Are You Really Ready for Customer Experience?

 

Ginger Conlon, Editor-in-Chief

Marketers can talk a good game about customer centricity, but actions speak louder than words.

“Putting customers at the center of your business is what it takes to create a competitive advantage.” This simple, yet bold, statement by Lars Birkholm Petersen launched a discussion around where most businesses are in terms of their customer experience maturity level.

During a presentation at Sitecore Symposium 2014, Petersen reviewed the marketing automation company's Customer Experience Maturity Model (see above). Its stages are initiate, radiate, align (the “attract” phase of the customer lifecycle); optimze, nurture (the “convert” phase); engage, lifetime customers (the “advocate” phase). “We've found that 85.4% of companies are in first two stages,” said Petersen, global director, business optimization services, at Sitecore. “Strategic value increases as you move further along the model. If you're in those first two stages, how do you get started?”

Ron Person, senior consultant, business optimization, for Sitecore, took the stage to answer Petersen's question. To move ahead to stage three, “align,” connect corporate objectives to marketing objectives, Person advised. There are six steps to doing so: determining strategic themes (e.g., customer intimacy, top quality), setting strategic objectives (what's needed to make the theme become a reality), defining marketing objectives (specifically to support the strategic objectives), setting digital goals, creating an engagement value scale, and then selecting key performance indicators. This process will help show the link between business objectives and customer actions.

“All marketing tactics, down to A/B testing, need to contribute toward reaching strategic objectives,” Person stated emphatically.

The fourth stage is “optimize,” which includes such activities as rules-based personalization, A/B and multivariate testing, and campaign attribution. Chris Nash, who joined the stage with Person, explained that optimization, such as in-the-moment personalization, is exceptionally powerful. “Use it in all phases of the customer lifecycle,” said Nash, senior consultant, business optimization, for Sitecore. “Use customer data [in real time] as it becomes available; use the data that makes the most sense to use for that personalization. Use different types of data in different combinations.” Doing so can be complex, he said, but its effectiveness is worth the effort.”

“Optimization is the low-hanging fruit of experience marketing,” Nash said. “Focus on the buyer journey.”

Get connected

After the presentation Petersen, Person, and Nash met with several anaylists and journalists (myself included), to dive deeper into the topic of customer experience maturity, which is a central theme of their new book Connect: How to use data and experience marketing to create lifetime customers.

Considering the complexity of the customer experience, and of marketing, I asked: Are companies at multiple stages of customer experience maturity with different aspects of their marketing? For example, is the email customer experience more mature than in other channels? If so, how can they better connect those stages?

“Companies are being forced by customers to create a connected experience,” Nash said. “It puts pressure on organizations to think about the quality of the overall customer experience. Email, for example, is an ‘advanced' silo, but it's often a disconnected experience from other channels.” Nash explained that this type of disparity creates “moments of disconnected frustration” for customers.

Eliminating the disconnect and creating a consistent customer experience is all about people, process, and technology, Peterson noted. Marketers need to focus on the customer across channels in a connected way; they should collect and use data create relevant, contextualize experiences. “This goes back to marketers needing to ensure that they align the customer experience with strategic objectives to get business value,” he said, referring to the Customer Experience Maturity Model. “Once they're doing that, they can optimize and nurture. Then can moved to engage and lifetime customers.” But tread purposefully: “It takes time, maybe years to move up the maturity model, espcially in a big organization,” Petersen said. 

The journey, however, is more necessary than ever before.

“Businesses that excel at customer experience,” Nash said, “make it so other companies need to do the same.”

The Pros and Cons of Automation for Small Businesses

 Small business owners say the top benefit of automation is increased productivity and the top drawback is the need for excessive technical support, according to research from Skynova.

The report was based on data from a survey of 288 small business owners in the United States with at least one employee.

An infographic (below) explores key findings from the survey, including the five biggest benefits and drawbacks small business owners see from automation.

The piece also looks at how valuable small business owners perceive automation to be.

https://bit.ly/3alDX1x

Where machines could replace humans—and where they can’t (yet)

 

By Michael Chui, James Manyika, and Mehdi Miremadi

The technical potential for automation differs dramatically across sectors and activities.

As automation technologies such as machine learning and robotics play an increasingly great role in everyday life, their potential effect on the workplace has, unsurprisingly, become a major focus of research and public concern. The discussion tends toward a Manichean guessing game: which jobs will or won’t be replaced by machines?

