Top Use Cases of AI in Supply Chain Optimization

The related data will help them target their customers with customized offerings. AI-enabled tools can help businesses improve the efficiency of their existing workforce. This article explores the scope of AI in logistics and supply chains and the benefits on offer. Pfizer was able to design special shippers enabled with GPS-powered data loggers, also equipped with reusable batteries which provided total visibility on the temperature, location, and status of every single dose.

  • ASCM is an unbiased partner, connecting companies around the world with industry experts, frameworks and global standards to transform supply chains.
  • Digital transformations can force internal teams to overcome silos and even restructure to facilitate increased collaboration.
  • Rather it may not make sense to run them in real-time as it will create more confusion!
  • Web scraping, social media listening, and translation can help to track data from internal and external sources.
  • Cost inefficiencies, technical downtimes, labor shortages, and bad customer experience can be disastrous for any business.
  • With the latest demand and supply information, machine learning can enable continuous improvement in the efforts of a company towards meeting the desired level of customer service level at the lowest cost.

Supply chains have seen the need for resilience exacerbated in recent times, given the massive global disruptions caused by the pandemic. This has triggered the industry’s response to look towards advanced technology, like artificial intelligence and machine learning, to optimize their current operations. AI is being used at every stage of the supply chain to improve efficiency, minimize the impact of global worker shortages, and find safer and smarter ways to get goods into the hands of consumers. Gramener is a design-led data science company that excels in building custom data and AI applications for supply chain and logistics companies. Growing businesses have various sources of inventory that require receiving and sharing their ‘available-to-promise’ picture in real-time. As such, inventory management has become increasingly complex due to accessing information on a global scale.

How Gramener can Help With Its Data and AI Solutions for Supply Chain

For instance, if you go through companies using AI in supply chain case studies, you will find they manage to strike the right balance and shorten lead time. I have led or supported the growth of multiple products and services in the AI, IoT, cloud , software, hardware, integrated systems, and consulting markets. I am a marketing, product and business leader with a broad technology and business experience in organizations and groups ranging from tens to thousands of people.

AI Use Cases for Supply Chain Optimization

These systems can also help you understand which products are best suited for different shipping containers and how many boxes each product needs to ship efficiently. While there are several benefits of AI in the supply chain, let’s look at the essential ones in detail. The world’s leading producer of premium cars is using AI and ML in its supply chain to ensure sustainability.

Enables Improved Storage Efficiency

As a result, it is possible to cut through the most trafficked area and uneven road conditions. Simultaneously, it helps you save time, money and reduce the wear and tear of your truck tires. As per reports, it is believed that using such advanced AI enabled GPS for supply chain delivery; you can save an estimated $50 million per year. Using machine learning models, companies can enjoy the benefit of predictive analytics for demand forecasting. These machine learning models are adept at identifying hidden patterns in historical demand data.

  • But with more data sources, more computational power and more server capacity will be needed.
  • Both data modeling and AI precision are needed to determine the most efficient ways to get the goods on and off the containers.
  • This will improve customer satisfaction, which can help you to build stronger relationships and ultimately—secure more sales.
  • With thecloud, a company can connect this data to create one single and trusted source of truth.
  • The AI may be used to report unrecognized safety hazards promptly so that a contingency plan can be activated.
  • Therefore, to manage the complexity of the modern supply chain, your business needs to embrace these smartly designed solutions aligned with your everyday needs.

As the largest non-profit association for supply chain, ASCM is an unbiased partner, connecting companies around the world to the newest thought leadership on all aspects of supply chain. With UbiOps you can deploy and maintain your Python / R models easily, quickly and without any IT dependency. Simply put, if the model stays on the developer’s laptop, the end-user cannot access it. And what if the laptop gets stolen, has insufficient processing power, or gets hacked?

Why is Machine Learning Important to Supply Chain Management?

Mosaic built a multi-phased model that not only predicts demand at the item level, but also optimizes the pricing mix while accounting for potentially limiting elements like inventory drawdown. The supply chain is a diverse and complex domain and manufacturing industries must align with its workflow to remain competitive. High calibrated competencies are required to sync and manage multiple activities during warehouse management, inventory management and product delivery. Even a small technical glitch and machine downtime can cost you billions of dollars in revenue loss and time to fix the issue on time. Given the imperfections of human resource input into the inventory management system, artificial intelligence can fill the gap. When the flow of goods in and out of factories is controlled through AI, there is increased productivity in terms of processing times for orders.

