Breaking it down — the 4th Industrial Revolution for You.

What is 4IR? The term Industry 4.0 has been bouncing around the industrial world for quite some time now, and it’s beginning to gain traction in the business world. We have been talking about how it will change our lives tremendously. Most people think of it as the emergence of Artificial Intelligence (AI), Machine Learning (ML), Data Analytics, Augmented Reality (AR), et al. While that is true, how will it affect our lives again? It is also essential that you know what it can mean for your business exactly, for you to leverage the maturation of 4IR to your advantage.   

History tells us that every time there has been an Industrial revolution in the past, it has raised the level of income and improved the quality of living globally. Therefore, to understand 4IR, shouldn’t we start at the very beginning? Knowing what preceded it will tell us how the Fourth Industrial Revolution will impact businesses and why. 

The First Industrial Revolution in 1765: It all began with the coal-fired steam engines that powered the wheels of mechanized factory production and accelerated the economy, shifting us from an agrarian to an industrialized society.  

The Second Industrial Revolution in 1870: A century later, the world saw the invention of electricity as a source of energy besides gas and oil. The electrification of factory production enabled mass production, which further led to tremendous technological advancements and culminated in the invention of the automobile and airplanes by the 20th century. Industry 2.0 thus earned the title of the Technological Revolution.  

The Third Industrial Revolution in 1965: As the rule of three impressed, nuclear energy emerged as yet another energy source half a century later. The era marked the rise of electronics, which automated production, followed by the advent of computers and the invention of the internet. Advanced telecommunications and information technology initiated globalization and the digitization of manufacturing. Thus, it went down in history as the Digital Revolution.  

After witnessing the three steps in the evolution of technology, now we find ourselves in the middle of the fourth major industrial era — Industry 4.0.  

The Fourth Industrial Revolution in 2015: Dynamically different from the previous industrial revolutions, 4IR is progressing at an unprecedentedly fast and exponential pace. Powered by a confluence of many technologies such as the Industrial Internet of Things (IIoT), AI/ML, and big data analytics, Industry 4.0 has presented a dramatic new business model for every possible industry. Business leaders are now gearing up for this revolution to transform their organization, given the potential increase in income and benefits. Manufacturers expect it to impact every stage of the product lifecycle and optimize throughput. 

Below are 10 facts about Industry 4.0 for you: 

  1. Industrie 4.0 — the term first coined at the industrial trade fair, Hannover Messe 2011 in Germany, ignited the vision of a new Industrial Revolution and slowly captured global attention. Industrie 4.0 (I4.0) represented the strategic vision of Germany for the future in an ambitious bid to preserve global manufacturing leadership by reaffirming commitment to economic and social transformation through innovation, collective and multi-stakeholder participatory processes, and policy experimentation. The rapid diffusion of the term I4.0 across the globe has positioned Germany as a reference for strategic approaches to harnessing the Fourth Industrial Revolution. [1] 
  1. Klaus Schwab, Founder and Executive Chairman of the World Economic Forum (WEF), is credited for popularizing the phrase Fourth Industrial Revolution by publishing an article in the American magazine Foreign Affairs.  
  1. At The Great Reset, the 50th annual meeting of the WEF held in June 2020, their proposal included the Fourth Industrial Revolution as a Strategic Intelligence for rebuilding the economy sustainably after the COVID-19 pandemic. 
  1. Industry 4.0 is the era of the most ground-breaking evolution in the history of technology. It is not only fundamentally changing the way we conduct business, but it is also offering new opportunities for entrepreneurs and manufacturers, creating new niches for people to up-skill and progress professionally.  
  1. Over the next few years, you will see a phenomenal upsurge in cooperation between human and machine intelligence, blurring the difference between our real world and the metaverse — the physical and digital worlds.  
  1. IR 4.0 is driven by data, which will be the currency dominating our digital lives henceforth. British mathematician Clive Humby called data the new oil. It is indeed a fitting metaphor. Just like oil data is useless in its raw form, once refined becomes a valuable asset. With the amount of data we generate, we could be sitting on a big pile of untapped wealth. [2] 
  1. These are exciting times for the manufacturing industry. However, contrary to popular belief, IR4 is not about automation and robot-driven processes. The fact is that the core idea of Industry 4.0 is to make production processes more predictable, efficient, and beneficial to all parties involved. 
  1. A factory is deemed IR4.0 ready or digitally transformed when it operates with data transparency, intelligently connected machines enabled for interoperability, a virtual twin of the physical shop floor, and AI applications that provide end-to-end workflow efficiency and optimized production. 
  1. Industry 4.0 ushers the implementation of AI/ML technologies in workplaces, factories, and companies, allowing micro-planned production processes with real-time insights, wastage reduction, and leveraging data to improve business processes. Industry 4.0 promotes flexible production processes that utilize data for customer-oriented solutions. 
  1. Fei-Fei Li, director of Stanford University’s new Human-Centered AI initiative has rightly pointed out that Industry 4.0 has a human-centric approach even its propagation of the adoption of AI. Digitalization must not be perceived as disruptive, and AI applications are not competitors, but partners in securing our well-being. Technology has always enhanced human capabilities, not diminished or replaced it in manufacturing.[3] 