In fact, as our research has begun to show, the story is more nuanced. While automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail. Automation, now going beyond routine manufacturing activities, has the potential, as least with regard to its technical feasibility, to transform sectors such as healthcare and finance, which involve a substantial share of knowledge work.

From science fiction to business fact

McKinsey’s Michael Chui explains how automation is transforming work.

These conclusions rest on our detailed analysis of 2,000-plus work activities for more than 800 occupations. Using data from the US Bureau of Labor Statistics and O*Net, we’ve quantified both the amount of time spent on these activities across the economy of the United States and the technical feasibility of automating each of them. The full results, forthcoming in early 2017, will include several other countries,1but we released some initial findings late last year and are following up now with additional interim results.

Last year, we showed that currently demonstrated technologies could automate 45 percent of the activities people are paid to perform and that about 60 percent of all occupations could see 30 percent or more of their constituent activities automated, again with technologies available today. In this article, we examine the technical feasibility, using currently demonstrated technologies, of automating three groups of occupational activities: those that are highly susceptible, less susceptible, and least susceptible to automation. Within each category, we discuss the sectors and occupations where robots and other machines are most—and least—likely to serve as substitutes in activities humans currently perform. Toward the end of this article, we discuss how evolving technologies, such as natural-language generation, could change the outlook, as well as some implications for senior executives who lead increasingly automated enterprises.

Understanding automation potential

In discussing automation, we refer to the potential that a given activity could be automated by adopting currently demonstrated technologies, that is to say, whether or not the automation of that activity is technically feasible.2Each whole occupation is made up of multiple types of activities, each with varying degrees of technical feasibility. Exhibit 1 lists seven top-level groupings of activities we have identified. Occupations in retailing, for example, involve activities such as collecting or processing data, interacting with customers, and setting up merchandise displays (which we classify as physical movement in a predictable environment). Since all of these constituent activities have a different automation potential, we arrive at an overall estimate for the sector by examining the time workers spend on each of them during the workweek.

Exhibit 1

Analyzing work activities rather than occupations is the most accurate way to examine technical feasibility of automation.

In practice, automation will depend on more than just technical feasibility. Five factors are involved: technical feasibility; cost of automate; the relative scarcity, skills, and cost of workers who might otherwise do the activity; benefits (eg, superior performance) of automation beyond labor-cost substitution; and regulatory and social-acceptance considerations.

1)      Applying expertise to decision making, planning and creative tasks.

2)      Unpredictable physical work (physical activities and the operation of machinery) is performed in unpredictable environments, while in predictable physical work, the environments are predictable. 

Technical feasibility is a necessary precondition for automation, but not a complete predictor that an activity will be automated. A second factor to consider is the cost of developing and deploying both the hardware and the software for automation. The cost of labor and related supply-and-demand dynamics represent a third factor: if workers are in abundant supply and significantly less expensive than automation, this could be a decisive argument against it. A fourth factor to consider is the benefits beyond labor substitution, including higher levels of output, better quality, and fewer errors. These are often larger than those of reducing labor costs. Regulatory and social-acceptance issues, such as the degree to which machines are acceptable in any particular setting, must also be weighed. A robot may, in theory, be able to replace some of the functions of a nurse, for example. But for now, the prospect that this might actually happen in a highly visible way could prove unpalatable for many patients, who expect human contact. The potential for automation to take hold in a sector or occupation reflects a subtle interplay between these factors and the trade-offs among them.

Even when machines do take over some human activities in an occupation, this does not necessarily spell the end of the jobs in that line of work. On the contrary, their number at times increases in occupations that have been partly automated, because overall demand for their remaining activities has continued to grow. For example, the large-scale deployment of bar-code scanners and associated point-of-sale systems in the United States in the 1980s reduced labor costs per store by an estimated 4.5 percent and the cost of the groceries consumers bought by 1.4 percent.3It also enabled a number of innovations, including increased promotions. But cashiers were still needed; in fact, their employment grew at an average rate of more than 2 percent between 1980 and 2013.

The most automatable activities

Almost one-fifth of the time spent in US workplaces involves performing physical activities or operating machinery in a predictable environment: workers carry out specific actions in well-known settings where changes are relatively easy to anticipate. Through the adaptation and adoption of currently available technologies, we estimate the technical feasibility of automating such activities at 78 percent, the highest of our seven top-level categories (Exhibit 2). Since predictable physical activities figure prominently in sectors such as manufacturing, food service and accommodations, and retailing, these are the most susceptible to automation based on technical considerations alone.

