#Artificial Intelligence In Logistics

[medicaldevicesindustrynews]

Germany was the Leader of Smart Shipping Container Market

The smart shipping container market will grow at a compound annual growth rate of 18.4% in the years to come, to touch a value of USD 15,341.5 million by 2030. The development of the industry can be chiefly credited to the guideline of temperature, recover security, and instantaneous GPS tracking, which these containers allow. The sensors combined into gathering and tracking data on the…

[arvistchicago]

AI and Analytics: Optimizing the Supply Chain

AI and Analytics: Optimizing the Supply Chain

Artificial intelligence and data analysis are already taking off. While many industries still struggle to recover from the effects of the pandemic, there are some industries who have embraced these technologies on a large-scale. Know more about supply chain ai software at arvist

The supply chain industry is one of them. Statista data shows that AI solutions have improved inventory management, smart manufacturing and dynamic logistic systems.

AI is primarily used to improve efficiency and productivity in the supply chain. The introduction of AI to supply chain management has resulted in more sustainability. This makes every company wonder if digitalization at this scale will benefit their supply chain business.

McKinsey conducted a recent study that determined the implementation of AI enabled supply-chain management led to significant improvements. Adopters of this technology saw a 15% decrease in logistics costs, 35% in inventory, and a 65% increase in service. This shows the power of AI-enabled Supply-Chain Management to revolutionize an industry and its importance for the modern business landscape.

Let's look at the impact of AI on the supply chain. We will also look at the impact of integrating AI services into your enterprise on your workforce, machines and software.

Artificial Intelligence in supply chain management: Data analytics and modern supply chains

AI and/or Analytics in Supply Chain refers to a process whereby smart machines can solve problems. The IIoT (Industrial Internet of Things) can automate the smart manufacturing process, which will drive the supply chain. AI-driven supply chain operations are aimed at making the supply chain more efficient.

Instrumented

Machine-generated data streaming out of IoT devices

Intelligent -

Data analytics and modeling can help you make more accurate and reliable assumptions

The Interconnected System -

Connectivity for better decisions

Supply chain data analysis can optimize workflows where large data sets are used to identify inefficiencies, forecast and drive innovation. You can use four types of supply chain analysis to make data-driven business decisions.

Here are a few examples of supply chain analytics:

1. Predictive Analytics

Predictive Analytics is a technique which uses statistical modeling and regression to understand and identify trends in historical data to make predictions for future trends.

It helps companies in the supply chain predict future outcomes and their business implications. Predictive analytics can be used to reduce risks and disruptions.

2. Descriptive Analysis

Data mining is a type of descriptive analytics that uses large datasets for the purpose of identifying patterns and generating summaries to help users gain an understanding of a situation. This type of analysis uses historical data to identify trends and make conclusions that can help inform decision making.

You can also use descriptive analysis to help you better understand analytics in the supply chain. This provides visibility and certainty for all types of internal and external information across the supply management.

3. Prescriptive Analysis

Prescriptive analysis is a powerful tool that can be used to explore how changes in the supply chain will impact outcomes. This allows for the identification of potential improvements and recommendations, which is a great resource to optimize supply chain operations.

It is important to work with logistics partners in order to maximize business value. SRM (Supplier Relationship Management) is a popular analytic method that uses a prescriptive approach.

4. Cognitive analytics is the best way to learn advanced analytics for supply chain management. It is most effective in improving customer relationships and experience. The data collected by AI-driven systems are analyzed, and then used to create dashboards and reports that answer complex questions.

Supply chain analytics is a powerful tool that can help your business pursue new ideas and meet customer needs.

[probablyasocialecologist]

The supply chain capitalism of AI. This image partially captures the supply chain of AI as a global and complex phenomenon. Natural resources, components and materials to build AI infrastructure are extracted, shipped, manufactured and produced across the globe. For instance, NVIDIA obtains tungsten from Brazil; gold from Colombia and tantalum from Kazakhstan. Minerals are assembled to manufacture GPUs by TSMC. NVIDIA sells GPUs across data centres in the world. Given the refresh rates of these materials, data centres sent their components to recycle plants or dumps. The human labour wrapped-up in this chain includes, data labellers, logistics drivers, data scientists, miners, data centre operators and electronic waste dismantlers, who are also scattered across different geographies. Source: NVIDIA (2022) and fieldwork.

