during the carburizing of coal, i.e., heating it at high temperatures in the absence of oxygen. Steel is crucial to many industries, from modern engineering and construction to automobile production and many more. On average, the global steel industry burns about 1 billion tons of metallurgical coal to produce 1.7 billion tons of crude steel, contributing to 7-10% of global greenhouse emissions. [2] EU Climate and Energy Legislation prioritizes digitalization and de-carbonization of steel and related manufacturing.

The onus of reducing greenhouse gas emissions is now upon the steel manufacturers. Many have already adopted digital transformation as an opportunity for increasing efficiency and improving their production processes.

The Vedanta Spark program has acknowledged Neewee as one of the top startups, having worked on the prediction and optimization of coal blend for Vedanta’s Sesa Goa Iron Ore and ESL Steel Ltd.  

Sesa Goa Iron Ore Business of Vedanta Limited is engaged in exploration, mining, and processing of iron ore. The company was founded in 1954 as Scambi Economici SA Goa and acquired by Vedanta. Since then, it has grown to be one among the top low-cost producers of iron ore in the country. During 1991-1995, it diversified into the manufacture of pig iron and metallurgical coke. Sesa Goa Iron Ore also has a 60 MW power plant that produces clean power by using the waste heat recovery from its coke ovens and blast furnace gas. Sesa Goa Iron Ore operations in India are in Goa & Karnataka. The company has implemented AI-powered energy recovery while manufacturing coke, compliant with advanced global emission norms. 

Located in Siyaljori village in Bokaro district of Jharkhand, ESL Steel Limited, a part of Vedanta Group, is a leading integrated primary steel producer. It has a 1.5 million tons per annum (MTPA) greenfield integrated steel plant that produces pig iron, billets, TMT bars (V-XEGA), wire rods (V-WIRRO), and ductile iron pipes (V-DUCPIPE). The facility, equipped for manufacturing a diverse range of steel products, uses high-quality raw materials and endeavors to achieve consistent quality output.

Neewee’s proprietary AI applications collect, connect, and analyze data acquired from every point of the process to optimize manufacturing operations. Since the data information is distributed over the various systems in the workflow, such as operational maintenance systems and quality management systems, it gets challenging to control and reduce wastage. While optimizing the processes, maintaining the metallurgical coal and the yield at a stipulated level without overloading assets becomes essential. 

Aside from achieving the primary objective of optimizing coal consumption, Neewee’s AI apps also helped reduce production costs. Since coal is the primary constituent in the raw material, optimal usage during every production campaign directly impacts the market price of steel.

By controlling consumption and wastage of fossil fuel, Neewee AI apps help pave the pathway to the de-carbonization of steel manufacturing, making a significant contribution to environmental sustainability.

The other advantage of AI-enabled process control and predictive maintenance is that a modern blast furnace can operate continuously for at least half a century with overhauls or relines every 15-20 years while maintaining efficiency in operations. The industry can easily extract a regular lifespan from the existing facility via periodic relining instead of retiring the blast furnace early for switching to a more carbon-efficient technology. [3]

The strategic integration of AI apps provides a competitive advantage while also catalysing environmentally responsible manufacturing. 

It is imperative to go with AI because there is no planet B!


[1] Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24, 38–50.

[2] Ellis, Dr. B., & Bao, W. (2021). Pathways to decarbonisation episode two: steelmaking technology. BHP.

Forrester Lists Neewee Amongst Digital Twin Service Providers in 2022 Report

In the recent report published by Forrester, Now Tech: Digital Twin Service Providers, Q1 2022, which analyses the digital twin service provider market, vendors were listed based on market presence and functionality. The overview provides a list of digital twin service providers. All have been segmented into three categories based on digital twin solutions revenue—large established players (>$1,000 million in Annual Category Revenue), midsize players ($100 million to $1,000M in ACR), and smaller players (<$100M in ACR). Also, as per functionality, the market is divided into three segments. 

Neewee is honored to have been listed as a small vendor in the digital twin operator primary functionality segment with a vertical market focus on chemicals, pharmaceuticals, metals, aerospace, food, and beverage. Neewee is also proud to have provided information for the research, too.  

Tech execs must read Forrester’s full report now published here (available to Forrester subscribers and for purchase)

For those who would like to learn more about the digital twin, read on!

What Is a Digital Twin?

According to the Forrester Now Tech report, a digital twin is “a digital representation of a physical thing’s data, state, relationships, and behavior.” [1]

All the data of the physical counterpart—its state, relationships, and behavior—is taken in to create a virtual model. The link that forms the bidirectional loop of data between the physical manufacturing system and its digital model is called the Golden Thread. A cleverly deployed digital twin can turn your manufacturing towards productive and efficient processes.

