Internet of things in future logistics

How the Internet of Things Drives Innovation for the Logistics of the Future

Herbert Ruile

Abstract The Internet of Things (IoT) is considered one of the most important trends, drivers, and enablers for business transformation. The field of logistics is affected manifold by this novel trend promising competitive advantages and growth. This chapter addresses an innovation value chain framework that relates advanced technology developments to business innovation in the field of logistics. The proposed framework contributes to clarify the complexity within the technology- driven innovation chain and helps organizations define, discuss, and develop their own IoT-driven business model. Based on three case studies, the framework is explained and discussed in terms of its relevance and value for an organization.

Keywords Business transformation · Internet of things · Innovation · Logistics · Case study

1 Introduction

Logisticsisoneofthemajorandstillgrowingmarketsintheworld.IMARC,aleading market research company providing detailed industry analysis, estimates that the global logistics market reached a value of US$ 1,171 Billion in 2017 [1]. They have identified three major factors that drive the growth of the global logistics market (a) the rapidly expanding e-commerce industry, (b) the growing focus on sustainability and compliance, especially environmental issues and corporate social responsibility (CSR), and (c) the rise of international trade agreements coupled with an increasing demand for reverse logistics services. The increasing use of technologies such as RFID (Radio Frequency Identification), Bluetooth, and newly used technologies such

Formerly Institute for Information Systems, School of Business, FHNW University of Applied Sciences and Arts Northwestern Switzerland.

H. Ruile (B) Logistikum Schweiz GmbH, c/o Business Help Point, Dätwylerstrasse 27, 6460 Altdorf, Switzerland e-mail: [email protected]

© Springer Nature Switzerland AG 2021 R. Dornberger (ed.), New Trends in Business Information Systems and Technology, Studies in Systems, Decision and Control 294, https://doi.org/10.1007/978-3-030-48332-6_18

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as drone delivery and driverless vehicles today increases the chances of efficiently enhancing logistics and its services.

Due to the constant competitive pressure and the limitation of infrastructure in industrialized regions logistics is becoming a major bottleneck in the value chains and requires innovative approaches [2].

Although innovation has become one of the key economic indicators, a guar- antee of social prosperity and health, the logistics industry still lacks innovation [3]. We distrust this, because we see Schumpeter’s five types of innovations are also applicable to logistics: product/service and process innovation, demand and supply innovation,andnewindustrystructures[4].Weseehugeeffortsinscienceandtechno- logical developments that are perceived as prerequisites and key elements for radical innovation. Since the increased use of technologies in the late 18th century, various phases of industrialization have followed. At the very least, the introduction and use of the internet as a commercial platform has radically changed the way of doing busi- ness: digital networking and autonomous interaction of distant objects, information and people became possible [5] and has steadily changed the way logistics business is done.

The latest digital developments offer new opportunities for growing prosperity and have therefore become popular for major investments by governments and economies [3]. These developments are described through a common and least differentiated use of terms such as “Industry 4.0”, “Internet of Things”, “Cyber Physical Internet”, “Digital Transformation” or “Digitalization”. The World Economic Forum investi- gated the impact of digitalization on logistics and identified the overall economic impact: “$ 1.5 trillion of value on stake for logistics players and a further $2.4 tril- lion worth of social benefits as a result of digital transformation of the industry up until 2025” [6]: p. 4.

Based on literature reviews and expert panels, Kersten et al. [7] studied the most important trends for logistics, which are essentially caused by digitization: digitiza- tion of business processes, increased transparency in the supply chain, networking, business analytics, automation and decentralization that give regional and urban transport platforms a more important role. These trends are still effective [6, 8].

Growing markets, competitive pressure, limited infrastructure, and technological advances require further innovations in logistics. How does it work? What are the right approaches and processes that logistics can rely on? What does it mean for logistics organizations to take advantage of the opportunities offered by advanced technology?

To provide a contribution to technology-driven innovation in logistics, this chapter introduces an integrated conceptual framework for IoT-driven innovation value chains in logistics. It is organized in three sections: (a) description of the fundamental terms of logistics and IoT, (b) the introduction and explanation of an IoT-driven innovation framework and (c) the application and discussion of the framework.

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2 Fundamental Terms

2.1 Logistics as the Object of Innovation

More than five decades have passed since business logistics was first scientifically addressed in the mid-fifties, without finding a satisfactory answer to the question of the identity of logistics (What is logistics?) [9]. From an academic point of view, the impetusfortheprogressofknowledgehasitsoriginsinthediscussionofthetermsthat describe the object of analysis [10]. From the point of view of management science, with increasing functions, tasks, and responsibilities in the field of logistics, the conceptual clarifications and definitions of logistics become increasingly important. The understanding of logistics in science and practice has developed in recent years. In the following, empirical-inductive explanatory approaches will be pursued that take up and summarize concrete problems of logistics practice (see Table 1).

