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.

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.

AI Assistance Imperative for Net Zero Steel Manufacturing!

Digital Transformation Can Help Save the Planet.

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

Myths about Digital Transformation/Manufacturing.

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

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

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

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


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

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


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

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


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

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

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

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


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

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

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


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

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

Final Takeaway: 

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

How to get a 3x productivity boost using Lean Manufacturing with AI

Does the philosophy of Lean Manufacturing with AI have actionable meaning? Simply put, Lean Manufacturing is a management philosophy that urges you toward eliminating waste and improving productivity. It is a legitimate methodology for manufacturers and business owners to adopt, which will help them optimize production, give value to customers, and thus transform into market leaders. But, what is a manufacturer really expected to do to join the burgeoning crowd in Lean Thinking? 

Even Henry Ford, regarded as the Father of Lean Manufacturing, inadvertently created waste in his ambitious attempts at minimizing it. At the Highland Park manufacturing plant in the early 20th century, they tightly monitored the production line of the Model T automobile. The process ‘flow’, from raw material being sourced to the point of sales of the automobile, got planned to the T. (Pun intended!) Although high production standards got achieved and maintained, flexibility in the processes was zilch! There was no scope for variations or any modifications. Also, Ford failed to consider consumer demand and kept pushing finished automobiles into the market. What could one expect from the large pile-up of cars pending sales? He had eliminated operational inefficiencies, but the unsold inventory was another form of waste and monetary loss.  

Offloading the costs incurred by wasteful production processes onto the customers is a big no-no! On the contrary, customer satisfaction is at the core of Lean Manufacturing. Not giving customers value for money will only damage your business relationships. For retaining position between tough competition, production must be flexible and adaptable to customer demands. So how can a manufacturer pull off this feat, improve production efficiency while also reducing wastage and being sensitive to consumer expectations? 

There are a few ways to boost productivity triple-fold (3X) with Lean Manufacturing, and it begins with just one step — embracing Artificial Intelligence (AI). Manufacturing gets Lean when AI breaks down siloed data and facilitates transparent coordination between diverse production teams. AI applications can help manufacturers streamline processes, eliminate waste, and achieve the ultimate goal — customer satisfaction. 

How it gets done — 

  1. Reducing batch cycle time: Manufacturers have to fulfill orders within tight deadlines. Any delay in fulfilling orders can cause huge losses for manufacturers, so being ahead of the game is critical. Bodhee® Golden Production Run AI app helps you identify quality parameters impacting batch performance through real-time visibility of patterns created by the Golden Digital Thread. Experience quality improvement by 10-12% and reduction of batch cycle time by 1-2 %. 
  1. Increasing workforce productivity: Lean thinking with AI brings a radical change in company culture with improved workforce management. Bodhee® Production Performance Monitoring app helps monitor production and Key Performance Metrics such as Overall Labor Effectiveness (OLE) with just one click. It gives manufacturers a competitive edge by reducing errors and requirements for reworking by 15%. It optimizes warehouse operations and improves product delivery time by 8-10%. 
  2. Increasing capacity utilization: It is essential to have dynamic planning in response to the events on the factory shop floors or in the supply chain or user feedback that keeps the businesses agile. Bodhee®Integrated Micro Scheduling AI app facilitates synchronized and Live micro-planning of the entire production process. Manufacturers can work out every tiny detail and customize it, thus ensuring shorter lead times, quality improvement, tailored solutions, and many other desired production goals. 

Studies suggest that 80% of manufacturers who adopted AI over the past two years realized a value increase between a moderate 23% and a significant 57%. Many of them who had latent IT and OT data assets harvested data from IoT sensors, which AI then leveraged to optimize processes and business results (1).   

As the manufacturing industry evolves with the Industrial revolution 4.0, and realizes the multiple potential benefits and value addition in Lean manufacturing with AI, it will soon become a norm or even mandatory for assured productivity.

How Artificial Intelligence Is the Change Agent for Manufacturing Success

Data does not lie, and artificial intelligence creates transparency!

Change begins with an idea, and a change agent can make or break an idea or turn it towards becoming the next innovation.

That has always been true, but now more than ever before. The innovations that are changing the world are happening at lightning speed. Companies can launch an idea, iterate on it with customers, and scale it globally in the blink of an eye. We are talking about artificial intelligence, robotics, virtual reality, advanced materials, 3D printing, and more.

The Fourth Industrial Revolution is reshaping the world. New capabilities are getting rolled out daily. But are we thinking ahead of the curve or are we resisting the drivers of change? At the core of Industry 4.0 are transformational technologies such as Machine learning (ML) and artificial intelligence (AI), backed by Big Data — change agents that gave a solid foundation to the biggest revolution in innovation. Are you wondering why and how your business will get impacted? If you think this is another fad that people will eventually forget, here is our $0.02.

Let’s face it! Manufacturing success can be rare to come by. Even for the biggest and best manufacturers, it is a constant struggle to compete in a global economy. There are two perpetual goals of any manufacturing business — two qualitative and quantitative goals — increased output and improved efficiency, which fetch the ultimate goal of Golden ROI.

