Top 5 Trends in the Logistics Industry to Look Out for in 2021

Logistics has been around for ages and has undergone major transformations time and again. With new advancements in technology, it continues to stretch its horizons. The burgeoning eCommerce sector has further propelled its demand. The logistics market globally is expected to touch $12,975.64 billion by 2027, at a CAGR of 6.5% for the forecast period 2020 to 2027.

Supply chain optimization technology companies Locus and Shippo recently announced $50 million in funding to expand geographically and invest in additional technology enhancements for last-mile optimization as eCommerce continues to grow globally. The eCommerce sales surged in the first quarter of 2021 by 39 percent compared to the first quarter of 2020, while the US domestic parcel market is expected to touch 100 million packages per day by 2023.

With logistics automation, IoT-enabled connected devices, and tech-driven logistics services coming into play, it’s safe to assume we are in for some significant changes in the industry. But then, change is not always bad because it brings opportunity too. In the current scenario, it has ushered in new business models and greater customer expectations. Amazon and many others are already putting customers into the habit of expecting same-day delivery. Needless to say, fast, flawless service has now become an industry standard.

There is no denying technology and changing times have sparked new trends that are all set to shape the future of transportation and logistics. While companies like Locus are leveraging technology solutions to improve visibility and on-time performance, those like FedEx are leveraging blockchain to increase their competitiveness. So let’s look at the top 5 trends that are forcing logistics companies to adjust their sail.

1. Artificial Intelligence (AI) and Machine Learning (ML)

According to a McKinsey survey, AI can help enterprises maximize their gains by more than 50 percent a year. Not surprising then, all forward-thinking organizations are now eager to adopt AI technologies. AI and ML can address problems early on and propose solutions that can help tide over challenges and improve operational efficiency. AI algorithms with the help of ML can help companies address demand fluctuations effectively. They help reduce operating costs, plan supply chain processes, and bring intelligence to administrative tasks to accelerate data-based processes. AI and ML are improving every aspect of warehousing operations, thus increasing profits. For instance, AI helps them access critical information, while machine learning helps them make sense of this information to predict and track trends and make smarter business decisions.

2. Internet of Things (IoT)

IoT sensor technology and connected IoT devices have simplified logistics chores to a great extent. From tracking shipments and inventory to vehicles and equipment, just about everything is easily accessible thanks to IoT. Modern enterprises now rely on IoT-powered container management to increase fuel efficiency, ensure preventative maintenance, and enable real-time monitoring. Drones and self-driving automated vehicles come with IoT sensors to ensure timely deliveries.
IoT startups and logistics companies are joining hands to adopt a proactive approach to container operations. Hapag-Lloyd, for instance, collaborated with Globe Tracker to come up with Hapag-Lloyd LIVE that offers powerful features like real-time GPS location, temperature information, and power-off alerts. With its fleet of around 100,000 containers equipped to serve better, this initiative will ensure enhanced supply chain transparency.

Juan Carlos Duk, Managing Director Global Commercial Development at Hapag-Lloyd, elaborates, “Customers expect more reliable supply chains, so the industry needs to change and invest sufficiently. It is imperative that we understand and fulfill our customers’ needs faster than our competitors. Inviting our customers to further shape our real-time monitoring products right from the beginning will allow them to receive products that are tailor-made for their needs – while giving us a chance to deliver the best possible service at the same time.”

3. Radio Frequency Identification (RFID)

While sensors continue to hold an important place in cargo ships, trains, and alarm systems for tracking and monitoring purposes, tags or sensors are also placed on products enabled by RFID technology. Data is sent via radio waves to be processed for tracking inventory. This is a popular labor-saving technique that allows businesses to scan tags, barcodes, and labels to get information pertaining to their containers. RFID tags have been used increasingly in the apparel sector, among many others.

The logistics industry is now leveraging RFID to get real-time visibility of goods, reduce errors, plan product locations in warehouses, and even measure temperatures in case of chemicals and medicines to ensure that the right storage requirements are met. RFID systems can pinpoint the exact location in real-time, giving logistics managers a bird’s eye view on trucks, pallets, and inventory to see things exactly the way they are across the supply chain. In sudden events or unforeseen circumstances, RFID systems work proactively by changing a delivery route.

