TRANSPORTATION AND LOGISTICS
The top challenges faced by transportation and logistics companies worldwide are the management of costs and margins in a volatile and dynamic business landscape with rising fuel price and aggressive business competition. Utilization of predictive analysis in managing transport and logistics operations can be useful to make business transformations, specifically in terms of cost efficiency. For instance, road freight and transportation, fleets of vehicles are out-numbered, and it is a difficult job to monitor and maintain these vehicles despite the necessity of constant maintenance. The advanced analytics instead of preventive maintenance enables companies to perform predictive maintenance.
The predictive analytics solutions gather vehicle data from sensors, which is then examined to identify components that are most likely to break or underperform. Thus, allowing technicians to make necessary repair/replace decisions and avoid expensive post-damage repairs. , Furthermore, linking historical data with consumer profiles, economic indicators, and geo-localized market data, logistics and transportation providers can forecast demand with increasing accuracy. This helps in anticipating daily volumes, optimizing delivery routes, and allocation of resources to deliver the service efficiently, ultimately enhancing customer satisfaction. For instance, ArcelorMittal, a global steelmaker that operates as two interdependent companies, ArcelorMittal Mines Canada and ArcelorMittal Infrastructure Canada optimized its supply-chain logistics by implementing predictive analytics.
In order to remove the logistics bottlenecks and improve resiliency, ArcelorMittal used predictive analytics solution to transform and correlate data from its distinct systems for creating intelligent insights for the business decision making supporting both strategic decisions and everyday business decisions for augmenting the use of the company’s equipment and transportation infrastructure.
COMPETITIVE LEADERSHIP MAPPING TERMINOLOGY
The vendors of predictive analytics software in Transportation and Logistics are placed into 4 categories based on their performance in each criterion: “visionary leaders,” “innovators,” “dynamic differentiators,” and “emerging companies.” The top 23 vendors evaluated in the data quality tools market include Agilone, Alteryx, Inc, Angoss Software Corporation, Dataiku, Domino Data Lab, Exago, Inc., Fair Isaac Corporation (Fico), Good Data, Greenwave Systems, Inc, IBM Corporation, Information Builders, Inc., Knime Ag, Microsoft Corporation, Microstrategy Incorporated, NTT Data Corporation, Oracle Corporation, Qliktech, Inc., Rapidminer, Inc, SAP SE, SAS Institute Inc, Sisense, Tableau Software Inc and Teradata Corporation.
Use Cases of Predictive Analytics Software in Transportation and Logistics
Use Case #1: Optimize operations or reduce operational costs
Understanding variables such as weather and season which might influence preferred mode of public transportation, or traffic patterns and levels of congestion that influence cost of travel (cost of delay, cost extra fuel burnt, etc.) or fluctuations in fuel prices, workforce utilization, and more are key to optimal transport operations. With so many variables to factor in, predictive analytics could essentially empower transportation sector to enhance their capabilities in making intuitive decisions based on robust data and industry models for thorough forecasting while factoring in numerous possibilities simultaneously. Companies using the predictive analytics witness improved performance and productivity; thereby resulting in improved profit margins and top line revenue growth. With better planning using predictive analytics certain benefits such as –
- Project optimization for reduction time, cost, and workloads
- Increase employee retention to reduce cost of recruitment and training; or
- Optimizing travel routes to reduce time which is proportional to costs
Use Case #2: Capture or address operational risks:
Predictive analytics solutions can help in avoiding potential incidents by analyzing factors such as:
- Driving behavior & employee health and safety
- Operational process/work flow (systems and resources)
- Probable high accident routes and/or time of the day
- Traffic congestion
- Potential breakdown of vehicles from wear and tear
- Regulatory compliance
Higher the availability of multiple variables that are specific to analyze the root cause of an incident, the easier it is to understand and prepare for the future that are inline with the reality using predictive analytics.
Use Case #3: Supply chain management
With dynamic patterns in consumer behavior predictive analytics solutions can help in optimally managing the supply chain in an effort to reflect higher degree of seamless collaboration between the various entities involved in the process, thereby potentially avoiding delays in delivery of goods or services, and reducing cost of storage and distribution, with just in time deliveries.
Use Case #4: Vehicle maintenance and inventory prediction
Using analytics predictive maintenance is applied to avoid breakdown of vehicles and redundant or unnecessary maintenance scheduling. Predictive analytics can help in initiating timely and necessary maintenance based on current information of leading indicators for breakdown.