For starters, there is a lot that AI can do for optimized manufacturing. The best way to understand that is by breaking down each word:

• Output: AI can pinpoint where your company is experiencing loss in productivity and recommend ways to correct your processes. It could begin with detecting bottlenecks that cause WIP and also do as much as providing real-time guided production models that enable end-to-end synchronized production planning.

• Efficiency: Close at the heel of increased output comes efficiency through AI. The more efficient the workflow and production processes, the better revenue generated for the company. And that can also translate into increased investments in applications that can bring further growth. By utilizing this fantastic catalyst or change agent of business success called AI, companies can ensure optimized manufacturing.

And what is fueling those recommendations and insights from your AI applications? That which tells you where and how your processes are flawed? Big Data is the massive collection of information that gets analyzed by AI applications. It is from Big Data that companies can extract meaningful business insights. And that can help them optimize manufacturing, improve customer experience and satisfaction, and thus eventually gain an edge over market competition.

The demand for data-driven decision-making is growing as businesses enter the 4th industrial revolution (IR4.0). For some time, manufacturers have been looking for new ways to boost production while saving costs. Many turned to the internet of things (IoT), but data analytics with AI applications came as game-changers.

The power duo creates clarity out of complexity. Human perceptions and biases from experiences can obstruct the optimization of manufacturing. Whereas data does not lie, and data analytics give unbiased observations. It helps distill facts and creates transparency in operations. Artificial intelligence applications can provide integrated analytics and actionable insights in real-time. Manufacturers can achieve optimized quality production through timely informed decisions.

The report published by PwC based on their study conducted regarding Exploiting the AI Revolution says that AI could contribute up to $15.7 trillion to the global economy by 2030. Of this, $6.6 trillion is likely to come from increased productivity. Research has also suggested that 45% of total economic gains by 2030 will come from product enhancements, stimulating consumer demand. That is because AI will drive greater product variety, with increased personalization, while also reducing production costs considerably (PricewaterhouseCoopers, 2016).

So, understand, invest and embrace. Don’t just resist and reject. Informed and progressive decisions today will lead you to success in the future. And see if you can borrow some artificial intelligence to do that!

How to Get Golden Production Runs Consistently in 5 Easy Steps.

“I don’t spend my time pontificating about high-concept things; I spend my time solving engineering and manufacturing problems.”

Elon Musk, CEO and product architect of Tesla, Inc. 

If finding the Golden Batch in your production run is a big challenge, repeating it every time is a bigger problem. 

The golden batch is an ideal production run, which works as a template of gold standards. All future mass-production lines would have to measure up to it for quality control. You find a golden batch when all the stages of production get perfected, from design to the process workflow and finally to your customer approval. It may take several trials and revisions and thus a lot of time and effort gets invested here, but the golden batch is a manufacturer’s pursuit. Not only does it act as a benchmark of optimum quality, production efficiency, cost-effective processes achieved with minimal wastage, but also proves that you can reliably supply what the customer has ordered. 

However, several components continue to influence the production line. Raw materials, equipment, labor, space, and costs are the usual suspects. You would need to keep a keen eye out to see how these elements influence each other. Then, for a perfect quality production run, you would need to measure the variables carefully, regulate, and change the production processes accordingly. Thus, the problems of the manufacturer do not end with the finding of a single golden batch. You face the challenge of consistently delivering uniformly excellent yields from every production line thereafter.  

Despite all your efforts, there is a high error rate causing quality issues? There are many likely reasons for that. 

Hurdles in Repeating the Golden Batch 

  • The R&D Golden batch does not consider process variables, e.g., raw material variations, asset performance deterioration, ambient conditions, etc. 
  • Multi-step and often complex processes require multiple variable analysis over long and varying batch cycle times (BCT). This evaluation is difficult and requires significant manual effort. 
  • Most of this analysis is offline and dated and may not be most relevant to current production runs. 
  • Operators do not get the intimation of alerts and recommendations in real-time, and lack of timely guidance hampers the production run. 
  • Subject-Matter Experts can detect where and when the processes deviate, but it is tough to get to the exact root cause of parameter variations. 

When there are thousands of parameters interacting and varying simultaneously, the impact of inadequacies directly affects the output. Fluctuating yields across batches, sub-optimal production, quality issues, and rejected batches can create instability. The business may suffer severely, too.  

The good news is that it is not too late. You could still consider implementing AI in your manufacturing processes to improve operational decision-making and productivity. 

AI for real-time data analytics is the simplest solution to hitting big wins by identifying the Golden Batch and the best manufacturing process that will repeat it consistently. 

AI applications can show you every aspect of your manufacturing batch or production process, using multivariate data analysis techniques (MVDA). Simplifying the identification of the best range of parameters to guarantee optimized production consistently. It also monitors operations, creates simulations, and offers real-time guidance for easy implementation on the shop floor. Integration of hardware with the Golden Batch recipe enables end-to-end digitalization.  