4. EDI/API integrations

Both EDI (electronic data interchange) and API (application programming interface) are crucial for logistics companies to integrate data across communication channels. APIs, however, bring more power and flexibility to enable companies to exchange data with cloud-based apps and other digital ecosystem systems seamlessly. API integrations can be used to connect eCommerce stores with fulfillment centers to meet consumer demands successfully when same-day or next-day deliveries are becoming so popular.
Modern businesses are now exploring new possibilities by integrating EDI and API rather than choosing one over the other. They serve as a smarter solution for those who wish to modernize but are reluctant to give up on their traditional EDI solutions. In fact, the allure of an integrated platform is simply impossible to resist. It allows companies to upgrade their legacy systems and evolve into an environment that facilitates end-to-end visibility to conduct business rapidly.

5. Disruptive technologies

Technology adoption in warehouse automation globally is expected to grow from 8 percent in 2019 to 45 percent by 2030. Supply chain and logistics companies worldwide are accelerating digital transformation initiatives to make their operations more responsive. Disruptive technologies are now taking over every sphere of logistics, positively impacting businesses and those who run them.

83 percent of those participating in a survey by MHI in collaboration with Deloitte believed digital supply chains would become the predominant model in just five years. Says John Paxton, CEO of MHI, “Supply chain resilience has never been more important. Companies that made investments in digital technologies prior to the pandemic were more prepared and able to adapt, survive, and even thrive during this disruption. They will also be ready when the next crisis inevitably hits.”

Some of the top technologies that are making waves and helping organizations brave new storms include:

Blockchain – Relatively new but extremely powerful, blockchain is helping industry leaders induce transparency into their business. It facilitates safe transactions through an irrefutable decentralized ledger system and ensures quicker approvals and clearance. Blockchain with its trustless peer-to-peer network increases efficiency, reduces human error, and prevents fraud. For companies that are committed to enforcing digital initiatives, blockchain should be on the cards.

Robotics – Robotics play a significant role in increasing the speed, productivity, and accuracy of supply chain processes while ensuring that human jobs stay intact. Rather than replacing humans, they play a collaborative role to increase overall efficiency. For instance, collaborative robots offer assistance to humans in picking up, packing, and placing goods as required. On the other hand, autonomous mobile robots can help pick up goods and transport them to storage facilities. There are software robots that can do mundane, repetitive tasks to allow human workers more time to focus on chores that need human intervention. Logistics companies are leveraging Robotic Process Automation (RPA) for managing simple clerical tasks in areas like order management and after-sales service to reduce overhead costs and eliminate human error.

Related: Automated pricing operations powered by RPA helped a leading 3PL improve its revenue by 40%

Predictive analytics – Predictive analytics adoption, which currently stands at 31 percent, is expected to grow to 79 percent in the next 3-5 years. A good 43 percent of respondents plan to up their spending on predictive and prescriptive analytics to more than $ 10 million. Predictive analytics drives supply chain companies towards resiliency, helping them manage inventory, maintenance, pricing strategies, and forecasts.

Predictive analytics helps choose faster routes based on traffic, distance, weather, fuel consumption, and vehicle condition. It also helps anticipate maintenance of equipment and vehicles to minimize downtime. It forecasts demand accurately across any logistics network using historical data and market analysis data. It also helps companies adjust their prices based on need. Demand forecasts also help supply chain managers maintain an optimal level of inventory to ensure that demand is met at reduced costs by storing stock at appropriate distribution centers.

Cloud Technology – Software-as-a-service products hosted in public clouds are now a given, considering public cloud solutions are easier to implement. They allow logistics companies to leverage pay-per-use models, thereby necessitating low capital investment. Companies do not have to pay for the hefty cost of maintaining the IT infrastructure and yet get the security and scalability that the cloud offers.

Logistics companies are now leveraging cloud integrations to collect data from management systems, collaborate, and communicate to build process efficiencies and garner better business outcomes. Cloud-integrated logistics is not confined to time or space and gives greater freedom and accessibility that we desperately need today.

Sharpen your digital edge with Trigent

Trigent, with its decades of experience in the logistics sector and a process-driven approach, has been helping supply chain leaders and their ecosystem partners respond intelligently to market disruptions. Our technology experts help create lasting value by giving you keen insights into market trends and empowering you to adopt the latest innovations. Our solutions are custom-made to help you manage diverse aspects of transportation and logistics with amazing ease.

