What Is the Right Approach to Resolving Operational Pain Points?

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

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

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

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

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

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

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

Most people never listen.”

– Ernest Hemingway

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

Capitalizing on IR4 Technologies to Improve CX

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

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

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

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

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

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

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

References:

[1] IDC InfoBrief. (2022, January 21). 5 Critical Statistics to Improve Manufacturing Customer Experience in 2022. Www.liferay.com. https://www.liferay.com/blog/customer-experience/5-critical-statistics-to-improve-manufacturing-customer-experience-in-2022

[2] Team Ameyo. (2017, July 28). 193 Customer Experience Quotes to Make You Think Differently about CX. Ameyo. https://www.ameyo.com/blog/customer-experience-quotes/

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

How Does Industrial Edge Computing Increase Your Competitive Edge?

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

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

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

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

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

Advantages of Data Processing on the Edge

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

Some of the advantages of Edge Computing are:

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

Edge AI: The Future of Smart Manufacturing

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

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

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

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

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

References

[1]  Vailshery, L. S. (2022, February 25). Edge computing market size worldwide 2025. Statista. https://www.statista.com/statistics/1175706/worldwide-edge-computing-market-revenue/

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

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!

References

[1] Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24, 38–50. https://doi.org/10.1016/j.esr.2019.01.006

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

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

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

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

What is Zero-Defect Manufacturing?

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

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

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

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

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

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

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

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

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

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

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

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

User’s Story….

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

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

The Business Problem:

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

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

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

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

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

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

Business Benefits Delivered:

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

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

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

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

References:

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

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

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

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

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

The Business Challenges That the Food Manufacturing Factories Face:

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

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

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

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

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

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

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

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

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

  • Recommendations for the Current Plan 

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

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

  • Rescheduling Your Campaign

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

  • Optimized Delivery Target

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

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

  • Optimizing WIP Inventory

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

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

The True Benefits of Bodhee® Integrated Micro Scheduling AI App

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

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

● 8-10% Capital Reduction

● 5-7% Improved Capacity Utilization

●  3-5% Reduced Batch Cycle Times

Unpacking the Intelligent F&B Manufacturing and Packaging System!

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

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

Peter Turla, author, president of National Management Institute

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

What Do You Mean by Radical Transparency?

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

Radical Transparency in Leadership:

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

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

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

Transparency Builds Trust!

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

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

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

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

Neewee’s Value Based Collaborative Company Culture – YouTube

We Walk the Transparency Street.

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

References:

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

Data Experts Can Help Improve Manufacturing; Here’s How…

The way we manufacture anything today is drastically different than how our grandfathers did. Machines get connected to the web and are ripe for data analytics. The SMART machines are data-driven, enabling newer endless possibilities for manufacturing. In fact, realized intelligent factories depend on data science methodologies and collaborative manufacturing systems to make operations more flexible, adaptable and optimized. 

Data Science methodology is a set of techniques and processes used when solving manufacturing problems by utilizing data. This methodology has a significant impact on the practical application of data-driven products. Critical business data is analyzed with the objective of aiding enterprises better understand their business. The focus gets expanded from ‘what happened?’, ‘how often?’ and ‘where?’ to answer ‘why?’, ‘what if this trend continues?’, ‘what will happen in the future?’, and ‘what is the ideal scenario?’. Using subject-specific jargon, these questions correspond to analytical tasks widely known as statistical analysis, forecasting, predictive modeling, and optimization. [1]  

In other words, Data Analytics is about making the most of all data using a set of tools and technologies to deliver business value. The process begins with acquiring data from multiple sources (preferably clean and possibly in large amounts) in layman’s terms, the information you can leverage. Even the smallest of data can get integrated and analyzed to reveal whether or not a manufacturer is making profits, saving money, or over-spending to improve performance. Let experienced professionals or the right data analytics tools handle the data to get real value from your production processes.  

Aside from data analysts and statisticians, you can make the most out of available data by harnessing advanced AI apps, leading you to customer-centric goals and process efficiency. The AI apps work with the collected business data, bringing the numbers in the statistics to life, diagnosing, and correcting the manufacturing through informed decisions that drastically improve the production processes. 

“The goal is to turn data into information, and information into insight.” 

– Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co. 

A smart factory monitors the entire production process by linking physical and digital worlds. From building tools to individual operators in the workshop are rich data sources. It is an end-to-end connected and flexible production system, which uses a constant stream of data to provide learning from the machines. Based on the integrated data analytics, AI Apps provide real-time insights to adjust the process workflows, adapt to new needs, and optimize production. Intelligent factories thus achieve machine maintenance in advance, inventory management, and efficient production processes throughout the manufacturing network. 

Data can take many forms and fulfill many purposes in an intelligent factory environment. Data makes discrete information on manufacturing environmental conditions such as humidity, temperature, and pollutants accessible. It also helps self-optimize devices, addresses numerous manufacturing problems, improves process control, and guides while responding to new requirements. 

According to a recent study conducted by the World Economic Forum, nearly 75% of surveyed manufacturing executives consider advanced analytics to be critical for success. However, only a few companies capture the full value that data and analytics can unlock to help address the manufacturers’ most pressing challenges. [2] Establishing a strong technological backbone is a prerequisite to effectively scale data and analytics applications for manufacturing success in the post-pandemic world.  

