凱捷（Capgemini）：2020釋放工業物聯網的商業價值（英文版）（28頁）.pdf

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1、Unlocking the business value of Industrial IoT Executive Summary At many organizations, Industrial IoT initiatives are failing to achieve their goals and reach meaningful scale. We found that more than six out of ten organizations have failed to take Industrial Internet-of-Things (IIoT) initiatives

2、past proof- of-concept stage or beyond implementation at one or two sites. Business and technical challenges stand between the organization and successful wide-scale adoption: No clear business case: Around half of organizations struggle to establish a clear business case for IIoT investments. Secur

3、ity concerns: Many organizations (62%) are grappling with cyber risks that could have significant reputational and financial consequences. Constrained analytical capabilities: 60% of organizations say they do not have the analytics capabilities to take advantage of the data generated from IIoT sourc

4、es. Uncertainty about IIoT standards and protocols: More than half of organizations say uncertain standards are a significant challenge. How should organizations think about managing these challenges? Based on what we have learned from our researchas well as our experience working with clientswe bel

5、ieve that organizations should focus on three critical areas: Create a clear and compelling vision for how IIoT can address critical problems for the business. Put together a leadership team that has the business and technical acumen to devise a coherent IIoT strategy and navigate the organization t

6、hrough the changes required. Drive scale by focusing on a subset of high-potential use cases and moving them quickly to enterprise-wide adoption. Using this framework as a guiding principle, organizations will also need strong technology competence in the following areas: Robust analytics and develo

7、pment platforms to take advantage of growing volumes of structured and unstructured IIoT data. Advanced analytics and AI capabilities that are both delivered centrally as well as “at the edge.” A “security-by-design” approach that addresses cybersecurity threats and which follows best practices for

8、data management and security controls. 2Unlocking the business value of Industrial IoT Introduction Organizations around the world are placing big bets on the Industrial Internet of Things (IIoT), including platforms, hardware, and applications. In 2016, global spending on IoT reached $700 billion a

9、nd is expected to reach 1.3 trillion by 2020, largely driven by IIoT spends.1 However, turning investment into a positive return is another question. A number of research studies have established that organizations are struggling to establish a clear business case for IIoT with organizations unconvi

10、nced about the financial benefits.2 The critical question for executives leading IIoT initiatives therefore becomes: how do we define the ROI and how can we justify capital investment in the IIoT? To answer this questionand understand how organizations can maximize IIoT investmentswe surveyed senior

11、 executives from over 300 organizations across the globe who are already implementing IIoT initiatives. We also analyzed more than 300 cross-sector, real-life uses cases for manufacturing and operations spanning industrial manufacturing, retail, consumer products, energy how do I execute?”10 Figure

12、3. Struggling fi rms face business case and cyber security/privacy challenges Share of organizations facing challenges in IIoT implementation Source: Capgemini Research Institute, Industrial IoT survey, N=316 organizations that are implementing IIoT, October 2017 62%49% Lack of a clear business case

13、 Cybersecurity and data privacy concerns Why are organizations struggling to move beyond pilots? 6Unlocking the business value of Industrial IoT Absence of uniform standards and protocols poses a serious challenge: There are no central IIoT standards today related to machine-to-machine communication

14、, routing, and networks. It is only in the past year that a handful of alliances for IIoT standards have started collaborating to define unified protocols for data sharing, networks, and interoperability.11 More than half of the organizations we surveyed cite uncertain standards as a significant cha

15、llenge (see Figure 4). It will take several years for different technology standards to be consolidated and provide the backbone of IIoT deployments across the globe. Connectivity issues: Connectivity is a challenge across planning, deployment, and scaling-up for two reasons. First, connectivity iss

16、ues are magnified as organizations move from the proof-of-concept (POC) stage to full scale. For example, when deploying IIoT solutions at the POC stage, network connectivity is seamless and has low latency because the number of devices are limited and the network is homogenous. However, when deploy

17、ing IIoT at scale, where the number of devices increases significantly, multiple issues can disrupt connectivity: different cell towers, varying connectivity speeds, proxy servers, and firewalls. Second, because we do not yet have a widely- accepted set of standard connectivity protocols, there is u

