WHITE PAPER BIG DATA IN LOGISTICS
CONTENTS INTRODUCTION INDUSTRY CHALLENGES IN LOGISTICS UNDERSTANDING BIG DATA BUILDING A VALUE-DRIVEN BIG DATA STRATEGY BIG DATA IN LOGISTICS LOGISTICS AS A DATA-DRIVEN BUSINESS OPERATIONAL EFFICIENCY CUSTOMER EXPERIENCE NEW BUSINESS MODELS SUCCESS FACTORS FOR IMPLEMENTING BIG DATA ANALYTICS OUTLOOK LIST OF FIGURES 5 6 10 11 12 12 16 18 21 23 25 26 3
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INTRODUCTION DEAR READER, Big Data Analytics has been recently dominating the headlines and it is surely one of the most influencing current trends in the world of information management. However, unlike many other topics that have shaken the IT industry in the past, it is not primarily driven by a discussion on technology. Admittedly disruptive technology approaches propel the performance of data processing to a new order of magnitude. But first and foremost, Big Data Analytics is about leveraging extensive information assets in order to enhance business process performance or to even create entirely new business models. Consequently, fully understanding Big Data always means to look at it in front of a particular business context. In this respect, the logistics industry is a promising target for applying Big Data techniques as it heavily relies on extensive information flows. A timely and accurate delivery can only be assured if data travels ahead of every single shipment. On a global scale, the tracing of goods from origin to destination creates an enormous data set that fuels a variety of valuable Big Data use cases. With the paper in hand T-Systems is proud to present a comprehensive analysis of Big Data applications along the logistics value chain. We elaborate a series of use cases that help to increase the efficiency of logistics operations or that enhance customer experience and loyalty. Moreover, opportunities for logistics providers to capitalize on their own information assets and to deploy innovative business models are discussed. Spotlights on successful real-world applications show what Big Data can achieve in logistics environments today. In summary, the study provides a tool to logistics providers for navigating their journey towards an information-driven business. It has been jointly developed with the Solutions & Innovation department at Deutsche Post DHL and with experts from Detecon Consulting. The research team has combined world-class experience from both the logistics domain and the information management domain. I now invite you to explore the game-changing power of Big Data Analytics for the logistics industry. Enjoy reading! Yours sincerely, Dr. Stefan Keh, Industry Leader Travel, Transport & Logistics 5
INDUSTRY CHALLENGES IN LOGISTICS The world is changing faster and faster. The accelerated economic development across the globe has created entirely new business networks and flows of goods. In the light of global competition, companies focus on core competencies and reduce their own vertical integration. In addition, variations of a particular product are ramped up to satisfy regional demand while at the same time the product lifecycle shortens. As a result, the complexity and dynamics of global supply chains are taken to a higher level. Customers require sophisticated services like just-in-time supplies, same-day delivery and dynamic shipment routing. In many vertical segments, logistics become a critical element of competitiveness and market success. In order to meet these rapidly changing requirements logistics providers have to master a number of challenges imposed by political, economic and demographic movements. URBAN POPULATION BY MAJOR GEOGRAPHICAL AREA PER CENT OF TOTAL POPULATION 90 80 70 60 50 40 30 [%] Urbanization More than half of the world s population already lives in urban areas. Projections indicate that many regions will experience a continued growth of megacities in the coming decades, cf. figure 1. In these areas the density of deliveries will surge as will the traffic density and the occurrence of congestion. In order to further ensure timely delivery of shipments, logistics providers have to reshape their delivery process. Innovative solutions for the last mile and real-time guidance for optimized delivery performance have to be put in place to keep up with urban population growth. In the same way as megacities grow, population density in remote areas will decrease. Consequently a dedicated delivery network in rural areas will be expensive to maintain as the cost per delivery is going up. In order to protect margins, logistic providers are forced to enter into partnerships. Shared networks and cooperative last mile delivery may become popular among CEP (Courier, Express and Parcel) companies in the future. 20 10 0 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 North America Latin America, Caribbean Europe Oceania Asia Africa FIG. 1, Source: World Urbanization Prospects, the 2011 Revision, United Nations Department of Economic and Social Affairs, 2012; cf. http://esa.un.org/unpd/wup/analytical- Figures/Fig_1.htm. 6
Volatility Changing economic conditions are the new normal. The continuous transformation of operating models has become an imperative for enterprises in the light of changing demand, new competitors, substitute products, scarce resources as well as political and social developments. As major shifts in economy gain in size and geographical reach, global players must frequently scale their production up or down according to their volatile business environment. As a consequence, logistics providers are in turn facing high amplitudes in the demand for transportation services. The figure below illustrates that after a long period of steady growth the demand for freight capacity has become virtually unpredictable in recent years. EU-27 PERFORMANCE BY MODE FOR FREIGHT TRANSPORT 1995 2011 2000 1800 1600 1400 1200 1000 1997 1999 2001 2003 2005 2007 2009 2011 Road Sea FIG. 2, Source: EU Transport in Figures Statistical Pocketbook 2013, European Commission, 2013. In order to constantly adapt to changing demand with regards to quantity as well as quality logistics providers have to enhance their strategic and operational capabilities. On the one hand a breathing infrastructure is required to quickly reshape network capacity and service performance. On the other hand means for comprehensive prediction of market and supply chain dynamics have to be established to anticipate changing economic conditions well in advance. Customer Expectations Current research on logistics trends shows that market players are increasingly concerned about satisfying their customers expectations. Across different categories of firms, managers have ranked meeting customer requirements as their top priority, cf. figure below. Why do customer expectations finally become of importance in logistics? Basically the effect originates at both ends of the supply chain. In the downstream direction, consumers have a heightened demand for flexible shipment options like self-collection, follow-me routing or same-day delivery. In this respect, consumers may even switch e-commerce suppliers when experiencing poor delivery services. With an ever-increasing volume of goods ordered online and shipped directly to the consumer, there is a rising power on the recipient side that logistics providers have to consider. In the upstream direction, manufacturers and retailers are also increasingly demanding with regards to performance and agility of logistics services. Driven by the development of a global economy, companies are serving more regional markets and are sourcing from more different countries. Consequently the complexity of supply chain systems is growing while requirements for tight delivery windows stay unchanged. In addition industry-specific features like real-time tracking, cold chain management and shelf stocking are stretching the capabilities of logistics providers to the limit. PERCENTAGE OF LOGISTICS OBJECTIVES RANKED AS TOP PRIORITY [%] meet customer requirement on-time delivery green logistics delivery time innovation csr logistics cost logistics quality schedule flexibility 0 5 10 15 20 FIG. 3, Source: Trends and Strategies in Logistics and Supply Chain Management, p. 16, BVL International, 2013. 7
Fragmentation and Complexity Many logistics domains like road freight and supply chain solutions are highly fragmented markets. The majority of market participants owns a comparatively small market share and is limited to either geographical boundaries or vertical segments. Sub-contracting and collaboration exists but the level of standardization and business interoperability is low. In fact, many providers operate in a niche of industry or local knowledge. In contrast, globalization forces many logistics customers to expand the complexity of their operational model as part of their efforts to serve more markets with more product variations through different sales channels. Subsequently the requirements for logistics services with regards to adaptability, scalability and localization are increasing. The complexity becomes even higher as the management of sustainability, governmental regulation and risk on a global scale are demanding. How do logistics providers overcome the divergence that lies in the fragmentation of their own market landscape and a rising complexity of services to be delivered? The concept of smart business networks1 offers a solution to the problem. It is about the capability to quickly rearrange an operational model by orchestrating transactions and resources from a number of business partners, cf. figure below. What differentiates a smart business network from just a partnership is the ability to execute this change in an agile and cost-efficient way. However it takes transparency, standardization and interoperability to rapidly interconnect complementary business functions across different companies. Successful logistics providers will master the development and adoption of a networked business operating system as a platform to link up business processes and create new modular services. ORCHESTRATION OF A BUSINESS NETWORK business layer networked business operating system transaction layer producer forwarder agent retail shipping line bank insurer end consumer rfid data scanning data rfid data scanning data inland shipping physical layer logistics & production producer seaship road transport distribution terminal FIG. 4, Source: Detecon. seaship 1) Smart Business Networks, P. Vervest, E. van Heck et. al. (Editors), Springer, 2005. 8