Industry 4.0 is data-driven and when its technologies are growing exponentially, the frequency of data collection will also increase. Those organizations that are under-using data or are still not AI-ready run the risk of remaining on a lower rung of the business ladder.  

On a lighter note…. 

Although the D word is of BIG importance in the manufacturing world, we stand divided on the pronunciation. There is a valid reason behind the ‘proper’ pronunciation of the word Data. Ideally, it should be pronounced: day-taa, (not daa-taa) as data is the plural of datum (day-tum). 

References:

[1] Omar, Y. M., Minoufekr, M., & Plapper, P. (2019). Business analytics in manufacturing: Current trends, challenges and pathway to market leadership. Operations Research Perspectives, 6, 100127. https://doi.org/10.1016/j.orp.2019.100127

[2] Weber, A. (2021, August 24). The Big Data Dilemma. Www.assemblymag.com. https://www.assemblymag.com/articles/96570-the-big-data-dilemma

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! 

Empathy in Company Culture is the Sign of an Emotionally Intelligent Organization

“When dealing with people, remember you are not dealing with creatures of logic, but with creatures of emotion.” 

– Dale Carnegie. 

Business is people, and empathy is an essential business skill. Empathy in company culture is the sign of an emotionally intelligent organization. But what does all that mean? How can you build it into your business to make it more efficient and effective? Let us look at what it takes to become an emotionally intelligent organization, and why empathy in company culture is an essential aspect of modern business. 

Empathy is a foundational characteristic of emotional intelligence (EI). At its core, empathy is about making others feel heard and understood. The concept of empathy calls for feeling with as opposed to feeling for another person—sympathy. It gets a tad complicated in the context of business. Most often, empathy gets you focusing on dealing with individual customer problems and complaints as they occur. It is considered a strength of character to have the empathy to understand how others feel and then do something about it, thereby avoiding possible conflicts or misunderstandings. It does come naturally to most, but it is also a skill you can learn and develop.  

Why Have Empathy? 

Empathy can make or break business performance as it is one of the building blocks in a business relationship. Whether it is a manufacturer-customer relationship or the management-employee rapport, empathy is an essential business skill. Businesses today have begun to understand the importance of cultivating a culture of empathy and displaying a high emotional quotient (EQ).  

Under emotionally intelligent leadership, a company can grow into a well-rounded organization that is productive and competitive. Empathy in company culture ensures that employees get treated with respect and dignity. Their opinions are valuable; they enjoy the freedom of expression and ease of communication without feeling the pressure of the corporate hierarchy. Higher the level of employee empathy in the company culture, better the level of organizational EQ/EI, which is a sure sign of positive outcomes in areas such as employee satisfaction and team cohesion. Better company culture can also mean improved staff retention. Happy, comfortable, and satisfied staff are the ones who will render better customer service. Thus, empathy in company culture can help an organization succeed at all levels of the business.  

For significantly improved business relationships, they must strategize an increase in empathy in the company culture. The organization will then be able to: 

  • Enhance workforce engagement and boost motivation and productivity. 
  • Ensure more effective employee performance through better leadership and management of teams and individuals. 
  • Promote overall emotional well-being and reduce stress among employees. 
  • Improve customer service and sales approaches by helping employees understand customer needs better. 

Customer centricity is when a company focuses on the customer in all company actions, processes, and decisions. It is a strategic approach that seeks to understand customer needs, wants, and expectations to deliver a seamless customer experience that is relevant to them. Customer centricity is a philosophy that says success is a direct result of the ability of a company to understand and meet customer needs. If you wish to deliver customer satisfaction and emerge as a successful business leader, heightened empathy in company culture and customer-centricity must go hand-in-hand.  

How Do We Build Empathy? 

There are three steps to building Empathy that every employee of the organization can consider: 

Step 1: Learning to pay attention and listen. 

Before doing anything else to build empathy for business success, start by learning how to listen with complete attention. Do not get busy thinking about what your response will be while the other person is still speaking. Remain in the present and be truly interested in the point of view of others.  

Step 2: Asking questions. 

Ask questions that will give you more insight into the situation or experience of another person or even another group of people. Maintain open, two-way communication. 

Step 3: Humanize the data. 

Data-driven empathy is about working with personalized insights and aligning your business with customer-specific requirements. Data can give you a real-time view of the customer’s evolving needs. Business problem solutions can thus be tailored to the T, establishing the company as a customer-centric organization.  

Step 4: Create awareness about empathy as a Key Success Factor (KSF). 

Propagate empathy in the company culture as a winning strategy for all. Employees who look at empathy in a different light will focus on getting to know customers better, treat them with more respect and undiluted attention, have complete transparency in their communication with customers, and provide them with appropriate solutions. That, in turn, will create a company that customers trust and think of going back to or conducting business with again.  

Essentially, that is the way to ensure golden ROI from happy customers and long-standing business relationships. And we can thus conclude that empathy is a sustainability tool too. Is it surprising that even the European Council has recognized Empathy as one of the core competencies for the future? 

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!