artificial intelligence

This article has been written by Kiran Kere pursuing an Executive Certificate Course in Corporate Governance for Directors and CXOs from Skill Arbitrage.

This article has been edited and published by Shashwat Kaushik


The global integration of artificial intelligence (AI)/Generative AI (Gen AI) is revolutionising various industries, with the manufacturing sector emerging as a focal point for AI/Gen AI advancements. This article deep dives into the crucial role played by generative AI (Gen-AI) in the manufacturing & FMCG industries, investigating its capacity to tackle challenges unique to the industry. Through an exploration of the most revolutionary applications of generative AI, we highlight the disruptive impact of AI on the manufacturing and FMCG industries landscapes.

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Current challenges in manufacturing and FMCG industries

Effective data management

There are a lot of challenges while we integrate data from disparate systems like SAP or NON SAP applications industries are using on day to day basis, leading to inconsistencies and difficulties in achieving a unified view of operations. There are a lot of challenges while securing sensitive manufacturing data , and there is a need for cybersecurity measures to protect against unauthorised access and security breaches.

Supply chain disruptions

There are a lot of dependencies on global suppliers, which exposes manufacturers to geopolitical, economic, or natural disruptions, impacting the timely availability of raw materials or packaging materials. There is very little real time visibility across the supply chain for business end decision makers causing supply chain disruption.


Automation needs a lot of investments and it even needs reskilling and upskilling of the existing workforce to adapt to the new ways of working in an automated way.

Environment and sustainability

Sustainability is the question of today, where all manufacturing industries need to meet environmental requirements, adapt the ecofriendly processes and meet the compliance requirements set for their respective end products.

Demand forecasting

Market conditions are unpredictable, and it is making it difficult to provide an accurate demand forecast, which in turn poses a lot of challenges in optimising inventory levels. Inaccurate or incomplete data can impact the effectiveness of demand forecasting models. This also adds to the overall distribution and transportation costs.

Increased Costs

Price fluctuations for raw & packaging materials can lead to increased production costs, affecting overall profitability. Especially seasonality has a large role to play in the FMCG raw materials ingredients.

Lower productivity

Inefficient processes and bottlenecks in workflows can lead to lower productivity, hindering the timely completion of production tasks. Lower productivity is impacting to meet market demands in an efficient way.

Quality control

Quality products are the nerve of manufacturing, which determines their business growth and repeat purchases. Due to manufacturing process variations, there are quality variations in the end products. Industries need to have robust quality control measures. Advanced quality control measures and technology integration and adoption can also have challenges in terms of increasing costs and implementation complexity.

What is Gen AI /AI

The fundamental distinction between generative AI and traditional AI resides in their objectives and operations. Traditional AI focuses on executing specific tasks using predefined rules and patterns, whereas generative AI, on the other hand, aims to produce entirely fictional data that mimics human-created content.

Generative artificial intelligence is a further extension of artificial intelligence capable of generating text, images, or videos, using generative models. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics. This is really going to make people more efficient, as real alike or synthetic data can be easily made available for users in a few hours, which can make deliveries more efficient. It’s particularly valuable in creative fields and for novel problem-solving. A lot of testing data or project documentation automation can be easily delivered through Gen AI.

Generative AI in manufacturing market size, growth and trend analysis

The global generative AI in manufacturing market size reached USD 225 million in 2022 and is predicted to surpass around USD 6,963.45 million by 2032, expanding at a CAGR of 41% from 2023 to 2032.

Generative AI has huge transformative benefits across various industries, showcasing its positive impact:

  • Content creation
  • Healthcare
  • Manufacturing
  • Finance
  • Marketing
  • Gaming
  • Natural language processing
  • Cybersecurity
  • Automotive
  • Agriculture
  • Entertainment
  • Human resources

Generative AI’s positive impact spans a broader industry class, including its digital disruption, innovation, and efficiency. There are various industry applications that are going to be impacted categorically by creative advancement.

We will be specifically focusing on the manufacturing/ FMCG industry and its processes in the below sections.

Role of generative AI in manufacturing

Generative AI is going to play a key role in the future of manufacturing. It can assist in the creation and optimisation of product designs, product content, process workflows, environmental aspects, sourcing, procurement, and supply chain management. This technology can predict the answers to business questions with the current business data fed into the models. This can help manufacturing production ,  store supervisors, and R&D people make quick decisions based on optimised model outputs. This is going to help in reducing the overall costs and will deliver more eco-friendly products with very high-quality standards.

The below table depicts the area in Manufacturing / FMCG industries where and how Gen AI can help or make a significant favourable impact.

Manufacturing AreaHow Gen AI Can help
Raw material storage·        Optimise inventory·        Reduce transportation cost·        Reduce waste·        Define/Trigger Reorder points
Manufacturing plant·        Improve efficiency and reduce downtime·        Predictive maintenance·        Reduce waste
Product Ingredients·        Suggest best ingredients combination to optimize cost·        Better quality product
Packaging material and Storage·        Optimize packaging material cost·        Reduce waste·        Identify and suggest more eco-friendly materials·        Reduce waste·        Follow FIFO , LIFO·        Reorder points
Supply chain management/ and demand forecastOptimise transportation costsDefine reorder pointInventory forecastingRaw and packaging material sourcing
Transportation managementSuggest optimised models to lessen the freight costOptimise primary, secondary and tertiary distribution costsDeal with situations like vehicle transit delays due to various conditions.
Financial invoicingGenerate automated reporting and invoices.
AuditsGenerate reports/documentation for ISO and food safety audits like HACCP.
Research and Development (R&D)Simulating and predicting molecular structures, product ingredient composition/recipes and packaging designs.

