Tech in Logistics: Part 4-Predictive Analytics for Decision Making in Logistics Management

    After our previous blog on the value of Real-time data collection, we shall explore the relevance of data in logistics in this blog. As the operational roles of logistics continue to expand, the extent of data generated from transactions across the supply chain also continues to increase at an exponential rate. Although data has been part of every transactional functionality since time immemorial, ever since technology unraveled the power of harnessed data, the demand for aggregating, filtering, validating, and integrating data to derive meaningful insights is on a steady increase. Data has certainly undergone a colossal makeover to ‘big data’.

    The logistics landscape was indeed quick to adopt big data technologies; however, both the ecosystem and the scope of data capture and analysis expanded beyond imagination. Hence, the current expectations from big data are humungous, with the logistics ecosystem expecting the insights to deliver real-time status of consignments, forecast trends and possible disruptions, and even manage driver rostering and route plan decisions efficiently.

    The logistics ecosystem has clearly moved much beyond real-time data announcing ongoing events and has started applying data analytics for two purposes:

    • Informed real-time decision making and
    • Predicting future incidents and situations based on extrapolations of existing data and current trends

    This innovative extension from data analytics to cognitive data insights forms the core of predictive analytics and in-memory optimization. Both approaches involve using statistical data, pre-recorded patterns, and earlier event response sequences to analyze every captured data for decision-making and predicting future trends.

    Understanding the value of data-enabled decision-making and Predictive Analysis

    While both the applications of data appear to be a spectacular improvement over raw data, the actual purpose of data-enabled decision-making and predictive analytics certainly needs clarification.

    Data-enabled decision making

    Data achieves its complete purpose only when utilized to make timely decisions. Let's take an example:

    Let's assume that a truck carrying a critical consignment is travelling along a pre-defined route but meets with a sudden unexpected disruption - a sudden breakdown.

    • If this information does not reach the decision-making authorities, every supply chain stakeholder associated with the consignment is likely to face huge losses.
    • If this data does reach the decision-making authorities, the next scenario will be possible chaos, with decisions taken manually based on the efficiency and skill set of the decision maker.
    • Now consider the situation where data is analyzed and converted into precious insights in terms of the exact location where the breakdown occurred, the implications of this breakdown on the overall transit time, the cause of breakdown, the probability of resolving the issue within a limited time, the resources required, the alternate options available, the probability of sticking to the original route plan with each option, and the most recommended option based on earlier such occurrences. Don't you think that the decision taken by the stakeholders in this instance will be the best possible solution to the disruption?

    This is indeed the power of data insights - they help you take the best decision, backed by rock solid data, and with zero latency.

    Data-enabled decision making

    Predictive analytics adds intelligence to the analyzed data, enabling users not only to stay on top of the current trends, but also to be ready to handle future situations. Let's take the example of transport management:

    • The in-memory optimization engine is critical in simplifying all the earlier complications of transportation management - especially fleet planning.
    • This engine uses pre-packaged algorithms and patterns of previously encountered instances to record scenarios.
    • This record of all patterns and previous outputs supports the engine in seamlessly considering all possible variants and influencers to suggest accurate options for route planning.
    • Some of these variants include driver availability, driver performance, vehicle availability, vehicle performance, consignment nature, time restrictions, weather condition, route condition, pick up and destination, possible routes, load planning, and warehouse availability.
    • The final set of route options are generated by extrapolating the possible scenarios that can be derived from each combination of variables.

    The scope of predictive analytics is indeed immense in predicting the ideal route for logistics transportation.

    Other benefits of Predictive Analysis & timely decision-making

    The benefits these two data-enabled approaches are innumerable, ranging from improved workforce efficiency to effective transport and warehouse planning.

    • Predictive analytics is critical in forecasting market demand and plan resources accordingly - especially a boon in the e-commerce industry
    • Warehouse planning - from consignment location to inventory management - can be streamlined through data insights
    • Data-enabled fleet management streamlines planning vehicle service schedules, choosing appropriate vehicles, and vehicle performance monitoring
    • Achieving efficient driver rostering - from constantly reviewing driver performance to ensuring OHS compliance - is no more a challenging task
    • These twin capabilities also bring in a lot of savings - from efficient workforce management to fuel savings and effective vehicle maintenance.

    Thus, predictive analytics and data-enabled decision-making are definitely simplifying the management of the entire logistics process across the supply chain, not only by enabling stakeholders to take well-informed decisions but also by delivering a clear, data-backed map of short- and long-term possibilities and implications of taking a specific approach.

    But the core functionality of data is to bring crystal clear visibility across the logistics ecosystem. Our next blog will evaluate the options that every logistics stakeholder has to achieve end-to-end transparency of the landscape.

    Ramco Logistics has taken innovative ways to meet the needs of 3PLs, Freight Forwarders and Courier Service Providers. We at Ramco Systems have enabled the collection of real-time data. Our Analytics module uses the real-time data to empower the customers to take data driven decision-making. To know more, leave a comment below or write to us at contact@ramco.com

    Part 3                                                       Part 5

    Logistics, Decision Making, ERP for Logistics, predictive analytics, 3PL, analytics, Freight Forwarders, Network Service Provider, logistics analytics

    Ramco Systems

    Written by Ramco Systems

    Related posts

    Subscribe to our Blog

    FOLLOW US

    Like Us