• Logistics is defined as the management of transport, warehousing and trans- shipments, which covers the functional management, technical, and organiza- tional design and optimization of transportation, fleet management, warehousing, handling of materials, order fulfillment, logistics network design, inventory management, and management of third-party logistics services providers [11]. Logistics can be experienced physically and technically. This view of the industry is characterized by technical advances in the areas of automation, autonomy, energy consumption, and pollution.

• Logistics is understood as a contemporary leadership concept for the develop- ment, design, management, and realization of effective and efficient flows of objects (goods, services, information, and financing) in the company-wide value- added system. Logistics links important business functions and business processes between the point of entry and delivery [9, 11]. Process integration, lean manage- ment, and IT integration approaches play an important role. The administration and modern use of master and transaction data of processes, products customers and/or suppliers in a more or less closed system (e.g. business data warehouse) will be managed via data mining, big data analytics, and deep learning approaches.

Table 1 Perspective of logistics and terms used based on [9]

Terms in use Describes logistics as management of

1 Transport, warehouse, trans-shipment isolated business function

2 Logistics management firm’s integrated flow of material and information

3 Supply chain management the overarching value chains from supplier to customer

4 Value creating networks interconnected partners of a value creation network

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• Logistics as Supply Chain Management (SCM) represents a further and innovative development stage. The intra-organizational perspective is replaced by a multi- tiered value chain with a consistent focus on the ultimate customer requirements and demand. In essence, supply chain management integrates supply and demand management within and across companies. SCM is a system approach to viewing the value-adding channel as a whole [8, 9, 11, 12]. The future of SCM will lie in its unique agility based on real-time capabilities. Suppliers, manufacturers, and customers can rely on information and decide on the availability of materials, capacities, and flexibility.

• Logistic as management of a value-creating network (VCN). This perspective will replace supply chains with strategic business networks. Sydow [13] defines: A strategic network is a polycentric form of organization of economic activi- ties between market and hierarchy, which aims at the realization of competitive advantages and yet is strategically managed by one or more enterprises. It is char- acterized by complex reciprocal, rather cooperative than competitive and rela- tively stable relationships between legally independent, but mostly economically dependent enterprises.

Younger schools of thought expand the linear SCM approach into a network [14]. The holistic view of value creation networks allows development, design, realiza- tion, and optimization of economic systems [15]. Networks are characterized by more or less free and dynamic interaction between those involved: suppliers, service providers, manufacturers, retailers, customers, consumers, competitors, regulators, and institutions. With an increasing number of participants and dynamics of reverse interaction,digitalnetworkingcreatesahighlycomplexeconomicsystem.Forfurther discussion, we use the general term of logistics but consider all four perspectives.

2.2 Internet of Things

The enabling technology in our investigations is termed the “Internet of Things”, which goes back to Mark Weiser’s work at the computer science lab at Xerox PARC where he formulated the ubiquitous computing vision and described a world, where algorithms are closely embedded in our daily life [16]. Later, in 2009, the term “Internet of Things” was coined by Kevin Ashton, RFID pioneer and co-founder of the Auto-ID center at the Massachusetts Institute of Technology [17]. The subsequent discussion will follow the IoT definition of the International Telecommunication Union as “the global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies” [18]. “Sensors and algorithms offer a system of interconnected smart devices, which enable real- time and intelligent communication from man to machine, machine to machine, and enterprise to enterprise. The term “thing” with regard to IoT is defined as an object of the physical world (physical things) or the information world (virtual things),

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which is capable of being identified and integrated into communication networks” [18]. “With regard to the ‘Internet of Things’, this [device] is a piece of equipment with the mandatory capabilities of communication and the optional capabilities of sensing, actuation, data capture, data storage and data processing” [18].

A related term for IoT is “Industry 4.0” which was introduced to the public at the Hanover Trade Fair in 2011, and presented as part of Germany’s high-tech strategy [19]. Meanwhile, the 4.0 extension is being used in almost all economic areas and for all functions to express the significant digitally induced changes that are expected (e.g. logistics 4.0, government 4.0, health 4.0, etc.). Although the term was coined in Germany, industry 4.0 shares some commonalities with developments in other regions where it has been labeled as “Internet of Things” or “Cyber physical Internet”. The latter terms reflect primarily the technical content and not the resulting business and social transformation.