Possible used cases with generative AI

Machine-generated events monitoring

AI in general can play a crucial role in revolutionising maintenance workflows and proactively implementing predictive maintenance strategies. Operational efficiencies can be improved by leveraging AI to analyse the online data from machines and equipment. It can help in minimising various downtimes (like breakdowns, replacing parts, changeovers, etc.). In turn, overall plant operations will be optimised and utilisation will be improved

Customer service automation

Manufacturers are increasingly relying on AI, specifically Gen AI, to meet customer expectations. Various value added services like ordering replacement parts, product troubleshooting, service scheduling , providing product details , operational guides are automated and expedited with the help of Gen AI

Documents search and reordering

Gen AI is facilitating rapid analysis and generating documents across end to end product lifecycle. Gen AI efficiently extracts and combines essential information for both sales, purchasing teams and technical teams. For instance, it simplifies servicing instructions into a user-friendly, step-by-step format, enabling technicians to promptly perform their tasks. Additionally, Gen AI can consolidate purchase orders & sales orders by efficiently generating customer quotes.

Product content catalog

By leveraging Gen AI text generation capability, manufacturers can build the product content catalogue quickly by having rapid alignment of requirements with the purchased product specifications.

Supply chain

GenAI can provide recommendations on the best suitable vendors and suppliers for raw materials and packaging materials by looking at the demand forecast, inventory in hand, sales demand, and delivery schedule. It can also help in showing the supply chain performance and provide necessary recommendations on top of that.

Synthetic data generation

AI algorithms produce synthetic data that mimics real-world manufacturing scenarios. There are millions of rows of data that can be generated quickly, adding to process efficiency and project delivery efficiencies. This synthesised data plays a crucial role in training AI models, improving their performance, and enabling manufacturers to optimise processes, anticipate failures, and reduce breakdowns/downtimes. 

What value factors generative AI can bring to manufacturing

Increased efficiency

To increase ROI, manufacturers are   in search of ways/strategies to enhance plant/shop floor operations efficiency and minimise waste coming out of manufacturing, packaging units, and stores. Generative AI plays a key role in optimising production processes, resulting in a considerable increase in overall efficiency.

Cost reduction

Industry leaders are always in pursuit of reducing operational expenses and thus increasing profits. Generative AI helps by proposing economical product design and production methods & processes, resulting in a reduction of manufacturing costs.

Personalised experience

Consumer demand and the final product experience are always at the heart of business, and they define the ultimate success of the product. Generative AI helps manufacturers efficiently create customised solutions that align with ever changing customer demands and needs, thus providing more personalised experiences to end consumers.

Quality improvement

Any product sales improvement and continuity, which we call brand reputation, requires high delivery and consistent products. If there are any major deviations in production batches, it is definitely going to impact end product quality and, thus, repeat sales or repurchases of the same products. In FMCG industries, maintaining product colour, look /feel, taste matters a lot for repeat sales. Generative AI helps improve product quality through the optimisation of product design and manufacturing processes, ensuring excellence in the final product or output.

Sustainability practices

Our future is completely dependent on how efficiently we are going to adopt sustainable and ecofriendly practices. Manufacturing/ FMCG industries are also part of this; in fact, they have to do quick adaptation and implementation so that it has positive impact on the surrounding environment. Generative AI plays a role in suggesting and building products that adhere to environmentally responsible design principles and methodologies.

Time to market

Generative AI can show you exactly what your product will look like through various prototypes. It helps simplify the product designs by performing various iterations. It can help you reduce the overall time to market, so product launches can be done quickly for new products that are in huge demand in the market.

Generative AI is definitely going to have a welcoming impact on the 8 P’s (product, price, place, promotion, people, positioning, processes and performance) of marketing and the 6 C’s (content, commerce, community , context, customization and conversation), which are very tightly integrated and revolve around the manufacturing of products and processes.

Risk association & security measures while gen AI in use

Data used is ethical with informed consent

Gen AI models run ultimately on data; every business data is confidential in nature and there are people who are generating the data and there are data owners. While this business will be used by generative AI models, it is essential to obtain informed consent from data owners while providing the data. This ensures transparency about how the data will be used and builds trust among various stakeholders. It is important for data owners to be aware of how their data will flow throughout the entire data pipeline.

Data privacy

It is key for the manufacturing or FMCG industries to keep information private about product designs, recipes, packaging designs , raw/packaging materials, production processes and technologies specific to their operations. In no way can this data be exposed to the outside world, especially to safeguard it from competitors or unauthorised hands. Gen AI makes sure data is handled with the utmost care, prevents any unauthorised access to data and protects the intellectual properties of the respective business.

Security measures

Before starting any project, it is very important to have very tight security measures in place to avoid any unauthorised access to Gen AI systems. This will help in avoiding any kind of data breach. As all the manufacturing processes, recipes, patents, and technology data is sensitive and there is a lot of valuable information, tight security measures are a must to avoid any kind of data theft or unplanned data exposures outside.

Accountability and responsibility

Clearly defining roles and responsibilities regarding data use and AI system operation is a key factor in the success of any implementation project. Once we have the RACI matrix established and accountability defined, any ethical concern arising out of the Gen AI process can be handled on an immediate basis responsibly.

Compliance and regulations

It is important to follow local and global compliance & regulations related to data protection, /security and AI usage. Manufacturers should stay informed about changes in legal frameworks and ensure compliance with ethical guidelines and standards in the industry.

By addressing these ethical considerations, manufacturers can reap the benefits of generative AI responsibly, ethically, and sustainably, promoting trust among stakeholders and contributing to the long-term success of AI-driven manufacturing processes.



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