Going From Descriptive to Prescriptive Analytics for Resilient Manufacturing: A Paradigm Shift

Research suggests that “Resilience” refers to the capability of manufacturing systems to adapt and cope with disturbances in their functions and recover from the partial setbacks. Manufacturing resiliency demands the generation of models that will guide production toward improved operational efficiency.[1] 

“Resilient Manufacturing” is a sought-after concept that has been around for decades. However, IR4 technologies have increased the ability to efficiently collect and analyze data accurately, leading to its resurgence after many years. AI/ML technologies with predictive and prescriptive analytics have enabled organizations of all sizes to implement data-driven decision making.

Manufacturing Analytics in the Past

Descriptive analytics has been the norm in manufacturing forever.  Analyzing production summaries and highlighting patterns in current and historical data, generating reports from KPIs and other parameter metrics to track production performance and trends. Descriptive analytics was the traditional tool for a better understanding of the manufacturing environment.

However, the modern manufacturing arena is much more demanding. Adopting flexible processes, real-time insights for process improvement, and timely forecasting to avoid unexpected downtime—a lot is expected of a manufacturing company to grow between competition and become economically resilient.

Most manufacturers have made unprecedented efforts to streamline operations and eke out maximum productivity from the business insights derived from traditional descriptive data analytics. However, the buck can’t possibly stop there anymore! 

Decision Making with AI apps and machine learning algorithms has helped a larger number of manufacturers to enjoy the benefits of predictive and prescriptive analytics for enhanced operational agility. 

Using AI-based Predictive and Prescriptive Analytics for Resilient Manufacturing

Predictive and prescriptive analytics go further with the descriptive data and provide actionable insights that help you make informed decisions. Predictive analytics will forecast how a process will behave in the future and what the outcomes might be. And prescriptive analytics recommend what you must do to improve the process for desirable outcomes.

The sure-shot way of delivering a desirable quality product is by looking at data from past manufacturing cycles. While that is true, AI-powered predictive and prescriptive analytics give you more. Utilizing both historical and current data, you get timely forecasts of downstream events and accurate recommendations of actions needed for optimal results.

Going from reactive decision-making to predictive and preventive will enable you to unearth hidden bottlenecks, prevent unexpected downtime, and plan the utilization of available resources—to name a few. AI-based data-driven production planning can adjust parameters and tweak operations to increase productivity and reduce operational costs.

“The oak fought the wind and was broken. The willow bent when it must and survived.”

― Robert Jordan.

Flexibility is key to resilience, and data-driven flexible & swift decision making will lead to resilient manufacturing. 

We envisage building resiliency into the DNA of every manufacturing company.

Talk to us. Let’s Optimize!


[1] Thomas, A., Pham, D. T., Francis, M., & Fisher, R. (2014). Creating resilient and sustainable manufacturing businesses – a conceptual fitness model. International Journal of Production Research, 53(13), 3934–3946. https://doi.org/10.1080/00207543.2014.975850

[2] Erieau, C. (2019, February 20). The 50 Best Resilience Quotes – Driven. Driven App. https://home.hellodriven.com/articles/the-50-best-resilience-quotes/

Gold Rush Mentality in Manufacturing: Strike It Rich with AI Apps.

A sawmill worker discovered gold in the rocks of the American River, triggering “The Gold Rush” of the 19th Century. The legend goes that more than 300,000 people rushed to California, risking everything to harvest the wealth. Not every stone bore gold, but it laid the foundation for turning America into one of the world’s largest economies today.

Similarly, IR4 technologies have become a source for manufacturers to dig and hit gold with their production data. The greater benefit, however, is that, unlike the stones from the stream, every bit of relevant data can bear nuggets of crucial inputs.

Like in the 19th Century, AI has shifted the paradigm once again. Shortcomings on the shop floor do not get perceived as final. On the contrary, manufacturers can take learning from process errors or defects in the outcome. Our AI apps have identified and even created opportunities to enhance process control and strike it rich.

Adopting the “Gold Rush Mentality”


Consider the use case of the pharmaceutical industry. Impurities are undesirable substances that stay with the active pharmaceutical ingredients (APIs) or have either developed during the fermentation process. The continued presence of impurities during the manufacturing of the drugs might impact the quality parameters and the productivity eventually. 

