Mobility-as-a-Service (MaaS) utilizes numerous modes of transport, from public transit and cycling to planes and automobiles. Vehicle management responsibilities to incorporate different modes of transport could be enhanced with MaaS. MaaS allows the tracking and coordination of huge amounts of data, incorporating trips, transactions, schedules, and employee mobility budgets.
To avoid traffic jams and save time, companies that are part of the MaaS movement present incentives every month for riding a bike to work rather than driving a vehicle. Instead of letting vehicles sit inactive in a parking lot, companies locate them to employ in car-sharing programs or ride-sharing for other employees which help in increasing their mobility.
The global connected logistics market is anticipated to reflect a strong CAGR of 11% during the forecast period (2019-2029).
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Key Takeaways of Connected Logistics Market Study
- Adoption by the retail & consumer goods industry is estimated to account for relatively high market share than other industry verticals, owing of the ease of approach offered by e-Commerce platforms to customers for ordering products online.
- Connected logistics devices continue to witness higher than software components and services, as the adoption of vehicle telematics and RFID is on significant rise.
- South Asia & Pacific’s market is projected to grow at a highly significant CAGR through 2029, as logistic organizations in developing nations such as India are implementing novel technologies to offer better solutions that help organizations reduce costs and save time.
“Key participants in the connected logistics market could gain remarkable profits by offering connected logistics with blockchain technology, since security and transparency achieved by blockchain aid in tracking information concerning receipts, orders, payments, invoices, and digital assets such as barcodes, certifications, and copyrights,” says the FMI analyst.
Substantial Need for Data-driven Logistics to Boost Market Growth
Rising usage of predictive analytics is assisting logistic operators which are utilizing historical data and real-time patterns to draw predictions to increase reliability, enhance customer satisfaction, and decrease costs. Predictive analytics utilizes algorithms to track historical data to expect need, adjust schedules, and avoid risks to facilitate logistic managers generate proactive solutions.
Logistic firms are utilizing the predictive analysis to comprehend the probability of shipment statuses by utilizing simulation models. The need for data-driven logistics is rising, as logistic companies could gain competitive advantage and achieve efficiency by utilizing predictive tools to avoid losses related to mismanagement of schedule and deliveries and inaccurate stocking. The logistics sector creates enormous amounts of data, and companies store and gather this data for analysis intends.
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To study this accumulated data, big data analytics is employed, which consecutively, utilizes advanced analytic proficiencies to draw out valuable information from enormous amounts of data, and additionally helps data-driven decision-making. By employing big data analytics, logistic firms could transparently track equipment performance, employee, and production that would lead to increased customer satisfaction via streamlining shipping, packing, and picking, in addition to providing real-time visibility and access to order statuses.
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