The increased adoption of IoT is creating a demand for clearly defined cybersecurity measures

Cybersecurity has seen many iterations in protecting data and endpoints for decades. From antivirus scanners to endpoint protection platforms, the methods for guarding against viruses and malware have needed to adapt as the threats themselves have adapted swiftly.

And now, with the globe’s digital footprint on an upward trajectory, the latest trend is the cybersecurity mesh. This method responds to the increasing number of connections that exist all around us and that have a murkier definition of network access.

According to Gartner’s Top Strategic Technology Trends for 2021, the cybersecurity mesh provides the plasticity needed to respond to digital business acceleration. The idea of the mesh is based on the platform that networks have no physical boundaries.

In light of this, the cybersecurity mesh is defined around a particular person, such as an individual employee within an organization, or a thing — like an IoT device. This way, the security infrastructure can build perimeters around access points comprised of a larger ecosystem instead of creating a cybersecurity perimeter around a central point and then expanding it to enclose all people and things within.

This also allows network management to maintain security at a differentiated level of access to various network parts. With digital connections continuing to spread and mission-critical data being communicated in future use cases of surgical robotics and autonomous vehicles, the need to secure each endpoint is essential.

This landmark legislation requires that guidelines be set forth by the National Institute of Standards and Technology (NIST). The NIST must create standards around identifying and managing security vulnerabilities, secure development, identity management, patching, and configuration management. IoT devices are categorized as hardware that can connect to the Internet and contain at least one sensor.

While this only applies to IoT devices in the government sector, this law targets manufacturers to sell IoT solutions to the government. This may ultimately create a trickledown to the private sector in which all IoT device manufacturers adhere to stricter security guidelines.

It may be unclear whether the new legislation will set off a tidal wave of rigorous cybersecurity enforcement. Still, it sets a good precedent as the world becomes more and more connected. Security by design is an essential strategy when implementing an IoT ecosystem.

Security by design covers more than just endpoints — it also encompasses the gateways, routers, data centers, and cloud security when creating an IoT ecosystem to help secure not just where data travels but how it travels.

With this method, security is designed at the forefront of an IoT project, making it easier and comprehensive to secure all components of an IoT stack as it is being built.

But security is an ongoing process, which is why network security as a service is becoming a popular option among IoT adopters. With insight into endpoints and the network, users have the greatest level of visibility into threats and anomalies.

Source: IOT For All

Applications of AI in Shipping and Logistics

1. Demand Forecasting: Demand forecasting depends on historical data, and using AI can further enhance analyzing historical and real-time data to provide precise demand forecasts. With more accurate demand forecasts, shippers can optimize inventory management, dispatches, and workforce planning, thereby increasing service levels. McKinsey stated in a report that AI-powered forecasting methods could reduce errors by 30-50% in supply chain networks.
2. Supply Planning: Supply planning is an essential part of logistics. Artificial Intelligence can aid in demand analysis based on real-time data. Businesses can dynamically adjust their supply planning parameters to optimize supply chain flow, increase efficiencies, and increase profitability.
3. Automation in Warehousing: The rising need for contactless processes in supply chains due to the current global situations seem to have propelled the necessity of advanced automated business processes. AI has the potential to revolutionize automation in the warehousing scenario. Combining robotics with AI, robots are equipped to track and locate inventory and perform pick and pack functions that usually require an additional workforce to do the job. With automation comes efficient resource allocation that enables assigning the workforce to do more value-added activities rather than manual chores. Deep learning further facilitates the learning in these robots, which allows them to make autonomous decisions regarding activities within the scenario they are deployed in.
4. Intelligent Computer Vision: Deep learning and AI have enabled advanced scanning, surveillance, and automation to visualize many logistics scenarios through images and videos and direct operations accordingly. This has changed how shipments are dimensioned or inspected for damage, labeling, and stacking arrangements while loading. Computer Vision, combined with Deep Learning in self-driving vehicles for automated and smart navigation, is now a reality.
5. Workflow automation: Workflow Automation is the utilization of Artificial Intelligence to streamline complex and manual back-office operations. Documentation in freight forwarding is a tedious task and has immense potential for automation using Robotic Process Automation (RPA) and Optical Character Recognition (OCR). Shipping documents are all not in a standard format, and this is where technologies like these can automate reading and understanding documents that are printed or handwritten with utmost accuracy. Such workflow automation can free up significant work-hours of the logistics personnel and assign them to do more value-added activities.
6. Predictive logistics: Different touch points across a supply chain generate extensive data. Better Machine Learning algorithms can extract predictive insights in logistics that are critical to decision-making. Artificial Intelligence can aid decisions related to capacity planning, forecasting, and network optimization, thereby streamlining operations and enhancing overall supply chain performance. AI is finding extensive use in dynamic route optimization, managing delivery time windows, optimize fuel consumption, and load capacity utilization, among many other activities in last-mile deliveries thereby propelling the digitization of supply chains.
7. Enhanced Shipment Tracking: Shipment visibility data is of critical importance to overall supply chain performance. AI-powered tracking and tracing capabilities help accurate prediction of ETAs and ETDs. Furthermore, the ability to alert on supply chain disruptions, delays, and risks in shipping routes can help businesses increase agility and employ backup measures to avoid significant losses. Machine Learning can also help analyze historical data to identify shipping patterns considering various factors such as weather conditions, seasonal demand fluctuations, congestion in trade lanes, etc. With extensive use of voice-based assistants or chatbots, customers or customer service personnel can extract the tracking information in seconds.