A large-scale pharmaceutical manufacturer was struggling with high batch-to-batch variability at one of its formulation plants in Italy. There were two types of recurring impurities in the batches, increasing beyond the threshold. They were actively seeking to identify the root causes but did not get success while manually analyzing the enormous production data.

The advantages of having the “Gold Rush Mindset” when your manufacturing is supported by AI apps:

Neewee’s meticulous problem-solving approach was to start drilling down for the Golden Run Analysis of 110 batches. Our specialized machine learning algorithms hit gold by identifying that the multiple parameters, simultaneously interacting and varying, were causing the impurities to creep in and affect the output quality significantly.

Our Golden Production Run AI App successfully identified a significant correlation between the raw material quality and increasing number of bad batches. Also, the harvest age affected productivity directly. These insights proved no less than gold when our AI app recommended the combination of process parameters to be avoided and the best range of upstream controllable parameters to reduce the impurity profiles.  

Striking it Rich with AI/ ML Technologies

Our Golden Production Run AI App got deployed to focus on unearthing the cause of the impurities in the batches and process optimization for improved yield. With the help of our proprietary Process Memory-based algorithm, the AI app derived the golden thread by monitoring the LIVE production, analyzing outliers, and recognizing patterns.

Within 8-12 weeks, the facility saw the reduction of both the impurities in the batches and improved productivity by 7-10%.

Significantly improved consistent quality output through process efficiency guaranteed quicker Golden ROI.  

Now, here we have a real-world story of transformation into resilient API production worth retelling. A replay of the Gold Rush of sorts!

History made, which Neewee not only witnessed but was instrumental in making the endeavor successful too.

How the Watchmaker Saved Time by Reduced Rework..

Under the Watchful Eye of the Bodhee® Production Performance Monitoring AI App

Today the technologically advanced society may stand separated into two halves—the watch lovers and those who foresee the end of the era of watch wearing. Whether you like Smart-watches or traditional watches, the timepiece can never go out of style. The recent world watch industry statistics have inspired the thought as it shows how the market size has recovered despite the economic crisis that followed the Covid shock. [2] 

However, watchmaking has changed dramatically in recent years. Automation and digital technology have revolutionized the way watches are made, and the demand for high precision quality has never been greater. Watchmakers must now have a keener eye for excellence at every step in the production process. And the monitoring must ensure that the production lines are running quickly and efficiently. 

Watches with defects get returned for reworking. Sometimes the errors are non-rectifiable, and the products may get entirely rejected as waste. While waste is a watchmaker’s nightmare, reworking also entails lost material, time wasted in the making, additional cost of labor to rework and repair the parts. Not to mention dealing with customer disappointment. 

In this blog post, we’ll enumerate how one of India’s largest watchmakers secured the future of its production and market share by proactively adopting our Bodhee® Production Performance Monitoring AI App. Here’s a real-world story of how our AI app helped the company overcome challenges, optimize its manufacturing processes, and reduce reworking. 

Before AI adoption- too much rework and wasted time, money, and resources.

Watch-making is a precision process. The margin for errors is minimal to zero. The process has multiple sub-steps, and not all steps are automated. So, some critical tasks were completed manually. That was when process deviations were creeping in and causing a disturbance in the manufacturing. Sometimes there were variations in the number of times the watch dials passed through the electroplating solutions. At other times, the loading of jigs was incorrect, or the electroplating solution did not get replenished on time. Also, the lack of automation created data silos, which made accurate data analysis a challenge. There was no visibility and no digital record of how the production process actually got completed on the shop floor or which processes were showing violations.  

Unfortunately, in multiple instances, the consequence of process deviations got noticed far too late. The company had to bear heavy losses when customers returned the watches due to quality issues. The watchmakers realized process adherence is crucial as the impact of errors in production proved significant in terms of effort and costs in reworking. Above all, customer satisfaction was another important manufacturing goal they were losing.

After Bodhee® Production Performance Monitoring AI App: 

Here’s the real-world story of how our AI app helped the company overcome challenges and optimize its manufacturing processes. 

Once Bodhee® Production Performance Monitoring AI App was implemented, the app monitored all production processes in real-time and recommended a plan in sync with shopfloor activities. A digital simulation of the workflow was created, and the app helped identify the critical process deviations in every sub-step.  

Installation of RFID readers at designated points tracked each jig at order level as it moved along the production process. By providing actionable insights for better process control, our AI app helped avoid critical process variations and improved production efficiency. 