Neewee’s Process Digital Twin

Industry 4.0. is all about the digital transformation of the manufacturing industry. However, for a holistic digital transformation of an entire facility, we need to consider the entire manufacturing value chain as a set of connected processes. Propagating connectivity is at the foundation of Neewee’s philosophy. To enable seamlessly efficient manufacturing, Neewee accordingly developed the AI apps. 

When a digital replica gets created, every manufacturing process—not just the assets—from start to finish get connected to provide an end-to-end view of every step in the workflow. Staying ahead of the curve, Neewee successfully innovated the Process Digital Twin for integrating data by mapping different processes in the manufacturing value chain. A digital twin is created for five different dimensions of the interacting parameters—process parameters, product parameters, asset, work centre, and external factors. Then each gets connected, from upstream to downstream activities, supply chain, etc., to simulate the overall outcome. 

The Value Manufacturers Can Expect from a Digital Twin Service Provider 

The digital twin is an Artificial Intelligence (AI) technology used for data-driven simulation and testing of various scenarios before publishing the actual production plan. It helps manufacturers monitor batches and make decisions about optimum utilization of their resources and the efficiency of their processes. They can also be used for training purposes, where employees can learn how to handle process variations and even predict problems before occurrence in reality.

By aligning digital twin AI solutions to your organization’s needs, you can tackle a host of business challenges: 

  1. Integrated data analytics can power the digital twin to identify important parameters influencing the process workflows or smooth functioning of an asset fleet. It simplifies the analysis of the root cause for variations. 
  2. The digital twin can detect the risk of accidents and predict unplanned asset downtime. Thus, it helps prevent bottlenecks and pile-up of WIP.  
  3. Real-time simulation of production processes provides end-to-end visibility to the planners across the organization and improves process control.   
  4. The digital twin allows the integration of legacy factory equipment into a digital environment. Thereby facilitating the collection of data that would otherwise have gone non-measured. That is useful in meeting the increasing need for adopting Lean manufacturing methods in Industry 4.0.
  5. Digital twin data can enable the production team to gain insights and make timely decisions for process correction and ensure quality at the same time. Saving the manufacturer lot of time and money that could be wasted in trial and error. 
  6. Product Prototyping and Quality Control Management are two massive manufacturing challenges that the digital twin helps overcome.
  7. A digital twin operator will not only provide tools for customers to build digital twins, but it also runs, analyses, and optimizes the digital twin for the customer. 

According to the Forrester report, you can improve operational effectiveness by engaging the services of a digital twin operator. With the help of a digital twin service provider, tech execs can derive the following benefits [2]:

  • Realize returns on digital twin investment more quickly.
  • Get to focus more on your business requirements and less on the undifferentiated heavy lifting or maintenance of the digital twin.
  • Extract insight from both IT and operational technology (OT) data silos.

The concept of the digital twin was first published in David Gelernter’s book ‘Mirror Worlds’ in 1991. Michael Grieves of the Florida Institute of Technology went forth and introduced the concept to the manufacturing industry in 2002. However, NASA used it first to create digital simulations of space capsules and craft for testing. Finally, in a 2010 Roadmap Report, John Vickers of NASA gave the concept its name. [3] And there’s been no looking back ever since! 

Digital Twin market size exceeded USD 5 billion in 2020 and is expected to grow at a 35% CAGR between 2021 and 2027. Increasing adoption of IoT, big data analytics, and the advent of AI/Machine Learning technologies in manufacturing sectors ranging from Automobile and Aerospace to Pharmaceutical & Healthcare are likely to fuel the industry’s growth. [4]

With the advent of AI, rolling out the digital twin is a proud pleasure for a digital twin service provider like Neewee and a joy to experience for the manufacturer. 

As always, feel free to contact us and schedule a demo’ now!


[1] and [2] Miller, P., Matzke, P., Kortenska, E., & Barton, J. (2022, February 17). Now Tech: Digital Twin Service Providers, Q1 2022. Forrester.

[3] TWI. (n.d.). What is Digital Twin Technology and How Does it Work? Retrieved February 24, 2022, from

[4] Digital Twin Market Size, Growth Forecast Report 2027. (2021, July). Global Market Insights Inc.

How Data and AI Apps Can Help the Blue-Collar Employees on the Shop Floor

“Employee engagement is an investment you make

for the privilege of staying in business.”

Ian Hutchinson – Human capital management strategist, Australia

Employee satisfaction is one of the most important factors that leads any manufacturing company to success. Many studies have demonstrated job satisfaction has a considerable impact on the motivation of workers. That, in turn, has an immediate effect on productivity, which affects the performance of business organizations. [1]  

A blue-collar worker is thus an equally important foot soldier in this era of Industry 4.0. Although the manufacturing industry has awakened to the fact, it still struggles to prioritize employee satisfaction. Consider AI and Machine Learning strategies that can help augment human resources. 

In manufacturing, the shop floor is where the magic happens. It is where products get made. Employees on the shop floor often work long hours and do the heavy lifting. The working conditions are challenging, and the pressure of getting optimum turnover is high. 