Source: Ocean Insights

SupplyChainBrain Magazine: Ten Predictions for Supply-Chain and Procurement Software in 2021

1. Knowing, managing and communicating with suppliers has never been more important. The COVID-19 global pandemic was a wakeup call for many supply chains from a variety of perspectives. By way of illustration, when the world stopped and manufacturers needed to ask their suppliers at scale “R U OK?” — they struggled. Large enterprises might have been able to get away with Excel, e-mail and phone calls in the past, but 2020 became the effective “light bulb moment” regarding supplier information management and digital communication. Supply chain executives will pursue numerous beefy initiatives in 2020 spanning risk management, supplier diversity, and sustainability. However, none will be possible without intelligent, ongoing supplier information management.

2. Artificial intelligence and machine learning: “You’re still early.” We aren’t quite ready for artificial intelligence in supply chain. To be sure, we see a handful of companies doing interesting things with A.I. around spend analytics and strategic sourcing, but from a mass-market, holistic perspective. it feels as though we’re stuck in inning one. We see a wide array of supply-chain data gaps, dirty data and human intervention still at play. This space is also still feeling the effects of on-premise systems, bespoke in-house technology, and siloed datasets scattered throughout the supply chain. Enterprises need to nail the basics and foundational aspects of supply chain (including spend management, supplier information management, the four walls of logistics, and transportation) before taking a swing at A.I. Once that gets done, we think A.I. and machine learning will have a profound impact on supply chain and procurement, given the complexity and volume of data at play. Twenty twenty-one will be a year of immense growth for A.I. and machine learning, setting the stage for wider adoption later on.

3. Sustainability, sustainability, sustainability. While we’d like to say that 2021 is “the year” for sustainability, that prediction is doomed to fall short. We can say, however, that the macro tailwinds around sustainability are stronger than ever, and underwent a stark acceleration in 2020. Whether enterprises are being altruistic or self-serving, it’s now undeniably important for large global entities to take on sustainability initiatives with fervor. The pressure to do so is coming from every direction, including consumers, employees and investors. The largest, most prominent brands in the world are focused on the sustainability of their suppliers, end-to-end supply-chain visibility, and transparency. For example, PepsiCo just announced a goal for net-zero emissions by 2040. In verticals such as food and grocery, we see the emphasis compounding even further, leading to the adoption of smart inventory management, order management and waste reduction. We think the role of chief sustainability officer will be much more prevalent by the end of this year. To that end, we’re seeing more chatter, infrastructure, groups and events being built around this role, which is a great sign, and exactly what we saw in the early 2010s around the title of chief procurement officer. Successful companies ensure that efforts in sustainability have tremendous business value by focusing on the right priorities.

4. Debunking the digital transformation myth. This trend really comes down to supply-chain visibility. In evaluating the technology stacks at many of the largest manufacturers globally, it’s clear that the number of organizations that have successfully “digitally transformed” their organizations are few and far between. We see several roadblocks, including the tenure of digital transformation projects outliving that of project leaders, turnover in key leadership roles such chief procurement officer, too much software, and continued reliance on legacy or on-premise enterprise resource planning (ERP) applications. New technologies such as robotic process automation (RPA), integration platforms and low-code platforms offer interesting alternatives, but the one overarching conclusion is the lack of a “one-size-fits-all” approach. Cloud-based ERP and master data management (MDM) platforms offer hope, but are still in the early innings of displacing on-premise deployments, which take years to implement, and typically don’t come up for tender for five or even 10 years. We believe supply-chain technology, especially that pointed at digital transformation with agile methodology, will continue to be “the Wild West.”