The supply chain capitalism of AI: a call to (re)think algorithmic harms and resistance through environmental lens

[scifigeneration]

AI could improve your life by removing bottlenecks between what you want and what you get

by Bruce Schneier, Adjunct Lecturer in Public Policy, Harvard Kennedy School

Artificial intelligence is poised to upend much of society, removing human limitations inherent in many systems. One such limitation is information and logistical bottlenecks in decision-making.

Traditionally, people have been forced to reduce complex choices to a small handful of options that don’t do justice to their true desires. Artificial intelligence has the potential to remove that limitation. And it has the potential to drastically change how democracy functions.

AI researcher Tantum Collins and I, a public-interest technology scholar, call this AI overcoming “lossy bottlenecks.” Lossy is a term from information theory that refers to imperfect communications channels – that is, channels that lose information.

Multiple-choice practicality

Imagine your next sit-down dinner and being able to have a long conversation with a chef about your meal. You could end up with a bespoke dinner based on your desires, the chef’s abilities and the available ingredients. This is possible if you are cooking at home or hosted by accommodating friends.

But it is infeasible at your average restaurant: The limitations of the kitchen, the way supplies have to be ordered and the realities of restaurant cooking make this kind of rich interaction between diner and chef impossible. You get a menu of a few dozen standardized options, with the possibility of some modifications around the edges.

That’s a lossy bottleneck. Your wants and desires are rich and multifaceted. The array of culinary outcomes are equally rich and multifaceted. But there’s no scalable way to connect the two. People are forced to use multiple-choice systems like menus to simplify decision-making, and they lose so much information in the process.

People are so used to these bottlenecks that we don’t even notice them. And when we do, we tend to assume they are the inevitable cost of scale and efficiency. And they are. Or, at least, they were.

The possibilities

Artificial intelligence has the potential to overcome this limitation. By storing rich representations of people’s preferences and histories on the demand side, along with equally rich representations of capabilities, costs and creative possibilities on the supply side, AI systems enable complex customization at scale and low cost. Imagine walking into a restaurant and knowing that the kitchen has already started work on a meal optimized for your tastes, or being presented with a personalized list of choices.

There have been some early attempts at this. People have used ChatGPT to design meals based on dietary restrictions and what they have in the fridge. It’s still early days for these technologies, but once they get working, the possibilities are nearly endless. Lossy bottlenecks are everywhere.Imagine a future AI that knows your dietary wants and needs so well that you wouldn’t need to use detail prompts for meal plans, let alone iterate on them as the nutrition coach in this video does with ChatGPT.

Take labor markets. Employers look to grades, diplomas and certifications to gauge candidates’ suitability for roles. These are a very coarse representation of a job candidate’s abilities. An AI system with access to, for example, a student’s coursework, exams and teacher feedback as well as detailed information about possible jobs could provide much richer assessments of which employment matches do and don’t make sense.

Or apparel. People with money for tailors and time for fittings can get clothes made from scratch, but most of us are limited to mass-produced options. AI could hugely reduce the costs of customization by learning your style, taking measurements based on photos, generating designs that match your taste and using available materials. It would then convert your selections into a series of production instructions and place an order to an AI-enabled robotic production line.

Or software. Today’s computer programs typically use one-size-fits-all interfaces, with only minor room for modification, but individuals have widely varying needs and working styles. AI systems that observe each user’s interaction styles and know what that person wants out of a given piece of software could take this personalization far deeper, completely redesigning interfaces to suit individual needs.

