That distinction is increasingly critical in today鈥檚 manufacturing environment. Our global manufacturing clients now operate with a level of product and operational complexity that was unimaginable a generation ago. This complexity propagates rapidly across functions and at speed. Products are more configurable, software-defined, connected, and tightly regulated. A single design change can ripple across supplier readiness, manufacturing processes, software configurations, field diagnostics, sustainability reporting, warranty exposure, and regulatory compliance. Similarly, a quality issue may originate as a component deviation, extend into production, surface in field performance, and ultimately impact customer trust. Even localized supply disruptions cascades across geographies and impact capacity, working capital, customer commitments, and service levels.

This is why the digital engineering conversation has moved beyond tooling. The question is no longer, 鈥淒o we have digital systems?鈥� Most large manufacturers already do. The sharper question is, 鈥淐an those systems create a trusted operating foundation for decisions across the product lifecycle?鈥�

Fig 1: What Changes for the CXO?

Why the Digital Thread Is Becoming the Enterprise Nervous System

That foundation is what the industry often calls the digital thread.

At its simplest, a digital thread is the connected flow of product and process information across the lifecycle, from concept, design, and engineering to manufacturing, quality, service, and eventual retirement. It links the 鈥渨hy鈥� of a product, such as customer requirements and engineering intent, with the 鈥渨hat鈥� of product structures and configurations, the 鈥渉ow鈥� of manufacturing processes, and the 鈥渉ow well鈥� of quality and field performance.

In our customer ecosystem, where a digital thread has been established, leaders are able to understand how decisions in one part of the lifecycle impact cost, quality, compliance, delivery, sustainability, and customer experience across the enterprise.

The digital thread enables synchronized execution of customer orders across design, manufacturing, supply chain, and service. It also provides traceability and accountability鈥攃apabilities that are particularly critical in automotive and industrial product segments.

However, the challenge in implementing digital thread is a manufacturing reality – product data rarely sits in one place. Companies leverage primarily PLM for managing product information and leverage MES, ERP, LIMS for executing shop floor operations.听 Supply chain works across planning, procurement, logistics, inventory, supplier collaboration, and risk systems leveraging SAP and Supply chain solutions. Service adds its own layer of warranty, maintenance, asset performance, and customer usage data.

For years, enterprises tried to deal with this by integrating systems. The next challenge is deeper: integrating meaning.

AI has made that gap impossible to ignore. A senior engineer can often compensate for fragmented context because she understands the product, supplier history, plant constraints, and organizational workarounds. AI agents cannot depend on tribal memory. They need structured context, governed relationships, clean taxonomies, and boundaries for action.

Manufacturers cannot scale AI in engineering and operations while leaving product context trapped in silos.

The right approach to this integration can have significant impact. For instance, the integration of ERP (SAP), PLM (Enovia), production planning and scheduling (Ortems), and MES (Apriso) enabled the creation of a digital thread for an automotive manufacturer. These systems exchanged information seamlessly, providing end-to-end visibility, component- and process-level traceability, and enabling optimization at the enterprise level rather than within individual functions.

From Digital Engineering to Enterprise Complexity Management

Today, a product is no longer a static object progressing through a linear lifecycle. It is a dynamic system of hardware, software, data, suppliers, services, and regulatory obligations. An elevator illustrates this well. It has evolved from a standalone mechanical system into a connected, integrated solution within a building management ecosystem. Continuously updated and enhanced through new features and software upgrades.

Product complexity now begins well before production. It is created through configuration options, embedded software, supplier dependencies, sustainability requirements, regional regulations, material constraints, service models, and customer-specific variants. Once that complexity enters the lifecycle, it travels. A poorly governed change in design may become a manufacturing issue, a service issue, a quality issue, or a compliance issue months later.

AI is helping engineers explore alternatives, extract requirements, accelerate validation, generate design options, and reuse knowledge. Yet these capabilities become enterprise-grade only when they are connected to a governed product context. Without that context, AI speeds up isolated tasks while leaving the organization鈥檚 complexity problem untouched.

The more advanced manufacturers use model-based approaches, digital twins, knowledge graphs, and AI-enabled engineering workbenches to detect these dependencies earlier. They simulate how design choices affect manufacturability, cost, quality, and serviceability before those choices become expensive. They treat engineering knowledge as an enterprise asset rather than something rediscovered project by project.听

The most serious manufacturing leaders are asking a harder question now: if AI agents are going to participate in engineering and operations, what enterprise context will they reason from?