Production processes optimized by AI apps and ML technology can help the manufacturing teams: from quality control to production planning and scheduling to inventory management. That primarily makes the work-life of employees easier by reducing workload and taking the stress of decision-making off their shoulders. Workers provided with a supportive factory environment feel motivated to do more for the company. 

AI/ML and other data-driven technologies can not only facilitate lean manufacturing but help blue collared employees on the shop floor to do their jobs better. Consider how AI apps enable monitoring of production lines and provide a real-time prediction of problems, which helps the production planners prevent process deviations. 

Maximized capacity utilization, optimum inventory management, and efficient utilization of resources are vital to any given manufacturing process. However, it is humanly impossible to forecast events that can cause process deviations, such as unexpected machine breakdown, change in production demand, unavailability of raw materials. 

Reinforcement Learning utilizes historical and LIVE production data. Powered by RL and integrated data analytics, our AI apps identify the parameters influencing the process workflow and causing variations. By providing actionable insights about constraints to be updated, the AI apps let the planning team optimize the processes for increased efficiency. When the production planners or shop floor workers get accurate recommendations, the scope for human errors gets considerably reduced. If your organization believes in imbibing empathy in your company culture, you can imagine how an employee will feel relieved to get guidance in the decision-making process. 

In the recent past, blue-collared employees on the shop floor felt threatened by the arrival of AI technologies. It was a growing fear that AI initiatives could disrupt manufacturing methods and replace manpower on the shop floor. That is now a proven misconception! 

Skilled workers are as vital as technology for manufacturing. With advancements in AI and machine learning technologies, the manufacturing industry could optimize the use of available human resources. Machine learning algorithms can also help the entire production facility get clarity on the realities of the shop floor, beginning with the availability of human resources. The production team can then customize a product campaign considering the over or under-utilized workforce on the shop floor. Avoiding bottlenecks or ineffective distribution of any manufacturing resource is important for running efficient production lines. Flexible production planning can also transfer flexibility into your blue-collar workers’ life.  

Future Forum reported that workers crave flexibility in their jobs. Those with schedule flexibility feel 3.2X better about their work-life balance and 6.6X better about their work-related stress. [2]

Employee retention should be the number one priority for all SMEs and big manufacturers. You must engage your employees and create a desirable company culture with long-term profitability in mind. Leveraging production data analysis with the help of AI Apps is the best way to ensure your blue-collared employees are happily engaged on the shop floor.


[1] Lavanya, V. (2017). A Study on Employee Job Satisfaction in Manufacturing Sector. International Journal of Engineering Technology, 5(10).

[2] Hartman, J. (2021, December 8). 19 Employee Retention Statistics for 2022. Fit Small Business.

Conversations at Neewee:

Everything you must know before pitching your business model to a venture capitalist.

The world of startups has got in a tizzy ever since Shark Tank India—a reality television series—started showing. Aspiring entrepreneurs have to pitch their business models to a panel of investors who would consider investing. They would get a share in the budding company in return.

That immediately reminded us of the very first webinar we had conducted as a part of “Conversations at Neewee,” a segment we created on our YouTube channel.

Learning from those who have achieved excellence and sharing great ideas that can lead you to excellence is at the heart of every conversation at Neewee. Growth through collaboration and open exchange of ideas is the fundamental value of our company culture. To throw some light on the evolution of a business idea from the conception to the stage of emerging as a successful organization, we invited Abhishek Taparia, Co-Founder and CEO of Radixcap, an investment banking company. 
The financial advisor has shared a wealth of information, so we thought of giving you a brief overview of some great insights.

How Does Startup Funding Work?

When one or more entrepreneurs come together with a common business idea and collaborate on a vision, they nurture a dream. Realizing a dream and bringing an idea to fruition needs capital primarily. When the entrepreneur develops a product or service, the company in its nascent stages is called a startup. Fundraising for a startup and growing into a full-fledged organization is a systematically chalked-out and arduous journey.

The sequence and segmentation are indicative. In actuality, startups might skip intermediate steps depending on individual transactional understandings and sector exclusivity. 

Stages of Funding:

  • Pre-seed/Seed Stage ($50k- $250k)

This initial stage of business usually requires a relatively small capital investment that the founding team might bring in themselves, sowing the seeds as the metaphorical name suggests. Or you can also attempt to generate seed money from friends and family who have more faith in you aside from the hypothetical business plan you may share.

  • Angel Round ($250k- $750Mn)

At this stage, a startup may enter a round of pitching for external funding where a venture capitalist may recognize potential and invest in your company in exchange for equity. However, your investor can prove to be an “angel” only when they share your vision above everything else. The investor is a right fit when they are as passionate as the founder members about building an organization with collective goals in mind. Your angel investor must be ready to open doors for you.