AI Use Cases for Supply Chain Optimization

Companies can optimize their supply chains to enable improved decision-making and reduce risks. This information enables companies to make fast decisions, so they don’t have to wait till month or quarter-end to find out how much stock they have at each location. When planning the delivery of goods on particular days and times, supply chain scheduling plays an important role.

Supply chain insights

Knowing the quantity and location of available-to-promise inventory and where it resides is critical for businesses to meet and exceed customer expectations. Being able to communicate to customers the estimated time a product will arrive is increasingly valuable and necessary in this highly competitive age. With automated rebalancing, companies can use AI-predictive rules to access in-store and warehouse inventory, going beyond linear, rules-based sourcing to predict and meet seasonal customer and margin needs.

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The data for analysis can be gathered from internal sources, such as orders and sales, or external sources, for example, macroeconomic factors, brand sentiment, and the number of COVID-19 cases. As a result of such planning, AI is capable to return a detailed report of potential consumption volumes, broken down by customer and location levels. And with the detailed demand data on hand, businesses can optimize their production volumes, reduce inventory levels, store more goods closer to their customers, and cut down on unnecessary shipments. As supply chain companies shift their focus from products to outcomes, traditional business models will become dated and then obsolete altogether, with the bodies and brands of the laggards and losers scattered along the way. With global supply chains strengthening their roots, competitive pressures will force firms to extract every possible ounce of cost from their respective operations. This is even more pronounced for local, regional, and national firms that are limited in their economies of scale, currency hedge capabilities, market concentration, and limited technology and operational budgets.

Machine Learning in Supply Chain

The use of artificial intelligence is particularly relevant for large volume transactions. Despite all challenges, the supply chain industry is on the cusp of new dawn due to artificial intelligence . As a consequence, NVOCCs, logistics service providers, warehouse operators, and freight forwarders can improve their operations in terms of service quality, efficiency, and pace. Issues faced in logistics and supply chain due to the scarcity of resources are well known. But the implementation of AI and machine learning in the supply chain and logistics has made the understanding of various facets much easier.

  • A recent study by Gartner also suggests that innovative technologies like Artificial Intelligence and Machine Learning would disrupt existing supply chain operating models significantly in the future.
  • In other words, it is intelligence that is operationalized by machines rather than human resources.
  • Ocado also put much effort into fraud detection using machine learning technologies.
  • This is useful in assisting employees put the pallet in the correct order and release product as per their shelf life.
  • Due to a lack of collaboration and integration with suppliers, many supply chains, such as food and automotive, faced serious disruptions during the global pandemic.
  • Supply chains can’t get the insight they need because data is siloed, and they lack end-to-end visibility — ultimately this impacts their ability to meet customer needs.

About 60% of projects dealing with AI in supply chain management are either delivered late or over budget. We have laid out an AI adoption roadmap to help you overcome AI implementation challenges and ease your supply chain transformation journeys. Our data & analytics applications enable logistics companies to boost their operational excellence & build a robust warehousing & distribution network. For example, imagine running a logistics company that ships products from point A to point B.

With access to advanced AI integrated with deep learning, it is easier to shuffle through essential data involving number of order placed, order types, location and type of shipment. This helps unearth real cause of charge back while reducing disputes among peers. LOCOMeX, with its AI-Powered Data-driven solutions andSupply Diversity Program Management Software, can assist you AI Use Cases for Supply Chain Optimization in achieving the Supplier Diversity objectives for your business. You can track your various Tier-2 spending with DivedIn Tier-2, a cloud-based, AI-powered invoice-to-pay automated tracking application. Also, The Supply Diversity Reporting Software feature suggests adjusting your category spending with compatible suppliers to get the greatest outcomes for your diversity goals.

AI Use Cases for Supply Chain Optimization

If you haven’t yet had formal discussions about new technology integrations, decide what these integrations might help you achieve. Weigh those against the hypothetical costs of implementation — including technology-acquisition expenses; the effects of temporary productivity disruption; and the labor costs of installation, setup and training. Bringing in the perfect balance here is mastering the art of inventory and warehouse management.

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Originally published on April 14th, 2022, updated on January 9th, 2023
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Top Use Cases of AI in Supply Chain Optimization

by David Harutyunyan time to read: 7 min
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