Thus, the real-time Monitoring dashboard and the Digital Twin traced the errors for all orders and sorted the causes by process and parameters. That enabled the customer to track the orders, gain process control, and avoid wastage or reworking. The increase in visibility created a substantial business impact through improved quality production, which also meant reduced wastage and increased savings on production costs.

Visibility of not only the entire LIVE process but the historical production runs as well, was instrumental in solving quality issues. 

Business Benefits: Reduction in Rework by 8-10 % 


“A stitch in time may save nine,” is an old proverb, which essentially means it’s better to solve a problem right away, to stop it from becoming a much bigger one. 

Let’s rephrase that to context, “An AI app in time saves 9…” 


[1] Neewee. (n.d.-a). Case studies Archive – Neewee. Neewee. https://neewee.ai/case-studies/ 

[2] Davosa Swiss. (2022, February 15). Watches Industry Statistics & Analysis. Davosa USA. https://www.davosa-usa.com/blogs/story-time/luxury-watches-industry-statistics-industry-analysis 

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:

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

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

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. https://news.mit.edu/2014/in-the-blink-of-an-eye-0116

[2] Peterman, M. (n.d.). 15 Statistics That Prove the Power of Data Visualization. Blog.csgsolutions.com. https://blog.csgsolutions.com/15-statistics-prove-power-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. Www.liferay.com. https://www.liferay.com/blog/customer-experience/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. https://www.ameyo.com/blog/customer-experience-quotes/

[3] Georgiev, D. (2020, November 12). 55 Customer Experience Statistics You Need to Know in 2022. Review42. https://review42.com/resources/customer-experience-statistics/#:~:text=By%202022%2C%20global%20spending%20on

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. https://neewee.ai/

How 1 Amazing AI/ML Strategy Can Propel Aerospace Manufacturing

At what stage would you expect the aviation industry to ensure 100% safety and dependability of every airplane flying out? Not at the tarmac! The assurance must start at the manufacturing level itself. Undoubtedly, it is a mission-critical industry, heavily relying on accurate Quality Gate checks before the aircraft roll out.

The “2022 Aerospace Industry Outlook” published by Deloitte reported the current macroeconomic trends, indicating the demand for small and medium-sized aircraft is growing again. Consequently, aircraft manufacturers need to shift toward digital and operational efficiencies that could accelerate time to market and reduce cycle times. [1]

The Importance of a Scalable and Robust AI/ML Strategy

What company would want to risk losing its competitive thrust? A global leader in the aerospace manufacturing sector was struggling with a terrible lag in its aircraft delivery rate.

Multiple factories were involved in the production of the vehicle structure. The aircraft assembly utilized 100+ workstations to handle 3,000,000+ parts. The final assembly line was also a complex system of a series of workstations designated to complete crucial tasks.

Quality Gates that were established after every few stations to assess the critical parameters detected several internal time and quality perturbations such as the late start of assembly, quality deviations need for reworks, etc. Also, there were external perturbations such as delays in supplier deliverables, crucial deliverables with open work items, etc. An early warning alerts system was also completely missing, which worsened the situation further.

Aside from the lack of visibility on the upstream processes and the pending work in progress (WIP), the failure to pass through quality gate checks affected their output performance.

Consider the enormous impact on business due to such anomalies in the production process!

Yes, AI/ML can give aerospace manufacturing wings!

Our Bodhee® Predictive Quality AI App facilitated integrated data analytics, providing foresight on bottlenecks, predicted success rate at quality gates, and identified failure patterns in critical downstream parameters. Predictive alerts and accurate recommendations for process correction were given by our AI app when the aircraft were only in the initial stages of assembly.

Our revolutionary and much-acclaimed Digital Twin technology enabled the accurate simulation of the entire complex flight engineering and intricate manufacturing processes. The virtual representation of the physical world provided the necessary levels of visibility for precision manufacturing.

The user-friendly and interactive real-time data visualization made the generated production models easily interpretable by production planners. End-to-end visibility of the manufacturing of flight vehicles allows AI-powered data analytics to provide actionable insights, which can minimize the scope for errors in the production line to slip through the Quality Gates.

The advanced ML algorithms utilized the training data and the LIVE production data to provide actionable insights for the operators to make informed timely decisions, which improved process control.