5. Rise of the CSCO and CDO. We see multiple new seats at the table, in no small part due to some of the trends highlighted above. The chief supply chain officer (CSCO) will have increasing influence in the boardroom, given inherent links to important initiatives such as digital transformation, sustainability, diversity and brand value, especially in the new omnichannel world. The chief data officer (CDO) is an even more nascent role, but is fast emerging as a staple for forward-leaning, large enterprise C-suites. We’re also starting to see enterprises hire several CDOs, each of whom owns a bucket of data (such as for customers and suppliers). Given that every company is a software company now, we expect organizations to double down on data. The next generation of software leaders will win with “software plus data,” not just applications.

6. The customer experience’s continued impact on the supply chain: the Amazon-like approach. We love Amazon. It his spurred a flurry of innovation across the e-commerce and supply-chain ecosystem from a variety of perspectives. There are obviously categories of software that exist to help brands sell on Amazon, but what excites us more are categories that help brands to compete with, or at least stand a fighting chance against, the e-tailing giant, such as feed management, co-warehousing, fulfillment, last-mile delivery, e-commerce shipping, and supply chain as a service (SaaS).

7. E-commerce continues to change the game. Anyone who has been entrenched in e-commerce over the past several years knows that the e-commerce gold rush of 2020 wasn’t a flash in the pan. It’s true that 10 years’ worth of acceleration occurred in eight weeks, but this adoption was bound to happen eventually. The pandemic simply got us there quicker. Generations that were forced to buy items online in 2020 aren’t going to forget the convenience of having groceries delivered to the doorstop, or the joy of receiving the perfect Christmas gift in the comfort of their homes. We saw this strongly form in the small-business arena, a customer segment that has been adopting e-commerce steadily over the past decade for certain verticals, but was forced online in Q2 2020. We’ve continued to observe emphasis and adoption in this segment, but see a long-term horizon for innovation. The continued flurry of activity is supporting multiple equally large opportunities, such as e-commerce shipping, payments and other banking products, while propping up newer opportunities around direct to consumer and drop-shipping.

8. Supplier diversity. The concept of suppler diversity has been around for a while, but we’re starting to see a turning point, in no small part due to the spotlight placed on this issue in 2020 in the U.S. Similar to our analysis on sustainability, supplier diversity is clearly becoming a board-level initiative with strong stakeholder support. diversity.

9. Best-in-breed point solutions > all-in-one platform. Zooming out from supply chain and procurement, we see several themes evolving across business software in general. Typically, we notice cycles during which multiple best-in-breed point solutions rise in popularity in a given industry. These eras are then typically followed by periods of mass consolidation. The cybersecurity and sales technology spaces have been through a few waves over the years.

10. Supply-chain and procurement specialization. As these spaces continue to mature, it becomes clear that a one-size-fits-all approach isn’t optimal. We believe that vertically oriented SaaS will become even more critical in supply chain and procurement, where complexity and nuance are high. Food and beverage, grocery and restaurant, pharmaceuticals, cold-chain, e-commerce and cannabis are all verticals that we view as exciting over the near term. Putting aside end-market vertical specialization, we similarly see a spend-category focus in multiple areas of opportunity, including travel, contingent workforce, software, aviation, and various flavors of direct procurement, which up to now has experienced very little innovation.

Bonus round: a 2022 prediction. In 2022, we hope to see payments take the supply-chain and procurement spaces by storm. Over time, we hope to see the office of the CFO, CPO and CSCO work more in unison around the physical and financial supply chains. Supply chains will first need to get a handle on their suppliers, spend and data, but with those basic pillars as a launching point, the opportunity for driving future savings through payment processing, cash-flow management, consolidated invoicing and financing products, to name a few, is robust.

Source: SupplyChainBrain Magazine

UPS invests in artificial intelligence

The logistics & delivery corporation UPS plans to spearhead a project to explore the way Artificial Intelligence systems can optimise charging electric vehicle fleets and integrate onsite renewables to reduce emissions and energy costs. 

The EV Fleet-Centred Local Energy Systems (EFLES) project is scheduled to start in May at UPS’s Camden depot. UK Power Networks Services will provide oversight, while Moixa is to contribute their GridShare AI platform to manage solar, storage and charging assets.

“We have the global expertise, smart-charging infrastructure and resources to host this first-of-a-kind testbed at our Camden facility,” said UPS sustainable development coordinator Claire Thompson-Sage, before adding: “This project will build on our EV infrastructure technology to help develop a holistic local energy system.”

Moixa’s software solution Gridshare will be instrumental in the management of the power system, as the company writes that the software can keep track of hundreds of data sources and “analyse data sources for energy prices, power demand, weather conditions and more” to help optimise power usage and grid stability. This way they aim to charge when power is cheapest and the mix of renewable energy sources is highest in concentration.