Logistics and Big Data are a perfect match It is obvious that neither bigger trucks nor faster trains are a relief from the challenges discussed above. Instead, transparency, foresight and responsiveness are the key controls to successfully navigate the transition of the entire industry. In this respect the logistics sector is ideally suited to benefit from the technological and methodological advancements of Big Data Analytics. In its ancient Greek roots, logistics means practical arithmetic2, which gives a strong hint that mastering data was a key factor in this discipline from the beginning. Today logistics providers manage a massive flow of goods and at the same time create vast data sets. For millions of shipments every day origin and destination, size, weight, content and location are tracked in order to efficiently operate the provider s global delivery network. But does that fully exploit the value within this inexhaustible stream of data? Probably not. Most likely there is a huge untapped potential for optimizing resources, improving customer experience or creating new business models. Moreover the integration of supply chain data streams from multiple logistics providers may overcome the current market fragmentation and enable powerful cooperative services. CURRENT AND PLANNED INVESTMENT AREAS FOR BIG DATA TECHNOLOGIES social networks (internally, b2b) business analytics platforms as a service network redesign software/ systems product life cycle management 0 10 20 30 40 50 60 Today Five Years FIG. 5, Source: Trends and Strategies in Logistics and Supply Chain Management, p. 51, BVL International, 2013. [%] Many market players become aware of the fact that Big Data is a gamechanging trend for the logistics industry. In a recent study on supply chain trends, sixty percent of the respondents stated that they are planning to invest in Big Data Analytics within the next five years3, cf. figure 5. However, the quest for competitive advantage starts with the identification of strong Big Data use cases. In the course of this paper we first demystify Big Data Analytics and examine the value it is driving. Thereafter, a number of use cases specifically targeted at the logistics sector are discussed. 2) Definition and development, Logistik Baden-Württemberg, cf. http://www.logistik-bw. de/definition.411+m52087573ab0.0.html, (accessed Oct. 23, 2013). 3) Trends and Strategies in Logistics and Supply Chain Management, BVL International, 2013. 9
UNDERSTANDING BIG DATA The sustained success of internet powerhouses like Amazon, Google and Facebook have provided evidence that there is a fourth production factor in today s hyperconnected world. Besides resources, labor and capital, information has become a new frontier of competitive differentiation. Companies from every vertical sector are making efforts to trade gut-feeling for accurate data-driven insight in order to make thoughtful business decisions. No matter if it is an expected sales volume, the preference of customers for a specific product or next month s work shift plan, data is the new oil that makes businesses successful. But like a quest for oil, it takes educated drilling before a well of valuable information is revealed. What makes the search for meaningful information so complex is the enormous growth of available data both inside companies as well as on the public internet. Social media, ubiquitous network access and a steadily increasing amount of smart connected devices have caused the number of available digital information pieces4 to pass the number of stars in the universe5 back in 2008. Currently, the digital universe expands at a speed of doubling the data volume every two years6, cf. figure below. EXPONENTIAL GROWTH: DATA VOLUME INCREASES BY A FACTOR OF 50 BETWEEN 2010 AND 2020 (Exabytes) 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 FIG. 6, Source: IDC's Digital Unsiverse Study by EMC, December 2012. 40,000 30,000 20,000 10,000 4) i.e. Bits, the smallest quantity of digital information. 5) The Diverse and Exploding Digital Universe, IDC, 2008. 6) The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East, IDC, December 2012. In addition to the exponential growth in volume two further characteristics of data have substantially changed. Firstly, the vast majority of newly created data stems from camera images, video and surveillance footage, blog entries, forum discussions and e-commerce catalogues. All of these unstructured data sources contribute to a much higher variety of data types. Secondly, the massive deployment of connected devices like cars, smart phones, RFID readers, webcams and sensor networks adds a huge number of autonomous data sources. Those devices that continuously generate data streams without human intervention increase the velocity of data aggregation and processing. Volume, velocity and variety is this Big Data? In literature, the 3V have been widely discussed as the characteristics of Big Data Analytics. But in our opinion, for businesses to leverage information as a production factor and strengthen their competitive position there s more to it than that. Becoming an Information-Driven Business When Otto Retail Group started out to optimize their sales forecast accuracy7, the company was already capable of processing millions of data records from the electronic storefront each day. Thus, handling a huge number of online orders at high speed was not the main issue. Instead, the key question Otto Group had to answer on the journey to significantly cutting excess stock by utilizing predictive analytics: What additional value does the existing bulk of data carry and how can we capitalize on it? While consumers are used to make information-driven daily-life decisions like purchases, route planning or finding a place to eat, companies are lagging behind. For exploiting their information assets companies have to above all change the attitude to how they use data. In the past data analytics have been used to confirm decisions that have already been taken. The cultural change largely consists of a transition towards a forward-looking style of data analysis that generates new insight and better answers. This shift in mindset also implies a new quality of experimentation, cooperation and transparency across the company. The second prerequisite for becoming an information-driven business is to establish a specific set of data science skills. This includes both mastering a wide spectrum of analytical procedures as well as a comprehensive understanding of the business. Finally it takes new technological approaches to explore information in a higher order of detail and speed. Disruptive paradigms of data processing like in-memory databases or eventually consistent computing models promise to solve large-scale data analytics problems at economically feasible cost. 7) Cf. http://www.blue-yonder.com/en/otto-automated-merchandise-planning.html (accessed Nov 13, 2013). 10
Coming back to the introduction if information is the new oil every company already owns a lot of information wells, but most of their data is like crude. Unless it is refined it cannot be transformed into business value. Ultimately, Big Data Analytics comprise the attitude, skill set and technology it takes for a company to become a data refinery and create additional value from its own information assets. BUILDING A VALUE-DRIVEN BIG DATA STRATEGY In this respect, capitalizing on the value of information assets is a new strategic objective for most enterprises and organizations. Apart from internet powerhouses that have successfully established informationdriven business models companies in other verticals are in an early stage of exploring how to benefit from their growing pile of data. According to recent research8 only 14% of European companies already address Big Data Analytics as part of their strategic planning. In contrast almost half of the respondents expect a yearly data growth in their organization of more than 25%. Big Data Value Dimensions When companies adopt Big Data as part of their business strategy the first question that surfaces is about what type of value Big Data is driving. Does it contribute to top or bottom line, or is it a non-financial driver? From a value point of view an application of Big Data Analytics falls into one of three dimensions, cf. figure below. The first and most obvious is operational efficiency. In this case data is used in order to make better decisions, to optimize resource consumption and to improve process quality and performance. It s