The 5 Easy Steps to Getting a Golden Production Run: 

Step 1- Feature Generation 

Feature engineering is extracting the most relevant variables and refining features from raw data, which will better represent the underlying problem to the predictive modeling algorithms.  That results in improved model accuracy on unseen data. 

According to a survey in Forbes, data scientists spend 80% of their time on data preparation [1]. But with the Golden Production Run AI App, acquiring integrated data gets sped up and simplified. 

It helps achieve optimal outcomes by: 

  • Feature Engineering based on long Batch Cycle Time  
  • Integration of a) Compositions b) In-Process Parameters c) Yield d) Production Quantity e) Quality Results f) Asset Downtimes and Maintenance  
  • Automated algorithmic data quality checks to ensure processing of good and relevant data  
  • Handling complexities arising from time dimensions and complex process interactions in terms of data preparation 

Step 2- Identifying the Golden Thread  

The Golden Thread is a term used to describe a performance model, which aligns your manufacturing objectives with the accurate measures of success. Identifying the golden thread means finding that perfect combination of raw material composition and process parameters that will guarantee the best output in quantity and quality every time. 

The Golden Production Run AI App establishes a relationship between various factors that influence the production line and assesses the Process Memory-based algorithms to derive the golden thread. It enables statistical pattern identification and gives real-time insights into correlations between varying parameters and the production output.  

Step 3- Generation of Parameter Scorecards 

A scorecard is the visual representation of the manufacturing objectives. It tracks and presents the success metrics based on your needs and the time frame you select while comparing the different values.  

The Golden Production Run AI App generates a simple yet effective parameter scorecard. It helps in the real-time identification and rating of prime contributors, causing variations in output metrics. When the production run output is not up to the mark, the scorecard identifies the combination of maximum impact parameters and the magnitude of fluctuations in terms of significance. 

Step 4- Validate the Golden Thread 

Subject Matter Experts (SMEs) can fine-tune the performance model when they zero in on the golden production parameters. SMEs use their extensive industry experience to confirm the parameter relevance and associated importance with output metrics while validating the golden thread. 

Step 5- Enjoy the Golden Production 

And Voila! You will have successfully implemented a Golden Production Model that delivers consistent optimal capacity yield of desired quality. Run time predictions, alerts for production leadership teams, and the real-time guidance system for operators create an immediate impact on the shop floor and the final product.  

By utilizing the real-time Process Memory algorithms and the real-world expertise of SMEs, the Golden Production Run AI app delivers consistent production runs excelling in quantity, quality, and efficiency. 

What is the Golden Production ROI? 

  • 8-10% improvement in production  
  • 3-5% improvement in yield 
  • 2-4% reduction in rejected batches 
  • 1-2% reduction in batch cycle times 
  • 3-5% improvement in quality parameters 

Read the Full Story of How a Large Pharmaceutical Company in Europe Improved their Yield… 

 The sub-process, which needed examination, was the API process for a blockbuster drug manufactured by a large pharmaceutical company in Europe. It generated a pivot product that gets crystallized in 2 salts. The production process involved reactions across various process steps and the crystallization process. A higher yield became desirable from each batch run because the manufacturing process had a long batch-cycle time. The crystallization process got repeated multiple times to ensure increased output. The recovery of ethanol was also a side step, and it got reused in the key chemical process of manufacturing the drug. 

The Business Problem 

The sub-process selected for this implementation had a problem of fluctuating yield between various batches; yields would fluctuate between 8-10% over the different production runs. The production operator’s team could see which area the yield was getting dropped, but they had a limited understanding of the process parameters and the reasons for the fluctuation. They, therefore, could not get to the root cause of the problem. Since the element was disturbing the production process of a blockbuster drug, low yield meant ineffective utilization of the production plant capacity. In summary, the process deviation was causing-  

a) Cost impact in terms of quantity of raw material used and 

 b) Production capacity wastage due to low yield. 

How Was the Problem Solved? 

After initial process understanding, the parameters got mapped at the Micro and Macro levels. At each chemical step, the process got broken down into multiple technical steps using fuzzy algorithms. After acquiring the break-up for all the batches, the process memory algorithm got run — both at macro and micro levels. Data harmonization and data quality checks across batch times provided real-time monitoring and comparisons. The parameters got identified, which kept varying locally and globally, causing the fluctuating yield. Simultaneously, optimal process parameters for the best output also got recommended in real-time. Subsequently, the production team verified all the parameters, and controlled and changed only those parameters that got selected for action. When provided with accurate recommendations and simplified solutions, the customer noticed a significant impact on business operations. Using Neewee’s AI App, the pharmaceutical company improved its yield by 2-4%. 

The Bodhee® Golden Production Run AI App had successfully identified that the quantity of water and ethanol used during one of the technical steps was the hidden cause of the hurdle in repeating the golden batches.  


[1] Press, G. (2016, March 23). Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says. Forbes.