AI can be used in two ways
In general, AI can be used in two ways. First, it can assist people in their day-to-day tasks, personally or commercially, without having complete control of the output. It can also reduce errors, for example, using a virtual assistant and in data analysis. Second, AI can help automate processes by functioning without the need for any human intervention—for example, robots performing process steps in a fulfillment center.
Using AI to assist people and automate processes helps the top and bottom line because companies waste a lot of time and money on having humans perform basic supply chain tasks.
Companies can significantly improve network, capacity, and demand-planning decisions with AI predictive capabilities using big data. Big data insights, along with AI, can improve supply chain transparency and optimization, and can potentially revolutionize the agility and efficiency of supply chain decision-making.
On a more tactical and operational level, companies are using AI in robotics and automated vehicles to track, locate, and move inventory within warehouses. While totally autonomous vehicles might not happen for a while, we already see technology such as assisted braking, lane-assist, and highway autopilot.
Streamlining procurement-related tasks can happen through the automation and augmentation of chatbot capabilities, which require access to robust and intelligent data sets. This can allow for automating actions such as placing purchasing requests, researching and answering internal questions regarding procurement functionalities, or receiving, filing, and documenting invoices and payment/order requests.
AI can also personalize relationships between logistics providers and customers. A logistics provider can now enable a customer to query Amazon’s Alexa to track a shipment. If there is a problem with the shipment, Echo users can ask for assistance and be directed to the logistics company’s customer assistance department.
Using predictive analytics for supplier selection and supplier relationship management with data generated from supplier assessments, audits, and credit scoring could provide a basis for decisions regarding supplier selection and risk management. The supplier relationship would be more predictive and intelligent.
There is no doubt that the potential of AI and machine learning will finally be achieved in the supply chain, enabling companies to eliminate waste, in many cases before it even occurs. Now that’s real artificial intelligence.
Source: Inbound Logistics
Talk about artificial intelligence (AI) and the Internet of Things (IoT) in the warehouse, and the room can get quiet very quickly. Are we at that point already?
Source: Logistics Management
Quite simply, yes, we are at the point where AI and IoT are real for the warehouse. Both are powerful new tools that better enable warehouse and distribution center activities to keep pace with rapidly shifting supply chain dynamics.
“Don’t be bamboozled by AI and IoT,” says Nate Brown, CEO of EVS. “Both are being used to solve the same problems as before. They just do it better. IoT provides data not previously available. It’s another level of understanding. AI analyzes micro-decisions and optimizes them to a level not previously possible.”
“IoT makes no sense without AI in the warehouse,” says Sean Elliott, chief technology officer at HighJump. “You need a combination of new data sources, that’s IoT, coupled with better solutions, that’s AI, to make sense of the data, develop insights and act on that knowledge. The two are front and center for improved operational performance,” he adds.
In addition, experts agree the two technologies are essential to accommodating the current shift from forecast-driven to demand-driven DCs.
All of that said, it is still very early in the game for both technologies, says Dan Gilmore, chief marketing officer at Softeon.
Some companies interviewed for this story don’t have a commercial offering yet. Others are in pilot. There are others that have offered a product for a short time. Looking forward, JDA, along with its partners, has committed $500 million in R&D over the next three years, says Steve Simmerman, senior director of sales/global partners and alliances.
Clearly, AI and IoT are knocking at the warehouse door. It’s a knock you want to answer.
Building out the IoT
Let’s face it, warehouses and DCs are under pressure like never before.
“Orders flow in all day long, and it’s expected that the DC will take action immediately. The challenge is figuring out how to best process those orders in a timely manner,” explains Adam Kline, senior director of product management at Manhattan Associates.
As he goes on to say, even with a warehouse management system (WMS) in place, those decisions are made with set rules, set capacities and set resources. However, there’s nothing static about those orders that just dropped. “Current conditions matter most going forward. Not preset rules. Now the systems intelligently balance capacity against resources, aiming to maximize utilization,” says Klin
“AI and IoT team up to make decisions based on current conditions,” says Gilmore.
So, where exactly is all that IoT data going to come from? Believe it or not, some of it is already in place in your facility.
Materials handling equipment from conveyors to automatic guided vehicles and automated storage systems and the like all receive and send data about their activities. So do handheld devices from scanners to voice systems.
“Most facilities are bringing in more and more data devices that are developing into a burgeoning IoT network,” says Mark Jensen, senior director of product management at Epicor. Many times, simple sensors provide information not previously available for decision making. Smart phones are part of that new network.