what automated data processing has always provided, but with an enhanced set of capabilities. Customer experience being the second dimension aims at increasing customer loyalty, at performing precise customer segmentation and at optimizing customer service. Covering the tremendous lot of data from public internet Big Data propels CRM techniques to the next evolutionary stage. Finally Big Data enables new business models to complement revenue streams from existing products or to create additional revenue from entirely new (data) products. VALUE DIMENSIONS FOR BIG DATA USE CASES BIG DATA AS A STRATEGIC OBJECTIVE IN EUROPEAN COMPANIES has your company defined a strategy? [%] no 63 planned 23 operational efficency customer experience new business models yes 14 0 15 30 45 60 75 FIG. 7, Source: Big Data Survey Europe, BARC, February 2013, p.17, Statistics from BARC study, n = 273. Usage of data in order to: Increase Level of Transparency Optimize Resource Consumption Improve Process Quality and Performance Exploit data for: Increase customer loyalty and retention Performing precise customer segmentation and targeting Optimize customer interaction and service Capitalize on data by: Expanding revenue streams from existing products Creating new revenue streams from entirely new (data) products FIG. 8, Source: DPDHL/Detecon. Depending on business priorities the identification and elaboration of Big Data use cases can be navigated by applying the above value dimensions. For all three dimensions, applications of Big Data Analytics specifically targeted at the logistics industry are discussed in the following chapter. 8) Big Data Survey Europe, BARC-Institute, February 2013. 11
BIG DATA IN LOGISTICS Companies are learning to turn large-scale data amounts into competitive advantage. Precise forecasting of market demand, radical customization of services and entirely new business models are lighthouse examples for the exploit of untapped data sources. As current best practices touch many vertical markets it is reasonable that Big Data will also become a disruptive trend for the logistics industry. However, the application of Big Data Analytics is not obvious. The particularities of the logistics business have to be thoroughly examined first in order to discover valuable use cases. LOGISTICS AS A DATA-DRIVEN BUSINESS A kick-start for the discussion of how to apply Big Data to logistics is to look at its characteristics with regard to creating and consuming information. Which parts are heavily dependent on data? Where are opportunities to generate data in unmatched detail or accuracy? The answer to these questions highlights sector-specific areas where smart usage of data unleashes a new order of efficiency or a unique asset for monetization. For the logistics industry, five distinct properties make Big Data Analytics a viable instrument for competitive advantage. 1. Optimization to the core When seen individually most logistics services 1 appear to be simple. From a functional point of 9 3 view moving a parcel from origin to destination 6 does not require a lot of planning. The complexity arises from the vast number of shipments and destinations, the deterministic lead time and the finite amount of available resources. As a consequence the optimization of properties like delivery time, resource utilization and geographical coverage is an inherent challenge of large-scale logistics operations. But how good can optimization be without data? Information on payload, quantity, size, location, destination and many more are fundamentally required to control efficiency and service quality in a transport network. The earlier this information is available and the more precise the information is the better the optimization results will become. Therefore, the strong dependency of logistics operations on timely and accurate information is a key opportunity for Big Data to address. Advanced predictive techniques and real-time processing of events promise to provide a new quality in capacity forecasts and resource control. 2. Tangible goods, tangible customers A number of vertical industries has sacrificed direct customer interaction in favor of standardization and cost efficiency. Travel, banking and all kinds of commerce are increasingly using indirect channels for customer interaction, above all internet and mobile portals. Although this move has inarguably driven down the cost of core sales and customer service processes it has at the same time abandoned a number of opportunities that result from a personal customer touch point. In contrast to flight booking or transfer of funds the transportation of tangible goods requires a direct communication at pick up and delivery. Apart from some automation efforts mainly for parcel delivery, logistics providers largely stay in direct contact with their customer base. On a global scale, millions of customer touch points a day create a bi-directional communication link for market intelligence, product feedback or even demographics. Big Data concepts provide versatile analytic means as well as complementary data points from public internet sources in order to generate valuable insight on consumer sentiment and product quality. 9 12
1 6 3. In sync with customer business The global expansion of markets and supply 1 chains is further increasing and with it increases 9 3 the relevance of logistics services as a critical 6 factor in a company s operational procedures. Modern logistics solutions tailored to customerspecific requirements seamlessly integrate into production and distribution processes in various industries. On the whole, the tight level of integration with customer operations let logistics providers feel the heartbeat of individual businesses, vertical segments as well as regional markets. Leveraging this comprehensive knowledge puts logistics providers in a unique position to become valuable advisors for their business customers. The application of analytic methodology reveals supply chain risks and provides remedial action in case of disruption. 4. A network of information A distinct asset of 1 a logistics provider is the maintenance of a transport and delivery network. This 9 3 highly optimized infrastructure that constantly 6 forwards goods around the globe is at the same time a high-resolution data source. Apart from using data for further optimizing the network itself (cf. Optimization to the core above) it may also provide valuable insights to external parties. On an aggregated, macroscopic level logistics data has been used as an economic indicator already in the past. Today, applying the power and diversity of Big Data Analytics moves the level of observation down to a micro-economic viewpoint. A detailed analysis and segmentation of the global flow of goods is a powerful tool for every business to detect developing trends and changing conditions in any given market. 5. Global coverage, local presence Another characteristic that separates logistics from many other industries is that decentralized 3 operation is a necessity. Local presence is required in order to serve customers at the starting point and endpoint of far-flung supply chains. Therefore, many logistics providers engage a fleet of vehicles that moves across the country with blanket coverage in order to pick up, forward and deliver various types of shipments. When picking up shipments, why not picking up data at the same time? Automated collection and analysis of local information along the transport routes can create a valuable zoom display for demographic, environmental and traffic statistics. Google has been successful with its Street View service for providing local footage as a business model. However, creating this data required customized cars being sent to every single location. For global logistics providers, the ability to collect and monetize local data virtually comes for free. The five characteristics above help to discover Big Data fields of application as they provide a roadmap to the unique information assets of a logistics provider. The next step is to identify specific use cases that exploit information value and contribute to operational efficiency, customer experience or to the development of new business models. SPOTLIGHT: SMART PORT LOGISTICS Intelligent deployment of innovative ICT solutions at the Port of Hamburg has improved end-to-end operations. Thanks to a realtime overview of the traffic situation at the site and on connecting motorways, the digital distribution of cargo documents and additional services, trucks spend less time stopping and driving around unproductively. This lowers costs for all stakeholders. Moreover, despite having limited space, the port is now equipped to handle the forecasted increase in container transloading, thanks to greater process efficiency. 13
