Maximizing Returns through Advanced Analytics in Transportation

Maximizing Returns through Advanced Analytics in Transportation

Table of contents Industry Challenges 1 Use Cases for High Performance Analytics 1 Fleet Optimization / Predictive Maintenance 1 Network & Capacity Optimization 2 Dynamic Pricing Analytics & Revenue Optimization 2 Customer Analytics 3 Success Story: Risk Management & Cost Optimization 3 ActivePivot Delivering High Value through Advanced Analytics 4 The Data Management Challenge 4 Analytics beyond traditional BI / OLAP systems 4 Why ActivePivot? Real-time Insights High Performance Complex indicators Advanced simulations Self-service dashboards

The need for better customer services and improved efficiency has forced organizations to be more agile and flexible and the transportation industry is no exception. The growing complexity and volume of data ( Big Data ) has inspired transporters to transform their processes and add new dimensions to drive business results. This white paper highlights how high performance analytics can be leveraged in the transportation industry to maximize business returns on some critical use cases. We also discuss ActivePivot - Quartet FS s Analytical platform - and how it can address some of these challenges. Industry Challenges The transportation industry has been experiencing unprecedented amounts of data captured from different sources and a large part of this data is unstructured too. The need to extract instant insights from this captured data is significant to achieve a competitive advantage, optimize CAPEX and OPEX, improve service reliability and mitigate risks. Growth cannot be achieved simply by increasing capacity as it leads to higher costs both from financial and environmental perspectives. The challenge to improve efficiency and reliability, coupled with cost containment, has been a top priority for transport companies as they continue to scale. Advanced analytics paves the way for being cost efficient and highly competitive while driving quicker ROI (Return on Investment) and lower TCO (Total Cost of Ownership). The following sections highlight some critical use cases within the transportation industry where Big Data analytics can make a significant difference and help in cost containment, improved service reliability and efficiency and reduce risks. Use Cases for High Performance Analytics Fleet Optimization and Predictive Maintenance The transport industry is fleet intensive and the cost of maintenance could be formidable depending on the size and age of the fleet. The complexity and variability of maintenance data grows with the additional dimension of repair costs, safety mechanisms, compliance, labor and so on. A continuous analysis of operational data is imperative to keep a check on rising maintenance costs and the health of the fleet. Service disruptions due to failures have an instant impact on profitability, customer satisfaction and retention and safety regulations. In addition, the rising insurance costs of the fleet add to the challenge of maximizing returns from the assets. Analytics solutions can consolidate and aggregate this large variety of data and help you in reducing costs by: Improving fleet utilization and performance Real-time view of fleet operating conditions Usage and wear patterns, maintenance cycles - 1 -

Reduce the risk of unscheduled maintenance Accelerated service delivery Minimize out-of-service time Predictive Maintenance Real-time analytics Prevent expensive failures, extend life of spares Reduce revenue loss due to service disruptions Event / incident monitoring Insurance expenditure optimization Warranty analytics Optimize asset costs and inventory levels TCO of an asset / lifetime value Reduce supply chain costs Optimized spare inventory Measure and monitor SLAs / KPIs Real-time warnings / alerts Network and Capacity Optimization Accurate demand forecasting is central for optimizing the available capacity and has a direct bearing on the overall revenue. This demands for continuous analysis (even in real-time) of data sourced from a variety of systems. As most companies struggle to meet the challenges of ever changing requirements, it needs complex analysis to warrant better yield and ROI. External dynamics like weather, natural disasters, strikes etc. can lead to peaks or troughs of capacity and network. A predictive approach safeguards against such encounters by consistent analysis of current and historical data. By identifying relationships and patterns, improved forecasting accuracy can be achieved to optimize capacity planning and return. The key benefits include: Improved return and availability of assets Optimized operations Real-time event management Reduced transportation costs Faster time to value Dynamic Pricing Analytics and Revenue Optimization Considering the complexity of operations coupled with the high degree of competition, transporters are increasingly looking at new strategies to improve their pricing models. There are multiple challenges while dealing with this as it involves buying capacity based on forecasts, carrier costs, margins etc. These characteristics create the potential for very large swings in the opportunity cost of sale. Complex analytical models can be developed to churn historical pricing data along with an insight into competitor pricing. These models can help filter pricing information and accurately priced offerings. Some of the benefits include: Accurate demand forecasts Improved and accurate pricing decisions margins by client/product/region/country Compile better pricing offers Meaningful customer segmentation Tracking and monitoring pricing effectiveness Real-time supply chain visibility - 2 -