Removing democracy’s bottleneck

These examples are all transformative, but the lossy bottleneck that has the largest effect on society is in politics. It’s the same problem as the restaurant. As a complicated citizen, your policy positions are probably nuanced, trading off between different options and their effects. You care about some issues more than others and some implementations more than others.

If you had the knowledge and time, you could engage in the deliberative process and help create better laws than exist today. But you don’t. And, anyway, society can’t hold policy debates involving hundreds of millions of people. So you go to the ballot box and choose between two – or if you are lucky, four or five – individual representatives or political parties.

Imagine a system where AI removes this lossy bottleneck. Instead of trying to cram your preferences to fit into the available options, imagine conveying your political preferences in detail to an AI system that would directly advocate for specific policies on your behalf. This could revolutionize democracy.Ballots are bottlenecks that funnel a voter’s diverse views into a few options. AI representations of individual voters’ desires overcome this bottleneck, promising enacted policies that better align with voters’ wishes. Tantum Collins, CC BY-ND

One way is by enhancing voter representation. By capturing the nuances of each individual’s political preferences in a way that traditional voting systems can’t, this system could lead to policies that better reflect the desires of the electorate. For example, you could have an AI device in your pocket – your future phone, for instance – that knows your views and wishes and continually votes in your name on an otherwise overwhelming number of issues large and small.

Combined with AI systems that personalize political education, it could encourage more people to participate in the democratic process and increase political engagement. And it could eliminate the problems stemming from elected representatives who reflect only the views of the majority that elected them – and sometimes not even them.

On the other hand, the privacy concerns resulting from allowing an AI such intimate access to personal data are considerable. And it’s important to avoid the pitfall of just allowing the AIs to figure out what to do: Human deliberation is crucial to a functioning democracy.

Also, there is no clear transition path from the representative democracies of today to these AI-enhanced direct democracies of tomorrow. And, of course, this is still science fiction.

First steps

These technologies are likely to be used first in other, less politically charged, domains. Recommendation systems for digital media have steadily reduced their reliance on traditional intermediaries. Radio stations are like menu items: Regardless of how nuanced your taste in music is, you have to pick from a handful of options. Early digital platforms were only a little better: “This person likes jazz, so we’ll suggest more jazz.”

Today’s streaming platforms use listener histories and a broad set of features describing each track to provide each user with personalized music recommendations. Similar systems suggest academic papers with far greater granularity than a subscription to a given journal, and movies based on more nuanced analysis than simply deferring to genres.

A world without artificial bottlenecks comes with risks – loss of jobs in the bottlenecks, for example – but it also has the potential to free people from the straightjackets that have long constrained large-scale human decision-making. In some cases – restaurants, for example – the impact on most people might be minor. But in others, like politics and hiring, the effects could be profound.

[taobotics]

Cargo handling has always been a time-consuming and unavoidable work, the emergence of AGV unmanned trucks, instead of people to complete the different states of the handling operation, greatly reduced people's labor intensity and improved the efficiency of the factory. The factory transportation robot system is based on a HandsFree robot and open source system, realizing the robot from map building, navigation, and motion control; it can autonomously and accurately complete the delivery of production materials under the operation scenario of human-machine mixing and provide the flexible flow of materials between production lines.

[larrysavagejrbirmingham]

Larry Savage Jr Birmingham - Logistics Communication Skills Explained

Improving your logistics communication skills is important to create a strong network of contacts and become an expert in the logistics industry. You should know this because many profit and business opportunities in the logistics industry stem from the relationships that partners and clients form.

So, to compete in the logistics industry, one must build effective communication with both potential and existing partners and customers. This article provides tips for boosting your communication skills in logistics, offering insights from logistics networking specialists. You can also read Larry Savage Birmingham – 7 Books to Read to Improve Your Communication Skills to build connections with partners for your business.

Here Are Some Tips To Improve Logistics Communication:

Pay Full Attention

Effective communication always starts with listening. Misunderstandings in communications are common with people who don’t have listening skills. All people have responsibilities, so wasting other people’s time can be bad for someone in the logistics industry.