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OPERATIONAL EFFICIENCY A straight-forward way of applying Big Data Analytics in a business environment is to increase the level of efficiency in operations. Ultimately, it s what IT has been doing forever: Acceleration of business processes. In this respect, Big Data Analytics simply opens the throttle. Last mile optimization A constraint in achieving high operational efficiency in a distribution network occurs at the last mile9. This final hop in a supply chain is often the most expensive one. In turn the optimization of last mile delivery is a promising application for Big Data techniques in order to drive down production cost. Two fundamental approaches make data a powerful tool for increasing last mile efficiency. In a first rather evolutionary step a massive stream of information is processed to further maximize performance of a conventional delivery fleet. This is mainly achieved by real-time optimization of delivery routes. The second, more disruptive approach utilizes data processing to control an entirely new last-mile delivery model. Thereby, external transport capacity is aggregated in order to substitute dedicated resources by leveraging a scalable existing workforce. Use Case #1: Real-time route optimization The traveling salesmen problem has been formulated around eighty years ago, but it still defines the core challenge for last mile delivery. Route optimization on the last mile basically aims at saving time in the delivery process. Rapid processing of real-time information can support this goal in multiple ways. When the delivery vehicle is loaded and unloaded a dynamic calculation of the optimal delivery sequence based on sensor-based detection of shipment items frees the staff from manual sequencing. On the road telematics databases are tapped to automatically change delivery routes according to current traffic conditions. Finally, the routing intelligence considers availability and location information posted by recipients in order to avoid unsuccessful delivery attempts. In summary, every delivery vehicle receives a continuous adaptation of the delivery sequence that takes geographical and environmental factors as well as the recipient status into account. What makes this a Big Data problem? An execution of combinatorial optimization procedures that are fed from correlated streams of real-time events is required to dynamically re-route vehicles on the go. As a result, driving directions for the onboard navigation system are updated in an instant to guide the staff to the next best point of delivery. SPOTLIGHT: CONNECTED CAR TRAFFIC & DIAGNOSTICS T-Systems Connected Car offers a platform-based solution that provides value-added services, opening up new opportunities for auto-owners and companies with a large number of vehicles. Data processing and storage takes place within the cloud. Capture of data, e.g., diagnostics data and information from sensors (ambient temperature, rain sensor, fog lights, position/ speed, etc.) Additional information such as road repairs, local weather reports and hazard warnings improve the driving situation and enable changes in delivery routes. Use Case #2: Consolidated pick up and delivery Sharing infrastructures that do not contribute to the differentiation of a service has become a strong lever for increasing business efficiency. Telecommunications or railway networks are shared between a number of market participants that provide individual services on top of the same infrastructure. Applied to a distribution network the concept of a Share Economy may create substantial efficiency enhancements on the last mile. The idea is simple: Underused transport capacities like taxi, public transport and homecare fleets are aggregated to take over last mile delivery on routes that they are travelling anyway. Even a collaboration between competing logistics providers is an option to gain cost efficiencies. Scaling the number of these affiliates up to a critical mass of occasional carriers can effectively take load off the delivery fleet. Despite the fact that a consolidated pick up and delivery has to be incentivized it creates a potential to cut last mile delivery cost, especially in rural and sparsely populated areas. On the downside shared network approach also issues a vital challenge. The automated control of a large number of randomly moving delivery resources requires extensive data processing capabilities. That s where Big Data techniques like complex event processing and geo-correlation come into play. A real-time data stream is traced in order to assign shipments to available carriers based on their respective location and destination. Interfaced through a mobile application affiliates publish their current position and accept pre-selected delivery assignments. SPOTLIGHT: PLATFORM CEP (COMPLEX EVENT PROCESSING) Complex Event Processing (CEP) is a new technology to analyze real-time streaming data. CEP engines handle poly-structured data streams from different sources (M2M, Sensor Data, Social Media, Traffic Data, Video Data, etc) in real time (< 20 µs) to detect defined data patterns (Events) and to react on in a predefined manner. The actions can be warnings, exception handlings or start of an automated new business process. CEP is an important part of the overall Big Data landscape. 9) The term last mile has its origin in telecommunications and describes the last segment in a communication network that actually reaches the customer. Taken to the logistics sector, the last mile is a metaphor depicting the last leg within a supply chain where goods are handed over to the recipient. Source: The definition of the first and last miles, DHL Discover Logistics, cf. http://www.dhl-discoverlogistics.com/cms/en/ course/technologies/reinforcement/first.jsp, (accessed Oct. 23, 2013). 16
LAST MILE OPTIMIZATION 9 1 6 Optimization to the Core sources Telematics and traffic information services RFID tags attached to delivery items Location data of recipients Sensors attached to delivery vehicles Data from order management and shipment tracking Position and status of delivery crowd members FIG. 9 3 techniques Complex Event Processing Geo-Correlation Combinatorial Optimization usage Real-time Sequencing and driving directions for delivery staff Scheduling of assignments for crowd-based pick up/ delivery Real-time prediction of ETA (estimated time of arrival) The two use cases above illustrate approaches to an optimized last mile delivery process, but they are diametrically opposed. However, in both cases massive real-time data originating from sensors, external databases and mobile devices is combined to operate delivery resources at a maximum level of efficiency. Apart from the advanced capabilities in data processing the pervasion of mobile technologies is another key enabler to these Big Data applications. Predictive network and capacity planning The optimal utilization of resources is a key competitive advantage for a logistics provider. On the one hand excess capacities lower profitability which is critical for low-margin forwarding services. On the other hand a capacity shortage impacts service quality and puts customer satisfaction at risk. As a consequence a thorough planning of resources has to be performed both on a strategic and on an operational level. Whereas the strategic view focuses on the long-term configuration of the distribution network, the operational planning scales capacities up or down on a daily or monthly basis. For both perspectives Big Data techniques improve planning results in their reliability and level of detail in order to perfectly match demand and available resources. Use Case #3: Strategic network planning On a strategic level, the topology and capacity of the distribution network are adapted according to an anticipated future demand. The results from this stage of planning usually drive investments with long requisition and amortization cycles like warehouses, distribution centers or custom-build vehicles. Therefore, more precise capacity demand forecasts increase efficiency and lower risk for investments in storage and fleet capacity. Big Data techniques support network planning and optimization by analyzing comprehensive historical capacity and utilization data of transit points and transportation routes. In addition seasonal factors and emerging trends of freight flows are considered by learning algorithms that are fed with extensive statistical series. External economical data like industry-specific and regional growth forecasts are included for more accurate prediction of specific transportation capacity demand. In summary a much higher volume and variety of information is exploited by advanced regression and scenario modeling techniques to push the predictive value to a higher order. The new quality of planning results and the expansion of forecast periods effectively take the risk out of long-term obligations for infrastructure investments or contracted external capacities. In the reverse case that strategic planning exposes an impending overcapacity an automated feedback accelerates the sales volume. This is achieved by dynamic pricing mechanisms or a transfer of overhead capacities to spot market trading. SPOTLIGHT: TELEMATIC ONE & ARRIVAL CONTROL Telematic One from T-Systems is an integrated, vendor-independent portal for logistics service providers. Truckage companies can take advantage of having access to freight-specific data from both truck and trailer telematic systems in one application. With TelematicOne logistics companies have a central control unit for all their logistics activities, regardless of the manufacturer of the truck or the trailer. Even subcontractors who use a different telematics software, can be integrated. Central monitoring of trucks, trailers or containers and parcels. Order-related monitoring of vehicle and cargo data for arrival prediction. Arrival Control App to communicate and monitor ETA (estimated time of arrival) in real-time. SPOTLIGHT: BUSINESS2CAR-PLATFORM T-Systems offers a standardized and highly scalable Business2Car- Platform based on WSO2 (WSO2 is a provider of service oriented middleware platforms). The platform integrates a broad variety of telematic services like real-time traffic information, driver related services, car related services like remote update, e-mobility, content and safety & assistance. It is able to connect to the car-headunit as well as mobile handsets, OBD-adapters or telematic boxes. In connection with Deutsche Telekom s secure connectivity worldwide, implementation of know-how and enterprise mobility services Business2Car- Platform delivers the comprehensive solution for modern vehicles. Use Case #4: Operational capacity planning On the operational level transit points and transportation routes have to be managed efficiently on a day-to-day basis. This involves capacity planning for trucks, trains or aircrafts as well as shift planning for personnel in distribution centers and warehouses. Often operational planning tasks are based on historical averages or even on personal experience causing resource efficiency to be unsatisfactory. Instead, using the capabilities of advanced analytics the dynamics outside and within the distribution network is modeled and the impact on capacity requirements is calculated in advance. Real-time information about shipments that enter the distribution network, those in transit or those stored is aggregated to predict the allocation of resources for the next 48 hours. 17