Meanwhile, the German term industry 4.0 has become a transitional term for the broader understanding of digital transformation, which is defined as the digitaliza- tion of analog machine and service operations, organizational tasks, and manage- rial processes [20] to create added value for customers and employees [21–23]. Or in other words: “Digital transformation is the evolving pursuit of innovative and agile business and operational models—fueled by evolving technologies, processes, analytics, and talent—to create new value and experiences for customers, employees, and stakeholders” [21].

Therefore, the terms IoT, digital transformation, and logistics will be amalgamated to “logistics 4.0” considering the aspects of IoT driven transformation processes. Thus, the definition of Logistics 4.0 by Hofmann and Rüsch [24]: p. 25 seems to be helpful, because it includes the aspect of business model innovation [25, 26]:

• Products, services, infrastructure, and environment are flexibly connected via the internet or other network applications;

• The digital connectivity enables an automated and self-optimized production of individual goods and services including the delivery without human interventions;

• The value-creating networks are controlled decentralized while system elements make autonomous decisions;

• Data-driven, networked business models are transforming industry structures, organization, the way of collaboration and required roles, skills and capabilities of network members.

3 IoT Driven Innovation Framework

3.1 IoT Solution Architecture

Because IoT and related business transformation have been defined so far, we propose an IoT architecture that integrates the definitions and consists of the five building blocks described below. The IoT architecture becomes an overarching and integrative

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Table 2 IoT solution architecture based on [17, 21]

Layer Descriotion Examples

1 Business application Business process Resource planning; source, make, and delivery planning, execution and control (system)

2 Analytics Analysis of data in order to create usable knowledge

Big data analysis, artificial intelligence, machine learning

3 Connectivity Data enrichment by integrating data fro different applications and sources

Cloud computing, data crawler, sementic web, block chain

4 Communication network Interoperable information and communication technologies

LAN/WAN, 5G, TCP/IP

5 Device Equipment with the mandatory capabilities of communication

Drone, self-driving vehicles

framework that logically connects technologies and organization. The architecture presented in Table 2 combines a technical IoT architecture [27] with a business model approach [28].

The IoT architecture consists of five complementary layers that transform tech- nology building blocks into new business processes and business models. In layer 1–3 we rely on the definition of ITU [17]. However, in the ITU definition the business layers are not considered. Therefore, we added the definition of digital transformation to create an integrated framework.

• Layer 1 represents devices: front-end technology such as sensors, actuators and RFID chips, smart computers embedded in mobile devices, or intelligent autonomous objects (e.g. drones, robots, vehicles, smart stores, smart infrastruc- ture). Information technology on level 1 creates, stores, processes and transfers data anytime and anywhere autonomously.

• Layer 2 consists of internal and external communication infrastructures. Networks that allow collecting, process, and disseminate valuable information, gathered from distributed devices. The required hardware consists of a secure data network infrastructure of scalable nodes (access points, storage) and linkages (wired and wireless).

• Layer 3 represents cloud-enabled services that provide SaaS (Software-as-a- Service), PaaS (Platform-as-a-Service), IaaS (Infrastructure as a Service), DaaS (Data-as-a-Service), and more. Layer 3 aggregates the “big data” and makes them available to create business applications.

• Layer 4 encompasses the business applications. Such an application uses the available data to simplify and improve existing processes; for instance, block chain and data analytics can help generate and capture value along the full product life by ensuring better coordination between all partners.

• Layer 5 defines the application in existing business functions such as purchasing, logistics, transport, warehousing, and production. The business functions embody

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methods and tools that are used to efficiently fulfill their tasks and responsibilities (e.g. planning and execution systems, risk management, etc.).

3.2 Integrated Innovation Value Chain Model

Business development, logistics, purchasing, and marketing functions are continu- ously integrated into new products, services, and business models through the use of new digital technologies. The transformation of technology into logistic applications becomes difficult due to the cross-functional character. The objective of the innova- tion model is to describe the overarching value chain and impact of IoT technology into business applications in order to enhance existing or create new business models within firms. The development and availability of IoT technologies is not sufficient to trigger innovation in products, processes, services, or business models. Technology has no value in itself. The value of technology comes with the width and depth of the application. However, how many value-added steps are necessary in between?

Groher and Ruile [29] proposed an integrated innovation system for logistics. The innovation value chain model describes a stepwise transformation of technology into business values within a business-to-business environment. They identified the following four parties across the value chain: research and technology development, tool development and tool integration, application at logistics service providers, and value creation on the side of shippers. The innovation value chain model does not consider the internal transformation: how people in organizations get to know the tools and how they use them in a specific context to achieve organizational advan- tage. A prerequisite for the successful use of a new technology and the tool is a learning-oriented organization [30]. “The adoption of new systems and processes is likely to improve effectiveness in the delivery of the logistics service. Organizational learning will also lead to reductions in the costs of transaction that will contribute to greater effectiveness in the delivery of the logistics service” [31]: p. 71. We integrate organizational learning into the structured problem-solving cycle [32] to obtain the IoT-driven value chain.