Call us today to book a business consultation.

References

Top 3 trends shaping the Insurance industry in 2021

According to a recent poll, 54% of CIOs believe that insurance companies are resilient and will continue to remain so if they move quickly and decisively. Although this is not big breaking news, we have all witnessed how insurers have evolved in the last few years, to meet the changing requirements of policyholders. Several new trends have emerged as more insurers adapt to these changes. Leading insurers like Allianz, Munich Re, Nationwide, and Liberty Mutual are pouring in money to find the next best thing in insurance.

3 key pillars defining the insurance industry strategy

As the business dynamic has changed in the last few months, insurance organizations continuously anticipate, adapt to, and manage risks and assess the appropriateness and completeness of their strategy during and post-pandemic. The following three pillars have defined insurers’ core value proposition, go-to-market, and technology adoption:

Redefine purpose: “There’s never been an era where the world was more in need of high-performing insurance industry. But to meet the moment and return to growth, insurers must rationalize and rethink their core strategies — from what products they offer, to how they operate, to which markets they serve”, stated EY in their Global Insurance Outlook 2021.

Agile and customer-centric approach: Everything insurers started doing -product portfolios, organization structures, and technology reflected deep insights into market needs. Right products delivered through the proper channels earn customer loyalty and enhances operational efficiency.

American Family Insurance started by implementing Agile within their digital experience team to aid informed decisions in 3-6 days. Today they use Agile CX Sprints for Marketing and Innovation programs as well.

Value-based services: As the pandemic changed the customer needs, the insurer in the health and auto sector shifted towards ‘usage-based insurance.’ This approach optimized the cost structures, strategically prioritizing investments for insurers.

Key insurance industry trends

Leading carriers worldwide have reimagined their insurance products and offerings to thrive in the new reality with these guidelines. Here are three trends you cannot miss in the insurance sector

Becoming more human with automation: 79% of insurance executives believe collaboration between humans and machines will be critical to innovation in the future. Intelligent process automation helped insurers transform their business, become more human, and better adjust to market volatility.

As per a report by Juniper, “Intelligent automation adoption will boom over the next five years, with more than 65 percent of carriers adopting the technology by 2024. The study found that they’ll do so to cut operational costs and remain competitive as they counter flat premium growth”.

Intelligent Automation helps in delivering significant benefits such as:

  • Efficiency, by automating the mundane tasks and minimizing manual data handling.
  • Customer satisfaction, by reducing the turn-around time and improving speed and accuracy.
  • Scalability, by enabling faster decision-making processes and new business generation.

Tailored solution for the customer: Millennials and Gen Z, who are used to stellar online services by Amazon and Apple, expect every company and industry to offer the same level of personalized services. Your customers want immediate access to payments, claims status, and policy information.

Allianz uses five steps to offer personalized policies:

  • Listen to customer
  • Figure out their stated needs and latent needs
  • Review current scene of system and process
  • Re-align them to customer’s requirement and
  • Deploy the right technology

Other insurance brands like Lemonade, Allstate, Nationwide allow full customization, which can be achieved through their app or their company website, within a fraction of a second.

Another category of personalized insurance products that are in high demand in 2021 is Switch-on and Switch-off insurance. An excellent example is Usage-based car insurance. It allows car owners to insure their vehicles for kilometers; they tend to drive instead of the run-of-the-mill full year.
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Auto-insurer Metromile Insurance Co.‘s revenue grew from $4 million in 2017 to a massive $106.4 million in the last quarter of 2018. Metromile tapered its services by providing personalized suggestions depending on miles driven.

Pro-active fraud and risk management: Insurance frauds are not new but have now been amplified. The Federal Bureau of Investigation (FBI) estimates that the total cost of insurance fraud in the United States alone is more than $40 billion per year. Gartner developed the CARTA approach that goes beyond single-point risk assessments to encompass continuous fraud prevention and detection across a customer’s journey on all channels.

The CARTA strategic approach specifies that effective fraud and risk management require:

  • 100% device visibility and automated control
  • Continuous monitoring, assessment, and remediation of operational risk
  • Micro-segmentation to contain breaches and limit lateral movement/damage
  • Technologies and products from several vendors
  • Detection, posture assessment, and control of physical and virtual devices as well as cloud infrastructure

Technologies and solutions to boost these trends

While Insurance organizations are tracking these trends, their key enablers are technological innovations to become an agile, customer-centric, and data-driven organization. In 2019, insurers spent nearly $225 billion in InsurTech and the number grew in 2020.

