Real-Time Predictive Visibility Platforms provide visibility to the cargo owner, exporter, and transporter on the status and location of cargo at all stages in the supply chain. In this article, we want to describe how this visibility facilitates better planning to optimize the holistic supply chain, besides taking prompt corrective steps where required.

Poor Schedule Reliability, its impact on Supply Chain planning, and implications for end-customers

Global freight transportation is disruption prone at the best of times, and the present situation has been exacerbated by supply chain disruptions post-2020. Incidents such as the Suez Canal closure and the Covid-induced lockdowns have caused schedule reliability to plummet alarmingly.

Container Liner schedule reliability has generally been unsatisfactory, declining from 78% in 2019 to 63.9% in 2020 and a precipitous decline to 35.8% in 2021. Also, we can see 20% reduction in carrier schedule accuracy in Jan 2022 (arrival within 1 day of difference) compared to 2019 and early 2020 based on our data:

Unreliable schedules create challenges by making planning difficult, if not impossible. A lack of planning necessitates reactive exception planning, resulting in poor customer service levels. To make matters worse, port congestion causes delays at destination ports and prolongs transit times, which is difficult to predict without comprehensive analysis using sophisticated systems.

Apart from these non-controllable factors, carrier strategies such as slow steaming and blank sailings also play a role in the unreliability factor.

In the end, it’s the customer that suffers. Continuous delays and unreliable schedules result in poor end-customer service, sub-optimal inventory planning, delayed hinterland connections, and more.

Current Supply Chain Planning tools and their limitations

The complexities of global transport make it imperative for shippers to utilize multiple data sources to make data-driven decisions. This is due to the fact that while carriers offer a mostly fixed sailing schedule to their customers, the schedule is being constantly revised to accommodate ever-changing market conditions.

A McKinsey survey of global Supply Chain leaders highlighted the current limitations of the supply chain planning processes. The study revealed that spreadsheets remain the primary tool for supply chain planning for 73% of the respondents. The survey concluded that in order to improve your supply chain, modernizing supply-chain IT infrastructure is crucial.

Another study commissioned by McKinsey further underscores the reliance on outdated or inefficient technologies. In the study, 85% of respondents admitted to struggling with inefficient digital technologies in their supply chains. 

Real-Time Predictive Visibility Platforms (RTPVP): Adopting technology to rectify lacunae in Supply Chain planning

With the challenges mentioned above unlikely to go away anytime soon, this is where Real-Time Predictive Visibility Platforms (RTPVP) come into play. RTPVPs fulfill the need to deliver end-to-end visibility and facilitate informed decision-making to ensure optimal supply-chain-related decisions.

These platforms identify and aggregate a diverse range of causative factors and variables and use complex mathematical algorithms to calculate predictive ETAs. The algorithms are able to account for such factors as weather conditions, drought, tidal flows, vessel design, bunker consumption, loading patterns, routing, emissions, and more.

The growing prevalence of RTPVPs can be gauged from Gartner’s research which predicted that through 2024, 50% of supply chain organizations would invest in applications that support artificial intelligence and advanced analytics capabilities.

Over the past few years, a number of companies have ventured into this space, offering products of varying degrees of intricacy and a vast swathe of functionalities.

A common challenge faced while using multiple data sources is conflicting data points. This could distort the analysis and throw misleading inferences, leading to incorrect / sub-optimal suggested courses of action.

Portcast: Offering superior functionalities and prescriptive solutions

Portcast has been one of the challengers in the RTPVP sphere. Its advanced system can intelligently pick the most accurate and relevant data points from all available options. To do that we use historical performance of each data point to then benchmark and contextualize it for a given container/vessel journey. Being aware of how vital data accuracy is for their customers, our technology intelligently selects, transforms and interprets source data to provide the most trustworthy information to our clients.

At the same time, we scaled up our Data Science team to apply cutting-edge technologies to make the model more intelligent, ensuring data is orchestrated with the optimal algorithm. Being an ML-first company, we firmly believes that automation provides scale, while also acknowledging that human presence helps to improve the modeling output further where edge cases occur. Understanding the imperfectness of everything, we have coined the use of ‘Human-in-the-loop’ methodology in order to accelerate anomaly detection at scale. It means delivering exceptional accuracy and levels of service for those relying on our data.

While thinking about the future, we focus on proactive rather than reactive solutions, such as actionable suggestions that optimize the supply chain, taking predictive modeling a step further. Cases include creating intelligent alerts in instances where cargo is substantially delayed or close to the port. These alerts enable supply chain managers to recalibrate their shipment plans and routes to avoid potential rollovers.

Fortunately, our proven technologies have benefited numerous customers, with one global freight forwarder reporting more than $500,000 cost savings per annum as buffer stocks and emergency shipments were reduced.

If you want to explore how our data and platform can help your business, please contact our representatives for a personalized demo.

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