The data is automatically sourced from warehouse management systems and sensor data along the transportation chain. In addition a detection of ad-hoc changes in demand is derived from externally available customer information, e.g., on product releases, factory openings or unexpected bankruptcy. Additionally local incidents are detected, e.g., regional disease outbreaks or natural disasters that skew demand figures for particular regions or products. The look ahead on upcoming resource requirements helps operating personnel to scale capacity up or down in a particular location. But there s more to it than that. A precise forecast also reveals upcoming congestions on routes or in transit points that cannot be addressed by local scaling. A freight aircraft that is working to capacity leaves any further expedited shipments behind at the airport of origin. Simulation results give an early warning to reassign shipments to uncongested routes and thus mitigate the local shortfall. In this respect the application of Big Data Analytics turns the distribution network into a self-optimizing infrastructure. SPOTLIGHT: EFFICIENT FLEET MANAGEMENT Efficient Fleet Management from T-Systems is a platform-based big data solution that cuts costs and protects the environment. It meets EU legislation requirements like 30% reduction of CO 2 emissions by 2020. Optimization of shipments and routes means cost savings (fuel and operating costs). Intelligent and transparent real-time collection of consumption and emission data through detailed vehicle information related to movement profiles which is compared with standard values to display deviations Complete transparency of driving behavior and fuel consumption for each vehicle in order to maximize fuel efficiency by improving driving behavior Supports multiple client systems (multitenancy), OEM-neutral hardware PREDICITVE CAPACITY AND NETWORK PLANNING 9 1 6 3 Optimization to the Core sources Shipment data of Warehouse Management Systems Shipment data of Transportation Management Systems Regional industry and trade growth forecasts Public news on regional incidents Customer data on product demand Weather data techniques Predicitve Analytics Scenario analysis Complex Event Processing Learning Systems Data mining for pattern detection Real-time Analytics usage Optimized transportation and delivery network planning Operational transportation capacity planning Operational workforce planning CUSTOMER EXPERIENCE The part of Big Data that currently catches the most attention is the acquisition of customer insight. For every business it is of vital importance to learn about customer demand and satisfaction. But with increasing business success the individual customer blurs into the anonymous crowd of a large customer base. Big Data Analytics help to win back individual customer insight and to create targeted customer value. Customer value management Data from the distribution network carries a significant value for the analysis and management of customer relations. Enriched by public internet mining, Big Data techniques can be applied to minimize the attrition on the sender side as well as to understand demand on the receiver side. FIG. 10 Both Big Data scenarios above aim to increase resource efficiency in the distribution network, but the style of data processing is different. The strategic optimization combines a high data volume from a variety of sources in order to support investment and contracting decisions. In contrast, the operational optimization continuously forecasts network flows based on real-time streams of data. 18
Use Case #5: Customer loyalty management For most business models the cost to win a new customer is by far higher than the cost to retain an existing customer. However, the fact that customer touch points become more and more indirect through portals, apps and indirect sales channels makes it difficult to trace and analyze individual customer satisfaction. As a consequence many businesses fail to establish effective customer retention programs. Again, it is the smart usage of data that enables the identification of valuable customers that are about to leave for the competition. Big Data Analytics allow a comprehensive assessment of customer satisfaction by merging multiple extensive data sources. For a logistics provider this materializes in a combined evaluation of records from customer touch points, operational data on logistics service quality and external data. How do these pieces fit together? Imagine a customer that lowers shipment volumes while publishing sales records through newswire at the same time. In addition, delivery records show that the customer has recently experienced delayed shipments. Looking at the bigger picture of this information, the need for immediate customer retention activity becomes apparent. In order to create this insight on a large customer base, various data sources have to be tapped. Customer touch points include responses to sales and marketing activities, customer service inquiries and complaint management details. This digital customer trail is correlated with data from the distribution network comprising statistical series on shipping volume and received service quality levels. Finally the internet also tells customer stories: Publicly available information from news agencies, annual reports, stock trackers or even sentiments from social media sites enrich the internal perspective on individual customers. From this comprehensive information pool the attrition potential for every single customer is extracted by applying techniques like semantic text analytics, natural language processing and pattern recognition. Pro-active countermeasures as well as customer loyalty programs are initiated based on automatically generated triggers. SPOTLIGHT: INTELLIGENT NEWS DISCOVERY Businesses in every industry have vast amounts of unstructured data with tremendous potential that they can make good use of. Petabytes of data are created every day. Intelligent News Discovery from T-Systems is a platform-based solution that enables you to find, index and analyze your media assets quickly and easily and to reduce decision uncertainty through more knowledge. Analysis of up to 2 billion web pages and videos on a daily basis without own IT capabilities. Indexing and semantic analysis of untapped information from audio and video material. Based on thematic user queries, contextually relevant sections in the indexed data can be found und utilized immediately. SPOTLIGHT: AFTERMARKET SOLUTION T-Systems aftermarket solution is an innovative combination of a cloud platform, a smartphone app and a Bluetooth enabled OBDadapter. It provides car repair shops and car manufacturers with a completely new way to interact with owners of used vehicles and to offer a far better service to their customers. But also logistic companies can make use of the solution, which provides over 20 data points from cars like mileage, distance, error codes and battery charge. It enables the monitoring of each used vehicle in the fleet and its performance and reliability. Fleet Management get s easier and better. Despite the fact that the business relationship usually relates to the sender side, loyalty management has to target the recipient side as well. Recipients are even more affected by poor service quality and their feedback influences the sender in selecting a shipment service. Especially in internet or catalog shopping recurring customer complaints let vendors consider a switch of the logistics provider. However, including recipients into loyalty management requires an even larger amount of data to be processed, especially in B2C markets. Big Data Analytics help to produce this integrated view on customer interactions and operational performance in order to keep senders and recipients satisfied. Use Case #6: Continuous service improvement and product innovation Collecting Customer feedback also provides valuable insight on service quality issues as well as on customer expectations and demand. Therefore it is the major source for continuous improvement of service quality and an important input for the ideation of new service innovations. To get solid results from evaluation of customer feedback it is necessary to aggregate information from as many touch points as possible. In the past ingests from CRM systems and customer surveys have been the single source of information. Today Big data solutions provide access to the gargantuan data volume stored in public internet sites. In social networks and discussion forums customers openly and anonymously share their service experience. However, extracting relevant feedback from natural language content created by billions of internet users by hand is like looking for a needle in a haystack. Sophisticated techniques like text mining and semantic analytics allow the automated retrieval of customer sentiment from huge text and audio repositories. In addition, the unsolicited feedback on quality and demand is broken down on region and time in order to identify correlations with one-time incidents or to track the effect of initiated actions. In summary, combing through the entire public internet brings unbiased customer feedback to light that empowers product and operational managers to design services properly to customer demand. 19