Data about people figures prominently, too. “It matters where people are located at a given moment, what they are working on and how can they best be used,” explains Justin Ritter, director of project engineering at Lucas Systems.
As Kline of Manhattan points out, real-time locator systems are moving into place to track people and their availability for specific tasks. In fact, several types of real-time locator systems are available, including smart phones, passive radio beacons and RFID.
“Many facilities kind of know where Pete is based on his most recent scan. But when you use real-time locator systems, you know exactly where Pete is at all times,” says Gilmore.
There’s also the matter of people and robotics, Gilmore adds. He calls it a pairing capability to get both the right person and the right robot to fulfill an order using IoT data. “It’s a matter of pairing up the locations and having the two work together to pick and make a run to packing,” Gilmore adds. “New thinking is required here.”
Building out AI
As Richard Lebovitz, CEO of LeanDNA, says, “while access to data is becoming much simpler, most facilities lack the ability to decide how to use that data and what actions to take. It’s all a matter of bridging the gap between the forecast and what’s really happening in manufacturing.” That’s where AI enters.
Brown of EVS offers his baseline definition of AI in the warehouse. “It learns and reacts to the current state, not just a set of pre-set rules,” he says. That’s straightforward.
AI and IoT are not two sides of the same coin, explains Elliott of HighJump. “But they do have a symbiotic relationship. The more data about actions and interactions that AI receives, the more it can learn about how to adapt to current conditions,” he adds.
While much of the IoT data comes from within the four walls, take the example of a late inbound load. “The DC is alerted by an IoT signal being managed by a control tower that a load will arrive late,” says Simmerman of JDA. “AI takes that information and determines the optimal time to release and deploy a specific amount of labor to unload the truck. AI also determines what portion of that load should go directly to fill orders or to storage. Suddenly you have a new level of visibility and intelligence into how to make the DC operate most efficiently,” says Simmerman.
Getting to that point really does require the granularity of data that IoT delivers. “Data granularity is the key enabler to allowing AI to learn as new situations present themselves,” explains Graham Yennie, data scientist at Lucas. This particular form of AI is known as machine learning
Bringing IoT and AI together
All of that is great. However, there is an even greater purpose to IoT and AI in the DC. The two technologies make it possible for a DC to move from being forecast driven to being demand driven. That is, when they are combined with WMS, warehouse execution systems and even work execution systems. Moving from forecast- to demand-driven operations is a huge but absolutely necessary pivot for DCs going forward, says Lebovitz of LeanDNA.
It’s all a matter of coping with the current shift from manufacturing and distribution calling the shots in the supply chain. Increasingly, customers are in charge to the extent that they have now transcended low costs as the primary driver of supply chain efficiencies.
As a result, a range of companies are investigating, piloting and fully integrating AI and IoT in warehouse operations.
Companies such as Lucas Systems and EVS are doing their due diligence to decide how to integrate the two technologies with their existing software packages. Lucas Systems expects to be deep into beta testing by next spring. Meanwhile, EVS is testing customer data with its WMS package.
LeanDNA has incorporated AI with its inventory analytics for manufacturing operations. Its software is used by a range of companies by connecting to their enterprise resource planning (ERP) systems to streamline operations.
Slotting and robotics are key to HighJump’s efforts to integrate the technologies with its WMS. Pilot projects are underway in both areas.
Softeon has an emphasis on tracking workers, their activities and equipment such as mobile robots using passive radio beacons. Slotting is also a key focus. Both have been integrated with Softeon’s WMS, which is now said to be able to make better decisions and faster.
Both IoT and AI are integrated with Manhattan’s warehouse execution package within its WMS. Order streaming, robotics and distribution control have all benefited because the capability was introduced almost 18 months ago.
Distribution management software from Epicor is just finishing beta testing of IoT. Meanwhile, AI is fully integrated into its virtual agent for its ERP system. The Cloud figures prominently here, too.
A little over a year ago, JDA purchased Blue Yonder and its AI capabilities. That has become a backbone in JDA’s strategy to digitize predictive analytics to create what the company is developing—a prescriptive state for the self-learning supply chain. IoT is just as integral to its long-term strategy. Control towers, the Cloud and warehouse tasking figure prominently.
It may be early in the use of IoT and AI in warehouse operations. And with all that’s calling for your attention day in and day out, it would be easy to overlook this development. But you would be better off if you didn’t let that happen.