Exhibit 2

It’s more technically feasible to automate predictable physical activities than unpredictable ones. 

In manufacturing, for example, performing physical activities or operating machinery in a predictable environment represents one-third of the workers’ overall time. The activities range from packaging products to loading materials on production equipment to welding to maintaining equipment. Because of the prevalence of such predictable physical work, some 59 percent of all manufacturing activities could be automated, given technical considerations. The overall technical feasibility, however, masks considerable variance. Within manufacturing, 90 percent of what welders, cutters, solderers, and brazers do, for example, has the technical potential for automation, but for customer-service representatives that feasibility is below 30 percent. The potential varies among companies as well. Our work with manufacturers reveals a wide range of adoption levels—from companies with inconsistent or little use of automation all the way to quite sophisticated users.

Manufacturing, for all its technical potential, is only the second most readily automatable sector in the US economy. A service sector occupies the top spot: accommodations and food service, where almost half of all labor time involves predictable physical activities and the operation of machinery—including preparing, cooking, or serving food; cleaning food-preparation areas; preparing hot and cold beverages; and collecting dirty dishes. According to our analysis, 73 percent of the activities workers perform in food service and accommodations have the potential for automation, based on technical considerations.

Some of this potential is familiar. Automats, or automated cafeterias, for example, have long been in use. Now restaurants are testing new, more sophisticated concepts, like self-service ordering or even robotic servers. Solutions such as Momentum Machines’ hamburger-cooking robot, which can reportedly assemble and cook 360 burgers an hour, could automate a number of cooking and food-preparation activities. But while the technical potential for automating them might be high, the business case must take into account both the benefits and the costs of automation, as well as the labor-supply dynamics discussed earlier. For some of these activities, current wage rates are among the lowest in the United States, reflecting both the skills required and the size of the available labor supply. Since restaurant employees who cook earn an average of about $10 an hour, a business case based solely on reducing labor costs may be unconvincing.

Retailing is another sector with a high technical potential for automation. We estimate that 53 percent of its activities are automatable, though, as in manufacturing, much depends on the specific occupation within the sector. Retailers can take advantage of efficient, technology-driven stock management and logistics, for example. Packaging objects for shipping and stocking merchandise are among the most frequent physical activities in retailing, and they have a high technical potential for automation. So do maintaining records of sales, gathering customer or product information, and other data-collection activities. But retailing also requires cognitive and social skills. Advising customers which cuts of meat or what color shoes to buy requires judgment and emotional intelligence. We calculate that 47 percent of a retail salesperson’s activities have the technical potential to be automated—far less than the 86 percent possible for the sector’s bookkeepers, accountants, and auditing clerks.

As we noted above, however, just because an activity can be automated doesn’t mean that it will be—broader economic factors are at play. The jobs of bookkeepers, accountants, and auditing clerks, for example, require skills and training, so they are scarcer than basic cooks. But the activities they perform cost less to automate, requiring mostly software and a basic computer.

Considerations such as these have led to an observed tendency for higher rates of automation for activities common in some middle-skill jobs—for example, in data collection and data processing. As automation advances in capability, jobs involving higher skills will probably be automated at increasingly high rates.

The heat map in Exhibit 3 highlights the wide variation in how automation could play out, both in individual sectors and for different types of activities within them.4

Activities and sectors in the middle range for automation

Across all occupations in the US economy, one-third of the time spent in the workplace involves collecting and processing data. Both activities have a technical potential for automation exceeding 60 percent. Long ago, many companies automated activities such as administering procurement, processing payrolls, calculating material-resource needs, generating invoices, and using bar codes to track flows of materials. But as technology progresses, computers are helping to increase the scale and quality of these activities. For example, a number of companies now offer solutions that automate entering paper and PDF invoices into computer systems or even processing loan applications. And it’s not just entry-level workers or low-wage clerks who collect and process data; people whose annual incomes exceed $200,000 spend some 31 percent of their time doing those things, as well.