CUSTOMER VALUE MANAGEMENT Tangible goods, tangible customers sources Customer data e.g., shipping volumes in CRM systems Data bases of financial analyst firms Transport management systems for shipment records Call center records Customer emails and feedback forms Social media like blogs or social networks techniques Data mining for pattern detection Semantic analytics Natural language processing Cluster analysis Data mining for pattern detection Geo-correlation usage Customer value assessment for business potential analytics Anticipation of customer attrition Continuous service improvement and identification of new product innovation requirement Supply chain risk management Uninterrupted supply of direct material is essential to businesses that operate global production chains. Lost, delayed or damaged goods have an immediate negative impact on revenue streams. Whereas logistics providers are prepared to control their own operational risk in supply chain services, disruptions increasingly result from major events such as civil unrest, natural disasters or sudden economic developments10. To anticipate supply chain disruptions and mitigate the effect of unforeseen incidents global enterprises seek to deploy business continuity management (BCM) measures11. This demand for improved business continuity creates an opportunity for logistics providers to expand their customer value in outsourced supply chain operations. Rapid analysis of various information streams can be used to forecast events with a significant or disastrous impact on customer business. In response to arising critical conditions counter-measures can be initiated early to tackle arising business risks. FIG. 11 SPOTLIGHT: CONNECTED CAR PREDICTIVE MAINTENANCE Logistics companies face the challenge of unexpected break downs with costly standstills. Reactive maintenance is leading to high cost of operation. Predictive and pro-active maintenance based upon real-time in-memory Analytics leads to cost efficiency and customer satisfaction. The continuous evaluation of data enables to recognize the typical pattern for normal operation as well as patterns for deviations (errors). Based on these data, machines may be continuously monitored and possible deviations from normal operation are recognized early. Use Case #7: Risk evaluation and resilience planning Contract logistics providers know their customers supply chain in great detail. In order to cater to customer needs with regard to a predictive risk assessment, two things have to be interrelated. On the one hand, a model describing all elements of the supply chain topology has to be continuously checked against forces that can affect supply chain performance on the other. These checks require data from a plethora of sources on local developments in politics, economy, nature and health to be aggregated and analyzed. This data originates from social media, blogs, weather forecasts, news sites, stock trackers and many other publicly available sources. As most of this information stream is unstructured and continuously updated Big Data Analytics power the retrieval of meaningful input for detecting supply chain risks. Apart from semantic analytics it needs complex event processing techniques that detect patterns in a host of interrelated information pieces12. In case a pattern points to a critical condition arising for one of the supply chain elements, e.g., a tornado warning for a region where a transshipment point is located, the customer gets notified. The report contains probability and impact of the risk as well as suitable counter-measures that mitigate the potential disruption. This can lead to a re-planning of transport routes or to ramping up supplies from other geographies. Robust supply chains that are able to cope with unforeseen events are a vital business capability in today s rapidly changing world. On top of a resilient and flexible supply chain infrastructure it is the foresighted risk detection that keeps a business running when disaster strikes. Logistics providers are well-positioned to secure customer operations by performing predictive analytics on global affairs. 10) Are you ready for anything?, DHL Supply Chain Matters, 2011, cf. http://www.dhlsupplychainmatters.dhl.com/efficiency/article/24/are-you-ready-for-anything, (accessed October 12, 2013). 11) Making the right risk decisions to strengthen operations performance, PricewaterhouseCoopers and MIT Forum for Supply Chain Innovation, 2013. 12) The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems, David C. Luckham, Addison-Wesley Longman, 2001. 20
SUPPLY CHAIN RISK MAMAGEMENT sources Model of customer supply chain topology Operational supply chain data External data on politics, economy, nature, or health events, from: social media weather forecasts news sites other publicly available sources FIG. 12 In sync with customer business analytics Text Mining Social Media Analytics Complex Event Processing NEW BUSINESS MODELS usage Report on upcoming risks for supply chain disruption Recommendations for countermeasures for sustained operations SPOTLIGHT: PLATTFORM HADOOP Big data would be inconceivable without Hadoop because it provides the foundation for processing extremely high and unstructured data volumes. Hadoop is a Java-based software framework that helps users handle compute-intensive processes on server clusters with large data volumes. With the help of Hadoop, applications can distribute complex computing tasks across thousands of nodes and process data volumes in the petabyte range. B2B demand and supply chain forecast The logistics sector has been a macroeconomic indicator for long. Global transportation of goods can act as a benchmark for future economic developments. The type of goods and the shipped volumes indicate regional demand and supply levels. The predictive value of logistics data for the global economy is constituted by existing financial indices measuring the macroeconomic impact of the logistics sector. Examples are the Baltic Dry Index13, a price index for raw materials shipped, and the Dow Jones Transportation Average14, showing the economic stability situation of the 20 largest U.S. logistics providers. Use Case #8: Market intelligence for small and medium-sized enterprises The aggregation of shipment records comprising origin, destination, type of goods, quantity and value forms an extensive source of valuable market intelligence. As long as postal privacy is retained logistics providers may refine this data in order to substantiate existing external market research. The fine-grained information in the shipment data base significantly enhances the precision of conventional demand and supply forecasts based on regression analysis. The resulting high predictive value makes the compound market intelligence a compelling service for external parties. In order to serve a broad range of potential customers the generated forecasts are segmented by industry, region and product category. Particularly small and medium-sized enterprises that do not have the capacity to conduct customized market research are the primary target group for this advanced data service. SPOTLIGHT: PLATTFORM SAP HANA Main memory plays a key role. In-memory technology is based on an innovative method of storing and managing data. All data is kept in a server s main memory, eliminating time-consuming access to hard-drive systems. Database technologies with column based indexing and a hybrid data model (row- and column-based storage) are employed. This leads to data compression and significantly improves analysis capabilities. The bottom line: performance of transactional and analytical processes can be increased significantly. Use Case #9: Financial demand and supply chain analytics Financial analysts rely heavily on data to generate growth perspectives and stock ratings. Sometimes analysts even perform manual checks on supply chains as the only available source to forecast sales figures or market volumes. Therefore, access to the detailed information collected from a global distribution network is of particular value for the banking and insurance sector, for rating agencies and advisory firms. A commercial analytics platform that let a broad range of users slice and dice raw data according to their field of research is an option for logistics providers to create new revenue streams from the huge amount of information that controls logistics operations. The application of analytic techniques to vast amounts of shipment data presented in the use cases above illustrates the implementation of information-driven business models for a logistics provider. In addition, the monetization of data that already exists adds highly profitable revenue to the company s top line. By applying the power of Big Data Analytics logistics providers have the unique opportunity to extract detailed microeconomic insights from the flow of goods through their distribution network. The huge digital asset that is piled up from the millions of daily shipments is exploited by capturing demand and supply figures in various geographical and industry segments. 13) Baltic Dry Index, Financial Times Lexicon, cf. http://lexicon.ft.com/term?term=baltic- Dry-Index, (accessed Oct. 23, 2013). 14) Dow Jones Transportation Average, S&P Dow Jones Indices, cf. http://www.djaverages.com/?go=transportation-overview (accessed Oct. 23, 2013). 21