Agentic Operations: The Move from Visibility to Coordinated Action

That question leads directly from digital thread to agentic operations.

Agentic AI systems can perceive context, reason through options, plan steps, trigger actions, and coordinate workflows within defined guardrails. In manufacturing, this has significant potential as the operating environment is full of decisions that are too dynamic for static dashboards and too coordination-heavy for manual workflows.

Consider a production supply chain disruption. A conventional dashboard shows a material shortage in the production plan, leading to a potential line stoppage. On the other hand, we鈥檝e developed an Early Warning & Autonomous Control System – an intelligent supply chain management solution 鈥� that goes beyond this basic reporting. It assesses the impact of the shortage on production and customer delivery commitments and dynamically re-prioritizes the production plan to maximize capacity utilization and efficiency, with the objective of minimizing financial impact while still meeting delivery commitments.

With a human in the loop, the system then executes the next-best actions. The real business value lies in compressing the time between signal, interpretation, decision, and action.

The same logic applies to maintenance, quality, manufacturing, planning, inventory optimization, energy management, warranty analysis, and field service. These are areas where manufacturers often have enough data to see problems; yet lack the connected process intelligence to act fast enough.

The temptation will be to treat agentic AI as another layer of automation. In manufacturing, autonomy without context is dangerous. An agent making recommendations on production sequencing, quality containment, maintenance prioritization, or supplier response must operate inside a governed architecture. It needs access to the right data, visibility into constraints, clearly defined authority, audit trails, escalation paths, and human oversight proportional to business risk.

The operating question is: which decisions can be safely accelerated because the enterprise has the data, process discipline, governance, and human accountability to support them?

Fig 2: From Digital Thread to Agentic Operations

The Workforce Is the Real Adoption Layer

Technology programs often underestimate the human system and, in our experience, this is one of the biggest challenges of Digital Transformation in the manufacturing organization. A supervisor who does not trust the data will work around the system. An engineer who sees PLM workflows as administrative overhead will keep using spreadsheets. A planner who cannot understand an AI recommendation will ignore it under pressure. A plant manager who sees digital transformation as corporate theatre will protect daily output first.

This is why manufacturing transformation depends on operating routines, incentives, skills, and trust. As AI systems take on more coordination and execution tasks, organizations will need to redesign roles around supervision, exception management, decision accountability, and continuous improvement. The workforce will need to become comfortable working with intelligent systems, questioning them, guiding them, and improving them over time. Increasingly, change management is an integral part of the digital transformation programs.听

The CXO Agenda: Build the Operating Architecture for Intelligent Manufacturing

For manufacturing CXOs, the priority is not to choose between PLM modernization, smart manufacturing, planning tools, digital twins, GenAI, and agentic AI. These are connected parts of one operating architecture.

The digital thread provides trusted lifecycle context. PLM governs product truth. Smart manufacturing connects operational reality. Digital twins simulate decisions. GenAI changes how knowledge is accessed and created. Agentic AI coordinates action across workflows.

The sequencing will differ by enterprise. One manufacturer may begin with engineering change velocity. Another may focus on supplier-driven quality variation. A third may tackle software-defined product complexity. Another may target downtime, service performance, or regulatory evidence. The architecture can be common, but the value thesis must be specific.听

The uncomfortable truth is that AI will expose fragmented enterprises faster than it fixes them. If product data is inconsistent, AI will surface inconsistency faster. If processes are poorly governed, agents will accelerate poor decisions. If engineering and manufacturing operate with different versions of truth, GenAI will produce confident confusion. If workforce adoption is treated as a training checklist, expensive systems will sit on top of unchanged behavior.

The manufacturing enterprise is becoming too complex to run through disconnected systems and retrospective intelligence. Leaders now need a digital engineering foundation that can understand the product, sense the operation, govern the decision, and coordinate the response.

That is the real journey from digital thread to agentic operations: building a manufacturing enterprise that can reason, adapt, and execute with discipline.

Author:

Nitin Kumar Kalothia
Associate Partner – Business Consulting Group
乐播传媒视频

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Sustainability Management imperatives are taking more prominence due to environmental concerns and will continue to be in focus in the coming days with increased awareness on the subject from governments, business & society at large.