It takes more than just physical presence to attend a logistics event. You must give your full attention in every meeting to avoid missing important details and forcing your partner to repeat information they've already said. Anyone can tell when you're not paying attention, and that's not how you should interact with current or future partners.

Practice Active Listening

You must listen actively to truly understand your logistics partner. Active listening in logistics communication requires understanding, responsive engagement, and retention of the information the other person is conveying.

Understand What’s Not Being Said

You should also be able to recognize when a prospect is lying. These are essential skills to have, and you won’t succeed until you develop the ability to read between the lines.

Speak In Specifics

You should be specific to be influential in logistics. You can prove to others that your freight forwarder is the ideal fit for his services and working style by providing concrete instances or tales to bolster your arguments.

Be A Logistics Expert

It is impossible to be particular if you do not understand what you're talking about. You should prove that you are an expert in the logistics sector and you know the issues, practices, and purchasing trends.

If you want to locate other logistics specialists who are true professionals in their freight forwarding industry, you should join a logistics network that carefully selects its members.

Know What They Don’t Know

It's not necessary to know everything about the logistics industry to be considered an expert. But you should be knowledgeable enough to manage the cargoes of your partners, have excellent assistance, and be an outstanding operator. You should also need to depend on your prospects to provide the missing information.

Recognize your knowledge gaps and ask your logistics partner to assist you in filling them in. You won't lose transactions due to erroneous assumptions; they will respect your candor about what you don't know.

Final Thoughts

As you can see, there are many ways via which honing your logistics communication skills can assist you in creating beneficial relationships for your enterprise. Finally, read Larry Savage Jr. Birmingham- 7 Skills That Can Help You Become an Expert Logistics Specialist if you want to become a master of logistics.  

[rajaniesh]

From Data to Decisions: Empowering Teams with Databricks AI/BI

🚀 Unlock the Power of Data with Databricks AI/BI! 🚀 Imagine a world where your entire team can access data insights in real-time, without needing to be data experts. Databricks AI/BI is making this possible with powerful features like conversational AI

In today’s business world, data is abundant—coming from sources like customer interactions, sales metrics, and supply chain information. Yet many organizations still struggle to transform this data into actionable insights. Teams often face siloed systems, complex analytics processes, and delays that hinder timely, data-driven decisions. Databricks AI/BI was designed with these challenges in…

[mayurblog1604]

Transform Your Supply Chain with Optimization!

At TheCodeWork, we understand that effective supply chain optimization is key to enhancing efficiency and reducing costs. By leveraging advanced technologies and data-driven strategies, businesses can streamline operations, improve inventory management, and respond swiftly to market demands.

Discover how our solutions can help you achieve seamless supply chain performance.

Learn more about our approach to supply chain optimization: TheCodeWork Supply Chain Optimization.

[solveprogrammingproblems]

ChatGPT Prompts for Supply Chain Manager

[immensitylogistics]

Immensity Logistics: Latest Trends in the Logistics Industry

The logistics industry is undergoing a profound transformation driven by technological advancements, evolving consumer demands, and a growing emphasis on sustainability. As we progress through 2024, several directions are in how goods are transported and delivered.

Adoption of Advanced Technologies

One of the most significant trends in logistics today is the increased adoption of Artificial Intelligence (AI) and Machine Learning (ML). These technologies enhance operational efficiency across various logistics functions. For instance, AI algorithms can analyze vast amounts of data to improve demand forecasting, allowing companies to optimize inventory levels and reduce stockouts.

Additionally, automation technologies, such as robotics in warehousing, are revolutionizing how companies handle inventory. Automated guided vehicles (AGVs) and automated components streamline picking and packing processes, reducing labor costs and minimizing human error. As a result, logistics companies are experiencing enhanced productivity and efficiency. 2. Decentralized Ledger Technology

The introduction of blockchain technology is another trend that is poised to revolutionize the logistics sector. Blockchain provides a decentralized and secure way to record transactions across multiple parties in a supply chain. This level of transparency is crucial for tracking the provenance of goods and ensuring compliance with regulations.