The integrated innovation value chain model describes a stepwise transformation of technology into business value (see Fig. 1):

• Step 1: from technology to digital tools. Digital tools are software programs assisting people in their functional task and responsibility. Software tools for plan- ning and controlling are well known: transport management system (TMS), ware- house management systems (WMS), resource/material planning (ERP), customer and supplier relationship management systems (CRM, SRM), and so on.

• Step 2: from digital tools to learning organization. Management tasks are based on a structured problem-solving cycle: identify, analyze, find and select a solution, plan and implement, and finally evaluate. Logistics management is supported by these advanced tools and should increase efficiency of the management processes.

• Step 3: from a learning organization to specific supply chain processes. According the SC reference model [33], we define the application areas within supply chain

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Fig. 1 Integrated value chain framework for IoT driven innovation in logistics based on [31, 32]

processes: plan, source, make, deliver, and return. Each of the process functions have developed their own expertise and tools. It is expected that cross-functional integration and competitive networking enable organizations to adapt and leverage IoT-Solutions to achieve logistics objectives [34].

• Step 4: from SC processes to the business model. The business model integrates supply chain management into the overall strategic alignment with the product, customer, and business improvement model [35]. According to Gassmann [35], business model innovation is achieved when two or more elements of the business model have been changed in a coordinated and integrated way. The alignment of supply chain design and market requirement is well investigated and documented [36]. Organizations with high SC alignments achieve higher capitalization in the market.

• Step 5: from the business model to order-winning criteria. Criteria for winning customer orders are cost, delivery, quality, and flexibility. A tailor-made business model (product, customer and value chain) is essential to meet the criteria for winning customer orders [37].

4 Application

The following sections represent three applications using the IoT system description and the innovation value chain framework. Both elements are necessary to understand IoT technology and business value to enable innovation. Innovation is understood as the successful implementation of novelty in the market.

4.1 Case 1: Drive Net

DRIVE net is an IoT-based prototype proposed for a region wide web-based real- time transportation decision system that adopts digital roadway maps and integrates

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Table 3 Drive net: IoT system

Level 5 Planning transportation for optimized delivery and mobility

Level 4 Travel time estimation and prediction, dynamic routing, incident-induced delay calculation, statistical analysis

Level 3 Incident tracking system, highway safety information system

Level 2 Satellite server with hardware, software, and data processing tools, Bluetooth-based travel time detectors

Level 1 Sensors on vehicles, roads, freeway loop sensor, real-time traffic data, tracking data (GPS)

Table 4 Drive net: innovation value chain

Tool Intelligent transportation system with real-time data collection from various data sources, data qualification analysis

Learning Support of traffic analysis and decision making for alternative vehicle routings: sharing, visualizing, modeling, and analyzing transportation data

Application Optimized real-time delivery planning

Business model Customer: road users (private, business) Product: traffic information platform Value chain: data processing from multiple source Earning: not yet defined

Order winning criteria Road users (private, business, people, and cargo transport): flexibility, quality (real-time), faster delivery

multiple data sources (e.g., traffic sensors, incidents, accidents, and travel time) [38]. The objective is to improve traffic flow and transportation time.

The application of the case uses a functional understanding of logistics: transport management (Tables 3 and 4).

4.2 Case 2: Used Car Trade

An exemplary blockchain implementation took place in used car trading in Zurich [39]. The blockchain is used to create trusted data throughout the life of the car, even if car owners change over time. A digital Curriculum Vitae (CV) records all vehicle data in a file. The car importer supplies the new car data for the vehicles, the insurance company includes proof of insurance, and the Road Traffic Licensing Office takes care of traffic licensing. Anonymous usage and event data for the vehicle come from a car-sharing provider. The repair and maintenance services of the workshops are also recorded on the blockchain in unalterable form. Dealers and buyers receive much more data than before, and they can still trust the information.

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Table 5 Used car trade: IoT solution

Level 5 Re-selling platform

Level 4 Data collection and history analysis, check of compliancy Further analysis not defined

Level 3 Blockchain for data collection from insurances, licensing offices, car service workshops, etc.