Businesses have a significant role to play & sustainable practices are considered a corporate responsibility with leading companies taking sincere initiatives to measure and minimize environmentally unfriendly operations and to address various stakeholders鈥� (internal & external) priorities:

• Brand reputation 鈥� As companies introduce sustainable methods in their manufacturing systems, their reputation among investors, stakeholders and consumers improves
• Operational efficiency 鈥� Reduced usage of energy and other resources leads to reduction in costs which eventually leads to improved operational efficiency
• Societal impact 鈥� Creating a strong image with sustainable methods sends out a strong message to the society and this in return creates a positive impact on consumers鈥� minds
• Transparency from Business Partners 鈥� Companies are expecting from their partners Sustainable business engagement & moving away from ties with partners at risk
• Shareholders 鈥� Beginning to use ESG scores as one of the criteria to make investment decisions
• Regulatory Requirements: These are becoming more stringent
• Customer perspectives 鈥� Becoming more conscious about sustainable Products & Practices & do not like Greenwashing

The manufacturing industry is setting ambitious and sustainable targets for improving the planet with meaningful environmental changes. Intricately connected with this are corresponding opportunities for technology companies. According to recent surveys, the sustainability market size for green technologies is expected to grow significantly with a Compound Annual Growth Rate (CAGR) of >25%, with market size expanding to almost $45b+ by 2028. The management of greenhouse gas emissions, energy consumption, waste management, green product development, and water conservation鈥攊s seen no longer as a cost but as a critical value differentiator.

Challenges faced by manufacturing companies:

Operational Inefficiencies

• Manual Method of collecting sustainability data & transformations leading into errors
• Auditability/ Traceability issues due to manual methods of operation
• Deficiencies in ability to do basic analysis on the data

Newer Asks:

• Suppliers following the organization鈥檚 environmental standards 鈥� Whether the suppliers are complying with the organization鈥檚 green standards with the components that they are providing
• Lifecycle assessment of products 鈥� Assessing the sustainability footprint of the product (e.g., from cradle to grave)
• Climate Risk Analysis (e.g., while setting up new units 鈥� Challenges of setting up new factories as per climate standards and how these setups are going to affect the environment)
• Evolving regulatory frameworks & higher reporting frequencies

These challenges need to be intrinsically addressed by a digital solution to improve efficiencies, enable auditability, reduce non-compliances & make the enterprise future-ready.

Conclusion

When it comes to sustainability in manufacturing companies, a remarkable change is afoot, resulting in more significant thinking鈥攅specially on the factory floor and value chain partners. Manufacturers prepared to adopt the change will find opportunities for innovation鈥攚ith sustainability targets inspiring green design, manufacturing, sourcing and novel technology applications. The time has come for data platform solutions for ESG and other newer asks that are emerging.

Author:

Sandip Mitra,
Business Consulting Group, 乐播传媒视频

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Imagine you are buying a few bags of potato chips to snack on. One bag has fewer chips than expected; the chips do not match the weight mentioned on the package. You will be disappointed. You will either complain to the manufacturer or simply stop trusting the brand. Worse, you could take to social media to express your anger, affecting the broader reputation of the brand. If there are a few extra chips in the bag, you will not mind it. But those extra chips can quickly add up to massive losses for the manufacturer. Either way, not controlling the grammage of products leads to unwelcome outcomes. This is not a new problem for the F&B industry. But new solutions are now available to contain and practically eliminate-the problem.

Variances in packaged food products is expected. The manufacturer of a 78 g bag of potato chips can perhaps tolerate a variance of +/- 2 grams. But by identifying the actual variance and tracking trends, upstream and downstream systems can be improved, costs can be lowered, compliance norms can be met, waste reduced and customers kept happy by offering a more consistent product. However, usually individual checkweigher or multi head weighers and baggers are used at the packaging stage to accept/reject products for the market鈥攚hen it can be too late. To overcome this, the science of Extra Grammage (EGA) Optimization needs to be mastered.

The weight of packaged food products is a tricky affair. Something as simple as extra moisture in the chips or extra oil can increase the weight. The thickness of input materials (for example, potato slices, masala or salt deposition etc. ) or even the temperature of the cooking oil or frying time can cause higher or lower weights, or the vibratory nature of the manufacturing equipment can lead to variances.听听听

Often the selling price of a product cannot be changed. In such cases the manufacturer must resort to controlling the weight.