  • Pre-series A ($750k-$2Mn)

You need to regularly water the plant to keep it growing. As your business gradually flourishes and matures, you will need to fortify it with more funding. When a typical institution gets interested in your company, you have a legitimate shot at growing larger. However, some companies may not progress beyond this round for lack of a strategically developed business model or resources for monetizing the business.

  • Series A ($2Mn- 10Mn)

This stage onwards is your growth journey. Large institutions will start taking notice of you.

  • Series B ($10Mn-$25Mn)
  • Series C and beyond ($25Mn and above)

Generally, investment bankers are not the source of capital for your early-stage enterprise. However, several other capital pools are accessible such as an angel network, micro VC, incubators, and government initiatives. Before choosing a capital partner, a startup should consider the impact of the kind of instrument it chooses, the dilution it offers, and the value they bring to the startup. Choice of instrument has multiple implications across the spectrum like future valuation, dilution of funders’ stake, cash flow, etc.

Whether within the founding team or diluting with investors, consider the capital structure carefully. Equity dilution is neither cheap nor a reversible phenomenon, and therefore, it should be respected and used well.

Protecting your company is crucial. Before signing on the dotted line, the clauses in your agreement must be scrutinized and understood clearly. The terms sheet will play a pivotal role in building your business and attracting the right pool of capital for acquiring funding. Before committing, venture capitalists will astutely analyze metrics, such as your business model, traction and competition, company evaluation, stakeholders, and alignment with investors’ expectations. Minimum stake expectation coupled with runway requirement at the early stage and the revenue multiple at the growth stage are the two key factors that determine majority of the startup valuation.

Organizations aiming to grow through the pre-series A and Series A stages of funding must perform thorough due diligence, which will place you in a much better position to handle the demands of the investors. Abhishek Taparia said that statistics and data of the last five years in the Indian startup ecosystem show that out of the 1200 companies that got funded at seed level, only 70 reached the Series C mark. 

What is the history of venture capital funding? How is venture funding different from bank loans? Who are the venture capitalists, and where can we find them?  How is company valuation executed? What metrics get evaluated, and who does the valuation?

Those and many more such questions got answered in the erudite discussion.

“Business building is never a sprint; it is a marathon!” 

Listen to Abhishek Taparia elucidate the meaning of Dr. Phil’s quote in the startup funding scenario. Watch the entire conversation that transpired in the video here!
Conversations @ Neewee e01 with Abhishek Taparia – The brain behind RADIXCAP – YouTube

Top 5 AI Predictions for Manufacturing in 2022 (And How to Prepare Today)

The year that has gone by—2021 was an eye-opener at many levels. Both process industries and discrete manufacturers have woken up to the crucial benefits of AI implementation. It is now a proven fact that leveraging Big Data and AI can facilitate the detection of patterns, accurate interpretations, and real-time communication of actionable insights to catalyze efficient workflows, faster decision-making, and optimized production.  

In 2022, Big Data and AI apps will undoubtedly impact how manufacturers do business, but what impressive strides will the powerful technologies take in the upcoming New Year? 

Top 5 AI Predictions for Manufacturing from experts: 

  1. Monetization of manufacturing data will be at the crux of agile manufacturing. 

Organizations that maximize their investments in data now will be the early birds to catch the business benefits and rule the roost ahead of their competitors. Stronger data teams and AI-enabled integrated data analytics will streamline processes for optimal efficiency in operations.   

Aside from that, data-drivennetworking between organizations will become the norm as AI apps will enable safe interoperability and data exchange. Ethical acquisition and exploitation of data, not only within but also across organizations, will establish data alliances like never before.  

The monetization of manufacturing data is the process that provides a measurable economic advantage by using (aggregated and transformed) manufacturing data to exploit data-driven knowledge spillover effects. AI apps can help shed inhibitions around data privacy. Strategies for monetizing manufacturing data can be manifold, offering an added economic value of more than $100 billion is predicted. [1] 

  1. A more holistic approach by factories toward AI adoption will prove to be a veritable game-changer. 

Factories dipping their toes and testing the waters will take the plunge. Instead of working in phases with incorporating AI applications in their processes, the push will be toward a system-wide change. Realized intelligent factories using integrated data from all the assets—operational and human—in the manufacturing network will become prevalent features. The gap between AI leaders and the companies following in step will get mitigated with accelerated digitalization. In fact, we can expect organizations to work with a blueprint, clearly prioritizing AI initiatives for business value and definitive manufacturing goals. 

  1. Green AI adoption for sustainability initiatives will be the mark of a responsible manufacturer. 

With the help of AI, companies will make a conscious effort of switching to environmentally sustainable manufacturing. The shift from a manufacturer-centric approach to eco-centric decision making will turn the industry on its head, allowing more responsible players to get into the front row.  

AI applications can offer tremendous new possibilities to help manufacturers adopt green manufacturing methods and reduce their carbon footprint. AI can optimize and ensure responsible energy consumption in manufacturing facilities and reduce wastage of natural resources. Integrating data analytics and AI in the manufacturing processes can facilitate the designing of products for sustainability by taking into account the environmental impact at every step of the product lifecycle.  