B2B DEMAND AND SUPPLY CHAIN FORECAST sources Shipment data from logistics production (origin, destination, type of good, quantity, price) External market intelligence sources FIG. 13 A Network of Information techniques Regression analysis Data fusion and integration Predictive Modeling Cluster Analysis Statistics Time Series Analysis usage Demand and supply forecast figures for different industries and regions Data analytics platform for financial analytics Real-time local intelligence Information-driven business models are frequently built upon existing amounts of data, but this is not a prerequisite. An established product or service can be extended as well in order to generate new information assets. For a logistics provider the opportunity for a complementary new business model emerges from the pick up and delivery of shipments. The constantly moving and geographically distributed fleet of vehicles maintains a local presence with blanket coverage that no other industry can provide. With the Internet of Things driving a continued miniaturization of cameras, sensors and mobile connectivity a delivery vehicle can easily be equipped with devices that collect rich sets of information on the go. This unique capability enables a set of value-added data services that a logistics provider can present to existing and new customers. SPOTLIGHT: SMARTER PROCUREMENT Smarter Procurement from T-Systems is an end-to-end solution. It helps enterprises cut costs, shorten lead times and protect liquidity throughout their organization, around the world. Cash flows can be forecasted and controlled proactively to gain a solid basis for judging and managing out-going payments. There is no need of high cash reserves just in case anymore, ratings and analyst reviews can be improved. But the accurate prediction of lead times and generation of cost savings not only effect the procurement process but also the supply chain in total. The company can achieve visibility throughout the entire enterprise including the logistic value chain. Use Case #10: Address verification The verification of a customer s identity and delivery address is a fundamental requirement for online commerce. Whereas address verification is broadly available in industrialized nations, for developing countries and remote areas the quality of address data is poor. This is also due to the lack of a structured naming scheme for streets and buildings in many remote areas. Generated from daily freight, express and parcel delivery, logistics providers can automatically verify address data, store geographic coordinates and provide verification schemes as a premium service to retail and other businesses as well as to public sector entities. SPOTLIGHT: ADDRESS DATA QUALITY & VERIFICATION Internationally operating businesses depend on correct customer or supplier addresses. Part of doing business worldwide is making sure that your databases are correct and in good quality. Companies have to face different languages, alphabets and spelling formats especially in B2B and B2C cross-border traffic and e-commerce. Big Data address processing in realtime enables dynamic sorting, sequencing, routing and re-routing for logistics. Direct match of input data with reference data. Return incomplete or incorrect incoming data with validated data from database. Significant increase of data quality for planning purposes and realtime route planning and optimization. Prerequisite for a correct calculation of ETA (estimated time of arrival) of shipments and deliveries. Use Case #11: Environmental intelligence The continued growth of urban areas15 has caused both city planning activities and environmental monitoring to become a matter of importance. Through the use of a variety of sensors attached to delivery vehicles logistics providers are enabled to produce rich environmental statistics. Data sets may include ozone and fine dust pollution, temperature and humidity as well as traffic density, noise or parking spot utilization along urban roads. As all this data can be collected en passant, a data service offered to authorities, environment agencies and real estate developers creates complementary revenues that subsidize the maintenance of an extensive delivery fleet. 15) According to the United Nations, by 2050 85.9% of the population in developed countries will live in urban areas. Taken from: Open-air computers, The Economist, Oct. 27, 2012. cf. http://www.economist.com/news/special-report/21564998-citiesare-turning-vast-data-factories-open-air-computers (accessed Oct. 12, 2013). 22
REAL-TIME LOCAL INTELLIGENCE sources Vehicle-attached sensors and cameras Driver-collected data (e.g., adress data) Sensor data from mobile devices GPS location data FIG. 14 Global coverage, local presence techniques Image/video recognition Ecent processing Statistics Cluster Analysis Geo-correlation usage Microstudies for urban development, marketing, demographic statistics, real-estate and construction industry Road condition screening Environmental monitoring Address verification SPOTLIGHT: SMARTER ENERGY MANAGEMENT Smarter Energy Management from T-Systems is a platform-based solution that verifies, aggregates and processes and turns it into actionable information. It provides businesses and private households with the information they need to increase their energy efficiency and to gain greater visibility into power usage in order to reduce their spending. Meter Data Management is the link between technical systems for electronic meter reading / reporting and the economic management systems of actors in the market. High-performance, parallel / distributed processing of data from 50 million virtual metering points. Ad hoc processing of requests from the consumption data analysis over the entire dataset. There are numerous other local intelligence use cases exploiting the ubiquity of a large delivery fleet. From road condition reports that steer plowing or road maintenance squads to surveys on the thermal insulation of public households logistics providers are in a head-start position to become a search-engine in the physical world. Innovative services that provide all kinds of data in microscopic geographical detail equally attract advertising agencies, construction companies and public bodies like police and fire departments. Thereby, Big Data techniques that extract structured information from real-time footage and sensor data are building the technical backbone for the deployment of new datadriven business models. SUCCESS FACTORS FOR IMPLEMENTING BIG DATA ANALYTICS Up to this point the discussion of Big Data Analytics has been focused on the value of information assets and how logistics providers can leverage data for better business performance. So far, so good, as solid use cases are a fundamental requirement for adopting new informationdriven business models. But beyond assessing business value it is important to notice that when it comes to implementation Big Data is not going to be like any other IT project. Five success factors have to be taken into account before getting serious. Business and IT Alignment In the past advancements in information management have been clearly targeting either a business problem or a technology problem. While trends like CRM has been strongly affecting the way sales and service people work, other trends like Cloud Computing has been primarily giving headaches to IT people regarding how operate dynamic IT resources across the internet. Consequently adopting capabilities and managing change had to take place predominantly on one side. For Big Data Analytics this does not apply. For a successful transformation towards an information-driven company business and IT departments have to contribute with substantial changes in their respective environments. In this respect a strong alignment between business unit and IT department on objectives, benefits and risks of a Big Data use case is essential. There has to be a mutual understanding of the challenges as well as a joint commitment of knowledge and talent in order to complete a Big Data implementation. Data Transparency and Governance Big Data use cases often build upon a smart combination of individual data sources that jointly provide new perspectives and insights. However the reality in many companies reveals three major challenges that have to be addressed to ensure a successful implementation. At first there has to be full transparency on information assets and ownership in order to locate data that is already available in the company. Secondly data attributes have to be clearly structured and explicitly defined across multiple data bases to prevent ambiguous data mappings. Finally strong governance on data quality has to be maintained. Effective cleansing procedures must remove incomplete, obsolete or duplicate data records that otherwise compromise the validity of mass query results. It is of greatest importance that a high overall data quality has to be assured on the individual data sources as the boosted volume, variety or velocity of the joint data set inhibits efficient validation and adjustment procedures. 23