Financial services and insurance provide one example of this phenomenon. The world of finance relies on professional expertise: stock traders and investment bankers live off their wits. Yet about 50 percent of the overall time of the workforce in finance and insurance is devoted to collecting and processing data, where the technical potential for automation is high. Insurance sales agents gather customer or product information and underwriters verify the accuracy of records. Securities and financial sales agents prepare sales or other contracts. Bank tellers verify the accuracy of financial data.

As a result, the financial sector has the technical potential to automate activities taking up 43 percent of its workers’ time. Once again, the potential is far higher for some occupations than for others. For example, we estimate that mortgage brokers spend as much as 90 percent of their time processing applications. Putting in place more sophisticated verification processes for documents and credit applications could reduce that proportion to just more than 60 percent. This would free up mortgage advisers to focus more of their time on advising clients rather than routine processing. Both the customer and the mortgage institution get greater value.

Other activities in the middle range of the technical potential for automation involve large amounts of physical activity or the operation of machinery in unpredictable environments. These types of activities make up a high proportion of the work in sectors such as farming, forestry, and construction and can be found in many other sectors as well.

Examples include operating a crane on a construction site, providing medical care as a first responder, collecting trash in public areas, setting up classroom materials and equipment, and making beds in hotel rooms. The latter two activities are unpredictable largely because the environment keeps changing. Schoolchildren leave bags, books, and coats in a seemingly random manner. Likewise, in a hotel room, different guests throw pillows in different places, may or may not leave clothing on their beds, and clutter up the floor space in different ways.

These activities, requiring greater flexibility than those in a predictable environment, are for now more difficult to automate with currently demonstrated technologies: their automation potential is 25 percent. Should technology advance to handle unpredictable environments with the same ease as predictable ones, the potential for automation would jump to 67 percent. Already, some activities in less predictable settings in farming and construction (such as evaluating the quality of crops, measuring materials, or translating blueprints into work requirements) are more susceptible to automation.

Activities with low technical potential for automation

The hardest activities to automate with currently available technologies are those that involve managing and developing people (9 percent automation potential) or that apply expertise to decision making, planning, or creative work (18 percent). These activities, often characterized as knowledge work, can be as varied as coding software, creating menus, or writing promotional materials. For now, computers do an excellent job with very well-defined activities, such as optimizing trucking routes, but humans still need to determine the proper goals, interpret results, or provide commonsense checks for solutions. The importance of human interaction is evident in two sectors that, so far, have a relatively low technical potential for automation: healthcare and education.

Overall, healthcare has a technical potential for automation of about 36 percent, but the potential is lower for health professionals whose daily activities require expertise and direct contact with patients. For example, we estimate that less than 30 percent of a registered nurse’s activities could be automated, based on technical considerations alone. For dental hygienists, that proportion drops to 13 percent.

Nonetheless, some healthcare activities, including preparing food in hospitals and administering non-intravenous medications, could be automated if currently demonstrated technologies were adapted. Data collection, which also accounts for a significant amount of working time in the sector, could become more automated as well. Nursing assistants, for example, spend about two-thirds of their time collecting health information. Even some of the more complex activities that doctors perform, such as administering anesthesia during simple procedures or reading radiological scans, have the technical potential for automation.

Of all the sectors we have examined, the technical feasibility of automation is lowest in education, at least for now. To be sure, digital technology is transforming the field, as can be seen from the myriad classes and learning vehicles available online. Yet the essence of teaching is deep expertise and complex interactions with other people. Together, those two categories—the least automatable of the seven identified in the first exhibit—account for about one-half of the activities in the education sector.

Even so, 27 percent of the activities in education—primarily those that happen outside the classroom or on the sidelines—have the potential to be automated with demonstrated technologies. Janitors and cleaners, for example, clean and monitor building premises. Cooks prepare and serve school food. Administrative assistants maintain inventory records and personnel information. The automation of these data-collection and processing activities may help to reduce the growth of the administrative expenses of education and to lower its cost without affecting its quality.

Looking ahead

As technology develops, robotics and machine learning will make greater inroads into activities that today have only a low technical potential for automation. New techniques, for example, are enabling safer and more enhanced physical collaboration between robots and humans in what are now considered unpredictable environments. These developments could enable the automation of more activities in sectors such as construction. Artificial intelligence can be used to design components in engineer-heavy sectors.

