Gen AI Applications in Supply Chain Management

The influx of Gen AI has taken the world of technology by storm. How can we forget, the jaw-dropping one million user base created by OpenAI’s ChatGPT just in the first week of its launch, setting a record-breaking benchmark for any other platform? This is good enough validation for the potential Gen AI brings to the table.

Gen AI slowly and steadily is making an impact on almost every industry, trying to support humans in bringing out better output with minimum effort. We are aware of how AI brought a seismic shift in the way businesses operate and use data to offer the best possible user experiences. With the entry of a new generation of Large Language Models (LLMs) what we also know as Gen AI, that exhibit human-like capabilities can do a lot in the realm of Supply Chain Management (SCM). But the million-dollar question is how prepared is supply chain management to onboard Gen AI in its operations?

The supply chain industry has relatively been slow in adopting Gen AI. But we cannot deny the fact it is catching up, as there is significant potential.

According to statistics shared by Gartner, 70% of business leaders believe that the benefits of Generative AI outweigh the risks. Supply chain leaders also mentioned that they are planning to allocate 5.8% of their budgets to technology and increase employee spending to deploy Gen AI. Whereas 65% of respondents to the Gartner survey mentioned, they will hire dedicated staff and experts to help deploy the technology in 2024.

The opportunities are endless!

This blog is mindfully curated to highlight how Gen AI is impacting the supply chain management space. We give our readers a deep understanding of how logistics 4.0 is way better than conventional SCM, by explaining Gen AI applications in modern supply chain management.

Before we get into the depths of the topic, let’s have a brief look at the digital supply chain process that consists of four stages as shown in the figure below:

1. Occasional Manual Workflow: Initial stage where processes are manually executed with sporadic frequency, lacking automation.
2. Periodic Analytical Process: Transitioning phase integrating periodic data analysis to optimize operations and decision-making.
3. Advanced Continuous Operations: Maturing stage employing continuous improvement methodologies for sustained efficiency and adaptability.
4. Real-time Self-learning Protocol: Peak stage utilizing AI-driven systems for instant insights, enabling proactive decision-making and adaptive strategies.

Challenges in Traditional SCM

Supply chain is the backbone of practically every industry. Be it retail, manufacturing, healthcare, food, and beverage, or even IT, the supply chain is integral for uninterrupted and smooth operations. Operating as an extremely complex and intricate industry, traditional supply chain management that depends on old or obsolete systems and human intuitions is bound to face a saga of challenges.

The figure gives a broad overview of implementing an intelligent supply chain in the logistics 4.0 era. It displays the shift from conventional linear architecture to a more dynamic, interconnected, and AI-enabled portfolio.

The traditional supply chain is now an outdated concept, and one of the primary issues in this setup is the lack of real-time visibility across the supply chain landscape, leading to inefficient decision-making and difficulty in prompt responses to disruptions. Additionally, legacy systems often struggle to integrate data from various sources, hindering the optimization of inventory levels and distribution routes.

Another challenge lies in forecasting demand accurately, as traditional methods may not effectively account for fluctuating consumer preferences or market trends. Moreover, manual processes increase the likelihood of errors and delays, impacting overall efficiency and customer satisfaction. These challenges highlight the pressing need for modernization and the adoption of advanced technologies in supply chain management.

Here is a brief view of challenges:

Visibility Gaps

In supply chain management, one significant hurdle is the limited or sometimes complete lack of visibility across the chain. This gap makes it difficult to track the movement and status of goods effectively. Consequently, it leads to inefficiencies and potential disruptions.

Fragmented Supply Chain Collaboration

Another critical challenge in the conventional supply chain management setup is the isolation and disconnection among different participants. When stakeholders operate in silos in the lack or absence of effective communication and collaboration, it is bound to hurt the smooth flow of goods and information, causing delays and misunderstandings.

Product Traceability Deficiencies

The absence of robust product traceability and monitorability is one of the most pressing issues in the realm of supply chain. Without reliable systems to trace products’ journey and monitor their condition, ensuring quality control and timely delivery becomes daunting.

Supply Chain Transparency Shortcomings

There is no alternative to transparency in supply chain operations, yet it’s often lacking. When information is not readily available or accessible to all relevant parties, it creates mistrust and inefficiencies.

Operational Punctuality Challenges

Disorganized and untimely operations pose a significant challenge in supply chain management. Delays at various stages disrupt schedules, increase costs, and impact overall performance.

Calsoft dedicatedly works with a modern approach and understands that retail in the digital age is all about automation, and digital transformation focused on minimizing human intervention with cutting-edge AI-driven solutions. Get a glimpse of our potential offerings in retail on the digital front.

Gen AI to the Rescue: Demystifying Gen AI Applications in SCM

It is difficult to keep control of personal inventory these days, we can only imagine monitoring the intricate landscape of the supply chain with precision. Traditionally, supply chain management was an area that focused on attributes such as cost, speed, and quality. But with the outbreak of uncertainties such as the pandemic and most recently the Ukraine war has shifted the focus to prioritizing resilience from unforeseen circumstances, sustainability, and risk mitigation.

With Gen AI to the rescue, businesses now have the necessary tools to cope with this situation. Research projects that between 2023 to 2032 Gen AI in supply chain management is forecasted to reach a CAGR of 45.6%, leading to a surge in the total market value of Gen AI from $300 million to $12,941 million.