The timing for the project couldn’t be better for UPS: Only last month, the logistics giant announced their investment in Arrival and the plan to purchase a total of 10,000 electric vans over the next four years. The vehicles will be integrated into fleets in Europe and the USA. In combination with their already existing fleet of various electrified vehicles, there will be a lot of batteries to charge in their fleet. This is also not the start for UPS relationship with Arrival, as the UK electric vehicle manufacturer previously developed  the electric transporter together with UPS.

This project builds on the Smart Electric Urban Logistics initiative, in which UPS was involved alongside UK Power Networks and Cross River Partnership, which kicks off on 1 May.

Source: Electrive

Artificial Intelligence has grown massively over the past few years

From managing the customers at your store to improving their overall experience to providing them with personalized solutions, there is a lot that the technology can do for your eCommerce outlet.

AI, though it is implemented in most businesses, is still a futuristic technology. According to a report by PwC, this technology will contribute close to $15.7Tn by 2030 to the global economy. While businesses are still not sure about investing in this technology, they are aware that investing in it will help them unleash their opportunities and improve their business offerings.

Customer interaction is an important aspect of eCommerce. It is said that by this year-end, 85% of the customer interactions will not involve a human being. 

As a number of eCommerce stores are getting ready to implement AI to their businesses, it is time to understand how the technology will improve and transform the eCommerce businesses in 2020. In fact, we will also see how this technology will boost eCommerce owners.

#1 Incorporating visual search

Searching on an eCommerce site for long has been text-based. A lot of people tend to enter keywords that will help them search for a particular product.

However, what if you don’t know the name or, you are not aware of the keywords that can be used for the particular product? That’s precisely why you need a visual search. The new way of searching for eCommerce stores would be visual. You can search using the image of the product.

Add the image of the product to the search bar, and the tool will analyze all the details of the image. This will include color, size, fabric, etc. for the particular product. Eventually, the tool will return with a wide range of product options for you.

As a result, your search results will be optimized to your needs, and your experience in the eCommerce store will be enhanced. The visual search will improve customer experience that will lead to happier customers in your store.

#2 Improve personalization

Artificial Intelligence is data-driven, as most often it learns from the data made available, and builds insights accordingly. In the near future, you will notice the effects of personalization as a result of AI in the eCommerce segment. The eStores will be able to personalize their product offerings and can even improve the overall experience for the end-users.

Based on the data collected from past purchases, order history, and the likes, the technology within the store will be able to enhance the user’s experience and recommend products that interest them. in fact, the technology can even help the store owners predict the purchase choices as well as the behavior of these customers.

In case you own an eStore and a brick and mortar store, then AI will help close the gaps that exist in the operations and improve the functioning of both the store types. In short, the channel experienced will be seamless for the end-users.

#3 Reduce cart abandonments

Cart abandonment is a cause of concern for the eCommerce business owners. They are most often unable to understand why the users decided not to purchase the product. Was it their WiFi connection or, was it that they changed their mind?

Artificial Intelligence will give better insights into how users purchase and what makes them complete the buy. It will also analyze the user’s behavior and improve the personalization offering for the user.

For instance, automating the email marketing and sending cart abandonment emails can help get the user back on the store, and complete the purchase. When you understand why the user did not complete the purchase, you would be able to send them content that will make them convert.

Combining automated emails and personalized content, there is a high chance you will be able to convert the customer.

#4 Extend better customer service

AI-based chatbots and virtual assistants will improve the customer service you offer to your end-users. Basically, it will be akin to being attended by the customer service people at the brick and mortar store. The salespeople will understand what you want and accordingly devise product recommendations that are meant for you.

Source: TechGenyz

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.

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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:

1. What are the major challenges in front of the global Artificial intelligence (AI) in Supply Chain and Logistics market?
2. Who are the key vendors of the global Artificial intelligence (AI) in Supply Chain and Logistics market?
3. What are the leading key industries of the global Artificial intelligence (AI) in Supply Chain and Logistics market?
4. Which factors are responsible for driving the global Artificial intelligence (AI) in Supply Chain and Logistics market?
5. What are the key outcomes of SWOT and Porter’s five analysis?
6. What are the major key strategies for enhancing global opportunities?
7. What are the different effective sales patterns?
8. 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

Source: Forbes

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

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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.

Preventive 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.

Cloud Services

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.

Optimizing Inventory

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.

Predictive Analysis

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.

Enhanced Management

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.

Strategized Logistics

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