EGA optimization is a latest Industry 4.0 solution integrates all weigher 鈥淢achine Data鈥� & 鈥淧rocess Parameters鈥� and where continuously monitors the trend of Underweight & Overweight Rejections and Extra Give Away of Materials.听 Then Auto-Insights are generated basis factual data at frequent intervals, minute level & KPIs are measured EGA%, rejections% etc. and any deviations beyond acceptable limits are alerted to respective operators to fix the production parameters causing the variance immediately, minimizing the volume of rejected products at the end of the line. The solution works for both linear and vertical manufacturing and packaging processes.听

Vertical manufacturing and packaging processes need to be dealt with a little differently from linear processes (example: potato chips versus cookies). In a vertical process, the product drop happens to multiple buckets during production. The weight may vary because of product breakage or due to residual recipe material, accumulated in the assembly line, sticking to the product.

In current practice, many CPG companies have not integrated their Machine data. Right set of Analytics not used to create meaningful alerts for operators and to improve output quality. Our implementation of this solution in a large food production plant has shown significant results, that even a 1% impact is saving thousands of dollars. But this is what production plant managers will like to hear the most: ROI in our implementation was less than six months. This is a high-impact, quick-return intervention that every manufacturer must consider.

Author:

Siddaraju G,
Senior Principal Consultant,
Business Consulting Group,
乐播传媒视频

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Vision-based computing or computer vision, aka machine vision, has grown tremendously in the last few years. The key reasons for the growth lie in the availability of affordable hardware, the growth in data sets and maturity in the science of Artificial Intelligence (AI) and Machine Learning (ML). With the COVID-19 pandemic accelerating digital adoption, vision-based analytics is bound to find wide-spread applications in several industries including manufacturing. The extraordinary drive for work place safety, the need for automation to improve productivity, and access to better quality will be the key drivers for the growth in vision-based computing. One indicator of its bullish future is reflected in a that found AI in computer vision market size would reach $144.46B by 2028 from $7.04B in 2020 (a CAGR of 45.64% from 2021 to 2028). Manufacturers who do not study use cases for vision-based computing in their organizations could lose out on the promise of generating business value and creating competitive differentiation.

An area where vision-based technology can be applied with great success is quality inspection. Take the case of manufacturing cookies in the food and beverage industry. Operators are deployed on production lines to check the quality of the output. They must examine each cookie for conformance to color, texture, shape, size, nut coverage and several other factors and benchmarks. These are important in an industry where consumers assess products visually. Human bias 鈥� even human fatigue or distraction — can affect the inspection process. Options such as interval sampling of products in a lab can improve the process but they too leave the gates open to flawed products reaching the market.

The solution is in using AI and ML-based algorithms to examine each cookie. Cameras scan the live production line before packaging, accurately picking out cookies that do not meet the pass range for quality. Even when samples are examined in an off-line mode in a lab, vision-based computing removes subjective biases.

Once a cookie on a production line is identified for non-conformance, a simple drop mechanism removes the cookie from reaching the packaging process.

Vision-based computing has several applications in factory environments. Most manufacturing plants already have several CCTV cameras used to monitor activity, human safety and business security. The number of cameras on a production floor can number anywhere between 30 and 100 鈥� a number impossible for humans to continuously scan for aberrations. This is why these cameras are usually used after the fact, to investigate incidents.

However, the camera feeds can be analyzed in real time based on EHS guidelines and even used for automated risk audits. This means the investment already made in video monitoring can go the extra mile by identifying hazards in different work areas such as slippery floors, accumulation of materials, obstacles in pathways, missing or inadvertent obstructions around firefighting equipment, violation of safety norms by employees (such as not wearing hard hats or fall arrestors), unauthorized entry of personnel in restricted areas, and the entry and exit of vehicles in plants, warehouses, collection points and so on.

Not only can vision-based computing prevent issues or generate real-time alerts, but the data can also improve processes. For example, supervisors can immediately know how many vehicles are parked in a certain area, what type of vehicles are parked and for how long, the time taken to offload material from any vehicle鈥攅ach data point allowing the supervisor to take quick and accurate decisions.

Vision-based computing is here to stay. And the race to adopt it has just begun.