Artificial intelligence (AI) in Supply Chain and Logistics Market Size | Growth Analysis | Key Players and Forecast To 2026 | IBM, Google, Microsoft Corporation, Amazon Web Services Inc
The global Artificial intelligence (AI) in Supply Chain and Logistics Market is expected to reach at xx % in the forecast period, stated by a recent study of Contrive Datum Insights. It offers a complete overview of the global market along with the market influencing factors. Furthermore, it offers a detailed description of the global market with respect to the dynamics of the market such as internal and external driving forces, restraining factors, risks, challenges, threats, and opportunities. Analysts of this research report are predicting the financial attributes such as investment, pricing structures along with the profit margin. This research document has been prepared by using advanced research methodologies like primary and secondary research.
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Following Artificial intelligence (AI) in Supply Chain and Logistics Market factors are explained in the report:
Market dynamics: The report shows the prospect of the numerous commercial opportunities over the future years and the positive revenue estimates for the upcoming years. It also studies the key markets and the mentions the several regions i.e. the geographical spread of the industry.
Competitive Market Share: The Artificial intelligence (AI) in Supply Chain and Logistics Market report offers a whole estimation of the market. It does so through in-intensity qualitative perceptions, recorded perceptions, and future predictions. The forecasts included in the report had been founded employing recognized research assumptions and procedures.
The report has analyzed several players in the market, some of which include: IBM, Google, Microsoft Corporation, Amazon Web Services Inc, Oracle Corporation, SAP, Facebook, Alibaba, Baidu, Tencent.
Goal of the Artificial intelligence (AI) in Supply Chain and Logistics Market Report: The central goal of this research study is to offer a clear picture and a better understanding of the market for research report to the manufacturers, traders, and the suppliers operational in it. The readers can gain a deep insight into this market from this piece of information that can enable them to convey and develop critical approaches for the further growth of their businesses.
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Geographically, the global market has fragmented across several regions. The major regions include North America, Latin America, Asia-Pacific, Africa, the Middle East, and Europe. It also offers a comparative study of the global market to understand the difference in performance among global competitors. Also, it represents how those competitors competing against each other’s to drive the businesses rapidly. This publication includes market segmentation such as applications, end-users, and geography. Researchers present informative data in a clear and professional manner. Historical growth rate, as well as forecasted rate, is also mentioned in the report.
Report Content Overview:
-Qualitative and quantitative analysis of the market based on segmentation involving both economic as well as non-economic factors
-Provision of market value (USD Billion) data for each segment and sub-segment
Indicates the region and segment that is expected to witness the fastest growth as well as to dominate the market
-Analysis by geography highlighting the consumption of the product/service in the region as well as indicating the factors that are affecting the market within each region
-Competitive landscape which incorporates the market ranking of the major players, along with new service/product launches, partnerships, business expansions and acquisitions in the past five years of companies profiled
-Extensive company profiles comprising of company overview, company insights, product benchmarking and SWOT analysis for the major market players
-The current as well as the future market outlook of the industry with respect to recent developments (which involve growth opportunities and drivers as well as challenges and restraints of both emerging as well as developed regions
-Includes an in-depth analysis of the market of various perspectives through Porter’s five forces analysis
-Provides insight into the market through Value Chain
-Market dynamics scenario, along with growth opportunities of the market in the years to come
Advanced Technologies, Trends, In-Depth Analysis, Regional Demand, Growth Strategy, Company Profiled Players
The major key questions addressed through this innovative research report:
- What are the major challenges in front of the global Artificial intelligence (AI) in Supply Chain and Logistics market?
- Who are the key vendors of the global Artificial intelligence (AI) in Supply Chain and Logistics market?
- What are the leading key industries of the global Artificial intelligence (AI) in Supply Chain and Logistics market?
- Which factors are responsible for driving the global Artificial intelligence (AI) in Supply Chain and Logistics market?
- What are the key outcomes of SWOT and Porter’s five analysis?
- What are the major key strategies for enhancing global opportunities?
- What are the different effective sales patterns?
- What will be the global market size in the forecast period?
Table of Content (TOC):
Chapter 1 Introduction and Overview
Chapter 2 Industry Cost Structure and Economic Impact
Chapter 3 Rising Trends and New Technologies with Major key players
Chapter 4 Global Artificial intelligence (AI) in Supply Chain and Logistics Market Analysis, Trends, Growth Factor
Chapter 5 Artificial intelligence (AI) in Supply Chain and Logistics Market Application and Business with Potential Analysis
Chapter 6 Global Artificial intelligence (AI) in Supply Chain and Logistics Market Segment, Type, Application
Chapter 7 Global Artificial intelligence (AI) in Supply Chain and Logistics Market Analysis (by Application, Type, End User)
Chapter 8 Major Key Vendors Analysis of Artificial intelligence (AI) in Supply Chain and Logistics Market
Chapter 9 Development Trend of Analysis
Chapter 10 Conclusion
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2020: 25% of the Fortune 500 will add AI building blocks to their Robotic Process Automation (RPA) efforts to create hundreds of new Intelligent process automation (IPA) use cases
As a quarter of Fortune 500 enterprises redirects AI investments to more mundane shorter-term or tactical IPA projects with “crystal-clear efficiency gains,” around half of the AI platform providers, global systems integrators, and managed service providers will emphasize IPA in their portfolios.