  1. Reinforcement learning will bring a radical transformation to the manufacturing scenario. 

Reinforcement learning (RL), an unsupervised machine learning algorithm, will deliver a transformative advancement in 2022.  

AI applications will get more intelligent with the simulation-based dynamic programming method. RL algorithms converge toward optimal process control strategies with data-driven solutions to problems in the production domains, enabling an autonomous manufacturing system. The key will be having suitable data sets to train algorithms, which requires a culture of data sharing within companies. Then machine learning can step out of the lab, where the training is slow and expensive, and get into production environments, where things get real. 

As it trains on the shop floor, instead of working with pre-programmed training modules, the complex real-time evaluation helps the AI applications to determine which actions are suitable in the long term. Reinforcement learning can thus bring a turning point in the world of manufacturing.  

Read about RL in detail HERE

  1. The Internet of Behavior (IoB) will enhance customer-centricity 

AI applications can help organizations collect and analyze behavioral data of employees engaged on the shop floor. The collection and usage of that data to influence future behavior is called the Internet of Behavior (IoB). As organizations improve the quality of data captured, they can also combine data from various sources and leverage IoB to improve the process workflow or influence interaction with customers, understanding their needs, overcoming challenges, and deriving customer-specific solutions. 

As per Gartner, IoB will tightly link customer experience and employee experience to transform the business outcome. It can help differentiate a business from competitors, creating a sustainable advantage. This trend enables organizations to capitalize on COVID-19 disruptors, including remote work, mobile, virtual, and distributed customers. [2] 

That is how we see things shaping up!  

As the world transitions from the pandemic to a state of normality, 36 % of manufacturers say they are currently engaged in AI projects, and 23 % more are planning to use AI in the coming months to unlock $13 trillion in value that industry experts anticipate from industrial sectors.  Manufacturers learned a lot during the pandemic and are now looking for the best way to become more productive, safer, and more agile by leveraging the petabytes of data insights harvested from connected factories. [3] 

We hope this article gives you a great insight into where the industry is heading in 2022. 


[1] Trauth, D. (2020, July 27). Monetization of manufacturing data. Senseering. 

[2] Panetta, K. (2020, October 19). Gartner Top Strategic Technology Trends for 2021. 

[3] IOT World Today. (2021, November 9). AI led Digital Transformation of Manufacturing: Time is NOW. IoT World Today. 

Reinforcement Learning, the Effective Solution for Process Control Optimization.

Gradual and deliberate ‘change for the better’ eventually improves productivity and facilitates efficient processes.

Kaizen, the Sino-Japanese business philosophy, urges you to strive for continuous improvement. Applying the principles of Kaizen to manufacturing focuses on improving the four main elements of manufacturing — product, process, people, and environment. Kiichiro Toyoda, the founder of Toyota Motor Corporation, championed the philosophy by saying, “The ideal condition for making things gets created when machines, facilities, and people work together to add value without generating any waste.” [1] 

Harnessing the power of data analytics thus becomes crucial to a company’s continual process improvement strategy. Manufacturing process control optimization drives you toward achieving several business goals, from improved production efficiency to reduced production costs and increased customer satisfaction. However, integrated data analytics by solely relying on limited human capabilities is not only a terrible challenge but can also be misleading and flawed. Measurement and monitoring of Key Performance Indicators (KPIs) are essential for process control, but with limited analysis capabilities, it gets difficult to control wastage completely.  

The manufacturing sector has thus undergone a radical transformation over the last few years with the advent of Artificial Intelligence (AI). Machine learning is a subfield of AI that enables machines to become intelligent with precise data analytics. It makes manufacturing process optimization possible by utilizing a simulation-based dynamic programming method called Reinforcement Learning (RL). 

Reinforcement learning is an unsupervised machine learning algorithm that has revolutionized the continuous manufacturing scenario. It enables an autonomous system that learns from interactions with the industrial environment in real-time, trial and error, and does not rely on preprogrammed training. RL catalyzes SMART manufacturing by learning from accomplished tasks, incorporating changes in behavior based on feedback about the results of previous actions.  

The biggest challenge for manufacturing optimization lies in collecting and cleaning vast amounts of data from different sensors every day while maintaining consistent uptime of production facilities. In many manufacturing systems, continuous state variables and discrete actions get detected. Therefore, process optimization becomes a highly complex task. Breaking down siloed data is essential and controllable parameters need monitoring and adjustment as they directly affect the production throughput.  

Though these nonlinear dynamics can make process optimization challenging, it is not that difficult for RL algorithms to converge toward optimal control policies. Since experimentation on the shop floor could get expensive, the best action path gets derived through simulation. A digital twin of the physical production system gets created by domain experts who understand how the system works. The algorithm then learns the complex relations between the various parameters besides their effect on production and output.  The feedback loop helps the goal-oriented algorithm learn and make sequential decisions while building a framework for the ideal process flow to generate desired results. In operations, RL-based approaches have delivered consistently impressive results.  