18、ncertainty around the relevance of key connectivity technologies such as Bluetooth, ZigBee, NFC, Wi-Fi, and in the future, LoRa As the CEO of an IIoT solution platform provider said: “The concern here is around future proofing and support to interoperate across the myriad of legacy, new, and unknown

19、 machine protocols. Being able to ingest, combine, and correlate data from any device that comes along is critical.”12 Figure 4. Inability to leverage IIoT data is a major hindrance Share of organizations facing challenges in IIoT implementation Source: Capgemini Research Institute, Industrial IoT s

20、urvey, N=316 organizations that are implementing IIoT, October 2017 60% Current analytics capabilities not ready to take advantage of IIoT data 57% Uncertain regulations and standards 57% Current data landscape not ready to manage IIoT data 53% Lack of technological readiness 7 Identifying and prior

21、itizing use cases To help organizations choose optimal use cases, we segmented them by business value and the payback period. The high potential use cases we identified are therefore the ones that combine higher benefits with a shorter payback time (see Figure 5). By focusing on these use cases, org

22、anizations will be in a better position to drive greater value from their IIoT investments and secure a competitive advantage. 8Unlocking the business value of Industrial IoT High FastPayback period of investmentSlow Low Benefi ts derived from implementation High = Greater than average benefi t on a

23、 normalized range Fast = Greater than average payback period on a normalized range Source: Capgemini Research Institute, Industrial IoT survey, N=316 organizations that are implementing IIoT, October 2017 Figure 5. Distribution of use cases by benefi ts and payback period of investments Environment

24、monitoring for telecom Equipment Smart metering Telematics for vehicle monitoring Smart water management Vehicle trip/route management Smart fridges Smart shelves Cold chain logistics Warehouse management- Inventory monitoring Telematics for transported goods monitoring Staff workload management Fle

25、et management Energy management based on ambient conditions (retail store) Smart product tracking Asset tracking (man/machine/material) Facility management Manufacturing intelligence Supply adequacy with demand Situational awareness (personnel safety and environmental hazard) Monitoring storage cond

26、itions Monitoring of inventory (level) Monitoring and controlling of climate conditions Product quality optimization Monitoring shipment conditions (E.g., temperature and humidity) Distribution network losses monitoring (leakage detection) Production asset maintenance Capacity utilization and worklo

27、ad management (factory) Telecom base stations remote monitoring and management Layout optimization (retail store) High gestation Low potential High potential Quick wins 9 However, we found that many organizations across sectors are not focusing on these high potentials (see Figure 6). If we look at

28、the sector distribution, we find that telecoms lead the way in implementing high potentials, with around four out of ten organizations (see Figure 6). Many organizations, however, are missing out on the performance opportunity offered (see “High Potential Use Cases: Sector Examples”). Figure 6. Aver

29、age implementation percentage of high potential use cases by industries IndustryImplementation at selective sites and full scaleHigh potential use cases Monitoring of inventory (level) Telecom base stations remote monitoring and management Environment monitoring for telecom equipment Manufacturing i

30、ntelligence Monitoring shipment conditions (E.g., temperature and humidity) Monitoring and controlling of climate conditions Production asset maintenance Capacity utilization and workload management (factory) Manufacturing intelligence Supply adequacy with demand Product quality optimization Smart s

31、helves Staff workload management Cold chain logistics Asset tracking (man/machine/material) Production asset maintenance Monitoring shipment conditions (E.g., temperature and humidity) Monitoring and controlling of climate conditions Smart metering Average multi-site implementation percentage Asset

32、tracking (man/machine/material) Production asset maintenance Vehicle trip/route management Telematics for vehicle monitoring Telematics for transported goods monitoring Fleet management Telecommunication 22%14% 36% Consumer Products 27% 13%14% Industrial Manufacturing 17%11% Retail 15%8% Energy and

33、second, “at the edge,” insights are fully embedded in the place of action, be it shelf, warehouse, car, production line, or drill site. Addressing IIoT security concerns A number of steps are critical to address security concerns: First, IIoT devices need to be built for security from the ground-up

34、and run on an OS that treats security as a primary concern. Currently, organizations are not doing enough to prevent security shortfalls at the conceptualization and design stage. Bruce Schneier, a renowned security analyst, says: “Security is an afterthought in product design and not something that