Level 2 Bluetooth, public telecom network (4G)

Level 1 Used car identifier, GPS location

Table 6 Used car trade: innovation value chain

Tool Digital platform, cloud-based control system

Learning Identification of invisible, undeclared incidents

Application Support of the sourcing process of the car buyer

Business model Customer: used car buyers and sellers (private, business) Product: transparency Value chain: data processing from multiple sources and partners Earning: pay-per-use, license, member fee

Order winning criteria Car buyer: quality, transparency and avoidance of fraud

The composition of this ecosystem is not accidental: it is the smallest survivable value system. However, the choice of the right partners is key to the survival of this ecosystem.

The application of the case uses a network understanding of logistics: value- creating network through efficient information flows between partners (Tables 5 and 6).

4.3 Case 3: Smart Farming

The example of smart farming [40] shows the digital transformation in the primary economic sector through the creation of networked farms for smart farming [40]. A network of small farmers, urban farmers and gardeners is integrated with their food or flower production. Production is controlled by multiple sensors (humidity, temperature, etc.) and actors (water pumping, fertilizer, shadow system, etc.). Moni- toring and sharing of these data will provide insight into the farming practice and

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Table 7 Smart farming: IoT solution

Level 5 Monitor and control on mobile devices

Level 4 Not yet developed

Level 3 IoT service platform (called Mobius). Each device is registered and allows machine-to-machine communication

Level 2 Wirelessly connected with the Raspberry-Pi installed with the &Cube through a ZigBee-based network

Level 1 (a) compound sensor (i.e., temperature, humidity, and CO2), (b) photosynthetic photon flux density (PPFD) sensor, and (c) soil moisture sensor Devices are intake and exhaust fans, an air conditioner, sprinklers, LED lights, a cover controller and an irrigation and nutrient management system

Table 8 Smart farming: innovation value chain

Tool Expert data base, knowledge management system

Learning Problem identification, scenarios, evaluation of experienced farmers

Application Optimized source and manufacturing process (e.g. which seed and plant protection to buy, how to care for plants, how to harvest them, etc.)

Business model Customer: (experienced and unexperienced) farmers, industry Product: expert system Value chain: data processing from multiple devices and sensors from various partners Earning: no clear description (we estimate: platform)

Order winning criteria Quality (in terms of reliability, actuality and scope)

should improve its productivity. The parties estimate that agriculture could be further developed by using the Internet of Things.

Theapplicationofthecaseusesanetworkperspectiveoflogistics:avalue-creating network by providing a more effective agriculture for the cultivation of their crops (Tables 7 and 8).

5 Conclusion

The objective of the chapter was to describe how IoT drives innovation in logistics. We developed an IoT-driven integrated framework that considers a) the technical and business architecture and b) an integrated innovation value chain for logistics application. We applied the frameworks to three IoT cases from the literature.

The application of the framework to the three cases shows commonalities und differences within the setup of the solution and the value chain. The usage of the IoT architecture allows a systematic analysis of the technological structure, and the application of the innovation value chain provides insight into the value added stream

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and the applied business model. We see some commonalities and differences in the IoT architecture:

• Advanced IoT solutions provide an interconnection of sensors, devices, and value chain partners by using internet technologies.

• IoT solutions create much more transparency by collecting and analyzing data from different sources.

• The data collection opens opportunities for expert systems and machine learning systems.

• Not all of the cases presented have fully completed the IoT architecture. The missing layers can provide opportunities to add functionalities that can add value to the overall system.

The application of the innovation value chain framework provides insight into the following

• Due to the technical structure, the preferred business model is a platform. Plat- forms are new players across the value chain and provide additional shared information to optimize the source, create or deliver processes.

• The partners of the platform do not change their business model (transport company, car dealer, farmer), but they have the opportunity to improve their operations.

The IoT architecture and the innovation value chain framework provide advan- tages for a better understanding of the proposed IoT cases. The mutual view of technology and economy expands our understanding of Logistics 4.0, which is an amalgam of IoT, industry 4.0 and digital transformation. The framework makes it possible to identify technical and business opportunities. In order to successfully implement an IoT-driven radical innovation, we need to experience value adding networked designs. The framework will help to analyze, design, and evaluate such technical and economic networks that are closely related.

The framework presented in this chapter is descriptive. The model is able to analyze, describe, and evaluate (to a certain extent) existing IoT solutions in logistics. However, the framework is not able to predict future solutions, nor to give advice on a process for developing innovative solutions.

Nevertheless, the framework was tested in three cases, and requires further valida- tion and proof in practice. Our future research work will follow the raised questions: (a) what are the success factors, driving forces, and barriers for building successful IoT-driven innovations in logistics? (b) how do companies and networks manage the transitory steps in the cross functional innovation value chain? and (c) how do companies deal with the growing IoT-driven platform economy that will substitute their traditional business model?

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