Building on the proven success of these IPA use cases, IDC predicts that by 2022, 75% of enterprises will embed intelligent automation into technology and process development, using AI-based software to discover operational and experiential insights to guide innovation.
And by 2024, AI will be integral to every part of the business, resulting in 25% of the overall spend on AI solutions as “Outcomes-as-a-service” that drive innovation at scale and superior business value. AI will become the new UI by redefining user experiences where over 50% of user touches will be augmented by computer vision, speech, natural language and AR/VR. Over the next several years, we will see AI and the emerging user interfaces of computer vision, natural language processing, and gesture, embedded in every type of product and device.
Emerging technologies are high-risk technologies. In 2020, warns Forrester, 3 high-profile PR disasters will “rattle reputations,” as the potential areas for AI malfunction and harm will multiply: The spread of deepfakes, incorrect use of facial recognition, and over-personalization. By 2021, predicts IDC, 15% of customer experience applications will be continuously hyper personalized by combining a variety of data and newer reinforcement learning algorithms.
Accentuating the positive, Forrester is nevertheless confident that “these imbroglios won’t slow AI adoption plans next year. Instead, they will highlight the importance of designing, testing, and deploying responsible AI systems — with sound AI governance that considers bias, fairness, transparency, explainability, and accountability.”
IDC predicts that by 2022, possibly as a result of a few high-profile PR disasters, over 70% of G2000 companies will have formal programs to monitor their ‘digital trustworthiness’ as digital trust becomes a critical corporate asset.
Leadership matters, says Forrester, and companies with chief data officers (CDOs) are already about 1.5 times more likely to use AI, ML, and/or deep learning for their insights initiatives than those without CDOs.
In 2020, senior executives like chief data and analytics officers (CDAOs) and CIOs who are serious about AI will see to it that data science teams have what they need in terms of data. The real problem, says Forrester, is “sourcing data from a complex portfolio of applications and convincing various data gatekeepers to say yes.”
And IDC observes that “effective use of intelligent automation will require significant effort in data cleansing, integration, and management that IT will need to support. Resolving past data issues in legacy systems can be a substantial barrier to entry, particularly for larger enterprises.”
AI adoption is not consistent across all companies and we are seeing a new digital divide, a divide between the AI haves and the AI have-nots, those with or without the required highly-skilled engineers.
In 2020, says Forrester, the “tech elite” will ramp up AI plus design skills while other will “fumble.” Pairing human-centered design skills and AI development capabilities will be key. As for the rest of the workforce, by 2024, 75% of enterprises will invest in employee retraining and development, including third-party services, to address new skill needs and ways of working resulting from AI adoption, predicts IDC.
What constitutes “the workforce” will continue to expand and IDC predicts that the IT organization will manage and support a growing workforce of AI-enabled RPA bots as intelligent automation scales across the enterprise. Another addition to the workforce will an army of chatbots, assisting with a variety of tasks in the enterprise. But Forrester predicts that four in every five conversational AI interactions will continue to fail to pass the Turing Test. By the end of 2020, predicts Forrester, conversational AI will still power fewer than one in five successful customer service interactions.
Where the work is done will also continue to expand. As compute power moves from the datacenter to the edge, says IDC, IT will be challenged to manage and control edge processing devices. By 2023, nearly 20% of servers that process AI workloads using AI-optimized processors and co-processors will be deployed at the edge. And by 2025, 50% of computer vision and speech recognition models will run on the edge (including endpoints).
AI will be here, there, and everywhere, and IDC estimates that by 2025, at least 90% of new enterprise application releases will include embedded AI functionality. However, adds IDC, truly disruptive AI-led applications will represent only about 10% of this total.
So we have to wait another 5 years to see the “truly disruptive” potential of AI finally realized and only in a few cases? Another Forrester predictions report indeed warns that in 2020, “the exuberance in AI will crescendo as expectations come back to earth.” While Forrester predicts another new peak in AI funding in 2020, it asserts it will be the last one—“with more than 2,600 companies globally, the AI startup ecosystem is a saturated market.”