The value derived within just 8-12 weeks of having incorporated our Bodhee® Predictive Quality AI App and ML in their production process proved to be transformative, to say the least. The work closure rate improved by 20%, and there was a commendable quality improvement of 15%. [2]

That was just a high-level retelling of our data-intensive, scalable, and robust AI/ ML technique, which catalyzed the streamlining of their manufacturing, lifting it out of the doldrums!


[1] Deloitte. (2021). 2022 Aerospace and Defense Industry Outlook. Deloitte United States. https://www2.deloitte.com/us/en/pages/manufacturing/articles/aerospace-and-defense-industry-outlook.html

[2] Neewee. (n.d.). Case studies Archive – Neewee. Neewee. https://neewee.ai/case-studies/

Brunton, S. L., Nathan Kutz, J., Manohar, K., Aravkin, A. Y., Morgansen, K., Klemisch, J., Goebel, N., Buttrick, J., Poskin, J., Blom-Schieber, A. W., Hogan, T., & McDonald, D. (2021). Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning. AIAA Journal, 1–26. https://doi.org/10.2514/1.j060131

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. https://www.statista.com/statistics/1175706/worldwide-edge-computing-market-revenue/

[2] I-Scoop. (n.d.). Edge spending 2024: expenditure, drivers, and industries. I-SCOOP. Retrieved April 15, 2022, from https://www.i-scoop.eu/edge-computing-explained/edge-spending-expenditure/

Our 5 Most Popular AI and ML 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.

AI & 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. https://www.elementai.com/news/2020/the-fastest-path-to-building-state-of-the-art-ai

Hinton, G. (n.d.). Geoffrey Hinton Quotes. BrainyQuote. Retrieved April 8, 2022, from https://www.brainyquote.com/authors/geoffrey-hinton-quotes

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. Www.mckinsey.com https://www.mckinsey.com/business-functions/operations/our-insights/industrial-iot-generates-real-value-if-businesses-overcome-six-myths

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. https://www.gartner.com/en/newsroom/press-releases/2019-02-20-gartner-survey-reveals-digital-twins-are-entering-mai

[2] Marr, B. (2017, March 6). What Is Digital Twin Technology – And Why Is It So Important? Forbes. https://www.forbes.com/sites/bernardmarr/2017/03/06/what-is-digital-twin-technology-and-why-is-it-so-important/?sh=1b2ef4c52e2a

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. https://doi.org/10.1016/j.esr.2019.01.006

[2] Ellis, Dr. B., & Bao, W. (2021). Pathways to decarbonisation episode two: steelmaking technology. BHP. https://www.bhp.com/news/prospects/2020/11/pathways-to-decarbonisation-episode-two-steelmaking-technology

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. https://www.forrester.com/report/now-tech-digital-twin-service-providers-q1-2022/RES177089

[3] TWI. (n.d.). What is Digital Twin Technology and How Does it Work? Www.twi-Global.com. Retrieved February 24, 2022, from https://www.twi-global.com/technical-knowledge/faqs/what-is-digital-twin#WhatChallengeshasitSolved

[4] Digital Twin Market Size, Growth Forecast Report 2027. (2021, July). Global Market Insights Inc. https://www.gminsights.com/industry-analysis/digital-twin-market

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). http://www.ijetmas.com/admin/resources/project/paper/f201710261509008553.pdf

[2] Hartman, J. (2021, December 8). 19 Employee Retention Statistics for 2022. Fit Small Business. https://fitsmallbusiness.com/employee-retention-statistics/

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

How to Achieve Zero-Defect Manufacturing with Bodhee® Predictive Quality AI App

Work SMART, not hard; you need to correct your production process, not your people!

Discrete manufacturing is a complex environment where products get assembled on a production line in small batches, usually customized to specifications, and even a single defective product is unacceptable. When the production process gets complex, there is greater scope for errors leading to defects in a product. The slightest defect can cost the company precious time and money as the product gets scrapped. Quality production is essential in discrete manufacturing, as rejected defective goods can only mean lost profits and unhappy customers. Maintaining Zero Defects Quality (ZDQ) standards is crucial to keep up with the competition, too.

What is Zero-Defect Manufacturing?

Zero-defect manufacturing (ZDM) is a quality management philosophy, where all steps in the production process ensure that the resultant goods do not have any defects. Non-conformance to stipulated quality standards is recorded as a defect, and every process gets scrutinized to detect the cause. Besides the primary goal of improving product quality, zero-defect manufacturing can secure significant financial savings. Zero Defect Manufacturing aims to eliminate the losses incurred from the rejection of defective products and reduce labor hours wasted on inspection and rework, thus increasing productivity.