Manufacturing is a complex system, where failure at balancing the customer’s demands with production costs, inventory, batch-cycle time, and quality can result in catastrophic outcomes. Manufacturers need to take a data-driven approach while developing robust, adaptive, and scalable solutions. Reinforcement Learning is an effective tool for predicting and solving specific problems in manufacturing processes. In fact, today 40% of all potential value created by data analytics comes from AI and ML techniques. In totality, machine learning can account for about $3.5 trillion to $5.8 trillion in the annual value — as per a McKinsey report. [2] 

To summarize, RL enables data-driven and flexible processes in production domains. It has, therefore, already been deployed in production systems, proving to be an effective solution for process control optimization. 

Myths about Digital Transformation/Manufacturing.

What is a myth, but a traditional narrative that catches people’s imaginations and soothes their sentiments! The stories get told with much conviction, with minimal or no corroborative proof, and soon become a widespread belief. Business myths are misconceptions arising from the assumptions made by ill-informed minds. Mythological tales can be very entertaining and form a part of a rich culture. Business myths might not serve any purpose and can be misleading, discouraging, and detrimental to company culture, too. If only we were to read between the lines, we would discover truths. 

Ever since tales of the Industry 4.0 became rife, innumerable ideas — true and false — regarding the digital transformation of manufacturing have started doing the rounds.  

Here are some myths we thought needed busting so that you can make informed decisions based on facts: 

Myth #1: Digital transformation is a big-budget affair, therefore, not meant for small companies.  


The first thought that comes to mind when discussing digital transformation is the costs involved. As nothing comes for free, digitalization comes at a cost, but strategically planning an AI adoption strategy will not drill a hole in your pocket. On the contrary, the resultant business benefits in terms of ROI will outweigh all expenses. Phasing out the transition could also help SMEs streamline their processes and enjoy profits. Industrialists have been reinvesting their gains into leveling up on the digital platform. AI applications for manufacturing optimization are accessible and affordable by all.  

Myth #2: AI is a disruptive technology that will remove the ‘man’ from the equation in manufacturing.  


All of the previous industrial revolutions since the late 17th century changed our life drastically.  History stands as proof that technological development has only created more jobs and better opportunities for humans to progress. AI is yet another technology designed to enable human capabilities and not cripple society. With AI assistance, humans can increase productivity by completing job tasks faster with precision accuracy, making businesses profitable. That will also leave more scope for skilled workers to get employed in more meaningful roles.  

Myth #3: The outcome of Smart Manufacturing is not measurable in tangible values. The benefits are not immediate.  


 For an average USD 5 billion company with a 10% margin, investments in digital technologies produce an additional USD 425 million in profit. Of all surveyed industrialists, more than 60% have stated that digital transformation can help organizations address the business objectives of prime importance — reducing operational costs and growing market share organically. [1] 

Neewee’s own ready-to-deploy AI apps can be integrated with any IoT platform to streamline the manufacturing process end to end, deliver higher ROI and improve product quality within six to eight weeks of implementation. We do this by creating Process Digital Twins that connect and mirror the manufacturing life cycle, revealing cause-and-effect relationships between components, raw materials, and processes. The apps then provide predictions and actionable recommendations using Machine Learning and AI.  

  • Improving process yield by 8-10% 
  • Enhanced product quality by 10-12% 
  • Reduced working capital by 8-10% 
  • Cut maintenance costs by 15-20% 

Myth #4: You can take your own sweet time to digitalize your manufacturing processes. Also, it’s optional!  


The general tendency is to wait and watch how the early adopters of technology are faring with the new intelligent manufacturing methods. However, sitting on the fence is not a healthy strategy if you wish to stay relevant in the rapidly increasing competition. A digitally connected lean manufacturing eliminates wastage, reduces batch-cycle times, and repeatedly delivers consistent quality and throughput. This directly translates as increased customer satisfaction. While traditional ‘unintelligent’ linear manufacturing systems will continue to struggle with delivering on promises, which can end up putting off the customers. Modernization of manufacturing is no longer an option but an imperative for business growth. 

Harvard Business Review sums up: “By the time a late adopter has done all the necessary preparation, earlier adopters will have taken considerable market share; they’ll be able to operate at substantially lower costs with better performance. In short, the winners may take all and late adopters may never catch up.” [2] 

Myth #5: For successful AI adoption in manufacturing, you need to digitalize the entire factory in one go! 