Artificial intelligence (AI) in Supply Chain and Logistics Market Set to Surge Significantly During 2019 to 2024
The Artificial intelligence (AI) in Supply Chain and Logistics Market Report comprises of an exhaustive study of the market, thereby encompassing every facet of the market including, but not limited to market size at global level, shortcomings, growth spectrum, opportunities, mergers and acquisitions, key players, company profiles. It does through Artificial intelligence (AI) in Supply Chain and Logistics detailed qualitative insights, past data, and verified estimations about Artificial intelligence (AI) in Supply Chain and Logistics market size. Also, the experts have used proven methodologies and assumptions to mark the projections in the Artificial intelligence (AI) in Supply Chain and Logistics Market, which, in turn, helps both current players and new members to avail benefit from this report as it offers beneficial source of advice and guidance.
Artificial intelligence (AI) in Supply Chain and Logistics Market Forecasts 2019-2024 & Explore information Globally by Leading Top Key #Companies: -IBM,-Google,-Microsoft Corporation,-Amazon Web Services Inc,-Oracle Corporation,-SAP,-Facebook,-Alibaba,-Baidu,-Tencent
Reports Presents an In-Depth Analysis of “Artificial intelligence (AI) in Supply Chain and Logistics Market” Booming Globally by Key Futuristic Trends, Growth, Share, Size, Future Demand, Revenue, Outlook, standardization, deployment models, opportunities, future roadmap and more with five year of forecasts.
(Source: The Chicago Sentinel)
DLA Looks to AI for Predictive Logistics Capabilities
(Source: News, Technology)
Jesse Rowlands, data scientist for the Defense Logistics Agency’s Analytics Center of Excellence, has said that the agency has been partnering with industry on artificial intelligence research and development efforts, National Defense Magazine reported Friday.
Rowlands told the publication that AI has “unlimited applications” at DLA, incuding predictive capabilities that can provide estimates on demand, lead time and component requirements. The agency may also leverage the value of AI in small iterations and models, he noted.
“That’s not a groundbreaking newspaper story, but you do a bunch of those and you’re having a big effect on the bottom line of the agency that you’re working at,” he added.
According to Rowlands, the recently established Joint AI Center may help Department of Defense agencies establish governance structures and streamline AI implementation.
Artificial Intelligence in Retail Market Growing Popularity and Forecast Till 2025
(Source: Hitech News Daily)
The Artificial Intelligence in Retail Market overview, growth prospect and Forecast Till 2025. The Report offers majority of the latest and newest data that covers all over market situation along with future prospect.
Artificial Intelligence Market is estimated to be US$ 27,238.6 million by 2025 from US$ 712.6 million in 2016. The growth in market revenue is attributed to proliferating adoption of game changing technologies, growth in mobile market and emergence of various AI based start-ups catering to retail industry. Proliferation of enhanced technological awareness and varied product choices among the consumers have resulted into a noteworthy shift in global retail industry landscape.
Artificial intelligence in retail market by application is segmented into Sales & Marketing, Supply-chain & Logistics, Shelf Analytics, Pricing, In-Store Navigation, Auto-checkout and others (staffing and product mix optimization). Customer experience and management is projected to acquire majority share in the market. Digital convergence in retail is considered to bring glowing opportunity for retailers to retail the customer and set themselves apart. In the coming few years, interaction with a variety of technologies such as bots is expected to reinvent the customer experience.
This has further foisted pressure on the traditional retailers to reimagine the strategies for creating and capturing value in order to explore the optimal usage of their assets. Public policy liberalization is also one of the key factors supporting the flow of knowledge, information and resources, further generating pressure on the brick mortar retailers to tackle with the lowered entry barriers to the online retailers in the market.
Key trend which will predominantly affect the market in coming year is rising adoption of multi-channel or omni channel retailing. In forthcoming years, the retail industry is anticipated to witness higher growth in the trend of Omni channel retailing. Artificial intelligence will be having a key role as this technology would be bridging the gap between online and offline retailing in coming future. Companies like Amazon have already enrooted its focus towards development of an AI enabled offline retail store that would enable the shoppers to move out of the store without waiting in the long queues of billing.
The overall market size has been derived using both primary and secondary source. The research process begins with an exhaustive secondary research using internal and external sources to obtain qualitative and quantitative information related to the market. Also, primary interview were conducted with industry participants and commentators in order to validate data and analysis.
Six ways deep learning is already changing your life
Artificial Intelligence for Military Logistics – Current Applications
Published by Millicent Abadicio Millicent is a writer and researcher for Emerj, with a career background in traditional journalism and academic research.