Here are the technologies that are driving transformation in the industry:

Robotic Process Automation (RPA) to overcome operational roadblocks

Insurers adopting process automation in areas ranging from underwriting and claims management to fraud detection and customer service have witnessed significant changes. Early adopters of Robotic Process Automation (RPA) have noticed reduced labor and claims processing costs and increased efficiencies in document and data management.

MetLife conducted a value stream analysis within its U.S. to determine how to minimize the mundane, rote tasks employees need to do, enabling them to focus on more value-added, customer-facing tasks. This exercise picked approximately 60% of processes that could be reengineered, and 40% could be automated. As a result, by the end of 2018, they have used more than 110 robots to optimize customer and employee experiences through simplified, digitized, and automated processes.

Automated data crunch

Data-related automation helps insurers unlock rich insights that range from understanding clients’ needs to make personalized promotions to offer data-backed advice to drive real-time decisions. For example, Planck, an AI-based data platform, reviews online images, text, videos, reviews, and public records and helps the insurer determine premiums, process claims, and give SMEs faster quotes.

Virtual assistants with AI-powered web & mobile chatbots

Another application of cognitive technology is AI-enabled Chatbots and Virtual Assistants. They interact with the customer in natural language processing and add a human-like touch. For example, Juniper Research claims that conversational AI chatbots for insurance will lead to cost savings of almost $1.3 billion by 2023, across motor, life, property, and health insurance. ( up from $300 million in 2019)

Predictive analytics in proactive fraud and risk assessment

The use of predictive analytics in identifying fraud risk indicators allows early flagging and response to any potential incidents. Here are three absolute favorite methods by the insurer to proactively detect fraud:

  • Social network analysis: The hybrid method includes statistical methods, network linkage analysis, organizational business rules, fraud-pattern analysis, etc., in identifying fraud clusters to see correlations between clusters and aid fraud detection management.
  • Big data, predictive analytics detection: This method is proactive and can handle Big Data sets and make predictive analytics reports.
  • Customer relationship management: This method interlinks to social media placing customers in control of their information and enabling customer transparency.
Wrapping up

The pandemic has elevated the insurer’s role in envisioning potential future disruptions and strategic opportunities and defining the future customer experience, business models, and capabilities needed to capitalize. Front-runners already see results; many others are following.

Among these, what trend do you expect to take the forefront of your organization? We can help you to pick the one that suits your requirements. Book a consultation today to know more.

Discover how predictive analytics is helping insurance companies minimize risk and fraudulent claims

As new technologies forge their way into diverse sectors, it comes as no surprise that the insurance sector is leveraging them for different reasons. Predictive analytics has found an important place in the insurance landscape for its ability to make data-based predictions.

The impact of predictive analytics in insurance sector

While predictive analytics helps the insurance industry gain great insights into customer activity and behavior, it also plays a massive role in preventing fraudulent claims and minimizing risks.

As per the Insurance Fraud Bureau, there has been one insurance scam every minute during the U.K.’s pandemic. Things are equally bad in the United States, where insurance fraud doubled to $100 billion last year.

The insurance industry is now moving quickly to mine data and track new rackets quickly. Explains Zurich’s head of claims fraud Scott Clayton, “By deploying the proper analytical tools, you can extract and interrogate the data, and use algorithms to highlight these links. By joining all the dots, you can soon identify persistent and prolific offenders.”

The pandemic’s unprecedented nature has set the tone for intelligent business practices that can shield them from fraud and help them strike back. Thankfully, predictive analytics, in tandem with big data, have answers to most of the problems insurers face.

How predictive analytics in insurance is minimizing fraud and risks

Understanding predictive analytics

To determine its role, we need first to understand that predictive analytics is an analytical tool that studies historical data to predict upcoming events and ensure business practices’ effectiveness. It gives organizations a competitive advantage and helps them stay abreast of changing trends. It looks into the data collected from different communication channels to analyze client interactions, agent feedback, customer behavior, etc., to build a more intelligent, data-driven ecosystem for all.