Zero-defect manufacturing has its roots in an idea sparked by Dr. Genichi Taguchi in the 1960s that turned Japanese manufacturing on its head. However, it was not until the 1980s that the United States and Europe began to see how their ways to ensure quality manufacturing were redundant compared to the Japanese. The old methods relied heavily on inspecting products as they rolled off the production line and discarding those pieces that did not meet a certain standard. However, Dr. Taguchi astutely pointed out that no amount of inspection could improve a product; quality had to be designed into the product from the start. [1]

ZDM is a holistic approach for ensuring process and product quality by reducing defects through corrective, preventive, and predictive techniques. It mainly uses data-driven technologies, guaranteeing no defective products leave the production site and reach the customer, aiming at higher manufacturing sustainability.

-This definition is a result of a CEN-CENELEC ZDM terminology standardization process. [2]

Championing this thought and taking a step further in this age of Industry 4.0 and the Industrial Internet of Things, Neewee is committed to providing our clients with Data Analytics and AI apps for manufacturing optimization. 

Our Bodhee® Predictive Quality AI App is an innovative technology designed to become an integral part of the entire discrete manufacturing process. The solution to ensuring zero-defect quality manufacturing is now most easily accessible. 

Bodhee® Predictive Quality AI App: Significant Commercial Benefits with Zero Defect Manufacturing

Zero defects! That may sound like an unattainable feat for many organizations, but not for manufacturers using our Bodhee® Predictive Quality (AI) App. ZDM is now easier to implement by leveraging big data algorithms and machine learning capabilities. Our advanced data-driven AI app has an extremely user-friendly interface that does not require special skills and expertise, saving a significant amount of time on implementation. 

Due to the uncertainty and variability threatening production, it is a common argument that ZDM is not feasible. For example, natural raw materials often come with inherent variable complexities, which generates a defect that will impact and propagate throughout the production line. In such a case, ZDM involves considerable effort in tracking the quality of the product at every stage in production. It can get time-consuming, costly, and even result in wastage. Bodhee® Predictive Quality (AI) App also demolishes all these hurdles by achieving what is deemed virtually impossible. 

  • Improved Quality Control- Bodhee® Predictive Quality (AI) App helps break down data silos. Seamless integration with the complete manufacturing system and interoperability with other IIoT platforms paves the way for quality-oriented approaches and improved process control.
  • Prediction of Upstream Disturbances- With data-powered end-to-end visibility of the manufacturing process, the AI app can predict a disturbance or deviation in the workflow in the immediate future, which enables the production management team to prevent it before it occurs. Real-time insights forecasting product or process variation can both facilitate defect minimization at the end of the production line.
  • Increased Productivity- Bodhee® Predictive Quality (AI) App has a two-step process of macro and micro-interaction mapping. While performing micro-mapping, the AI app carefully charts out how individual parameters impact one another across workstations. During the macro mapping stage, the app determines how these micro-level changes affect the quality of the raw material, in-process products, and the final product. Thus, it ensures the yield is defect-free every time, improving the throughput, too.    
  • Reduction of Wastage- Backed by Machine Learning algorithms, the AI app ingests LIVE production data—product and bill-of-material details, work orders, production flow updates, etc. Then, our trained Risk Score Algorithm, specifically designed for discrete manufacturing, assigns a risk score at each downstream workstation as the product moves through the manufacturing process. If the risk score crosses a threshold during the process, the app immediately sends out alerts and recommends downstream corrective actions for each workstation and quality gate. Thus, quality managers need not wait for the final output to do a quality check for defects. Predictive assessment and indications can help reduce the scope for errors and prevent wastage of product, time, and labor.   
  • Cutting Cost – Timely remedial actions that prevent wastage of all resources mean cost savings. Also, all the production data gets fed back into the system. The Reinforcement Learning algorithms that are ALWAYS learning work their magic, optimizing the future production processes. Efficient processes and zero wastage correspondingly lower production costs.
  • Improved Work-order Closure Rate – Bodhee® Predictive Quality (AI) App facilitates resilient production through flexible processes that can quickly adapt to any changes that need to be incorporated. Prediction of possible errors can prevent a defect from creeping into the product. Eliminating the chances of rejection helps reduce reworking and saves time at the quality gates.