Logistics in the military encompasses more functions than most people realize. In modernwarfare, that means large quantities of data to sift through in order to make decisions regarding supply, transport, communications, and so on. Using artificial intelligence (AI) and machine learning (ML) in one or more areas in logistics could help speed up that process and make it more agile.
Although AI and ML could have great benefits for military logistics, the military has focused on AI and ML applications in other areas. The military has been slow on the uptake, and it often accepts an amber status for its logistics in active war zones.
However, integrating AI for logistics in the military comes with its attendant concerns, although they are not the same as those when using AI for surveillance, intelligence, or weapons. Theissues with AI for logistics have to do with inherent unpredictability and vulnerability to exploitation.
Data scientists and engineers are making great progress in AI and ML with impressive results, but it is far from foolproof. This lack of certainty has until recently prevented more enthusiastic employment of available AI and ML solutions. A directive requiring all AI-based systems to have a human overseer or operator at all times should mitigate, but not eliminate, the specter of compromised and dangerous technology.
That said, the fact that the biggest adversaries of the US are aggressively pursuing the use of AI and ML in their own defense systems, and that these same systems face the same risks and vulnerabilities, might present unique opportunities for the Department of Defense (DoD) to level the playing field, so to speak.
Be that as it may, the DoD is addressing the issue of using AI for logistics in the US military, spearheaded by the newly formed Joint Artificial Intelligence Center (JAIC). The authors of the “Summary Of The 2018 Department Of Defense Artificial Intelligence Strategy” point out that “Other nations, particularly China and Russia, are making significant investments in AI for military purposes…The United States, together with its allies and partners, must adopt AI to maintain its strategic position, prevail on future battlefields, and safeguard this order.”
This article will run through some of the areas in logistics where AI is either in pilot mode or in active operation. The areas for discussion will include:
- Preventive maintenance
- Cloud services
- Supply chain management
- Medical aid
- Driverless resupply
We begin our analysis with a discussion of preventative maintenance.
JAIC director Air Force Lt. Gen. John N.T. “Jack” Shanahan stated, “we want to identify some smart automation initiatives that could provide near-term dividends in terms of increased efficiencies and effectiveness for back-office functions.” One important AI logistic initiative under JAIC is preventive maintenance, particularly for fighter jets.
An early version of automated preventive maintenance for the military was the F-35 fighter jet test performed by Lockheed Martin back in 2015. Below is a 2:50-minute video showing theAutonomic Logistics Information System (ALIS) at work:
There is no mention of AI or ML, but Air Force Chief Scientist Gregory Zacharias confirmed AI is the driving force behind the “smart” system. The system will probably get a lot “smarter,” probably with supportive AI software from defense contractor C3.ai. The AI company currently has nine projects with the DoD.
The military uses ALIS and, potentially, the C3 AI Suite for aircraft. However, if the pilot programs prove successful in the long term, the military might easily customize these and similar technologies to accrue the same benefits for other types of vehicles and equipment. We recently posted a more in-depth article on predictive analytics in the military for these other uses.
A central repository of information might sound like a bad idea for the military, but in the world of logistics, it is necessary. It means savings in time, effort, and money if logisticians have all the information they need to make informed decisions when moving supplies and equipment to support troops.
In line with this, the Army contracted IBM to provide cloud services as well as access to Watson to store and process logistics data coming from various sources. Logistics Support Activity, or LOGSA, (now Logistics Data Analysis Center or LDCA) Commander John Kuenzli stated in 2017:
Capabilities like Watson make it a very exciting time for us. This can be a way to free up our analysts from some of the more technical work, to let the machine do some of that and to leverage the analysts’ professional expertise to look up and look further toward where the Army is going.
The idea is to use the cloud to upload sensor data from vehicles and equipment and perform “conditioned-based maintenance (CBM),” which is a repair-when-needed approach, made possible with AI-driven data analysis. Below is a 4-minute proof of concept video on 360 Stryker combat vehicles from IBM:
While the idea of CBM falls under preventive maintenance, the bigger picture in this particular use of AI is in the move of military data into the cloud. According to DoD Chief Information officer Dana Deasy, data access is the key to effective warfare today. He explained:
One of the things traditional computing has always had a problem with is the warfighter sitting out on the tactical edge, [with the] cloud sitting [elsewhere]. Now imagine a world where we can take that compute power with new applications on top of it, and put the cloud right into the hands of the tactical fighter on the edge. That’s why the cloud is so important to us.
