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AI: A New Guardian In Supply Chain Management
Food and Beverages Tech Review | Monday, September 07, 2020
Defining the utilization of AL in increasing the efficiency and sustainability of supply chain in industries.
FREMONT, CA: The sustainable scheduling and management of logistics in many enterprises is not up to the mark; even today, supply chains have not been environmentally friendly. The fine line of control over factors like speed, flexibility, cost, and carbon footprint is blurred when looked in terms of shipping or transportation of the goods. It is highly unlikely for the enterprises to encourage sustainable practices with the current trends for shipping.
Newer technologies like big data analytics and AI have enforced a positive push over the companies, enabling the supply chains to function both efficiently and sustainably as possible. AI adoption has always resulted in causing dramatic effects on the supply chains, assisting the companies in benefiting from the most cost-effective and sustainable routes for shipping.
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To implement these technologies, firms have focused on jumping to a sharing economy in the case of supply chain management. By utilizing AI, data, and unique algorithms, a paradigm shift in the handling and operations of supply chains can be enabled along with the encouragement to work together. There are some recognized specific areas where these solutions can be applied to build a smart, efficient supply chain.
• The Shipping Co-Operative:
With the use of technology, AI-based algorithms can be utilized among organizations to incorporate a shared use of shipping and other transport methods. The algorithms developed specifically for this purpose will identify opportunities to share the commuting resources by collaborating with several transporting firms.
The algorithm also uses the data from GPS to schedule and manage the logs from collections and drop-off points of the shipping firms providing enhanced coordination. With real-time updates recorded as well, the entire system and the participants of the system are aware of the status of the shipment at all times with specific updates about the routes, stocks, and costs. The state of economic co-operation among the organizations has also opened up gates for mutual benefitting from various supply chains with other firms as well.
• Synchromodality:
As different packages need to reach at different time intervals, and most times the package delivery date may vary at the last minute. With the use of the physical internet, the syncromodalilty or the synchronization in the scheduling of shipping routes can be aptly adjusted depending on the methods of transport. This method can take into considerations the emergencies and can make the deliveries without compromising on quality or brand integrity.
With the use of real-time data system, the transportation method of the ongoing transaction can be adapted suitably while the shipment is en-route. Even during transportation, the algorithm has the power to select the most efficient and sustainable option in real-time.
• Deep Reinforcement Learning:
Deep reinforcement learning is an element of machine learning that involves the training of algorithms to enable better decision-making skills. The training method is a trial and error process where the technology is taught and diverted towards making the ideal solution at that point. With positive reinforcement, the robot is trained to streamline the random activities to repeat only those actions which show a good outcome for the institution.
Enterprises who use deep reinforcement learning can train its own AI module to conduct complex and positive supply chain decisions which include numerous variables. By doing the industry-specific learning program, the AI can accurately determine the details of the shipment to be sent, when to ship the products and the best mode of transportation.
The same program can be used to create algorithms, which encourage and efficiently carry out collaborative shipping, and use syncromodality approach to rejuvenate the company’s inventory. The interlinking of all the aspects mentioned above will result in creating the most efficient supply chain possible for an organization.
As the integration of newer technologies like AI to the supply chain occurs, the company converts into an environmentally supportive one with the reduction in the pollution and pressure on the atmosphere. The company’s carbon footprint will also reduce allowing the company to make a positive impression on the world.
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This article looks into the factors that affect the production and sales of poultry farming.
Over the past few years, shifting consumer behaviors have revolutionized the food industry. The 2010s saw a rise in popularity for chicken. Additionally, consumption has accelerated over time. The factors which affected the poultry production include:
Transparency Gained Significance:
The well-being of animals used for food became a significant problem during the 2010s due to harmful public relations initiatives by animal rights organizations. The majority of the respondents of a survey by the US Farmers and Ranchers Alliance said it was crucial for them that restaurants and supermarkets disclose information about the methods used to grow and raise their food.
As a result, several poultry growers and dealers tried to increase transparency regarding the care, handling, and feeding of birds.
There were Numerous Plant-Based Possibilities:
Ten years ago, vegans and vegetarians were the leading consumer group for plant-based cuisine. Almost 90 percent of consumers of plant-based proteins today do not consider themselves vegetarians or vegans. Flexitarians cite environmental and health issues as justifications for cutting back on meat intake but not altogether quitting it.