Big Data Analytics, Industrial Internet of Things, Machine Learning (ML), and Artificial Intelligence (AI) apps are different aspects that work together to turn your business operations toward Smart Manufacturing. Each involves cost and complexities. It is neither advisable nor practical to digitize the entire facility in one go. AI applications implementation into the system, working with integrated data analytics, is a step-by-step process involving reiterations to arrive at the best fit for optimizing different production processes. Since there can never be a one-formula-fits-all solution, only a well-developed strategy for AI Adoption guarantees astonishing results.   

It is not debatable anymore, manufacturers can optimize their business to achieve agility, efficiency, quality, and sustainability with AI Applications.  

Final Takeaway: 

The hype cycle about digital transformation of manufacturing carried many myths and even more facts. We have debunked some myths that might be keeping many from taking timely decisions for a digital strategy. It is therefore essential to confirm the credibility of the source before you believe the bit of news. Taking decisions based on myths would be like that notorious game of Chinese Whispers. You run the risk of falling for inaccurately transmitted gossip. 

Who is the SMART and Savvy Manufacturer?

S.M.A.R.T. The Oxford dictionary offers some definitions of what the adjective means. We all have grown up grooming our self to be just that. However, as the decades flew by, the meaning of the word kept evolving. 

In 1981, three management gurus, George Doran, Arthur Miller, and James Cunningham, published an article that changed the simple word into an acronym. They said that our goals had to be Specific, Measurable, Assignable, Realistic, and Time-related. “Sure,” we all said. Then the wheels of time turned some more and taught organizations that the letter ‘A’ had another role — Automation. And now, we are in the era of Industrial 4.0 where SMART has become the buzzword once again. This time it has a whole new definition, especially in the context of manufacturing.  

The new SMART is all about Augmentation. It coaxes you to adopt Augmented Intelligence to get to your manufacturing goals. Specifically, it is about using Artificial Intelligence (AI) technology — machine learning and integrated data analytics — not as a disruptive force but to enhance human intelligence.  

Customers today demand and settle for only the best, fitting their exact specifications, and they want everything delivered as scheduled. Manufacturers are feeling the pressure to accelerate and optimize production to stand up to customer expectations. Those at the forefront of intelligent manufacturing are redefining their production processes and becoming more efficient with digitized, networked production sites. Their goals are specific and measurable ROIs, realistic and time-sensitive customer satisfaction. 

Consider this recent use case scenario. Recently one of India’s largest defense aviation companies, which also works in the civil aviation space, was scheduled to supply critical component assemblies to a global aviation OEM. The complicated production process of the components required a specific raw material — Titanium plating. Besides, some parts also had to be exported for process completion. One can easily imagine the pressure! The monthly production volumes were high, and delivery timing was strict and contract-bound.  

To state the business problem: Their production plan got created at the Macro level once every quarter and reviewed in detail only once a month, leaving no scope to factor in the delays that the raw material suppliers could create or the variability of lead times for the special processing of the components in foreign countries. Their detailed production plan also sorely lacked clarity of daily production issues such as resource availability, shift adjustments, or even jig breakdowns. The cumulative impact of these process anomalies resulted in delayed production and failure in adherence to delivery commitments stipulated in the contract. Not to mention the significant losses incurred due to the increased WIP inventory, poor delivery rate, penalties for non-compliance with contract terms, and decreased customer satisfaction.  

Fortunately, the smart manufacturer took a timely leap, switching from traditional automation methods to an AI-enabled transparent, flexible, and efficient manufacturing ecosystem. And where there is theBodhee®Integrated Micro Scheduling AI Appthere are real-time actionable solutions and direct business benefits.  

The silos got broken down, and the cleaned-up master data inputs enabled integrated analytics. The app identified the gaps in raw material supply chain management. The algorithms calculated the actual lead times for process completion and recalibrated the production system. Constraints, as well as production goals, were configured for multi-objective scheduling. A detailed plan got customized considering every possible variability, from suppliers to lead times for the parts that needed special processing and resource availability, production capacity, factory holidays, staff shifts, and other shop-floor realities such as repetitive quality issues. The detailed production plans were made accessible to the production team with real-time actionable insights for timely adjustments. That ensured a significant reduction in WIP and efficient workflows, which translated as delivery targets getting achieved.  

Within just 8-12 weeks, the Tier-1 aero-component supplier began enjoying reduced working capital by 8-10%, 5-7% improved capacity utilization, and 3-5% reduced batch cycle time.  

So, now we know the answer to, “Who is the SMART manufacturer?”  

The one who leverages data through integrated analytics to generate golden ROI is a SMART manufacturer. Also, the one whose goal is to break through the competitive manufacturing landscape and emerge as a new market leader. Savvy?   

How IIOT Platforms are Getting Impacted by the Idea of Use Cases Driving Scalability and Accelerating ROI.

AI and use case-based applications are the future of IIoT, say experts and analystsWebinar Report

In the world of multi-dimensional data today, the traditional asset-centric and industry analytics IoT platforms can no longer provide competitive benefits or business impact. Thus, organizations are increasingly adopting Industry 4.0 and digitalizing their entire manufacturing value chain. Yet, while making this shift, many cannot understand the value and importance of the immediate impact that Digital Industrial Platforms can have on their manufacturing. 