In 2019, that early initiative has bloomed into a multi-billion single-provider contract for secure cloud storage and software under the Joint Enterprise Defense Infrastructure (JEDI) with several commercial companies vying for it. Unfortunately, legal problems are keeping the DoD from going forward with it.
Supply Chain Management
The government can also become a victim of fraud as much as any civilian, and perhaps more so because of the sheer volume of its acquisitions worth about $350 billion a year. For theDefense Logistics Agency (DLA), this is a big problem, as it provides what the military needs, such as weapons, repair parts, and fuel, as well as disposes of surplus equipment. As the DLA receives an average of a million bids a day, it can be fatally easy for some questionable transactions to get through.
To address this issue, the DLA turned to AI and ML software to sort through the mountain of private and public data to identify and flag suspicious or anomalous suppliers. DLA Logistics Operations Vice Director Michael Scott claimed its Business Decision Analytics (BDA) tool has made a big difference. In 5 months of operations:
It has identified more than 350 high-risk CAGE codes or supplier entities. It’s probably one of our top concerns right now from a process perspective is the rise in the number of fraudulent or bad actor companies who are trying to get into our business. This new tool has been very helpful in identifying that from the onset and allowing us to put controls in our systems to not allow them to get business from DLA.
It is unclear if the DLA developed the BDA tool in-house or contracted it out to one of several infotech and AI companies awarded contracts under the J6 Enterprise Technology Services(JETS) program.
How AI Is Driving the Next Phase of Growth in Logistics Industry?
Today we find ourselves in another transformational era in human history where Artificial Intelligence (AI) plays an increasingly central role in this transformation. AI stands to greatly benefit all industries and is prevalent in consumer-facing applications, clerical enterprise functions, online and offline retail, autonomous mobility, and intelligent manufacturing. AI in logistics plays an important role and beginning its journey to become an AI-driven industry.
We hope you will find this an insightful read, and we walk you through as How can AI be used in logistics to reinvent back office, operational, and customer-facing activities?
Well, let’s discuss the reason for why Logistics companies are facing an era of unprecedented change is that as New technologies are enabling greater efficiency and more collaborative operating models. It is the best time for the logistics industry to embrace AI because digitisation takes hold and customer expectations evolve.
AI can help the logistics industry to redefine today’s behaviours and practices, planning from forecast to prediction and services from standardized to personalized. It also offers logistics companies the ability to optimize network orchestration to degrees of efficiency that cannot be achieved with human thinking alone
Here are few things you should know about Artificial Intelligence and its Role in Logistics?
Artificial Intelligence (AI) has a growing presence in our personal lives and is rapidly being applied by businesses to increase efficiency and create new value. Many logistics companies around the world embrace digital transformation, transitioning away from legacy enterprise resource planning systems to advanced analytics, increased automation, and hardware and software robotics, and mobile computing.
With the help of AI, the logistics industry can shift its in operation from reactive actions to a proactive and predictive paradigm, which can generate higher insights at favourable costs in the back office, operational and customer-facing activities. For instance, AI technologies will use advanced image recognition to trace the condition of shipments and assets, bring end-to-end autonomy to transportation, or predict fluctuations in world cargo volumes before they occur.
More and more companies are adding artificial intelligence (AI) to their supply chain With the growing digitization of the professional world, in order to maximize their resources by reducing the time and money spent on deciding and when to send a package to a certain place.
Inventory Optimization means maintaining a particular level of inventory that may eliminate the out-of-stock situation and at the same time the cost of carrying inventory is not harmful to the bottom-line. Logistics plays a significant role as to scale back value of the product without reducing the material cost or process cost. The technology can also secure and manage the supplier inventory and the number of trucks that are available for delivery and Optimized logistics model. It becomes successful if it satisfies the Demand & supply equation.
Making brokerage processes easy
Customs declarations rely heavily on manual processes that involve knowledge of regulations, industries and customers. Cross referencing and validation are an effort-intensive process. Natural language process will modify and enable an AI software to extract relevant information from documents in various formats and present a declaration.
Tackling Unforeseen Circumstances
AI can be trained to learn from contingency plans that can guarantee corrective action in the future in and Using AI to scour the internet to observe trends can predict any increase in the demand of a certain category of products or to identify any risk way ahead in time.
Expect the unexpected when it comes to the logistical business as a series of circumstances could affect the expected delivery date of a product. Natural disasters such as hurricanes and floods, carrier bankruptcies and employee strikes can all affect the natural course of a company’s logistical workflow.