Designed keeping in mind High-Variation, Low-Volume Discrete Manufacturing, the AI app takes only 6-8 weeks to deploy impressive ROI for a medium complexity line.

Here is a use case that will give you a more detailed idea of how the app has helped achieve manufacturing goals in the real world.

User’s Story….

The customer is one of the largest cigarette manufacturing companies in the world. Their product manufacturing process comprises of two parts. In the first part, freshly harvested raw material gets cured and processed to obtain cigarette-grade tobacco. The second part of the process involves the rolling of cigarettes and packaging.

The production process in focus here is the first part where manufacturers need to treat and age the tobacco appropriately to enhance the flavors and aroma. At different stages of the process, moisture must be uniform and at pre-determined levels in the tobacco. The moisture levels significantly impact the overall quality and throughput yield, and therefore, it is a crucial parameter.

The Business Problem:

Since leaves from different types of tobacco get blended to create a particular flavor, and because the natural raw material is highly hygroscopic, it was getting challenging to maintain the correct moisture levels. There was a natural variation in the inherent moisture of tobacco due to the region it came from, season, atmospheric condition, the way the tobacco got stored, etc. The operator found it hard to determine the ideal steam pressure, adjustment of the water pressure, how many valves to open, etc., as these factors not only influenced the texture and qualities of tobacco but affected its grade, too. The output quality and quantity of tobacco were heavily dependent on moisture level post-primary conditioner.

The thrashing process produced a lot of waste since they failed to maintain moisture at appropriate levels. That also had a severe impact on the color of the tobacco, which in turn affected the quality.

Thus, the fluctuations in the moisture levels of tobacco had two consequences—1) Low Production and 2) Low Quality.

How Bodhee® Predictive Quality AI App Rolled Out the Solutions:

With a holistic view of the process, our AI App provided predictive insights and recommendations in real-time for course correction. Even though it was a fast-moving process, the AI app augmented process control. As the tobacco moved on the conveyor, the Reinforcement Learning algorithm collected learning about the moisture level and the resulting output quality at the end of the process. Based on the data inputs from the various processes parameters, our Predictive Quality AI app determined and recommended the ideal values to optimize process control, generating and maintaining appropriate moisture levels. Precision recommendations, such as the number of water and steam valves that must be opened, guided speedy process correction and helped achieve the desired results. Thus, significantly reducing the standard deviation of the moisture against the set points and preventing impact on the production costs.

With the help of our advanced algorithms and the Predictive Quality AI app, the production management team was able to identify and correct the errors in their production process. Our AI solutions helped the cigarette manufacturing company achieve zero-defect production, ensuring that every pack of its products was of the highest quality.

Business Benefits Delivered:

  • Optimized Productivity by 6 %
  • Improved Quality of Output by 18 %

Predicting the future isn’t magic, it is Artificial Intelligence! -Dave Waters

Manufacturers can use predictive analytics to take the trial-and-error out of their production campaigns. Our AI/ ML technology is revolutionizing the manufacturing industry with its ability to provide real-time data on production quality, improve efficiency, reduce costs, and deliver optimum yields. Additionally, adoption and implementation have proven cost-effective for SMEs and large enterprises.

Schedule a Demo for the Bodhee® Predictive Quality AI App to learn more now!


[1] Simpson, T. W. (2000). 32.3 Taguchi’s Robust Design Method. https://www.mne.psu.edu/simpson/courses/ie466/ie466.robust.handout.PDF

[2] Psarommatis, F., Sousa, J., Mendonça, J. P., & Kiritsis, D. (2021). Zero-defect manufacturing the approach for higher manufacturing sustainability in the era of industry 4.0: a position paper. International Journal of Production Research, 1–19. https://doi.org/10.1080/00207543.2021.1987551

Micro Scheduling AI: Unpacking the True Benefits of Scheduling for F&B Manufacturing

“Time is money!” Who better than a businessman to know the weight of those words written by Benjamin Franklin in an essay titled Advice to a Young Tradesman? To expand on the thought—poor planning and lack of time management come at a cost. Wise business gurus have stressed the importance of reducing non-productive time to minimize the amount of money lost in pursuits that do not deliver on results or goals. 

Of all the businesses, food manufacturing, processing, and packing factories are the most time-sensitive enterprises. As delivery time and shelf-life are crucial goals for the F&B industry, production planning, and scheduling forms the backbone of the high-risk business. Yet, we all know how traditional scheduling methods have failed the F&B manufacturers, time and again. 