One of the biggest technological breakthroughs would come if machines were to develop an understanding of natural language on par with median human performance—that is, if computers gained the ability to recognize the concepts in everyday communication between people. In retailing, such natural-language advances would increase the technical potential for automation from 53 percent of all labor time to 60 percent. In finance and insurance, the leap would be even greater, to 66 percent, from 43 percent. In healthcare, too, while we don’t believe currently demonstrated technologies could accomplish all of the activities needed to diagnose and treat patients, technology will become more capable over time. Robots may not be cleaning your teeth or teaching your children quite yet, but that doesn’t mean they won’t in the future.

As stated at the outset, though, simply considering the technical potential for automation is not enough to assess how much of it will occur in particular activities. The actual level will reflect the interplay of the technical potential, the benefits and costs (or the business case), the supply-and-demand dynamics of labor, and various regulatory and social factors related to acceptability.

Leading more automated enterprises

Automation could transform the workplace for everyone, including senior management. The rapid evolution of technology can make harnessing its potential and avoiding its pitfalls especially complex. In some industries, such as retailing, automation is already changing the nature of competition. E-commerce players, for example, compete with traditional retailers by using both physical automation (such as robots in warehouses) and the automation of knowledge work (including algorithms that alert shoppers to items they may want to buy). In mining, autonomous haulage systems that transport ore inside mines more safely and efficiently than human operators do could also deliver a step change in productivity.

Top executives will first and foremost need to identify where automation could transform their own organizations and then put a plan in place to migrate to new business processes enabled by automation. A heat map of potential automation activities within companies can help to guide, identify, and prioritize the potential processes and activities that could be transformed. As we have noted, the key question will be where and how to unlock value, given the cost of replacing human labor with machines. The majority of the benefits may come not from reducing labor costs but from raising productivity through fewer errors, higher output, and improved quality, safety, and speed.

It is never too early to prepare for the future. To get ready for automation’s advances tomorrow, executives must challenge themselves to understand the data and automation technologies on the horizon today. But more than data and technological savvy are required to capture value from automation. The greater challenges are the workforce and organizational changes that leaders will have to put in place as automation upends entire business processes, as well as the culture of organizations, which must learn to view automation as a reliable productivity lever. Senior leaders, for their part, will need to “let go” in ways that run counter to a century of organizational development.5

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Understanding the activities that are most susceptible to automation from a technical perspective could provide a unique opportunity to rethink how workers engage with their jobs and how digital labor platforms can better connect individuals, teams, and projects.6It could also inspire top managers to think about how many of their own activities could be better and more efficiently executed by machines, freeing up executive time to focus on the core competencies that no robot or algorithm can replace—as yet.

Could a machine do your job? Find out on Tableau Public, where we analyzed more than 800 occupations to assess the extent to which they could be automated using existing technology

https://mck.co/3evKBDF

Industry 4.0 - General Overview

The Fourth Industrial Revolution represents a fundamental change in the way we live, work, and relate to one another. It is a new chapter in human development, enabled by technology advances that are commensurate with those of the first, second and third industrial revolutions, and which are merging the physical, digital, and biological worlds in ways that create both promise and peril. The speed, breadth, and depth of this revolution is forcing us to rethink how countries should develop, how organizations create value, and even what it means to be human; it is an opportunity to help everyone, including leaders, policy-makers and people from all income groups and nations, to harness technologies in order to create an inclusive, human-centred future.

Industry 4.0 is a name given to the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things, cloud computing^[1][2][3][4] and cognitive computing. Industry 4.0 is commonly referred to as the fourth industrial revolution.^[5]

Industry 4.0 fosters what has been called a "smart factory". Within modular structured smart factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and with humans in real-time both internally and across organizational services offered and used by participants of the value chain.^[1]

Industrial revolutions and future view. By ChristophRoser. Please credit "Christoph Roser at AllAboutLean.com". - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=47640595

Name

The term "Industry 4.0", shortened to I4.0 or simply I4, originates from a project in the high-tech strategy of the German government, which promotes the computerization of manufacturing.^[6]

The term "Industry 4.0" was revived in 2011 at the Hannover Fair.^[7] In October 2012 the Working Group on Industry 4.0 presented a set of Industry 4.0 implementation recommendations to the German federal government. The Industry 4.0 workgroup members and partners are recognized as the founding fathers and driving force behind Industry 4.0.

On 8 April 2013 at the Hannover Fair, the final report of the Working Group Industry 4.0 was presented.^[8]. This working group was headed by Siegfried Dais (Robert Bosch GmbH) and Henning Kagermann (German Academy of Science and Engineering).