In a virtual conference on ‘The Emergence of Use Case-Based Applications on Digital Industrial Platforms’ conducted on Tuesday 7th September 2021, industry experts and thought leaders joined us as our esteemed speakers. Dr. Marie-Isabelle Penet — Global Industrial Operation Excellence & Transformation Manager at Euro API (Sanofi) along with Dr. Zoltan Finta, Digitalization Global Leader at Euro API (Sanofi), Dr. Paul Miller — Principal Analyst at Forrester, and Mr. Jaspreet Bindra, Thought Leader in AI and Digital Transformation together with Neewee’s very own Co-Founder & CEO Mr. Harsimrat Bhasin discussed the future of Digital Industrial Platforms and their importance, and the generation of quicker ROI by the use case-based applications approach. 

Mr. Harsimrat Bhasin opened the forum to invite featured guest, Dr. Paul Miller. Based in the UK, his area of research coverage is the Smart manufacturing space. Miller astutely directed attention toward a 2015 McKinsey report that said 80 – 90% of industrial IOT pilots failed to scale. He also observed that the organizational issues and processes around it were problematic — not the technology. Thankfully, there was a paradigm shift. The formerly mum manufacturers began speaking up about their business problems in improving their processes for better yield rates, understanding how to deploy predictive maintenance, and focused on solving problems.  

Dr. Marie-Isabelle Penet and Dr. Zoltan Finta shared their real-world experience of overcoming their manufacturing problems and optimizing production with the help of Neewee. Penet spoke about how Euro API, being a part of a big company like Sanofi, made it imperative to manufacture consistent quality Active Pharmaceutical Ingredients (API) on a big scale for Europe. The challenge was to identify correct process parameters and control them while also improving productivity and quality profiles.  

When asked why they turned to Neewee in particular, Finta said, “We wanted to work with a company that has extensive experience in manufacturing digitalization. Because Neewee is flexible enough to not only understand chemistry but also understand our most specific problems; dedicated to working together on those problems relentlessly until we find a solution.” He also added, “With Neewee, it is not a connection as a customer and the supplier. No, this is a partnership.” 

Penet confirmed that Neewee’s structured approach afforded clarity on what to do, what results to expect; they saw measurable benefits within three months. 

“Major benefits of the collaboration with Neewee is that there are actionable insights for implementation, and they always conduct work with maximum transparency. We have had not only one collaboration, but we did it again,” she said with a broad smile. 

However, Finta said it was challenging to convince the management at their manufacturing plant. The experts with 20-30 years of industry experience came to the table believing that they knew everything about API manufacturing and that there was no scope or requirement for digital transformation. 

“Thanks to the collaboration culture with Neewee, there was a total openness in discussions of any idea or feedback. Their agile response to anything the manufacturing site needed. We only needed to wait for the site representative to recalculate and realize they had reached maximized results, quantified as maximum yield. The management then committed to digitalization.” 

Bhasin explained the Neewee perspective, “It’s having a shared goal, which is very important. What will be the ROI at the end of it? Because then you know what you are working toward. We are not experts in their chemical production processes. So, what we bring is the way of looking at data, which can drive that value. Collaboration helps to scale it up and make it a sustainable long-term success. When you quickly identify problems in production processes and show them hidden values, it’s the proverbial tasting blood. It becomes self-perpetuating at that point.”  

Miller made it a thought-provoking discussion by asking Mr. Jaspreet Bindra to comment on failing fast at scaling success.  

“Successful digital transformations are successful experiments. Successful for many reasons, but failing for only one reason — lack of the organizational culture mindset.” Said Bindra. “A company becomes digital when it starts thinking and behaving in a different way. Business models must change to focus not only on the technology but also on customer problems,” Bindra added. 

As the webinar drew to a close, Bhasin spoke about how Neewee has helped Sanofi with initial success and digital transformation at a few plants and what is being done to replicate it globally across the entire organization in the future. 

“One is to have a solution that can work seamlessly across different sites similarly. Second, giving the same level of confidence to the management that we can deploy solutions rapidly. And last, giving on-ground actual users actionable insights,” Bhasin replied perceptively, adding, “Paul, sometimes you have to start small to show the success. Confidence in newer ideas builds upon success, and then it grows in circles, getting bigger and bigger!” 

The webinar ended on a high note with Finta’s words, who spoke like a true visionary, “Machine learning and artificial intelligence is a part of our digital transformation. I think that the future is to scale up what we started together with Neewee. The key element is to get reliable master data for data analysis. And instead of just one or two projects, we need to think 20 or 25. When the different digital tools get connected, we could use the same collected data for different purposes. We must not only scale up the fund to expand the portfolio but also increase it, to finally embrace the digital industrial platform in its entirety!”