The Business Challenges That the Food Manufacturing Factories Face:

Food manufacturing companies strive to provide consumers with the most delicious, fresh, healthy, and convenient foods. However, they face several challenges in their endeavours and deal with uncertainty. Factories need to ensure every product gets packed and delivered as per schedule. However, orders could change as per weather, season, or other market variations. In case of a sudden spike in demand, production planners need to be prepared to alter the processes and increase the yield within a short time, or they face the risk of losing out on the opportunity. All the products must be packaged and shipped quickly to avoid spoilage. Scheduling at such times needs to be precise to leave enough time for inspection and sorting of the products. Inventory also needs to be tracked and managed to avoid shortage or overstocking. 

A wide range of food products and drinks are made from fresh raw materials and packed in appropriate food-grade containers or wrappers for storage, distribution, and sale. Besides the packaging material, the production process and finishing time also play a vital role in preserving food quality, protecting from external elements during packing, storage, and transportation, guaranteeing safe shelf life until consumption. 

The production and packaging processes of these food and beverages require skilled workers to be involved, who must deliver perfection every time. As it employs human expertise at different stages of production, there is more scope for error and lapse in food safety. Factories need to establish a clearly defined system and provide proper training to the workforce. The workforce may still find it hard to identify why there is a deviation in the workflow. Pending WIP or accumulated inventory of perishable items could mean wastage, which can spell disaster for the manufacturer.

It can also get humanly impossible to assemble subject matter experts at the last minute to solve manufacturing problems. Avoiding failure at maintaining delivery schedules can seem like a superhuman feat.

Fortunately, Artificial intelligence (AI) has made planning possible, even when variables change. Machine learning (ML) algorithms or Reinforcement Learning (RL) can help with scheduling to save time and money by optimizing the workflow through a plant or factory. Micro Scheduling with our AI app can enable the food processing and packing industries to ensure efficient production, optimized yield, and timely output. 

Let’s look at how F&B manufacturing today is revolutionized by AI/ML technologies.

The Advantages of Switching to Bodhee® Integrated Micro-Scheduling AI App:

Micro Planning is defined as the planning and implementation of goal-oriented processes that get influenced by the decisions of the production planner. Our Bodhee® Integrated Micro Scheduling AI app is a data-driven problem-solving technological innovation for production planners with an interactive inquiry process and real-time actionable insights.  

Ithas an extremely user-friendly interface with readymade production models. However, your synchronized production plan can also get customized to suit your plant size or production volumes. 

  • Recommendations for the Current Plan 

Our AI app monitors manufacturing events—LIVE—to not only show what happens on the shop floor but indicate the impact too. It then provides recommendations for actions almost instantaneously.

The Reinforcement Learning (RL) algorithm is a powerful machine learning tool that enables a process improvement strategy based on accomplished tasks. It leverages the data acquired from the food sorting, filling, packing, sealing, etc., and recommends changes based on the results. Backed by RL, our AI app guides production managers to make better, informed decisions for future action. 

  • Rescheduling Your Campaign

Due to the convenient and simple User Interface, what might have otherwise been a daunting task becomes as easy as drag-and-drop. Rescheduling or planning a production campaign is always a multi-team activity. You have to work in coordination with the supply chain team, logistics team, production team, maintenance team, quality management team, and the HR management team also, in some scenarios. With our AI app, you can get asset-level planning, quality-level planning, and, if required, human resource planning too.

  • Optimized Delivery Target

The production, processing, and packing of various food and drinks— whether dry foods, dairy products, fresh produce, or meats— follow different procedures and scheduling. The production processes may vary, but accuracy and delivery adherence remain crucial for all. Our AI App helps you define critical orders so that you do not miss out on delivery dates.

RL algorithms utilize historical production data for scheduling micro-tasks of multiple steps. For predicting and solving specific manufacturing problems, our AI app uses the data information of key performance indicators (KPIs) such as processing times, setup times, breakdowns, etc. That substantially improves the scheduling process, outperforming the manual practices in most food processing, production, and packing companies.

  • Optimizing WIP Inventory

Our AI app helps you set capacity constraints to avoid under or overutilization of assets. Avoiding overutilization will save you from a surge in investments. Avoiding underutilization of capacity prevents the idling of resources that turn the cost curve upwards.