Author:

Siddaraju G,
Senior Principal Consultant,
Business Consulting Group,
乐播传媒视频

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Business Intelligence makes data driven decision-making possible at large scale. Using self-service analytics capability, the business leaders can tweak different parameters and simulate different scenarios before decision making. With this, they are better equipped to draw useful insights and make better informed decisions with higher probability of success.

Importance of Business Intelligence in manufacturing

Availability of technologies to source data at low cost from equipment and IT applications makes manufacturing, one of the most suited industry for applying BI. Data is already being measured and recorded in real-time using IoT devices, PLC, SCADA, MES, ERP, CRM and other platforms. Most of the times, this data collected is used only for general operations purpose. Getting insights from data is an area most organizations have not really looked at. Processing data efficiently can unlock a lot of value and can lead to great tangible monetary benefits. For example, in FMCG, processing quality data using advanced analytics and re-aligning process in real-time can lead to big savings by avoiding potential rejections and rework.

Business Intelligence tools convert text and numbers into images which are more readable and comprehendible.

Here are few applications of BI tools in manufacturing –

Inventory Management 鈥� Without complete visibility of products on hand, it may be challenging to minimize inventory costs without compromising service levels. BI dashboards take various data points like change in supply and demand, product obsolescence and other factors into consideration and help the manufacturers optimize safety stock, assess reordering point, control inventory costs, avoid out-of-stocks and improve the service levels.

Supply Chain Management 鈥� BI tools can help the supply chain managers get more insight into routes, carriers, wait times, freight delivery status and payments. It helps in finding more opportunities and do better negotiations. Tracking KPIs like OTIF helps identify delivery issues in real-time and address them.

When combined with Advanced Analytics, BI can solve even more complex problems like the following –

1. 听Energy Analytics – Energy cost is a major chunk of conversion cost in any manufacturing unit. There is a need to optimize performance of utilities like boiler, chiller and other energy intensive units. In absence of insights, the plant operators are more focused on meeting production targets than on optimization. 乐播传媒视频 has developed an advanced analytics-based solution on PTC Thingworx to address this challenge. The solution predicts optimum efficiency and prescribes operating parameters to achieve that. It leads to a 5-20% savings in energy costs.
2. Product Quality Analytics 鈥� 乐播传媒视频 helped a global FMCG giant to collect data from existing PLC & SCADA systems, apply artificial intelligence, make data models and show meaningful KPIs in real-time on responsive dashboards using which the operators could control the quality of products being manufactured. This has led to quality yield improvements of 1-2%.

Recommendations 鈥�
Now before you decide on your business intelligence tool, here are some points to keep in mind –

• Involve both Business management and IT team in developing BI tools
• Decide right KPIs from start
• Build database, don鈥檛 use data directly from different sources because different sources may have different benchmarks
• Select BI tools that allow customized report delivery and scheduling
• Develop BI tools which can work on different devices including smartphones to make it more useful
• Don鈥檛 delay the development thinking you don鈥檛 have enough data. Most companies already have enough data to start with
• In conclusion, BI tools are a must for manufacturing organizations today. It makes the whole process faster, optimized and transparent. The investments made are lesser compared to the long-term gains.

Author:

Nishant Jain
Associate Consultant, Business Consulting Group
乐播传媒视频

Reference:

1. Energy Advisor from 乐播传媒视频. Leveraging IOT and Data Analytics for Efficient and Greener Manufacturing

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Intelligent manufacturing, leveraging advanced technologies for end-to-end digitization and automation could be the answer. Let us delve deeper.

Siloed Data Ecosystems, Lack of Real-time Visibility and Reactive Approach Hinder Efficiency

A large number of manufacturing units run on aging equipment causing 45% unscheduled downtime. Operations are disrupted in factories as asset health is not regularly monitored. Maintenance of equipment becomes a major challenge and 80% of the time production managers and factory staff are fixing issues after the damage is done. The ability to proactively apprehend and resolve incidents is limited, leading one in three manufacturers to spend more than 10% of their operating budget on maintenance alone. At the enterprise level visibility on plant health, operating processes, and floor level performance is hindered with siloed data and access to actionable reports, on time. This creates an environment of vulnerability, posing challenges in meeting safety and regulatory compliance requirements. The end result is sub-optimal productivity and sub-par performance impacting the bottom line.