It’s no secret that insight-driven insurers are always better positioned to strengthen their capabilities in all five areas, namely, people, process, data, technology, and strategy. Predictive analytics helps them excel on all these fronts. 67% of those who recently participated in a Willis Towers Watson survey reported a reduction in expenses and a 60% increase in sales due to predictive analytics. Most importantly, it helps prevent insurance fraud.

The role of predictive analytics from a fraud prevention perspective

Insurance fraud has a significant bearing on the entire business, specifically on underwriting, and also causes a negative social impact. While undetected frauds drain finances and lead to many more scams in the future, those detected damage market reputation, and trust. Not to mention the legal issues that arise from them and the subsequent impact on future policies, procedures, and guidelines.

Predictive analytics helps insurers in the following areas to prevent fraud:

  • Pricing and risk mitigation – Offer insights that facilitate decision-making and estimate the level of risk that the insurance company has to assume while calculating the premium. For instance, those who go to the gym regularly may be eligible for a discount on health insurance.
  • Trends tracking – Helps insurers create new products, design new customer experiences, and deploy new technologies by keeping an eye on what’s trending in the world of insurance. This also gives insurance companies a competitive edge.
  • Fraud prevention – Helps insurers prevent fraud at different levels of the insurance cycle, including application, premiums, claims, etc. It offers a sneak-peek into public records such as criminal records, medical history, and bankruptcy declarations to review data for detecting inconsistencies and preventing frauds.

Dealing with insurance fraud

What’s frustrating is the fact that insurance frauds today are highly organized and occur digitally. Insurance companies have realized that the only way to fight and prevent insurance fraud is through data mining, analytics, and algorithms based on patterns in fraudster behavior.

Digital algorithms that have been hugely helping in timely scam detection are based on data pertaining to:

  • Referral history – Experts have created algorithmic models to estimate the probability of a claim going beyond a threshold level referred to Special Investigation Units or SIUs. This model typically uses the historical claims data referred to as the SIU to determine the probability value. Investigation scores are then calculated using investigation scoring automation techniques to distinguish between good risk and bad risk claims.
  • Historically rejected claims records – Based on the belief that claims that have been historically rejected stand a greater chance of being denied for doubtful potential frauds, digital algorithms automatically scan through the claims using several parameters. Claim Risk Indicators such as a customer’s SSN, address, contact number, etc., are carefully scrutinized using clustering-based data mining techniques. Claims are then categorized as ‘clusters with high claim frequency’, specifying the level of risk.
  • Individuals/groups – Digital algorithms, in this case, are based on data about individuals or groups that make fraudulent claims repeatedly. Flags are triggered every time fraudulent entities are detected, and these flags help identify fraudulent patterns.
  • Social media profiles – Algorithms, in this case, take into account social media profiles and interaction patterns of individuals along with other details such as lifestyle, attitude, etc. It takes into account mismatches between actual profiles of individuals on social media versus their claims. For instance, if an individual has forwarded an accident claim but their social media shows them partying with friends, there is certainly a mismatch that needs to be investigated. Algorithms based on social media posts are also useful in demarcating networks or groups of fraudsters.

Hurdles on the way to insure the future

Fraud detection is no longer static, limited to place or time. Every time a new detail is added, insurers now run predictive analytics at multiple touchpoints to enhance their fraud detection capabilities. Their efforts are now proactive instead of reactive and a great deal of effort is being put into fruitful collaborations with brokers and third-party vendors to build clear channels of communication and information exchange. But the quality of data still remains a big challenge.

Going forward, the focus should be on reducing data volumes and increasing data quality while ensuring that it is readily available as needed. The funnel needs to be narrowed in a way that competent individuals carefully review the results from machine analytics.

There are legislative barriers, too, concerning data sharing and individual privacy that sometimes stand in the way of data collection. Predictive analytics, however, is helping insurers make the best of what they have by sifting through information pools to help them produce intelligent products for the future.

Empower your insurance business with Trigent

Embark on a whole new journey with Trigent with predictive analytics at the helm. We can help you redefine your strategies to enhance risk management and ensure your future. Our disruptive suite of tools and solutions can transform your insurance business into a data-driven, efficient, and secure ecosystem.

Book a business consultation to know how we can help you keep insurance fraud at bay. Call us today.