The RL algorithms have end-to-end visibility on every step in the production process—from receiving the raw materials to delivering the finished goods—providing transparency on the inventory. Our AI app will give a complete visual of all the stock levels, the overstocked and the under stocked WIP, as per the consumption, typically your demand plan or sales order. Thus there is clarity on what alterations in the production parameters would be necessary. You could also create daily, weekly, monthly, or even long-term production schedules accordingly to minimize wastage or prevent incurring extra production costs due to pending inventory.

The True Benefits of Bodhee® Integrated Micro Scheduling AI App

As a production planner in the F&B manufacturing industry, you can become the wizard of Industry 4.0 by implementing the Bodhee® Integrated Micro Scheduling AI app.

Then, sit back and watch as, within just 8-12 weeks, the Bodhee® AI App delivers—

● 8-10% Capital Reduction

● 5-7% Improved Capacity Utilization

●  3-5% Reduced Batch Cycle Times

Unpacking the Intelligent F&B Manufacturing and Packaging System!

The best production systems can fail to meet their manufacturing goals when there is a gap between the planning and shop floor reality. Invest in data-powered AI/ML initiatives for the benefit of your business as you scale. A Smart manufacturer can achieve transparent and efficient manufacturing, maximizing resource utilization and reducing human error. 

“A plan is what while a schedule is when; it takes both a plan and a schedule to get things done.”

Peter Turla, author, president of National Management Institute

Contact us to book a demo or for more information on implementing intelligent food processing and packing in your manufacturing.

What Do You Mean by Radical Transparency?

When “Transparency” is attributed to people and not materials, it stops being just another physical quality and becomes more of a “radical” idea. In recent years, “Radical Transparency” has grown from a seed of revolutionary thought and flourished across the fields of governance, media, design thinking, and business, too. In business, radical transparency is about new actions and approaches that will bring change from the root up, increasing openness in organizational processes and data.

Radical Transparency in Leadership:

Operational transparency encourages organizations to embrace new technologies wile transparency in leadership means getting conditioned to welcome new ideas from any rung in the corporate ladder. No matter how young or old, a fresher or experienced professional, one must be open to learning. Due to radical transparency, leaders and organizations have realized the importance of being clear in their intentions and openly sharing with employees about all transformational developments. Whatever you are trying to accomplish, whether it is building company culture or increasing productivity, open dialogue with complete transparency can help achieve that goal.

Gone are the days when employee engagement started with arranging ping pong tables in the break room and ended with a fancy annual party. The WFH during the pandemic underlined the need for more meaningfully connected employee-employer relationships. Now, radical transparency in the (virtual or IRL) workplace keeps every moving part of the organization working in tandem. The different teams have access to information and developmental roadmaps and a say in discussions of company-wide goals. People working in the company feel empowered, valued, and motivated when they know how much their cooperation and teamwork means to the company. Unsurprisingly, happy people perform better in the workplace. 

Having highly engaged employees can lead to a 202% increase in performance. [1]

Transparency Builds Trust!

To create a happy and efficient workplace, employees need to feel trusted and respected. Being transparent generates trust between all levels of your organization, helps eliminate any hidden agendas, and keeps the employees from feeling left out in the dark. An open and healthy work environment allows ideas to get freely shared. It also values an employee who voices opinions and speaks the truth without inhibitions. Radical transparency in company culture supports open debates so that all the pros and cons of the topic of discussion get analysed to arrive at an unbiased conclusion. A transparent collaborative culture is a key to the success of any organization.

There is no “one-size-fits-all” solution for creating transparency, but the company culture that incorporates it successfully will thrive with ideas that will give it a competitive advantage too.

And yet, how many organizations have you seen walking the talk?

Neewee is proud to share our welcoming and inclusive, value-driven Collaborative Company Culture. Here isNeeWee’s Co-Founder & CEO, Harsimrat Bhasin’s message to the employees

Neewee’s Value Based Collaborative Company Culture – YouTube

We Walk the Transparency Street.

VALUES are not just words to write down and look at on a page once and forget about it later, but something you carry with you. Values must remain at the core of all your actions in life and work. Your chosen set of values must become almost like a toolkit—essential bits that come in handy and serve you well each day.


[1] Teamstage. (2020, December 6). Company Culture Statistics: Leadership and Engagement in 2022. TeamStage. https://teamstage.io/company-culture-statistics/