Intelligence Powered Digital Next Manufacturing Accelerates Growth and Amplify Outcomes

The manufacturing sector has the opportunity to reinvent their production and supply chain, augment the workforce, and drive deeper engagement with their customers with advanced technology adoption. The ability to harmonize, analyze, and drive real-time insights from multiple data streams define the strength of a digital enterprise. No wonder, more than 50% of manufacturers are keen to invest in building Big Data capabilities. The need for real-time inline quality control and visibility into the end-to-end process is driving 80% of technology leaders in the sector to increase investments in digitization, Artificial Intelligence, and Advanced Analytics.

Cognitive supply chain and operations with AI-based advanced planning and automation improves the ability to pre-empt issues, sense the operational challenges to respond with agility and speed. This removes vulnerability from systems and improves the health of assets by over 40%, saving huge maintenance costs and reducing unplanned downtime by almost 50%.

Companies improve the bottom line with a 5-10% reduction in operating cost and increased earnings by up to 10%. The advantages are obvious, the question is how do manufacturers accelerate their journey to digital next future?

Roadmap to Digital Next Manufacturing听

As manufacturers embrace emerging technologies the focus must remain on creating a definitive roadmap to achieve desired goals. Here is an incremental approach, that equips technology leaders to chart a sustained path to digital enablement:

Roadmap for Digital Next Intelligent Manufacturing

Connect Plant and Enterprise – A听strong data foundation built on IT-OT convergence and integration across multiple data streams forms the bedrock of a connected organization. Implementing gateway solutions that provide multi-device support and build a machine to machine (M2M) connectivity enable real-time visibility, optimize capacity, and improve the quality of the output.

Conduct Prescriptive and Predictive Analytics 鈥撎�Once the organizations gain the ability to harness data, they are enabled to leverage AI, ML, and advanced analytics to build predictive models and interactive dashboards. Real-time data management and AI/ML algorithms provide prescriptive analytics to improve overall equipment efficiency and reduce inventory. As they decrease the lead time for procurement there are substantial cost savings, equipping business leaders to optimize their budget.

Drive Cognitive Intelligence听鈥� As manufacturers operationalize self-tuning AI/ML algorithms at the enterprise level, they gain the ability to contextualize intelligence. Processes are automated with a digital feedback loop and systems are equipped to take decisions with scripted algorithms. These advanced cognitive capabilities pave way for a responsive and flexible value chain and proactive risk management.

Augment Intelligence on Edge听鈥�听Building on cognitive technologies, CIOs can adopt edge computing and advanced AI/ML modeling to connect products and services. The strength of their ML algorithms will create an ecosystem for autonomous decision making to keep the assets performing at peak. Augmented and Virtual Reality (AR/VR) adoption can bring them closer to floor level operations and create opportunities for product innovation. The technology prowess ultimately translates to enhanced productivity and efficiency, leading to improved margins.

Manufacturing Platforms of Intelligence

Under our , we offer Factory, Supply Chain, and Product Intelligence to equip manufacturers to build digital next manufacturing organizations that are cost-efficient, high performing, and more profitable. We can work with you to explore the use cases and assess the readiness with us through a discovery workshop and demonstrate value with a pilot MVP for a use case in 3 months.

乐播传媒视频 has been supporting manufacturers to gain streamline operations and gain a competitive advantage with Platforms of Intelligence. Our pedigree in CPG and manufacturing comes from our expertise and experience with our parent group ITC. We bring the advantages of Cloud, Big Data, Advanced Analytics, AI/ML, and Augmented Reality on a single platform, we accelerate the journey fully connected ecosystem. We leverage our various strategic partners like Microsoft, PTC ThingWorx, Anaplan, and Salesforce.

To know more, write to us.

Authors:

Amit Tulshiram Derkar
Senior Manager, DX Offerings & GTM

References

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One thing that leading manufacturing companies did during their low demand days is that they invested in preparing for the future. During the economic recession of 2008-2009, forward looking manufacturing companies invested substantially in training and development of their employee鈥檚 skills. A major portion of the workforce time was invested in driving continuous improvement projects, conducting trials, process innovations, etc., that were typically difficult to manage during business as usual scenario. Clearly, these companies benefited from their investments and were able to scale up quickly compared to competitors. The importance and role of operational excellence tools like Lean Manufacturing, Six Sigma, TPM and TQM in enhancing the maturity of the manufacturing sector can neither be questioned nor overlooked. Over the past few years, adoption of Information Technology in the manufacturing sector has increased significantly and has driven the continuous improvement journey.

