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.

Neewee Joins the SAP® PartnerEdge® Program

Bengaluru, India, September 23, 2021

Neewee announced today that it has joined the SAP® PartnerEdge® program as a partner that designs, develops, and builds software. 

“We are happy to announce Neewee is now an SAP partner,” said Harsimrat Bhasin CEO, Neewee. “This is a new milestone in Neewee’s journey and we would like to thank the entire team for this achievement. With Neewee’s leading-edge artificial intelligence apps in industrial analytics, manufacturing companies can improve their quality, efficiency, and productivity by leveraging the power of the internet of things (IoT) and AI.” 

Neewee enables manufacturing companies to see the larger context by collecting, connecting, and analyzing data from every point of the manufacturing process. This helps discover underlying patterns, uncover high-potential opportunities, predict deviations, and prescribe solutions. In turn, this intelligence augments day-to-day operations, empowering analysts, managers, and shop-floor staff to take crucial business decisions in real time. Neewee’s AI apps developed specifically for the manufacturing industry help identify parameters impacting yield, quality, production efficiency, consistency, and cycle times. They provide real-time recommendations and integrate with existing Information Technology/Operational Technology systems and platforms. 

As a partner in the SAP PartnerEdge program, Neewee is empowered to build, market and sell software applications that supplement and build on SAP software and technology. Neewee has its platform agnostic AI apps deployed on SAP Business Technology Platform. The SAP PartnerEdge program provides the enablement tools, benefits and support to facilitate building high-quality, disruptive applications focused on specific business needs – quickly and cost-effectively. The program provides access to all relevant SAP technologies in one simple framework under a single, global contract. 

About Neewee

We here at Neewee believe that a data-filled world needs a data-first approach. Neewee’s proprietary AI applications are optimizing manufacturing operations for leading global companies, and we have offices worldwide. We are driven with the vision of enabling complete digitalization of manufacturing to accelerate industrial transformation and profitable growth. 

Any statements in this release that are not historical facts are forward-looking statements as defined in the U.S. Private Securities Litigation Reform Act of 1995. All forward-looking statements are subject to various risks and uncertainties described in SAP’s filings with the U.S. Securities and Exchange Commission, including its most recent annual report on Form 20-F, that could cause actual results to differ materially from expectations. SAP cautions readers not to place undue reliance on these forward-looking statements which SAP has no obligation to update and which speak only as of their dates. 

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE in Germany and other countries.
Please see for additional trademark information and notices. All other product and service names mentioned are the trademarks of their respective companies. 

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Understanding the Difference between Digitization, Digitalization, and Digital Transformation.

91% of industry leaders have increased digital transformation investments in the past year,says the newly released 2021 State of Manufacturing Report.

Rising out of the phase of uncertainty brought by the global pandemic-driven shutdowns, the global manufacturing industry is now putting its back into regaining stability. Implementation of Industry 4.0 technologies is not an option for manufacturing companies but imperative to strategize the full recovery of business and achieve greener processes with more agility. The Fourth Industrial Revolution (4IR) leverages the 3 Ds of technology- Digitization, Digitalization, and Digital Transformation. 

Although the word ‘digital’ has been around for a few decades now, constantly advancing technology has changed its meaning dramatically over time. Also, the difference between digitization and digitalization is not clearly understood by many. And how these two terms are essential for digital transformation.

Digitization converts information available in the old analog form by scanning and encoding to store, process, and transmit in a computer-readable format. Essentially, digitization is the acquiring of data in the form of binary numbers to be processed by digital computers. 

In the manufacturing context, digitization of a product involves recreating an image of the physical product with the help of software tools, e.g., a clay model of an object gets designed in 3D CAD file format. The production process also gets digitized by creating a digital twin that mirrors every step in the process, end-to-end. With the help of AI applications, the entire manufacturing process- from product design and workflow of the production line to the finished product gets coded into the machines connected by the Internet of Things. Digitization replaces the paper-based processes even at the shop floor, where physical product design is no longer handed out but transmitted to a device. Thus, digitization is the foundation of digitalization — the latter cannot occur without the former.

The two terms, being closely associated, are often confused. But, scholars emphasize the analytical value in understanding the distinct difference between digitization and digitalization.

Digitalization is less about the specific process of going from analog to digital and more about a strategic and radical change in business operations. Gartner defines digitalization as the use of digital technologies to change a business model and provide new revenue and value-producing opportunities; it is the process of moving to a digital business. Accepting the overwhelming process of digitizing your traditional manufacturing processes and embracing automation and the Industrial Internet of Things is just one aspect. Digitalization also involves the workforce of your organization. In the wake of automation factory- workers and other employees need to shift from manual processes and adapt by acquiring digital skills relevant to their field of work. 

And thus, we arrive at understanding what digital transformation means. The strategic and considered move of upgrading the old factories to optimize your manufacturing processes by adopting technologies like Artificial Intelligence, Big Data Analytics, Machine Learning, Robotics, etc., is radical business transformation through digitalization. And equally vital to the transformation are the people working in the organization. But, digital transformation initiatives cannot stop at the implementation of digital technologies only. It goes beyond digitalization. For how can we forget that business is people, after all?

Digital transformation (Dx) is primarily a change in mindset that shifts the focus of organizational activities toward satisfying customer expectations, understanding pain points, and solving customer problems. Customer and employee experience is a crucial driver for successful digital transformation, as identified by the MIT Sloan Center for Information Systems Research. And the other driving factor is the operational efficiency of the organization. Embracing digital transformation means being willing to adopt artificial intelligence (AI) and automation to augment and free up workers for performing higher-value tasks. Harnessing technology for enhancing customer experiences while also aiming for a real-time lean manufacturing ecosystem will give massive business benefits —including cost efficiency, improved innovation potential, and the quintessential customer relationship. 

The unexpected advent of the COVID-19 pandemic made health and safety a major priority, which saw the whole world scrambling to cope with the new normal of remote working. And as a consequence, most enterprises converted into digitally functioning businesses for the sake of survival. There was an unprecedented change in human interaction, customer behavior, and people’s attitude toward exploring digital possibilities. There was a drastic paradigm shift, and it has come to stay.

Almost all business sectors have undergone slow but escalating digitalization. And we can surely expect that in the post-pandemic era, digital transformation — accelerated and deliberate — will become inevitable for all areas of manufacturing. If you wish to thrive (not just survive) in the next normal, your manufacturing industry has to turn agile and SMART.