The term — Industry stands for hard work but working hard to resist change is not industrious. Industrial manufacturers and businesses that get on board with IR4.0 today are undoubtedly the market leaders of tomorrow. May the Fourth be with you! 

AI Adoption—a SMART Business Strategy for Guaranteed Success

Digitalization has taken the manufacturing industry by storm. There is no living in denial now that AI is a transformational technology. AI has not only prevailed in the Gartner Hype Cycle, but it is also dominating this year’s technology landscape. Organizations are increasingly adopting AI applicationsolutions to create new products, improve existing products and grow their customer base. Integrating AI solutions for organization-wide applications and business workflows might seem complex, but there is no better time to get SMART than now! 

Senior Principal Analyst at Gartner, Shubhangi Vashisth has rightly pointed out, on average, it takes about eight months to get an AI-based model integrated within a business workflow and for it to deliver tangible value. Yet, Gartner predicts by 2025, 70% of organizations will have operationalized AI due to the rapid maturity of AI adoption initiatives (Goasduff, 2021). 

So, if you are wondering where and how to get started, here are our Pro Tips for turning your factory toward Smart Manufacturing:  

  1. Do not look at AI adoption as an experiment. It is a legit strategy that has proven its efficacy at reducing your business risks and improving productivity. 
  1. You need to focus on the quality of only two enablers of digital transformation—Data and AI application solutions. 
  1. Identify your business challenges first and make informed decisions about AI applications offering customized solutions for your pain points.  

The early adopters of AI, who embraced the IR 4.0 principles of making business processes more intelligent, have seen the technology deliver on its commercial promises.  

Research suggests that the BOE model provides a fitting AI adoption framework (Dasgupta & Wendler, 2019). Originally the BOE model got developed to understand the adoption of electronic data interchange (EDI) technology. It is now utilized as a general technology adoption model, too.  

The BOE model considers three factors:  

  1. Benefits-  

The perceived benefits of adopting AI applications in manufacturing organizations are  

  • Improvement of process yield by 8-10% 
  • Enhanced Product Quality by 10-12 % 
  • Reduction of working capital by 8-10% 
  • Cutting down of maintenance costs by 15- 20% 

The industry can get quicker ROI and start realizing benefits in 6 to 8 weeks of AI integration. The additional benefits of delivering such throughput with faster turnarounds and improved quality of service are improved customer satisfaction and long-term business relationships. 

  1. Organizational Readiness – 

Organizational constraints such as company culture, apprehension regarding AI adoption misconstrued as a disruptive technology, and legacy machines can pose challenges in deploying AI. However, under the skilled guidance of technology consultants and with the help of cutting-edge technology enablers, organizations can navigate the pitfalls to achieve relevant AI readiness. Systematic AI adoption increases the probability of successful transformation.  

  1. External Pressure (Competition) – 

Leveraging AI to secure significant business value can provide a competitive advantage to organizations. Industry-specific AI applications can lead to incredible improvement in manufacturing volume and process efficiency. 

And then again, there are three approaches to AI adoption: 

  1. Top-down approach for holistic organization-wide technology adoption 
  1. The Bottom-up approach, the opposite alternative, applies technology to various components and processes in a piecemeal manner. 
  1. The Agile approach involves continuous improvement at every stage in constant collaboration with technology service providers. The crossover into AI is a smooth process of evaluation, customized micro-planning, and execution.  

The alignment of AI adoption with your business strategy will elevate your manufacturing to the Plateau of Productivity. Machine Learning (ML), one of the technologies under the umbrella of AI, is also a commendable value-addition. ML fortifies AI with its capability of continual learning through experience, which plays a vital role in the fine-tuning processes. Start with apilot project and scale it up for heightened profitability.  

Wisdom is key to human evolution, but Artificial Intelligence is key to manufacturing evolution in this Smart Machine Age! 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.  

Reference:

[1] Press, G. (2016, March 23). Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says. Forbes. https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/?sh=369279086f63