By utilizing blockchain, logistics companies can improve their tracking capabilities, making it easier to trace the movement of products from origin to destination. It is particularly beneficial in industries such as pharmaceuticals and food, where traceability is essential to ensure safety and compliance. 3. Last-Mile Delivery Innovations

The last mile of delivery continues to be a focal point in the logistics industry, as it often represents the highest cost and complexity in the supply chain. Drone deliveries are gaining traction as a viable option for transporting goods quickly and efficiently, especially in urban areas. Drones can bypass traffic congestion and deliver packages directly to consumers, reducing delivery times significantly.

Another trend in last-mile delivery is the establishment of micro-fulfillment centers. These small warehouses closer to urban centers enable faster order fulfillment and delivery. By leveraging local hubs, logistics companies can significantly cost and improve service levels, meeting the growing consumer demand for rapid delivery. 4. Sustainability Initiatives

As environmental concerns become increasingly pressing, logistics companies are prioritizing sustainable practices. This trend encompasses a range of industries that desire to reduce the industry's carbon footprint. Companies are optimizing their transportation routes to minimize fuel consumption and investing in electric vehicles to replace traditional diesel trucks. Moreover, the circular economy is gaining momentum within logistics, with companies exploring ways to recycle and repurpose materials throughout the supply chain. By adopting sustainable practices, logistics providers can reduce their environmental impact and meet the growing expectations of consumers who prioritize sustainability. 5. Digital Transformation

The logistics sector is now experiencing a digital revolution marked by the integration of Internet of Things (IoT) devices and Big Data analytics. IoT technology enables real-time tracking of shipments, allowing companies to monitor the condition of goods throughout transit, enhancing visibility and control. This capability is for perishable goods, where maintaining the right conditions is crucial.

Big Data analytics further empowers logistics companies to make informed decisions by providing insights into supply chain dynamics. This level of intelligence allows for proactive decision-making, improving overall efficiency and customer satisfaction. 6. Customer-Centric Approaches

Finally, there is a shift towards a more customer-centric approach within the logistics industry. Companies are beginning to recognize the importance of providing exceptional service not just to B2C customers but also to B2B clients. It involves the customer experience by focusing on every touchpoint in the logistics journey.

By prioritizing customer satisfaction, companies can build long-term relationships and gain a competitive edge in the market.

Conclusion

In conclusion, the logistics industry is evolving rapidly, driven by technology, sustainability, and a focus on customer satisfaction. By embracing these trends, logistics companies can enhance their operational efficiency and adapt to the changing landscape of consumer expectations. As we move into 2024, staying ahead of these trends will be essential for success in the logistics sector.

[arvistchicago]

Moving from understanding operations to operational insights

Moving from understanding operations to operational insights

Understanding operations is not enough. You need operational insight

The difference between a successful transformation and a poorly executed innovation could come down to what organizations do in order to gain a real understanding of their own processes.

Moving from understanding operations to operational insights

Understanding operations is not enough. You need operational insight. Understanding your business is more important than ever.

Organisations with a heavy operations component are constantly under pressure to improve efficiencies, and digital natives continue to challenge them. Leaders are left with the choice of re-inventing operations and betting on creating new advantages or maximising insights to improve current operations and betting on their abilities to compete on current advantages.

It's not black and white but organisations are embracing technology and increasing their due diligence in order to ensure that transformation occurs in the right areas with the best possible intervention.

The difference between a successful transformation and a poorly executed innovation could be what organisations do to gain a real understanding of their own processes.

Understanding your organisation

Experts who are in touch with reality are at the heart of every organisation. By supplementing their expertise with automated and intelligent methods of extracting insights from operational data, experts can focus on solving problems instead of trying to prove why and how they are a problem.

Innovation is more than just building understanding. It's about guiding improvements. A compass instead of a road map.