Use Case #5: Customer analytics for enhanced customer service and experience
Predictive analytics of customer behavior for the travel industry helps in understanding the customer journey. Thus, transport companies can find opportunities to delight the customer with personalized service and relevant offers by combining different datasets and using historic and real-time information. This could include use of machine learning and techniques such as sentiment analysis, clustering, affinity analysis and propensity analysis to predict consumer’s behavioral patterns.
Use Case #6: Sales and Marketing
Use of real-time pricing solutions based predictive analytics of various variables such as customer information and competition, are widespread in the travel and transportation industry. It gives the companies a competitive edge by providing optimum price quotes while keeping in line with regulatory requirements.
Case Studies of Predictive Analytics Software in Transportation and Logistics
Tableau advanced analytics brought operational efficiencies for Tesla
Elon Musk’s goal to produce 1 million Tesla cars by 2020 failed to meet its projections more than 20 times in the past 5 years. As part of ongoing effort to tame chaos and improve manufacturing efficiency, Tesla turned to Tableau for advanced data analytics capabilities for root-cause investigation, quality defect tracking etc.
- Tesla could easily trace back the production defects and rectify the same in no time
- Improved operational efficiencies by predicting the production count and yield ratio
Case Study: Lytx Inc. helped USMC’s SWRFT Non-Tactical Vehicles dept. to address operational risks and optimize operational costs
Lytx helped United States Marine Corps’ (USMC) Southwest Region Fleet Transportation (SWRFT) Non-Tactical Vehicles department to address risky driving behavior using the DriveCam safety program’s analytics to proactively manage the fleet, fuel usage, and greenhouse emissions.
- Increased safe driving practices by 40%
- Increased durability of tires and brakes by 400%
Case Study: SAS helped NC DOT to optimize costs and operational risks of transportation projects
SAS helped North Carolina Department of Transportation (NC DOT) to optimize costs of transportation projects by using advanced data sources to predict most feasible choices for road corridors, thereby reducing cost of expensive soil surveys and assessment of impact on surrounding environment from the project, thus avoiding any environment regulatory compliance penalties.
- Reduce time to select and plan of road projects by 20%
- Cost savings to as high as USD 500,000 for each road project
Case Study: IBM helped Con-way Freight in efficient supply chain management
IBM helped Con-way Freight by providing predictive intelligence powered by IBM Netezza to analyze transaction-level details for deep customer understanding and data-driven business decisions. This enables the company to maintain high service levels over long periods in line with its commitments towards delivering guaranteed, day-definite regional and transcontinental service to more than 300 service centers in the United States, Canada, Mexico and Puerto Rico.
- Being in production within three weeks of purchase
Case Study: Dataiku helped Chronopost to optimize operational costs
Dataiku worked with Chronopost to improve on-time deliveries during peak activity times, for which geo-aggregation of historical delivery and parcel retrieval data was done to develop an application that evaluates an ease-of-delivery score for each address. This score-based approach enabled Chronopost to predict risky deliveries and proactively flag them to meet delivery deadlines. Due to the increased operational efficiency of their network, Chronopost is able to decrease package delivery costs and develop new commercial offers.
- Increased operational efficiency of the entire operational network
- Significant reductions in delivery costs
- Able to develop new commercial offers
Case Study: TIBCO helped TUI Group in analyzing its customers and enhancing sales & marketing
TIBCO helped TUI Group in analyzing customer journey and competitor pricing thereby enabling personalized customer service and smart/just-right/competitive pricing using business intelligence and predictive models.
- Rapid increase in Net Promoter Score
- Very competitive without giving up too much margin
- Significant financial impact with €50 million in revenue per day being generated
Case Study: BluePi helped Delhivery in supply chain management
BluePi’s analytical solution helped Delivery, one of fastest growing logistics company from India, to understand and optimize their ‘last-mile logistics’ machinery, by leveraging the predictive analytics powered by big data and real-time reports. It helps Delhivery to develop a scalable reporting system to track their logistical flow in real time.
- Increased operational efficiency
- On-time delivery with impeccable quality of service
Case Study: FourKites helped US Foods in supply chain management for its logistics operations
FourKits’s real-time load tracking platform implemented by US Foods, one of the largest food distribution companies, helped their logistics operations in provisioning on-time and hassel-free deliveries. With the help of the platform US Foods’ customer service team can proactively manage exceptions and provide answers immediately, without calls to dispatchers or drivers, by leveraging the access to rich information across their carrier base.
- Increased accuracy in real-time tracking of their logistics.
- Reduce redundancies and streamline operations, for providing customer service efficiently.