Technology Has Affected How People Eat:
With the rise in popularity of smartphones, new apps have made it simpler and more convenient to order meals from any restaurant imaginable, meal kits delivered to the doorstep, and groceries. Fast-paced lifestyles and hectic schedules have increased consumer interest in takeout, making up over 25 percent of all restaurant industry sales.
Organic and Antibiotic-Free Have Gained Popularity:
Demand for organic and antibiotic-free poultry has skyrocketed in the last ten years. The Organic Trade Association reports that organic food sales have more than doubled in past years.
Consumer attention has been drawn to antibiotics in chickens raised for human consumption due to worries about microorganisms resistant to antibiotics. Tyson Foods, a manufacturer of chicken, has switched all of its products to "No Antibiotics Ever."
In short, over the years, the production and sales in poultry farming have witnessed a drastic change due to these trends or factors.
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The global food supply chain, increasingly complex and interconnected, faces a persistent challenge in ensuring food safety. Traditional quality assurance methods—often characterized by time-consuming culture-based lab tests and reactive recalls—are no longer sufficient to mitigate the risks posed by foodborne pathogens like Salmonella, Listeria, and E. coli. The industry is undergoing a paradigm shift, moving from reactive testing to proactive, real-time safety management enabled by the integration of modern pathogen-testing systems.
The Three Pillars of Modern Pathogen Testing
Modern food safety advancements are anchored in three key technological pillars that work in synergy to create a faster, smarter, and more resilient pathogen testing ecosystem. The first of these is rapid detection technology, which has eliminated the lengthy delays associated with traditional testing methods. Molecular techniques such as Polymerase Chain Reaction (PCR) and quantitative PCR (qPCR) now enable results within hours, allowing manufacturers to halt the distribution of contaminated products before they reach consumers. Complementing these, biosensors and immunoassays offer portable, high-sensitivity solutions that can be deployed directly on production floors to detect contaminants almost instantly. Next-Generation Sequencing (NGS) provides a complete genetic profile of pathogens, making it indispensable for confirmatory testing and tracing contamination sources across the supply chain with unmatched accuracy.
Equally transformative are the advancements in automation and AI-driven intelligence, which have revolutionized how food producers monitor, analyze, and respond to safety risks. Automated sample preparation systems—powered by robotics and microfluidics—reduce human error while increasing testing throughput and consistency. IoT-enabled sensors now perform continuous on-site monitoring across processing lines, water sources, and packaging zones, triggering real-time alerts at the first signs of contamination. The integration of artificial intelligence (AI) and machine learning (ML) adds a predictive dimension, analyzing vast datasets—from historical test results and sanitation logs to environmental factors—to anticipate potential contamination before it occurs. In addition, AI-powered traceability systems, often supported by blockchain technology, can instantly pinpoint the origins of contamination during an outbreak, enabling precise recalls rather than broad-scale withdrawals. Automated compliance tools further streamline reporting and ensure continuous adherence to global food safety standards. Recoup Technologies , by integrating advanced AI and predictive analytics, is playing a crucial role in enhancing real-time monitoring and proactive safety measures.
Impact Across the Value Chain
Integrated pathogen testing systems are reshaping every stage of the food value chain. In primary production, early detection in agricultural water or feedstock prevents pathogens from entering the processing cycle. In processing plants, real-time monitoring of ingredients and surfaces enables immediate corrective actions, reducing the risk of cross-contamination. During distribution and storage, IoT sensors and AI analytics ensure cold chain integrity by tracking microbial and temperature fluctuations in transit. In recall management, the combination of rapid diagnostics and blockchain-enabled traceability empowers producers to execute targeted, data-driven recalls—minimizing waste, financial loss, and reputational harm while ensuring consumer safety.
Dalsorb provides advanced filtration technologies that remove contaminants, ensuring enhanced pathogen control and safety across the food supply chain.
By fusing rapid molecular techniques with automation and predictive AI, the food industry is transitioning to a proactive, prevention-focused food safety culture. This integrated approach is not just a technological upgrade; it is an essential strategy for ensuring consumer trust, upholding regulatory compliance, and building a more resilient global food supply.
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To assist breweries in the making beer more sustainably, Kerry has released an online calculator that can aid beer makers to see what the potential is for their brewery situation.
Kerry, the leading taste and nutrition company, launches a new sustainability calculator that allows brewers to seamlessly analyze the potentially significant advantages of using sustainable brewing processing aids and enzymes. The tool's purpose is to allow people involved in the brewing sector to quickly see what cost savings and environmental sustainability get could be delivered to their unique brewery situations.