As Industry 4.0 principles have been applied by companies they have sometimes been re-branded, for example the aerospace parts manufacturer Meggitt PLC has branded its own Industry 4.0 research project M4. ^[9]

Design principles

There are four design principles in Industry 4.0. These principles support companies in identifying and implementing Industry 4.0 scenarios.^[1]

• Interconnection: The ability of machines, devices, sensors, and people to connect and communicate with each other via the Internet of Things (IoT) or the Internet of People (IoP)^[10]
• Information transparency: The transparency afforded by Industry 4.0 technology provides operators with vast amounts of useful information needed to make appropriate decisions. Inter-connectivity allows operators to collect immense amounts of data and information from all points in the manufacturing process, thus aiding functionality and identifying key areas that can benefit from innovation and improvement.^[11]
• Technical assistance: First, the ability of assistance systems to support humans by aggregating and visualizing information comprehensively for making informed decisions and solving urgent problems on short notice. Second, the ability of cyber physical systems to physically support humans by conducting a range of tasks that are unpleasant, too exhausting, or unsafe for their human co-workers.
• Decentralized decisions: The ability of cyber physical systems to make decisions on their own and to perform their tasks as autonomously as possible. Only in the case of exceptions, interferences, or conflicting goals, are tasks delegated to a higher level.

Meaning

The characteristics given for the German government's Industry 4.0 strategy are: the strong customization of products under the conditions of highly flexible (mass-) production.^[12]The required automation technology is improved by the introduction of methods of self-optimization, self-configuration,^[13] self-diagnosis, cognition and intelligent support of workers in their increasingly complex work.^[14] The largest project in Industry 4.0 as of July 2013 is the BMBF leading-edge cluster "Intelligent Technical Systems Ostwestfalen-Lippe (it's OWL)". Another major project is the BMBF project RES-COM,^[15] as well as the Cluster of Excellence "Integrative Production Technology for High-Wage Countries".^[16] In 2015, the European Commission started the international Horizon 2020 research project CREMA^[17] (Providing Cloud-based Rapid Elastic Manufacturing based on the XaaS and Cloud model) as a major initiative to foster the Industry 4.0 topic.

Effects

In June 2013, consultancy firm McKinsey^[18] released an interview featuring an expert discussion between executives at Robert Bosch – Siegfried Dais (Partner of the Robert Bosch Industrietreuhand KG) and Heinz Derenbach (CEO of Bosch Software Innovations GmbH) – and McKinsey experts. This interview addressed the prevalence of the Internet of Things in manufacturing and the consequent technology-driven changes which promise to trigger a new industrial revolution. At Bosch, and generally in Germany, this phenomenon is referred to as Industry 4.0. The basic principle of Industry 4.0 is that by connecting machines, work pieces and systems, businesses are creating intelligent networks along the entire value chain that can control each other autonomously.

Some examples for Industry 4.0 are machines which can predict failures and trigger maintenance processes autonomously or self-organized logistics which react to unexpected changes in production.

According to Dais, "it is highly likely that the world of production will become more and more networked until everything is interlinked with everything else". While this sounds like a fair assumption and the driving force behind the Internet of Things, it also means that the complexity of production and supplier networks will grow enormously. Networks and processes have so far been limited to one factory. But in an Industry 4.0 scenario, these boundaries of individual factories will most likely no longer exist. Instead, they will be lifted in order to interconnect multiple factories or even geographical regions.

There are differences between a typical traditional factory and an Industry 4.0 factory. In the current industry environment, providing high-end quality service or product with the least cost is the key to success and industrial factories are trying to achieve as much performance as possible to increase their profit as well as their reputation. In this way, various data sources are available to provide worthwhile information about different aspects of the factory. In this stage, the utilization of data for understanding current operating conditions and detecting faults and failures is an important topic to research. e.g. in production, there are various commercial tools available to provide overall equipment effectiveness (OEE) information to factory management in order to highlight the root causes of problems and possible faults in the system. In contrast, in an Industry 4.0 factory, in addition to condition monitoring and fault diagnosis, components and systems are able to gain self-awareness and self-predictiveness, which will provide management with more insight on the status of the factory. Furthermore, peer-to-peer comparison and fusion of health information from various components provides a precise health prediction in component and system levels and forces factory management to trigger required maintenance at the best possible time to reach just-in-time maintenance and gain near-zero downtime.^[19]

During EDP Open Innovation conducted in Oct 2018 at Lisbon, Portugal, Industry 4.0 conceptualization was extended by Sensfix B.V. a Dutch company with introduction of M2S terminology. It essentially is characterizing upcoming service industry to cater to millions of machines, managed by the machines themselves.