Case Study: FarEye’s helped Blue Dart to enhance customer experience and reduce operational risks from Carbon Footprint
Blue Dart’s partnership with FarEye enabled real-time visibility of each shipment and by using predictive model or algorithms, FarEye optimizes the work schedules and routes for each delivery personnel thereby reducing costs. The platform also tracks each delivery attempts with various unique and innovative features like caller ID functionality to identify customer call with docket number and other details. This enables to efficiently plan and schedule for re-attempt delivery options for undelivered products, thereby increasing workforce efficiency and customer satisfaction.
- Value creation by provisioning personalized & seamless customer experience
- Reduced cost and carbon footprint by optimizing delivery schedules
Frequently Asked Questions
How will the Predictive Analytics Market perform in near future?The predictive analytics market size is expected to grow from USD 4.6 billion in 2017 to USD 12.4 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 22.17% during the forecast period. Proliferation of internet and the availability of various means for accessing the internet have led to a massive increase in the data volumes being generated. This will help in the advancement and expansion of high-speed internet services.
What are the opportunities in the predictive analytics market?With the rise in touchpoint and the need for collecting data to understand consumer behavior, every touch by a consumer has become an important data point that can be processed to reveal user behavior. With the exponential rise in individual and organizational data, businesses are now deploying teams of data scientists and analysts to process the collected data. Another factor accelerating adoption is the revenue generating potential of predictive analytics. This is compelling firms to invest in predictive analytics.
What is the competitive landscape in the market?The predictive analytics ecosystem comprises vendors, such as Alteryx, Inc. (US), AgilOne (US), Angoss Software Corporation (Canada), Domino Data Lab (US), Dataiku (US), Exago, Inc. (US), Fair Isaac Corporation (FICO) (US), GoodData Corporation (US), International Business Machines (IBM) Corporation (US), Information Builders (US), Kognitio Ltd. (UK), KNIME.com AG (Switzerland), MicroStrategy, Inc. (US), Microsoft Corporation (US), NTT DATA Corporation (Japan), Oracle Corporation (US), Predixion Software (US), RapidMiner (US), QlikTech International (US), Sisense, Inc. (US), SAP SE (Germany), SAS Institute, Inc. (US), Tableau Software, Inc. (US), TIBCO Software, Inc. (US), and Teradata Corporation (US). The exponential growth in data volume is due to the expansion of businesses worldwide, which is driving the rise in data volumes and sources. The accumulation of big data in a single location has rapidly developed the evaluation capabilities of data science experts in every organization. Additionally, companies prefer to provide stand-alone solutions rather than combined solutions. This is eventually resulting in a rise in the number of big data analytics startups, which are driving noteworthy innovations.
What are the regulations that will impact the market?Predictive analytics leads to ad hoc analysis, which assists companies to have all workable solutions for their business specific questions and forecast past, present, and possible future predictive scenarios. In the current competitive business scenarios, companies need more than accurate predictive statements and reports from its predictive analytics. Companies now need more forward-looking, predictive insights that can help them shape impactful business strategy and improve the day-to-day decision-making in real time.
How are mergers and acquisitions evolving the market?July 2017, SAP collaborated with energy and services company Centrica to help their customers in managing assets and energy consumption on insights available through the IoT. February 2017, Oracle announced the expansion of its IoT portfolio with the introduction of 4 new cloud solutions to assist businesses to fully utilize the advantages of the digital supply chain. By applying advanced predictive analytics to devise signals, IoT applications can help in automating business processes and operations across the supply chain to enhance customer experience. March 2016, the company has extended their strategic partnership to offer combined capabilities of cloud analytics and big data to their users. This will help users to automate and simplify the decisions while attaining greater business insights for smarter business decisions.
What are the dynamics of the market?Proliferation of internet and the availability of various means for accessing the internet have led to a massive increase in the data volumes being generated. This will help in the advancement and expansion of high-speed internet services. Globalization and economic growth are also playing major roles in driving greater data generation worldwide. Also, the rise in connected and integrated technologies has provided a platform to predictive analytics software vendors for leveraging this development and the unprecedented growth of the internet. Additionally, the eCommerce sector has modified the traditional shopping behavior of customers. Dedicated email campaigns, online/social media advertising, and cognitive analyzing of customers are the key enablers driving sales and increasing customers’ loyalty. With connected devices coming to the forefront, retailers are focusing on real-time analysis of customers’ shopping behavior and market basket analysis for analyzing consumers’ perception, which can be used for building tailor-made offers to increase customer retention. Similarly, with the rise in the global IoT analytics demand in the retail sector, the market is expected to have unprecedented growth opportunities for predictive analytics.