Again, forward-looking companies would like to use the current scenario to accelerate their Digital Manufacturing journey鈥攂ut the business situation doesn鈥檛 support it. While a few companies have been able to manage and continue the digital transformation journey, most manufacturers have put their plans on hold on account of financial constraints. As markets are slowly opening across the globe, normalcy will take time to return Most CXOs believe and understand the importance of investment in IT at this point of time but uncertainties in the economic scenario, cash flow challenges and short term returns in IT investments make them inclined to favor a delay in their Digital Manufacturing plans.

乐播传媒视频 understands the importance of technology in a manufacturing setup and has accordingly modified solutions and offerings to address some of the challenges of a CXO. In this blog, we talk about our 4D-MES framework that can be leveraged by companies to manage their ongoing Manufacturing Execution System rollouts or start the MES journey that has been put on hold. The 4D MES Framework allows manufacturers to extract maximum value at optimal cost from their MES landscape. The framework optimizes cost in the current program and generates additional value from the existing solution without new capex or major investments. The dimensions of the 4D framework are as follows:

• Domain Leadership 鈥� By pre-building industry templates leveraging our access to manufacturing plants of our parent company ITC Limited and utilizing the knowledge of domain experts, we reduce MES implementation lead time by almost 30% and help realize return on investment in a much shorter duration in addition to implementing industry best practices.
• Depth of Technology Expertise 鈥� Our Technology Assessment Framework is used for improving MES components utilization and to extract maximum value from the existing MES landscape. Automation toolkit (DevOps & Test Automation) helps automate the end-to-end MES lifecycle resulting in development and deployment effort optimization and business disruption reduction.
• Delivery Efficiency 鈥� The current team structure is designed and equipped to work remotely at 100 percent efficiency, helping us design, develop, test and deploy solutions even in a scenario where mobility is severely restricted. The team structure leverages the concept of shared resources to improve resource utilization and optimize overall development cost.
• Disruptive Business Model 鈥� The proprietary assessment framework helps quantify benefits from an MES implementation and enables us to work on commercials linked to an outcome-based model. Subscription-based pricing linked to the number of users and factories, variable bandwidth-based support engagements and commercials linked to outcomes help manufacturers reduce the overall initial investment and risks associated with a milestone-based payment model.

Recent surveys and articles by leading consulting companies highlight that technology will play a vital role for manufacturing and supply chains in the post COVID-19 era. CXOs believe in its importance and commit to its adoption but, with few exceptions, also agree that their digital journey is not well funded. The implication is clear: organizations should leverage the innovative and compelling business models and differentiated solutions being offered by technology companies to accelerate their digital manufacturing journey and prepare for the post COVID-19 era.

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It is said that with time evolution is inevitable. Change is inevitable. Darwinism is inevitable.
Mankind has always achieved excellence by a constant iteration of methods to do the same work given any field.
The basic philosophy behind iteration is to converge towards perfection through a constant change of ideas and methodology over time.
In the past few decades we have seen industries achieving marvelous breakthroughs, setting trends and making room for new ideas that would later change the way we do things.

Today, let鈥檚 look at the manufacturing industry and how technologies like artificial intelligence and machine learning are shaping the future to Industry 4.0. Imagine a manufacturing unit that produces 3 types of processed consumer goods using multiple assets and machine equipment required for the production process on the factory floor. Most manufacturers want to optimize their production costs by efficiently utilizing the assets, improving their reliability and efficiency. Let鈥檚 assume that one of the assets is 40% of annual production cost and if its performance and efficiency is not optimal then a major portion of your cost is consumed diagonally by the poor performance of an asset.

Moreover, the problem doesn鈥檛 end here, imagine asset managers and operators are only trained to analyze the endogenous factors that affect the system and manually operate them based on their own gut decisions which is not enough. It鈥檚 a limitation that is being bridged through experience and conventional R&D. Exogenous factors are equally important as the production lines won鈥檛 be completely isolated from the exogenous factors.

