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Big Data’s Role in Helping Shipping Firms Weather the Supply Chain Crisis

When it comes to the economy, the global supply chain was one of the biggest casualties of the COVID-19 pandemic. Port closures and soaring infection rates hampered logistics providers’ ability to schedule deliveries and predict risks. In turn, manufacturers’ demand projections were challenged, leading to empty shelves worldwide.

A lack of shipping containers has affected supply chains, with shippers scrambling to find usable containers before they disappear into service. Supply chain advisor Drewry highlights that the cost of an average 40-foot container is 79% higher than it was just a year ago, in February 2021.

Big data has been hailed as a solution to this crisis. What role can it play, and how can firms leverage analytics to deal with the challenges they’re facing?

Risk Assessment

Global shipping is fraught with risks. From unexpected weather patterns to changing global regulations, shippers have to consider a wide range of risks before providing their clients with delivery dates and expected routes. All of this is before accounting for last-mile delivery issues.

Collecting data isn’t an issue these days, thanks to the presence of IoT devices throughout the supply chain. However, supply chain stakeholders must analyze their data to spot patterns in them. For instance, a regular stream of delayed deliveries along a single route points to deeper issues.

Either changing regulations are delaying goods deliveries, or vendor technology along that route isn’t adequate for the goods in question.

Having visibility into what’s happening is, in this regard, the key to mitigating risk. Niko Polvinen, CEO of Finnish startup Logmore, is a huge believer in the power of data for superior supply chain management. “With enough data at hand, choke point analysis can show exactly what is causing a holdup, especially for complex last-mile delivery issues which can range from a lack of parking spaces to temporary diversions and regular weight of traffic,” he asserts.

With the right raw information and insights, companies can get granular with choke point analysis and improve their supply models. For instance, when shipping overseas, logistics providers can project container and vessel supply over the upcoming months to provide their clients with more accurate forecasts.

Accurate Chartering

Chartering plays a central role in delivering goods overseas. Finding the right ship for the cargo at the right price is a delicate balancing act. These days, the lack of vessel and container supply is making chartering extremely challenging. However, big data is ready to rescue supply chain stakeholders.

Chartering decisions are traditionally made based on information provided by brokers and ship owners. While having a network of this sort is valuable, it doesn’t lend itself well to accurate analysis. Big data platforms can provide charters with accurate information regarding vessel positions and expected times of arrival.

For instance, an analytics platform can integrate Automatic Identification System data, position reports, and vessel particulars while matching these datasets with cargo needs. The result is a list of vessels at the click of a button. Analytics platforms of this sort also increase transparency and enhance charter competitiveness.

Dor Raviv, CEO of Orca AI highlights further advantages of the big data approach. “Part of the importance of introducing technology throughout the industry is to increase automation—not only on ships for improved visuals and traffic updates but also for port operators to increase efficiency,” he says. What’s more, increasing automation frees charter planners to execute more value-added tasks.

Automating select portions of the chartering process also gives stakeholders more time to assess risks and determine the best way forward. The result is more efficient logistics planning and lower costs.

Operational Efficiency

Data from shipping operations offer fertile ground for increasing efficiency. For instance, determining optimal ship speeds is a challenging task. While builders rate their vessels for optimal speeds that minimize fuel consumption in test environments, the real world involves more complexities.

Everything from the specifics of the cargo hauled to the wind speed affects a vessel’s optimal speed. In these scenarios, a big data analytics platform can assimilate available data and calculate optimal speed in no time. Calculating fuel consumption projections is also easy, since dashboards can take bunker costs, freight rates, and upcoming schedules into account.

Transmetrics CEO Marc Meyer is attuned to the possibilities that big data offers supply chain companies. “Through predictive analytics, individual companies can then gain more visibility over operations resulting in healthy and optimal levels of safety stocks,” he says.

Meyer’s point is especially valid when considering the question of ship maintenance. Typically, operators conduct maintenance based on a schedule that utilizes intuition. A vessel that experiences abnormal usage might need more maintenance, something that will likely be missed by a non-data-driven approach. In turn, this increases running costs.

“With data authenticity and consolidation, predictive analytics can help better delegate shipping demands and thereby significantly slash costs,” adds Meyer. Modeling the cost-benefit analysis of maintenance, predicting asset life cycles, and designing optimal schedules are simple thanks to big data platforms.

Many Challenges, One Solution

Big data offers supply chain stakeholders optimal solutions to deal with the challenges they are currently facing. From predicting route maps to optimizing vessel operations for costs, big data analytics makes the supply chain more efficient and predictable for everyone involved.

By Evan Morris, Network Security Manager

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Todd Knarr  –  Mar 16, 2022 2:35 PM

One large reason for the container “shortage” is unbalanced flow of containers on routes. In the US it’s manifested as a build-up of empty containers in mid-continent and eastern inland depots because the flow of goods from west-coast ports eastwards is much greater than the flow back to the west coast. Similarly when manufacturing in Asia slowed down due to lockdowns the first things to be cut were the more expensive imports of raw materials where local materials were available while exports didn’t slow by nearly as much (US demand for tech goods in particular went up as remote work increased). The single most effective visualization would probably be a display of where the supply of empty containers is vs. where the demand is, combined with a concerted effort to move empty containers from where there’s an excess supply to where there’s excess demand.

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