Process Intelligence is driving the step change. This technology uses system data in order to quickly build digital twins for operations and identify scenarios that lead to process variances and lost productivity. The process mining technology has been around for a few years. However, the use of AI/ML to optimise processes has allowed the correlation of events and their impact on business performance.

Consider the Finance department, where early payment to vendors can lead to a reduction in working capital. The traditional methods of understanding payments processes can be time-consuming and complex. Interviews with operators only cover a limited number of people and managers might not know all the scenarios. Models that correlate working capital with variances in invoicing processes can give organizations insight into conditions in which early payments occur and their impact on working cash. The most effective interventions can be designed using this information.

Embedding your organization

Businesses with business intelligence can use this capability to drill down on performance and pinpoint improvements. They can answer the question "What is happening?". Then, using analytical engines, large volumes of operational information are processed to uncover inefficiencies and answer the question "why?". This should lead to an investigation of why inefficiency exists and the implementation of new processes - "how can we fix it?".

Process Intelligence Platforms are widely available and can fill this need. These platforms use event-based data from a standard data structure to generate visualizations and interactive analyses of processes. Vendors are primarily software companies that offer Process Intelligence and CRM/ERP providers who add Process Intelligence to their services. This makes it possible for organisations to find a solution of the right size.

Data engineering, AI/ML and seasoned operations experts are required to enable the ability of Process Intelligence, which is the ability to leverage common identifiers from multiple logs in order for a process to be reconstructed and visualized. These skills are scarce, but the ability to make more of data than your competitors is at stake. This makes it worthwhile to invest in them and integrate them into improvement initiatives.

Pulling the trigger

Process Intelligence is advantageous in three key areas:

Understanding systems and processes up front to guide the play. The speed of understanding that is gained by rapidly building a digital model of operations gives organisations an edge. Automated system crawlers are faster than workshops or documenting business process.

Monitoring the effectiveness of interventions and their uptake. As live system data are monitored, frequent and targeted corrections can take place while transformation takes place. A shorter feedback cycle reduces the risk of mediocre results after a sustained effort.

Incorporating insights into intelligent operational models. As air traffic controllers optimise flight paths digitally, operations leaders can use intelligence in process to monitor bottlenecks and simulate changes in operations to model their impact on performance. They can also make appropriate investments to intervene.

ERP transformations are a good example. The stakes are high and the success of these transformations is dependent on successfully navigating through massive organisational complexity in order to identify key value drivers.

The conclusion of the article is:

The ability of an organisation to integrate Data and AI into operations in a way that is fit for purpose, and not overlook the complex nature of those operations on the ground, is a frontier of new advantage. While most organizations look for a road map, they need a compass to guide them in the right direction.

[procurement-insights]

The Real Problem With GenAI - Too Many Arrows!

In what way is GenAI superior to "Traditional" AI using self learning algorithms?

To start, thank you, Dr. Tony Bridger. Your original arrow graphic above illustrates why technology implemented under an equation-based model doesn’t work. It also made me wonder how GenAI could improve the results of my self-learning algorithm platform, which I will share later in this post. I have asked solution providers, and GenAI advocates this question many times, but I have yet to receive…

[phoenixbizz]

Explore the impact of AI on warehousing and mobile apps, and how it's shaping the future of logistics.

[nextgeninvent]

Dear manufacturing executives here's what you should keep on your radar.

The promise of AI in manufacturing is immense, but so are the challenges. By addressing this head-on, with a clear strategy, vision, and roadmap, manufacturing executives can ride the wave of AI-led transformation, ushering their enterprises into an era of unprecedented efficiency and innovation.

Let's navigate these challenges together and unlock the full potential of AI in manufacturing.

Are you ready to embark on this journey, consult now for AI Based Software Development Services Company: https://nextgeninvent.com/ai-development-services

[mayurblog1604]

AI is Revolutionizing Industries!

From healthcare to finance, AI is driving innovation and improving efficiency. Learn how AI solutions are making a difference across various sectors:

Check out for more: https://thecodework.com/blog/how-ai-solutions-are-driving-innovation-across-industries/