With emerging technologies like AI/ML and Data Science taking a center stage, manufacturing industry has been adopting it for different use cases like predictive asset maintenance, asset performance optimization and augmenting human decision making for plant managers, operators and production managers. Let鈥檚 look at how AI/ML can be used to help manufacturers optimize the production cost. We adopted a holistic approach and focused on the following three areas:

1. Prediction and Optimization of Asset Performance based on exogenous and endogenous factors.
2. Simulation based operator assistance by using Machine Learning.
3. Comparative Analysis using Retrospective Data and Error Log Module.听听 听

We started off by analyzing the systems data both听exogenous and endogenous factors and discovered a varied performance of the asset under various conditions and various timeframes and realized that the asset was underutilized.听On further analysis, we discovered a scope for optimizing the performance by predicting the efficiency and using it as a tertiary input to optimize the controls that would in turn help achieve desired level of output.
We built a Machine Learning solution that could be utilized repeatedly over a fixed period and becomes a natural part of the process. We adopted all the safety regulations and process guidelines. We started off with simple Machine Learning solutions like Linear Regression and slowly moved on to final model i.e. Random Forest by a set of continuous trials based on our prior expertise in this field.

The manufacturing unit operated for 3 shifts in a day with each shift running for 8 hours and after analyzing the data, we found that even though the performance in each shift was almost the same but there was a slight discrepancy. So, to understand it better, we used Unsupervised Machine Learning Algorithm that detected the hidden performance pattern. We divided the entire year鈥檚 data into 4 principle clusters, each one with its unique centroid positions across the parametric space. We then incorporated this information in the Data Science Engine and generated a series of Machine Learning Models 鈥� Multiple Linear Regression, Generalized Additive Model and Random Forest 鈥� with exceeding performance. The Random Forest outperformed all the others with the help of the unsupervised model at the backend and at the same time none of the predictions were exceeding beyond the design specifications of the asset, so we selected it as the prediction algorithm.

Post Prediction, we took a step forward and formulated an optimization problem that minimized the sum of the squares of the error from the above mentioned model and in turn produce control limits for the other production unit that would enhance the process to assist the asset to reach the desired level of output. We achieved this using an evolutionary algorithm, Genetic Algorithm which is a metaheuristic inspired by the process of natural selection that belongs to a larger class of evolutionary algorithm. Genetic Algorithms are used to generate high quality solutions to optimization and search problems by relaying on biologically inspired operations such as mutation, crossover and selection.听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听 听

But we discovered that we cannot solve the problem by simply optimizing the controls, as easy as it seems in real life most of the control systems are analog in nature and you cannot fine tune them to an accurate decimal degree. So, in order to solve this problem, we decided to augment the floor manager鈥檚 decision making regarding the optimization. We developed a Machine Learning Algorithm to simulate the efficiency based on the desired level of the controls that the operator desires to operate in a given shift. This听Machine Learning algorithm helped the operators to simulate scenarios and possibilities听of the various control points in the chain and enable decision making. This augmented decision making enables the stakeholder to improve the asset efficiency over time. This evolution of operation assistance will enable us to get a deeper insight about the true capability of the asset.

Our solution has a potential to drop the portion of the production cost from 40% to 30%, that is, we had the potential to save at least 10% of the portion over a span of 12 months. The third component of the solution enables you to understand the听retrospective behavior of the asset听along with scope to increase the accuracy of the models at the backend by providing the end user with a log that will allow him/her to log errors/production efficiencies that happen over time on the production line. This error log at the backend enables the machine learning models to iteratively incorporate them in the solution and increase its accuracy over time. It also helps the stakeholders at the management and production line to identify key insights into the chronic issues on a regular basis to investigate any kind of malfunction that repeatedly affects the production line.听

Before we wrap up this short story about the advent of the new age solution using Data Science let me give you a snapshot of our solution.

Do get in touch with us to know more on how we can help you optimize the production costs and improve the operational equipment efficiency using data science.

Indranil Seal
Data Scientist, DX, 乐播传媒视频
Indranil.Seal@itcinfotech.com

Advisors:

Anindya Neogi
GM, Chief Data Scientist, DX, 乐播传媒视频
Anindya.Neogi@itcinfotech.com

Umashankar SM
Senior Principal Consultant, BCG, 乐播传媒视频
Umashankar.SM@itcinfotech.com

Nishant Jain
Associate Consultant, BCG, 乐播传媒视频
Nishant.Jain@itcinfotech.com

Author:
Indranil Seal
Data Scientist, DX, 乐播传媒视频

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