Now that we have a fair enough idea of the kind of impact Gen AI can have on supply chain, let’s dive deeper to understand Gen AI’s applications in Supply Chain Management. There is a range of Gen AI applications in SCM as displayed in the figure below. In this article, we will zoom in on the top five applications to deliver a clearer understanding.

1. Demand Forecasting
   Gen AI can be effectively trained to analyze various situations, to help businesses make informed decisions. With the rapid shift in customer preferences and demands, Gen AI models can be used to analyze historical sales and market trends data to strike a balance between supply and demand by improving forecasting accuracy.
   
   By integrating real-time data streams and using machine learning (ML) algorithms in SCM, businesses can smoothly be at par with changing market conditions and consumer preferences. This kind of flexibility plays a vital role in enabling proactive decision-making, minimizes stockouts, and reduces excess inventory, leading to cost savings and enhanced operational efficiency. This indeed helps businesses operate on a leaner SCM strategy playing an active role in mitigating disruption and reducing or getting rid of stocking issues.
2. Inventory Optimization
   Inventory management is inevitable to keep product shortages at bay and at the same time avoid cost accumulation caused by excess inventory. Gen AI in this realm proves extremely helpful in identifying inventory levels with the use of historical data, demand trends, and other variables. Additionally, it aids in the reduction of surplus inventory, prevents overstocking, and improves supply chain responsiveness.
   
   Gen AI models can be leveraged to determine ideal distribution strategies and storage practices considering delivery times, demand variations, and logistics costs. Gen AI can eliminate product deficits, lower holding costs, and control surplus inventory by providing options such as safety locks and reorder points.
3. Supplier Selection and Relationships Management
   Optimizing supplier selection ensures reliability, quality, and cost-efficiency in the supply chain ecosystem. Nurturing relationships fosters collaboration, trust, and innovation, driving sustainable success in supply chain management. By working with comprehensive data sets, including elements such as performance indicators, pricing structures, and quality assessments, Gen AI helps businesses find optimal suppliers and fortify supply chain resilience.
   
   Additionally, Gen AI algorithms can be used to mindfully handle supplier relationships by analyzing previous interactions, performance records, and contracts. These insights actively help in identifying potential risks or strengths of working with a particular supplier, and areas of improvement, and can propose a great foundation to develop strong negotiation strategies to nail beneficial collaborations. Gen AI can also help businesses diversify supplier networks to reduce risk and dependability.
4. Reverse Logistics and Returns Management
   Generative AI optimizes reverse logistics by analyzing returns, repairs, and refurbishment data. It guides decisions on product routes, repairs, and refurbished inventory distribution, minimizing costs and waste. By evaluating transportation costs, product condition, and demand, AI predicts whether items should be repaired, refurbished, recycled, or disposed of, reducing unnecessary expenses and waste.
   
   In routing, generative AI analyzes transportation data to identify the most efficient routes for returns, cutting transportation costs and time. Moreover, it utilizes historical sales data and demand forecasts for inventory management, preventing overstocking or stockouts of refurbished goods. By strategically allocating refurbished items, generative AI enhances supply chain efficiency, ensuring they are placed where they are most likely to sell.
5. Financial Optimization
   Last but not least, it’s time to talk about the financial angle. Deploying Gen AI algorithms in the financial side of supply chain offers significant advantages to businesses by providing an array of solutions to deal with challenges like:

• Credit risk evaluation:
  As mentioned previously, Gen AI can effectively process large volumes of data inclusive of financial reports, market analysis, credit histories, and more, enabling the credibility of stakeholders including suppliers, customers, or partners.
  
  In the bigger picture, this information can be used by stakeholders to manage financial risks, provide credit, and flag probable defaults within the supply chain landscape.
• Detection and mitigation of fraud:
  Gen AI models can potentially examine transactional data to identify patterns and irregularities, highlight the probability of fraud, support businesses to control financial losses, protect reputation, and maintain the integrity of supply chain operations.
• Risk Management
  In the realm of risk management, Generative AI steps in as a powerful ally, capable of evaluating a spectrum of potential hazards. From fluctuations in currency values to unexpected geopolitical shifts, AI provides invaluable insights to businesses. These insights empower proactive risk mitigation strategies, ensuring supply chain stakeholders navigate financial uncertainties with agility and maintain steadfast stability.

In Essence

The applications of Gen AI in supply chain management are endless. By exploring its potential, businesses can make smart use of the technology to model diverse strategies and streamline decision-making. Apart from the conventional options, Gen AI can also be tactfully deployed to come up with innovative packaging approaches. From designing eco-friendly materials to optimizing packaging shapes for efficiency, Gen AI opens doors to novel solutions that resonate with modern consumers and address evolving market demands.

The more you train Gen AI with constant trial and innovation, it can offer game-changing solutions to take the supply chain management game a notch higher.

Calsoft has been engaged in transforming businesses with the power of technology for over two decades with its AI/ML capabilities. We have partnered with a range of clients to help them transform every aspect of their business with state-of-the-art AI/ML solutions. So, whether you are looking for a cutting-edge solution or have complex supply chain management issues, we can support you in everything AI, right from ideation to execution.