The sustainability calculator focuses on raw materials, brewing operations, and waste management. With these three components accounting for 35 percent of a beers overall environmental impact, the tool highlights attainable opportunities for brewers who want to reduce their environmental and bottom-line costs without incurring capital expenditures.
The calculator process starts by asking for the preferred measurement in barrels or hectoliters and then the brewery size. Respondents are asked to check boxes regarding five objectives that will assist in analyzing each brewery's unique situation. Each of these manages with brewing solutions that don't need a capex budget to implement, making them highly accessible. Respondents are asked which of these are of interest to their brewery,
The sustainability calculator then quantifies the positive impacts of using Kerry's brewing solutions. Applying all five solutions can offer significant cumulative results. These outcomes, verified by a third-party sustainability technical analysis, can deliver up to 19 percent energy use reductions, CO emissions reductions of 41 percent, and cost savings.
While progress toward sustainability in every brewery has its unique trajectory, there are impressive possibilities available to aid all breweries in reducing environmental and brewing costs. The COVID-19 pandemic has accelerated consumer interest in sustainability, opening up opportunities for brewers to take a progressive position on this topic. The latest targeted brewing ingredients and enzymes offer brewers a suite of product options to enhance sustainability within their production processes as they simultaneously reduce costs. With sustainability never as essential as today, these solutions will appeal to brewers seeking to improve their sustainability efforts, mitigate their carbon footprint, lower production costs, and develop a brand message that will resonate with customers.
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The food industry can significantly reduce food waste, improve operational efficiency, and mitigate environmental impact by leveraging artificial intelligence algorithms and tools.
Food waste is a global crisis, with nearly a third of all food produced going to waste. This equates to significant economic losses and exacerbates the issue of hunger, affecting millions of people worldwide. Effective food waste management is crucial to address this problem and build a more sustainable and responsible food system. AI technology offers promising solutions to optimize processes, minimize waste, and improve decision-making throughout the food supply chain.
AI algorithms enable accurate demand forecasting by analyzing historical sales data, weather patterns, and events. This aids suppliers and retailers in optimizing inventory management, reducing overstocking and understocking, and preventing perishable items from going to waste. AI-powered vision systems examine images and videos of fruits, vegetables, and other perishable goods and detecting defects and spoilage early. Additionally, AI algorithms analyze sensor data to ensure optimal storage conditions and prevent food spoilage.
Models that use artificial intelligence estimate the remaining shelf-life of perishable items by taking into account factors like temperature, humidity, storage conditions, and product characteristics. This empowers retailers and suppliers to prioritize selling items with shorter shelf life, reducing waste caused by expired products. AI algorithms optimize transportation routes, scheduling, and logistics, streamlining operations and ensuring timely delivery of fresh produce. By considering factors like distance, traffic conditions, delivery schedules, and storage needs, AI mitigates delays and minimizes food waste within the supply chain.
AI analyzes real-time data from sensors, sales records, and other sources to optimize inventory management. It provides valuable recommendations regarding the timing and quantity of orders and inventory allocation, minimizing waste, and preventing stockouts. AI platforms connect food producers, retailers, and food banks or charities, facilitating the efficient re-distribution of surplus food. AI algorithms match supply with demand, considering factors like location, shelf-life, and transportation logistics, ensuring excess food is redirected to those in need. Personalized meal planning, recipe recommendations based on available ingredients, and storage and usage tips for food items are some of the ways in which AI-powered applications engage consumers in reducing food waste. With AI, consumers are empowered to make informed choices and minimize waste by raising awareness and providing practical solutions.
AI technology holds immense potential in addressing the global issue of food waste. Through the use of AI for demand forecasting, quality control, shelf-life prediction, supply chain optimization, inventory management, donation and re-distribution, and consumer engagement, food systems can become more sustainable and responsible. Collaborative efforts across the entire food supply chain, supported by AI, are essential in reducing food waste and ensuring a better future for our planet and its inhabitants.
Integrating AI into food waste management also aligns with broader trends in digital transformation and Industry 4.0. AI becomes a key enabler of innovation and efficiency as the food industry embraces automation, data analytics, and interconnected systems. By harnessing the power of AI, food companies can gain a competitive edge, improve operational performance, and meet the evolving demands of consumers for sustainable and responsible food practices.
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