Challenges

Challenges in implementation of Industry 4.0:^[20]

• IT security issues, which are greatly aggravated by the inherent need to open up those previously closed production shops
• Reliability and stability needed for critical machine-to-machine communication (M2M), including very short and stable latency times
• Need to maintain the integrity of production processes
• Need to avoid any IT snags, as those would cause expensive production outages
• Need to protect industrial know-how (contained also in the control files for the industrial automation gear)
• Lack of adequate skill-sets to expedite the transition towards the fourth industrial revolution
• Threat of redundancy of the corporate IT department
• General reluctance to change by stakeholders
• Loss of many jobs to automatic processes and IT-controlled processes, especially for blue collar workers
• Low top management commitment
• Unclear legal issues and data security
• Unclear economic benefits/ excessive investment
• Lack of regulation, standards and forms of certifications
• Insufficient qualification of employees

Role of big data and analytics

Modern information and communication technologies like cyber-physical system, big data analytics and cloud computing, will help early detection of defects and production failures, thus enabling their prevention and increasing productivity, quality, and agility benefits that have significant competitive value.

Big data analytics consists of 6Cs in the integrated Industry 4.0 and cyber physical systems environment. The 6C system comprises:

1. Connection (sensor and networks)
2. Cloud (computing and data on demand)
3. Cyber (model & memory)
4. Content/context (meaning and correlation)
5. Community (sharing & collaboration)
6. Customization (personalization and value)

In this scenario and in order to provide useful insight to the factory management, data has to be processed with advanced tools (analytics and algorithms) to generate meaningful information. Considering the presence of visible and invisible issues in an industrial factory, the information generation algorithm has to be capable of detecting and addressing invisible issues such as machine degradation, component wear, etc. in the factory floor.^[21][22]

Impact of Industry 4.0

Proponents of the term claim Industry 4.0 will affect many areas, most notably:

1. Services and business models
2. Reliability and continuous productivity
3. IT security: Companies like Symantec, Cisco, and Penta Security have already begun to address the issue of IoT security
4. Machine safety
5. Manufacturing Sales
6. Product lifecycles
7. Manufacturing Industries: Mass Customisations instead of mass manufacturing using IOT, 3D Printing and Machine Learning
8. Industry value chain
9. Workers' education and skills
10. Socio-economic factors

An article published in February 2016 suggests that Industry 4.0 may have a beneficial effects for emerging economies such as India.^[23] The aerospace industry has sometimes been characterized as "too low volume for extensive automation" however Industry 4.0 principles have been investigated by several aerospace companies, technologies have been developed to improve productivity where the upfront cost of automation cannot be justified, one example of this is the aerospace parts manufacturer Meggitt PLC's project, M4. ^[24]The discussion of how the shift to Industry 4.0, especially digitalization, will affect the labour market is being discussed in Germany under the topic of Work 4.0.^[25]

Technology road map for Industry 4.0

A "road map" enables whomsoever in industry to directly realize each move and what act need to be accomplish, who needs to make them and when. This method is decoded into a project plan, defining the characteristics of activity in each of the accompanying stages of formation. Considering an internationalized world, the need to actualize development strategies that can secure the sustainable competitiveness of establishments is the main issue. It is in this topic that Industry 4.0 road map grants itself as a visually pictured clear trail to boost the competitiveness of organizations.

The key benefits of technology road mapping

• Setting up coalition of technical and commercial master plans
• Making better communication across teams and companies
• Inspecting prospective competitive strategies and ways to carry out those strategies
• Competent time management and mapping out
• Conceptualizing outputs including goals, activities, and progresses.^[26]

References[edit]

1. ^ Jump up to:^a b c Hermann, Pentek, Otto, 2016: Design Principles for Industrie 4.0 Scenarios, accessed on 4 May 2016
2. ^ Jürgen Jasperneite:Was hinter Begriffen wie Industrie 4.0 steckt in Computer & Automation, 19 December 2012 accessed on 23 December 2012
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