35、s taken seriously enough. Companies are rewarded for features, price, and time-to-market. Its easy to slough off security because its not immediately obvious that youve done so.”23 Second, organizations need to follow best practices for data management and security controls to guard against potentia

36、l risks, particularly those emerging from the partner ecosystem (IIoT solution vendors and start-ups). The security of an IIoT platform is vital because most data transmission and operations run through it. As we show in our research “The Currency of Trust: Why Banks and Insurers Must Make Customer

37、Data Safer,” deploying strong controls for third-party data access helps strengthen security. Organizations should also consider deploying automated intelligence and security procedures, such as automatically updating patches when they become available. Automation reduces vulnerability by reducing i

38、ncident response time.24 Third, security solutions need to be tuned in to the specific needs of industrial IoT set-up. As Guido Jouret, CDO of ABB, a technology leader in electrification products, robotics and motion, industrial automation and power grids, says: “Even though security is paramount in

39、 the world of IIoT, there is one attribute that trumps even that: availability. By this I mean that many industrial machines can never be taken down to install patches or to fix a possible breach. Cybersecurity systems for the industrial IoT need to factor in the non-stop mission criticality of proc

40、esses and continuous availability.”25 Conclusion The benefits of Industrial IoT are compelling, with proven and multiple use cases showing the significant value that organization across sectors can generate. However, finding the sweet spot for the IIoT will require more than just deep- seated know-h

41、ow of the technology. Firms will need to have a coherent IIoT strategy and vision that address significant business issues, the right blend of committed leaders, and a strong focus on high-value use cases. Organizations that excel in these areas are delivering significant benefits and establishing a

42、 competitive edge that other firmsthose that struggle to get beyond experimentationwill find increasingly difficult to match. 18Unlocking the business value of Industrial IoT 19 Research Methodology Our research drew on quantitative and qualitative techniques. Between September and October 2017, we

43、surveyed 316 respondents from companies implementing Industrial IoT across a range of sectors and countries: Automotive, Industrial Manufacturing, Retail, Telecommunications, Consumer Goods, and Energy Sven Dahlmeier, Alexander Heeler, and Hendrik Wrdehoff from Capgemini Germany; Atul Kurani, Gita B

44、abaria, Satish Nayak, Vivekanand Sangle, Vinutha Naik, and Shalabh Dhankar from Capgemini India; Frederic Vander Sande and Pieter Schoevaerts from Capgemini Belgium; Joeri Van Geystelen from Capgemini Canada; Ron Tolido, Kees Jacobs, and Frank Wammes from Capgemini Netherlands; Johan Williamson from

45、 Capgemini Sweden; Nick Gill from Capgemini UK; Alexander Korogodsky, Sandeep Sachdeva, Debbie Krupitzer, and Robert Smith from Capgemini US. Anne-laure Thieullent Vice President, Insights & Data, Capgemini France Anne-Laure is Vice President and Global Head of Manufacturing, Automotive and LifeScie

46、nces for Capgeminis Insights & Data practice. With more than 17 years of experience in the field of Information Technology, data and analytics, she advises Capgemini customers on how they should put Big Data and artificial intelligence technologies to work for their organization. Her passion is to b

47、ring technology, business transformation and governance together and take customers to where they want to be as data- driven and innovative companies. Kunal Kar Manager, Capgemini Research Institute Kunal is a manager at the Capgemini Research Institute. He tracks the impact of digital technologies

48、on the financial sector and helps clients on their digital transformation journey. Pascal Brosset EVP-CTO, Digital Manufacturing Pascal BROSSET is the global CTO and North America leader for Capgeminis Digital Manufacturing services. He has 30 years of experience including a seven year stint as a Gr

49、oup CTO with Schneider Electric and ten years with SAP AG as a Chief Strategy Officer. Pascal successfully took a number of businesses through major shifts, combining technology and business model innovation. At Capgemini, Pascal is responsible for orchestrating and developing the companys strategy and portfolio of solutions for the Industry 4.0/IIoT/Digital Manufacturing market, and organizing the go-to-market for the related solutions. Yashwardhan Khemka Senior Consultant, Capgemini Research Institute yashwardhan_k Yash is a senior consultant