Data Privacy The exploit of information assets frequently touches personal data, especially when gaining customer insight is the target of the data analysis. In this case the adherence to data privacy regulations is compulsory. In countries with a strict data protection act in place particular use cases prove elusive due to data privacy rights. Thus, a consideration of regulations that apply is a given in the conceptual phase of every Big Data project. But legislation is not the only constraint. Even if the use case is compliant with prevailing law, a massive collection and exploit of data may arouse a public debate that subsequently damages company reputation and brand value. Data Science Skills A key to a successful Big Data implementation is the mastery of the manyfold data analysis and manipulation techniques that turn bulks of raw data into valuable information. The skillful application of computational mathematics makes or breaks the generation of reliable and meaningful insights. In most industries the required mathematical skill is rather scarce. In fact a talent war has begun as more and more companies recognize that they have to source additional data science skills externally. For Big Data projects it is important to consider that deploying the right technique for a particular data processing problem requires very specialized knowledge. Technology Usage Many data processing problems that are currently hyped as Big Data could have been technically solved five years ago. However, back then the required investment in technology would have shattered every business case. In the meantime not only raw computing power has exponentially increased but advanced data processing concepts have become available enabling a new dimension of performance at a fraction of cost. The most prominent approaches are in-memory data storage on the one hand and distributed computing frameworks on the other. The downside of these new concepts is that they require entirely new technologies to be adopted. Thus, for IT departments the implementation of Big Data projects requires a thorough evaluation of established and new technology components regarding their capability to support a particular use case. For example in-memory databases like SAP s HANA technology are very fast but limited in the data volume that can be stored. On the contrary, distributed computing frameworks like Apache Hadoop are able to scale out to a huge number of nodes but at the cost of a delayed data consistency across multiple nodes. In addition, earlier investments in conventional technologies have to be taken into account as they may also be scaled up for higher performance. The figure below illustrates a scheme that helps selecting Big Data technology options. Depending on requirements of a use case regarding volume, velocity and variety of the processed data, it will be placed into one of eight technology cubes. Use cases that get along with technology that is already in place (Number 2,3,7 and 10) are positioned in the blue cube. If the requirements for a use case regarding the volume, velocity or variety of data exceed the performance boundaries of the blue cube they are placed in the adjacent orange cubes. Each cube represents the application of a technology set that satisfies the respective requirements. The schema above provides generic guidance on major concepts that most Big Data technologies follow. For selecting a particular product or product family the individual features have to be analyzed in more detail. BIG DATA CUBE OF COMFORT: QUALITATIVE SELECTION OF BIG DATA TECHNOLOGIES in-memory storage external storage velocity 2 7 10 scale up 1 3 6 11 5 4 scale out 8 9 variety no sql structures blobs volume 1 2 3 4 5 6 7 8 9 10 11 Real-Time Route Optimization Consolidated Pick up and Delivery Strategic Network Planning Operational Capacity Planning Customer Loyality Management Continuous Service Improvement Risk Evaluation and Resilience Planning Market Intelligence for SME Financial Demand and Supply Chain Analysis Address Verification Environmental Intelligence FIG. 15, Source: Detecon, 2013. 24
OUTLOOK In the course of this paper we have first debunked Big Data Analytics as not being a kind of IT magic, but a disruptive way of leveraging information value. It is backed by rapid technological advancements that power the processing of vast data volumes while having an affordable price tag. With regard to logistics Big Data addresses a number of prevalent industry challenges and will take operational efficiency and customer experience to the next level. The expansion into information-driven business models that capitalize on unique data assets is also a future option for a logistics provider to consider. Looking ahead there are admittedly numerous obstacles that Big Data has to overcome before it has a pervasive influence on the logistics industry data quality, privacy and technical feasibility being only a few. In the long run these are all of secondary importance, given the fact that Big Data is first and foremost driven by entrepreneurial spirit. The Googles, Amazons and ebays have led the way of turning extensive information into a business. First movers in logistics will be next to exploit their data assets, as the analysis of reasonable use cases in this paper has foreshown. But apart from an implementation of a particular Big Data opportunity, how does the whole logistics sector transform into a datadriven industry? What evolution can be projected considering a world where virtually every single shipped item is connected to the internet? Logistics 2018+: The Ubiquitous Warehouse At higher level, the ability to synchronously trace supply chains and to spot every shipment at any given moment in time creates a detailed digital copy of the global flow of goods. This real-time data model enables a tremendous agility in the production, transportation and delivery of goods. In turn, business models in many industries will transform driven by the increased level of sourcing transparency and accuracy. Taking a retail business as an example, the ability to deliver a pair of shoes is no longer a question of having it in stock or of getting it from a distributor s stock. The question is if it is available anywhere on the planet and at what price. Consequently retailers will not be able to gain competitive advantage on the buying side anymore. Instead, with a transparent realtime view on global supplies the world becomes a single ubiquitous warehouse with a zillion of merchants selling its inventory. Finally, by eliminating the supply chain as a critical factor the market for consumer goods will feature the same dynamics as stock markets today. Apart from commercial trade the vision of the ubiquitous warehouse also affects business-to-business relationships in the manufacturing industry. The end-to-end control of direct material flows from production to consumption dramatically reduces the need for manufacturing in advance and intermediate storage. Complex supply chain scenarios like just-intime delivery will become a commodity and will push storage capacities from stationary warehouses out onto the road. In summary there is a lot of headroom for Big Data approaches to fill in the coming decade. The transformation into an information-driven business will make logistics smarter, faster and more efficient. But the biggest upside for customers is that Big Data will make logistics widely vanish from notice. 25
LIST OF FIGURES FIG. 1 FIG. 2 FIG. 3 FIG. 4 FIG. 5 URBAN POPULATION BY MAJOR GEOGRAPHICAL AREA PERFORMANCE BY MODE FOR FREIGHT TRANSPORT PERCENTAGE OF LOGISTICS OBJECTIVES RANKED AS TOP PRIORITY ORCHESTRATION OF A BUSINESS NETWORK CURRENT AND PLANNED INVESTMENT AREAS FOR BIG DATA TECHNOLOGIES FIG. 6 EXPONENTIAL GROWTH: DATA VOLUME INCREASES BY A FACTOR OF 50 BETWEEN 2010 AND 2020 FIG. 7 FIG. 8 FIG. 9 FIG. 10 FIG. 11 FIG. 12 FIG. 13 FIG. 14 FIG. 15 BIG DATA AS A STRATEGIC OBJECTIVE IN EUROPEAN COMPANIES VALUE DIMENSIONS FOR BIG DATA USE CASES LAST MILE OPTIMIZATION PREDICITVE CAPACITY AND NETWORK PLANNING CUSTOMER VALUE MANAGEMENT SUPPLY CHAIN RISK MAMAGEMENT B2B DEMAND AND SUPPLY CHAIN FORECAST REAL-TIME LOCAL INTELLIGENCE BIG DATA CUBE OF COMFORT: QUALITATIVE SELECTION OF BIG DATA TECHNOLOGIES 26
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CONTACT Marketing T-Systems International GmbH Uli Kunesch Market Intelligence Fasanenweg 5 70771 Leinfelden-Echterdingen, Germany Uli.Kunesch@t-systems.com Expert on Big Data T-Systems International GmbH Ralf Konrad Portfolio & Offering Management Sohnstr. 45 40237 Düsseldorf, Germany Ralf.Konrad@t-systems.com Expert on Logistics T-Systems International GmbH Veronika Söntgerath Strategic Area Connected Car Logistics Friedrich-Ebert-Allee 140 53113 Bonn, Germany Veronika.Soentgerath@t-systems.com PUBLISHED BY T-Systems International GmbH Hahnstraße 43d 60528 Frankfurt am Main, Germany Responsible for Content: Focus Industries http://www.t-systems.com Last revised: February 2014 authors: Martin Jeske Moritz Grüner Frank Weiß PowerEd By: IN Cooperation WITH: