Supply chain software draws private equity as pipeline bulges

A good example of AI in supply chain is how Ducab, a leading cable manufacturer, implemented an AI-powered supplier portal to streamline its supplier network. It enabled the automation of supplier pre-screening and self-registration, ensuring that only qualified suppliers get added to the database. AI-enabled SRM software can aid in supplier selection based on factors such as pricing, historic purchase history, sustainability, etc.

This mindset—that the supply chain team is a service organization that exists to serve constituents in other departments—is the basis for the more holistic and effective approach to supply chains we are seeing today. Supply chain digitization is the process of implementing technologies into different aspects of the supply chain. This could be via automation, data analysis, AI or other implemented technology, and it can serve varying purposes in boosting supply chain efficiency. Ultimately, the goal of supply chain digitization is to create a more agile and customer-centric supply chain that enhances accuracy and minimizes the need for human intervention. Corporations have been increasingly relying on artificial intelligence (AI) in supply chain for demand planning and procurement, while exploring its use in other areas, such as standardizing processes and optimizing last-mile delivery.

When it’s time to search for a new supplier, artificial intelligence can help you evaluate candidates by automating a scoring system across multiple criteria, such as delivery speed and compliance. After you find the right partner, natural language processing will assist you with contract drafting and review. However, he believes that many organizations have pivoted or begun to pivot from a “complete focus” on maximizing efficiency and rebalancing by increasing flexibility and preparedness. “People have tended to make supply chains lean because cost is a big factor. Yet the supply chain has challenges all the time,” Mohamed notes. To see more about how clean, connected data is the foundation for transformative supply chains, read the new thought leadership paper “Building intelligent, resilient and sustainable supply chains” today.

In this section, I’m exploring a selection of innovative supply chain analytics use cases that illustrate the transformative impact this discipline can have on organizational efficiency, profitability, and resilience. AI-powered analytics can analyze real-time data on inventory levels, sales trends, and customer demand to forecast future requirements accurately. This enables companies to optimize inventory positioning, minimize stockouts, and avoid costly excess stock, ensuring the right products are available at the right time and location. This white paper highlights the critical importance of real-time visibility in supply chains. It will provide an in-depth discussion of how real-time data can enhance supply chain resilience and offer insights into the technologies and strategies businesses can adopt to improve their supply chain operations. The ultimate goal is to demonstrate that real-time visibility is not just a competitive advantage but a necessity to remain profitable.

For instance, AI-powered computer vision systems can automate and improve the quality assurance of finished products. AI-enabled technologies such as cobots are helping drive efficiency, productivity, and safety through automated warehouse management. Driverless cars and last-mile delivery robots can transform supply chains by decreasing dependence on human drivers. Autonomous trucks can cross vast distances without the need to rest, while AI-powered drones are particularly useful for locations that are hard to reach or are dangerous for human drivers. McKinsey reports that using AI-driven forecasting tools reduces error by up to 50%, decreasing missing products and consequent lost sales by approximately 65%.

Demand is more granular and segmented, to satisfy differing fulfillment requirements in various categories and regional markets, while tolerating promotions and other variables that enhance volatility. The entire organization becomes more agile and customer-centric, leading to an increase in revenue of 3 to 4 percent. Given the rapid-fire shifts in demand due to the pandemic, there is a real risk that traditional

supply chain planning processes will be insufficient. Companies run the risk of product shortages, increased costs from stock, inventory write-offs, and related inefficiencies up and down the value chain.

What are the components of modern supply chain data analytics?

In this way, the blockchain tracked each batch of beans all the way through the supply chain. In addition to using blockchain to offer consumers the ability to track and trace yellowfin tuna, Bumble Bee is in the process of capturing data to provide the same level of visibility to the fishermen and the buyers. A private node, which contains a company’s private data, is owned and controlled by each company. A public node contains information that different companies need to share, such as product data. In May, Merck, IBM, KPMG and Walmart announced the completion of the pilot program, according a Merck press release. “When customers purchase a blockchain-enabled diamond, they can gain access to a password protected secure digital vault, including the chain of custody information for their diamond,” Gerstein said.

For instance, stock level analysis can identify when products are declining in popularity and are reaching the end of their life in the retail marketplace. Price analysis can be compared to costs in the supply chain and retail profit margins to establish the best combination of pricing and customer demand. AI-driven solutions for Machine Learning in supply chain will enable organizations to address supply chain challenges and reduce the risk of disruptions.

A question from a supply chain manager (“Where do I have excess inventory?”) or a buyer (“How is my vendor performing?”) becomes a simple question rather than a complex exercise in bringing disparate reports together. Keeping an efficient supply chain process reduces the risk of lost sales due to shortage of material or stock, ensuring that companies can manage optimal inventory levels. Generative AI solutions can integrate data from sales, marketing, production, and distribution to generate more accurate and comprehensive plans. This helps businesses align their strategies across departments, optimize resource allocation, and better respond to changes in demand and market conditions. Myriad use cases for supply chain analytics and AI exist, and the number continues to grow. Some are more difficult to scale than others, and the impact on key business priorities can differ across use cases.

It’s also important to create an additional approval process for abnormal activity to avoid ordering too much or misinterpreting rare occurrences, he said. “If one chain of events happens, I can automatically contact customers, notify accounting and submit [bills of lading],” Doris said. Doris’ team implemented Boomi Flow, an RPA service, to eliminate repetitive tasks from data entry and EDI, and to expand workflow into other areas. RPA technology is not as sophisticated or fast as some other integration techniques, but can be easier to implement. One use case that’s becoming increasingly important in the wake of COVID-19 is scenario modeling, often done with the help of a digital twin.

Data from various sources like point-of-sale systems, customer relationship management (CRM) systems, social media, weather data, and economic indicators are integrated into a centralized platform. Machine learning algorithms, statistical modeling, and predictive analytics Chat GPT are applied to the integrated data to identify trends, seasonality, and other factors influencing demand. This is especially true for supply chain management, where even subtle changes can significantly impact costs, customer satisfaction, and ultimately, profitability.

This method uses advanced analytics to model and evaluate various future scenarios that could impact a company’s supply chain. The design of the supply chain network will dictate the capacity of the business facilities, as well as the movement of raw materials, intermediates, and finished goods from source to consumption. Decision-makers must consider numerous complex variables, such as labor costs, customer locations, and available transportation networks. Due to the scale and complexity of modern supply chains, these decisions are typically supported by prescriptive analytics. AI systems are able to process huge amounts of data, such as news, images, market trends, and social media posts, and predict when and where potential risk events might happen. Knowing this information, companies can save money and avoid potential charges or penalties.

Similarly, the transition from autonomous vehicles overseen by humans to fully automated vehicles without human intervention is almost ready to expand from controlled closed-loop environments to public roads. Frequent communication between a company and its suppliers and between a company and its customers is key for an efficient supply chain, but making communication as effective as possible can be challenging. Instead of an all-or-nothing approach to supply chain automation, RPA is most effective when targeted at subprocesses to improve high-volume, repetitive, error-prone tasks.

AI-powered tools can also help track and analyze supplier performance data and rank them accordingly. To improve demand planning in your business, check out our data-driven list of Demand Planning Software. AI gives supply chain automation technologies such as digital workers, warehouse robots, autonomous vehicles, RPA, etc., the ability to perform repetitive, error-prone tasks automatically.

Modern supply chain analytics examples

The information on KPIs can be made available to management in real-time using a suitable dashboard. The demand numbers thus finalized are released to the next module (Supply Planning) in the desired time buckets (day, week, etc.). Companies have found that implementation is most successful when supported by four key elements (Exhibit 2). “So, either the supplier messed up or the shipping company messed up, and they didn’t manage the cases of beef patties in the right temperature range,” he said.

Organizations can use GenAI models on historical sales data, market trends and other factors to simulate potential supply-and-demand scenarios and improve their demand forecasting accuracy. Tracking demand patterns can help organizations mitigate disruption and avoid stocking issues. In many companies, processes have become increasingly complex due to global expansion and growing customer diversity—and, therefore, less efficient and more costly.

As investors pour cash into the technology, executives are racing to determine the implications for operations and business models. The shift to modern data analytics in the supply chain represents a significant transformation, with a broader range of data sources, advanced analytical techniques, and a more integrated, end-to-end approach. Brilliant Earth has integrated Everledger’s blockchain technology into its supply chain to more securely track the origins of its diamonds and provide greater assurance to customers of its responsible practices, Gerstein said. Partnering with a seasoned AI software development company like Intellias offers companies deep technical expertise and agility.

Just under half said the same about ML/deep learning and sentiment monitoring analytics. Simform partnered with a leading European car manufacturer (with operations in 12 countries and over 60 models in production) to optimize production planning and scheduling. They developed an AI-powered General Ledger Recommendation solution that analyzes historical purchase and invoice data to suggest the most appropriate general ledger account at the point of purchase. It was embedded directly into Accenture’s BuyNow procurement platform, which now helps buyers assign correct accounts and improve accuracy, efficiency, and cost of downstream accounts payable. The customer now has access to resources like online catalogs, specialized search tools, etc, to compare the prices of different products, which makes setting the optimal price a top priority for businesses. Build intelligent solutions to optimize your supply chain with Simform’s AI/ML development services.

The system continuously monitors production, enabling early detection of issues and facilitating root-cause analysis when problems occur. Companies can provide accurate ETAs and status updates to customers, enhancing their service quality. It also aids in risk management by allowing close monitoring of sensitive or high-value shipments and ensures compliance with regulations, especially for goods with specific handling requirements.

Why Use Graph Technology for Supply Chain Management?

Better visibility allows for better coordination and collaboration among supply chain partners, reducing delays, optimizing logistics, and minimizing waste. The future of AI in supply chain holds the promise of further optimization and automation, allowing businesses to predict demand, streamline inventory management, and enhance overall operational efficiency. AI-powered solutions are anticipated to play a pivotal role in driving cost savings and ensuring supply chains are more resilient and responsive to ever-evolving market dynamics.

Supply chain analytics examples are vast, limited only by the creativity of those who seek to leverage its powerful insights. Additionally, the role of automation and optimization has become more prominent, with autonomous, self-learning algorithms enhancing efficiency and driving continuous improvement. So, let’s dive in and uncover the secrets to unlocking the full potential of your supply chain with modern data analytics. GSF is part of the IBM Food Trust, a network that uses blockchain to track and trace food as it moves along the chain among wholesalers, suppliers and retailers and provide them with transaction details. Specific stakeholders were tasked with entering data at three stages of the chain — bean collection, local trader purchasing beans, and international trader buying beans from local merchant.

The result is few companies can run effective scenario analysis to determine the financial consequences of important decisions. Enabled with a raft of technology developments, a new paradigm is emerging in supply chain management. One where organizations can respond quicker to day-to-day requests, proactively address problem solving, and reduce errors and inefficiencies. Addressing these challenges requires a platform that enterprises can own, shape and scale per the business needs. At IBM we have embraced a hybrid cloud, component-based architecture that is built on open technologies. Ingesting high volumes of data at speed and contextualizing them to each persona is a given.

AI and other advanced technologies are quickly reshaping the very core of supply chain management. KPMG professionals believe organizations with the right approach and culture can harness these seismic shifts. A solution is to adopt a use case-driven approach to proactively address data quality issues. By focusing on specific use cases, organizations can prioritize data quality improvements where they matter most, thereby gradually refining and improving their datasets. With a future that promises autonomous, self-learning machines seamlessly managing the broader supply chain process, now is the time for organizations to overcome the inherent silos and enterprise systems that will restrict their progress.

But we must choose to embrace this new technology and make it part of the fabric of everything that we do. Generative AI models can analyze various sources of visual or textual data, such as traffic conditions, fuel prices, and weather forecasts, to identify the most efficient routes and schedules for transportation. The AI can generate multiple possible scenarios, and based on the desired optimization criteria, it can suggest the best options for cost savings, reduced lead times, and improved operational efficiency across the supply chain. For the first time, companies can actually capture data from across multi-echelon supply chains, consolidate it in the cloud and apply robust AI models to it to give companies a real-time view into the state of their suppliers. Scenario modeling can then help a company identify the best alternatives so the organization is prepared if a disruption actually occurs. As technologies such as digital twins, machine learning (ML) and the internet of things (IoT) continue to mature and proliferate, companies everywhere can begin to do things never before possible.

The prominent challenges of implementing blockchain in supply chains include scalability issues, regulatory compliance, interoperability, and industry adoption. Overcoming these challenges requires careful planning, collaboration, and a deep understanding of both blockchain technology and industry-specific requirements. Potential applications span planning, manufacturing, product life cycle, supply chain collaboration, and track and trace.

The system generates hyper-localized forecasts for every SKU and location by incorporating factors like local events, seasonality, pricing, and promotions. Once done, its AI-powered segmentation categorizes forecasts into “no-touch (no human intervention),” “low-touch (minimal human intervention),” and “high-touch (significant human intervention)” areas to streamline the planning process. Traditional demand forecasting methods like time series analysis and regression models rely on historical sales data to identify trends and seasonality.

Employing optimization algorithms and decision-support tools to recommend the best course of action based on the insights generated from predictive analytics. Enabling supply chain professionals to make more informed decisions on inventory management, transportation planning, and supplier selection. So, supply chain professionals should thoroughly approach inventory planning as it directly impacts a company’s cash flow and profit margins. Inventory management is one of the most typical Machine Learning use cases in supply chain. With ML, you can predict demand growth based on data sourced from many areas like the marketplace environment, seasonal trends, promotions, sales, and historical analysis.

“When we get a product back that we can resell, we type in that product identification information, which contains product data, manufacturer data and the serial number into MediLedger’s product verification system,” Hahn said. This is to ensure that no one can introduce a counterfeit drug into the supply chain as well as to ensure that the drug is actually coming from the person who bought it, Hahn said. The final report, which was submitted to the FDA in February, included several recommendations that discussed the value of ultimately moving toward an industry standard for interoperable blockchain. Merck and Walmart, along with IBM and KPMG, are testing blockchain as part of a program to improve drug safety and security. It’s exceedingly difficult to trace them from mining and the many handoffs along their supply chain.

You can foun additiona information about ai customer service and artificial intelligence and NLP. By leveraging historical data and current trends, AI can forecast potential supply chain disruptions. Supply chain risk management involves identifying, assessing, and mitigating potential disruptions and vulnerabilities across the supply network, from raw material sourcing to final product delivery. AI helps by enhancing capabilities to predict, prevent, and respond to supply chain-specific risks. AI’s ability to quickly sort through massive datasets, make predictions, and respond to queries in natural language is driving its rapid adoption.

Nearshoring supports risk reduction with the additional benefit of reducing logistics costs. It also allows for less capital tied up in inventory as the amount of inventory in the supply chain is reduced. For example, if an organization manufactures goods in China, they may have three months of work-in-progress at the supplier along with three months of inventory in transit. This translates to three to four months of inventory in the supply chain at any given time. However, if they source from Mexico and transition to three days of transit time, they can cut their inventory in the supply chain by roughly 80% and still be safe.

They can also work in conjunction with AI-based intelligent routing systems that coordinate between multiple logistics partners, such as road freight, cargo ships and air freight. The bots are capable of automatically assigning a delivery partner based on the location of the products. RPA bots act on this information by automating the process of scheduling maintenance, notifying affected customers and updating financial plans, she said.

Businesses can use data analytics in supply chain to set and track emissions reduction targets, optimize operations, inform supplier selection, and enhance sustainability reporting. It can be applied to transportation route optimization, energy source selection, product redesign, and supplier engagement. To mitigate disruptions, businesses can implement early warning systems, maintain flexible capacity, optimize inventory levels, and diversify suppliers. They can also enhance collaboration with partners, develop agile decision-making frameworks, and prepare financial buffers. The scope of supply chain analytics has expanded from siloed, function-specific views to a more integrated, end-to-end approach across the entire ecosystem. The timeliness and responsiveness of analytics has also improved, with modern approaches leveraging real-time data streams to enable rapid decision-making, in contrast to the lags of traditional methods.

Sustainability is currently a major focus for many organizations, and GenAI can potentially highlight areas for improvement. New tech and fluctuating demand can lead to operational challenges, and GenAI can potentially suggest how to improve. Business leaders should develop a resilience automation strike team and a roadmap to scale up any processes using automation, which makes them more resilient, said Craig Le Clair, vice president and principal analyst at Forrester Research.

This might involve diversifying supplier networks, implementing redundancy measures, or optimizing inventory levels – all informed by the insights gleaned from in-depth analytics. Modern supply chain analytics must provide robust visualization and reporting tools that allow supply chain professionals to access and interpret data-driven insights easily. Whereas traditional approaches relied on limited, internal data sources, modern analytics harnesses a much broader range of data, including external, unstructured, and real-time information. The analytical techniques have also advanced, moving from basic descriptive methods to sophisticated predictive modeling, machine learning, and prescriptive algorithms.

For example, the food industry is leveraging blockchain to improve traceability and ensure the authenticity of products. In logistics and transportation, blockchain is used to track the movement of goods and materials across the supply chain. The healthcare sector is using blockchain to securely manage patient data and streamline the sharing of medical records. The automotive industry is implementing blockchain to track the entire lifecycle of vehicles, from sourcing to delivery.

Beyond these performance improvements, the new data foundation means that supply chains can offer completely new capabilities that support better business models. For example, you can build insight-driven relationships with customers and deliver products “as a service.” IBM Systems does this by supporting long-term engagement with hardware customers. Based on usage data, support professionals can predict when new hardware might be needed and respond more quickly to service interruptions. Many capital-intensive products are good candidates to deliver “as a service,” but only if the provider has sufficient insight to support these products throughout their lifecycle and deliver the service seamlessly. AI in supply chain management will help enterprises become more resilient, sustainable and transform cost structures. Scenario planning and simulation is one of those supply chain analytics examples that helps businesses prepare for potential risks.

Adopting new technology (i.e., supply chain digitization) could be the solution to easily overcome many supply chain disruptions. There are limitations and risks to using GenAI in supply chains — especially when implementation is rushed or poorly integrated across organizations and supply chain networks. GenAI tools are only as powerful as their input data, so they are limited by the quality and availability of data from supply chain partners. Broadly, the risks that come with fewer human touchpoints — like lack of transparency or ethical and legal considerations — are best managed with strong governance and working with experienced partners. The module generates an optimal supply plan after considering current inventory levels at all storage points, inventory norms, push-pull strategies, production capacities, constraints defined, and many other design aspects in the supply chain. At its core, SNP involves generating & solving a large mathematical optimization problem using Mixed Integer Linear Programming (MILP) technique from the Operational Research (OR) tools repository.

The shift from traditional to modern supply chain analytics represents a significant transformation in how supply chain businesses leverage data and insights to drive their operations. Intellectually independent chatbots based on Machine Learning technology are trained to understand specific keywords and phrases that trigger a bot’s reply. They are widely used in supplier relationship management, sales, and procurement management, allowing staff to focus on value-added tasks instead of getting frustrated answering simple queries. According to the survey by Supply Chain Dive, the average cost of a supply chain disruption is $1.5M per day.

In addition to traceability, SAP’s blockchain service helps Naturipe solve another problem — timeliness. Bumble Bee believes blockchain is the safest way to share data between parties due to the technology’s reputation for being incorruptible and verifiable. SAP’s blockchain technology lets consumers access the origin and history of Bumble Bee Seafoods’ fair-trade-certified Natural Blue by Anova yellowfin tuna using their smartphones to scan QR codes on 12-ounce bags of tuna steaks. The response from the drug manufacturer comes back to FFF Enterprises’ private MediLedger blockchain https://chat.openai.com/ node, which is a server running FFF Enterprises’ copy of the software that provides the saleable product verification functionality. FFF Enterprises Inc., a pharmaceutical distribution company based in Temecula, Calif., is one of several companies using the MediLedger Network blockchain to verify the return of saleable drugs, said Jon Hahn, FFF Enterprises’ CIO. “What blockchain is doing is providing an underpinning augmentative layer across the drug supply chain and enabling that unit-level visibility to be traced as the [drugs] go all the way through,” she said.

“There are other areas of technology where there hasn’t been that same focus,” warns Harris. “In the UK there are plans in place, but as far as I know, little solid progress.” “You can’t predict everything, particularly if you look only for specific things,” Naus says. “You need supply chain use cases to understand what may happen, and you can’t forecast events that are too rare.” Emile Naus, principal at consultancy BearingPoint, broadly agrees, though he notes that a downturn from 2019 and into 2020 was predictable given the industry’s usual five to seven-year cycle.

MILP is a very effective optimization technique, where variables defined can be either continuous or integer (taking binary values). The optimization problem is generated by the SCM solution based on various configurations, master data (e.g., transportation lanes, capacity, etc.), constraints such as production capacity, and of course demand numbers. The output of the SNP module i.e., optimal supply plan is released to the next Production Planning module.

However, if a more rigorous and advanced approach is desired, then one can forecast demand numbers outside of the SCM system using advanced modelling and then upload them back to the SCM system. Inventory levels can decrease by 10 to 20 percent, often with a corresponding drop in inventory costs—while still meeting required service levels. Finally, the flexibility and adaptability of modern analytics stand out, allowing organizations to rapidly adapt to changing business needs and market conditions, a crucial capability in today’s dynamic environment. “One big value proposition of blockchain is the ability to run trusted business logic on the trusted data, which can help in dispute resolution,” said Ramesh Gopinath, vice president of blockchain solutions at IBM. The MediLedger blockchain network combines a “look-up directory” accessed through distributed ledger technology with a private messaging network that allows companies to securely request and respond to product identifier verification requests. Participants in the MediLedger Pilot Project, including FFF Enterprises, have been testing and developing a variety of products to run on the network that can help companies comply with the DSCSA regulations.

Supply chain analytics refers to the use of data to gain insights and make informed decisions about the various components and processes within a company’s supply chain. The insights are extracted through statistical analysis and advanced analytics techniques (AI and machine learning). AI tools enable demand prediction in supply chains with a holistic, multi-dimensional approach. In particular, AI services use computational power and big data to precisely predict what customers want and need every season of the year. Machine Learning algorithms can analyze vast amounts of data and draw patterns for every business to protect it from fraud.

To succeed, businesses need to invest in change management and staff training, in addition to studying and implementing the technology itself. Another example is optimizing supplier evaluation, flagging suppliers as low-, medium-, or high-risk. Leveraging AI in supply chain management can help design better delivery routes and optimize fleet utilization. When considering where a supply chain team can add value, there is also the concept of service. The idea that supply chain operations serve the larger organization is not new, but transformational supply chain leaders constantly ask if they are supporting the operations, facilities, and functions that create revenue.

Another example of the ML application in the supply chain is the case of computer vision (CV) in inventory management. With the help of computer vision, the software is also able to classify objects it “sees.” For example, robots equipped with cameras will inspect your storage and automatically build a real-time picture of your inventory. CV is one of the areas where all sorts of Machine Learning techniques—supervised, unsupervised, and reinforcement learning—can be applied.

To prevent that and ensure a smooth roll-out, map the development process to the initial supply chain digitalization strategy and keep in mind the key value you intend to tap into. Prioritizing the value-creation opportunities and dividing the development process into increments according to the set priorities might help navigate end-to-end AI implementation. Instead, manufacturers could seek to invest in better data analytics operations and logistics management. AI/ML powered simulations and digital twinning may deliver needed visibility of contributory dynamics, Fairbairn adds. Real-time visibility (RTV) in the supply chain refers to tracking and monitoring the movement of goods and materials as they move through the supply chain, from suppliers to manufacturers to retailers and ultimately to the end consumers. This is achieved by integrating various technologies such as IoT sensors, GPS, and RFID.

They’re very manageable first steps that can put companies on a path to more intelligent operations that can help them effectively compete with organizations that are currently setting the bar. AI systems can process vast amounts of data from diverse sources such as weather reports, geopolitical news, and transportation logs in real-time. For instance, an AI model might analyze satellite imagery and weather forecasts to predict flooding risks in key manufacturing regions, allowing companies to proactively adjust production schedules or secure alternative suppliers. Church Brothers Farms is a family-owned farming business committed to sustainability and producing fresh fruits and vegetables all year round.

You need to estimate TCO and the profitability you will gain in the short term and in the long run. Establishing a solid emissions baseline is essential for monitoring progress and setting ambitious reduction targets. Scope 1 and Scope 2 emissions are relatively straightforward to assess however, when extending this to the full supply chain, as in Scope 3, the complexity multiplies exponentially. Jacob Roundy is a freelance writer and editor, specializing in a variety of technology topics, including data centers and sustainability.

• However, he believes that many organizations have pivoted or begun to pivot from a “complete focus” on maximizing efficiency and rebalancing by increasing flexibility and preparedness.
• “We’ve built flexibility into our supply chain,” he adds, noting that typically, however, people don’t plan for something unpredictable to happen.
• Also, consider finding a reliable tech partner who will consult you on AI and help you build and customize AI-driven solutions.
• This allows route optimization algorithms to dynamically adjust routes and avoid congestion, saving time and reducing fuel consumption.
• For instance, Nike uses AI to predict demand for new running shoes even before they are released.

Fleur Doidge is a journalist with more than twenty years of experience, mainly writing features and news for B2B technology or business magazines and websites. She writes on a shifting assortment of topics, including the IT reseller channel, manufacturing, datacentre, cloud computing and communications. Tom Fairbairn, data specialist at software engineering firm Solace, says central banks now at least regularly examine supply chain issues when setting interest rates because those inflections affect inflation.

More important for the long term, the company also generated a set of future scenarios, along with recommendations to maximize both revenue and profit in each scenario. For example, in a scenario in which the forecast predicted low sales of a particular SKU, planners collaborated with marketing and sales to test that prediction through demand sensing and agree on the best path forward. Quality control analytics using statistical process control (SPC) in supply chain analytics is a data-driven approach to monitoring and improving product quality throughout the manufacturing process. It applies statistical methods to identify, analyze, and reduce variations in production, ensuring consistent quality and minimizing defects. Carbon footprint tracking and reduction involves measuring and minimizing greenhouse gas emissions across a company’s entire supply chain.

• Intelligent automation layers AI on top of RPA and can help prepare a request for quotation package and allow access to a wider set of vendors.
• Keeping track of the flow of goods in the supply chain on a system such as Food Trust helps participants track the temperature information and potentially settle any disputes, Gopinath said.
• By using region-specific parameters, AI-powered forecasting tools can help customize the fulfillment processes according to region-specific requirements.
• Another example of the ML application in the supply chain is the case of computer vision (CV) in inventory management.

For instance, Microsoft uses AI services and data science to automate document reviews and make it easier to search throughout contracts. AI leverages historical data to forecast future shopper demand and make sure the company has adequate inventory levels. For instance, Nike uses AI to predict demand for new running shoes even before they are released. Back in 2018, Nike precisely predicted demand for the Air Jordan 11, which were the most popular running shoes of the year.

This eliminates delays that would normally be attributed to manual labor, improves response times, reduces employee effort and enhances operational efficiencies. Zara has adopted AI and robotics to streamline its BOPIS (Buy Online, Pickup In-Store) service. AI robots fetch online orders from the warehouse to address long customer queues and waiting times. These robots can retrieve 2,400 packages, scan barcodes, and deliver items to designated pickup points. The automated system lets customers quickly retrieve their orders by entering a PIN and scanning a barcode. Zara has improved its online order fulfillment speed and efficiency by leveraging AI and robotics.

Brilliant Earth, a retailer of ethically sourced diamonds and fine jewelry, is tracking the provenance of its diamonds on the Everledger blockchain, said Beth Gerstein, co-CEO of the San Francisco-based company. The reality, though, is that blockchain in supply chain use cases are largely in the testing phase. To understand under what circumstances Machine Learning use cases in your supply chain would benefit your business, you need to conduct a Discovery Phase and calculate ROI.