Customer Analytics Improving customer experience is vital to customer retention and growth. Pinpointing the areas where a customer was dissatisfied, or where the losses came from, or identifying new revenue opportunities, is not a simple task when the complexity is high. Customer and services segmentation for high profitability becomes important to protect the bottom line. Strategic insights into customer behavior and buying patterns is instrumental in discovering value from each customer and instituting a trusted business relationship. Benefits from customer analytics include: Strategic insight into each customer Customer segmentation Develop relevant marketing strategies and campaigns Offers based on preferences Instant pricing and contract adjustments Service refinement and new avenues of growth Optimize service delivery monitoring, tracking Success Story: Risk Management and Cost Optimization One of the leading transportation companies in Europe involved in finished vehicle logistics had several challenges for risk mitigation and cost optimization. They had very complex and stringent SLAs that required constant monitoring and improvisation. The issues became multi-fold when encountering disruptions due to weather and port strikes, and the cost impact was formidable. Their previous systems were rigid, slow and lacked collaborative capabilities. The IT dependence was high and users did not have instant insights into the data, therefore a much delayed decision making process ensued. Their ActivePivot implementation has now helped improve efficiency, optimize costs, reduce risks and improve customer relationships. Instant insights and real-time alerts have resulted in better disruption / exception management, close monitoring of SLAs and improved visibility of the inventory. Social media analytics - 3 -

ActivePivot Delivering High Value through Advanced Analytics ActivePivot is an innovative Big Data analytics solution that meets the needs of transportation companies looking to extract actionable intelligence from large volumes of data quickly and effectively. ActivePivot consolidates operational data in real-time from multiple data sources, integrates business logic and complex calculations and provides real-time analytical capabilities for timely decision making. Data variety: Data can take many diverse forms: route plans, consignment information, inventory, customer data, pricing etc. Easily aggregating and reconciling heterogeneous data is imperative to derive maximum value. The Data Management Challenge Transportation companies need to respond quickly to unexpected changes in demand, adjust service levels, track and consistently monitor each process. These value added services create the need for high performance analytics as it directly impacts the volume, complexity and speed of data. Data volume: Data is generated in a variety of operational systems such as ERP, TMS, WMS, CRM as well as many other sources. Making sense of such a deluge of data is a non-trivial undertaking. Data velocity: The transportation industry requires that events and changes need to be dealt with quickly and efficiently as a decision based on incomplete facts can have a lasting negative impact. Consequently, coping with data velocity is vital for making timely decisions and controlling the value chain. Analytics beyond traditional BI / OLAP systems Traditional data warehouses and business intelligence tools are no longer adequate to meet the needs of instant intelligence. As these systems involve batch processing, they usually lack the flexibility needed to meet changing information requirements. They simply focus on what happened however transporters need predictive capabilities such as scenario analysis and simulations to better anticipate what might happen. Data sources: Bespoke client, Software component WMS API TMS ERP CRM Pricing Act ivep Aggregation Engine ivot In tegration Services Web Browser with ActivePivot Live, MS Excel, other BI tools el tin Sen t o v i ActiveP Alerts -4-

Why ActivePivot? ActivePivot applies in-memory processing to resolve the challenge of big data analysis. It is an in-memory multi-dimensional analytics engine that leverages computing power to significantly accelerate processing time. It continuously extracts data from a multitude of systems, loads that data into memory and calculates complex measures and indicators using advanced business logic. Real-time Insights ActivePivot provides real-time visibility into operations while exploring all types of data sources. It integrates data from heterogeneous systems and aggregates incrementally to immediately reflect new transactions in the analytical environment. High Performance As an in-memory based solution, ActivePivot is incredibly fast and uses the power of CPU multithreading to deliver results instantly rather than hours or days. This eliminates the need for typical batch processing that most traditional BI/OLAP systems are based on. Complex indicators ActivePivot helps you calculate, analyze and monitor complex indicators and KPIs. It applies statistical and predictive algorithms on very large data sets. It integrates advanced business logic, such as quantile calculations or any business rule, to produce complex indicators on-the-fly. Advanced simulations ActivePivot lets you simulate the impact of any change or decision before it takes place. What-if analysis can be conducted on any data set using any number of parameters. This allows you to evaluate alternative scenarios and make projections for assessing the impact of an unplanned change. Self-service dashboards The ActivePivot front-end interface, ActivePivot Live, can provide personalized, self-service views for valuable information. This could also be extended to customers where users can formulate any query, get answers in a split-second and/or explore data at any required depth and granularity. This helps in providing actionable information to the user, resulting in unmatched customer experience. - -

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