Source: S.N.A.K India
Artificial Intelligence are changing the Logistics Industry
There are an increasing number of people around the world who have significant purchasing power. Manufacturing and design technologies are capable of producing goods at some times blinding speeds. Likewise, new vehicular modalities increase the efficiency and capability of the transport industry.
What does all this mean for logistics? Each of these aspects challenge current business practices to compete at a faster pace alongside an increasing number of options.
Technology, such as artificial intelligence (AI), and other innovations, provide enticing solutions.WThe logistics industry itself is independent of the transportation industry. Logistics is involved with planning and analysis. Transportation implements the findings, goals, and long-term plans set forth by logistics professionals.
While transportation is action oriented, logistics technology makes the modern world possible.
“Current fragmentation in the logistics software market is an impediment to adoption. With the spike in blockchain and A.I. technologies, a myriad of new fragmented logistics software platforms has entered the market, offering more choice but at the same time confusing the markets. In 2019, a few selected that offer the most value will thrive while others will fizzle out as more of a novelty,” said Ashik Karim of 1Shift Logistics, an end-to-end logistics solutions company that uses technology to improve the industry.
Logistics technology brings efficiency to the supply chain. Its advantages can be employed for end-to-end solutions. From planning, tracking, control procedures, and implementation, logistics improves current operations. It also finds opportunities that may exist in production, storing, distribution, and transporting.
Many, if not most, of the top world leaders believe that artificial intelligence (AI) is the most profound technology to ever grace the human race.
Business leaders believe that AI has significant advantages for business.
By 2035, the AI market is expected to generate a 38 percent increase in profits.. AI’s ability to find inefficiency in current business-to-business transactions opens up a new world for data management. It provides the tools to enact operational order.
While AI can find, diagnose, and automate logistical solutions, digital ledger technology can record, secure, and make operations more efficient. Deloitte compiled a global survey and found that digital ledger technology adds speed (32%), encourages new business models (28%), and reduces costs (16%).
Each of these aspects alone are capable of a cascade of modernization for logistical practices. Together, they illustrate how disruptive a change is coming.
1Shift Logistics was created by the team at LiteLink Technologies. The 1Shift Logistics software provides cutting edge end-to-end logistics management solutions for the freight and logistics industry.
The revolutionary software empowers decision makers with increased visibility. It is a practical solution that can be accessed via desktop and mobile phones. It enables managers to directly connect with every member of the team. For instance, drivers find automated real-time routing, delay and incursion updates, along with payment processing.
The product works on desktops and on trucker mobile phones, eliminating the need for drivers to get involved in long discussions regarding route, delays and payments. The 1SHIFT Logistics platform seamlessly automates these typically manual features.
1SHIFT Logistics is a live and actionable solution. It offers decisive advantages for pricing histories, partnerships, and variable factors that determine operational decisions. As an enterprise solution, it delivers an improved understanding, speed, efficiency, cost reduction, and complete end-to-end monitoring with minimal need for manual intervention.
Artificial Intelligence how will supply chain management be promoted?
Logistics has been an integral part supply chain and business models. Unlike the past, businesses have now started to focus on its development for which they look up to new age technologies. Artificial intelligence (AI) is one such technology that holds the potential to leverage logistics to overcome present challenges. Retail logistics face the most challenges as it directly caters the consumers and is widespread making it more complex. Logistics currently needs to predict consumer needs, goods demand, a simpler process, and streamlined workflow to remain unhindered and profitable.
AI in Documentation
Organizations from around the world used to put in their efforts in managing the paperwork for logistics which was prone to errors, costly, and time taking. AI adoption allows automating the process and saving money as entities themselves could enter data and AI interface managed all by itself without human intervention. Also, the insights received from this data enables companies to enhance their payment and documentation methods and keep a track of them.
The major benefit of AI proves to be its predictive analysis of customer demands. Integrating AI in logistics will give manufacturers to retailers, an insight of consumer needs. Retailers will be able to understand the demand of particular good at a particular time or region and accordingly, they would procure them. Data received from retailers would help all other entities in the supply chain redefine their inventory. Improvements made at the top of the hierarchy would trickle down and benefit the rest.
Apart from customer needs, AI can also leverage organizations to improve their logistics management system as it will enable them to keep a track of their assets in real-time. From transportation to inventory everything could be sorted according to requirements of the market. A proper assessment of assets will allow optimizing resources and investments.
Tedious and mundane tasks can be shifted to the AI interface which will carry them out with the same efficiency each time. As AI can interpret bigger data sets companies can fetch more tenders and choose the most reliable and profitable logistics partners based on it.
Source: CIO Review