Assignment: Logistics operations management industry 4.0

1. Introduction –

2. Definition of Industry 4.0

According to () ‘Industry 4.0’ characterizes the Fourth Industrial Revolution that initiated in the beginning of 2010 and grounded on cyber-physical systems (CPS). Fundamentally, Industry 4.0 encompasses the integration cyber-physical systems into manufacturing functions and logistics and makes use of Internet of Things (IoT) in the industrial procedures. This has far reaching implication in regards to developing new business models, value creation, and downstream services and work organisation (). Therefore, () considers Industry 4.0 as a part of the Internet of Things and Services (IoTs). The use of IoTs helps to build networks involving the entire processes of manufacturing that transform factories into smart environment, incorporating smart buildings, smart products , smart grids, smart logistics, as well as smart mobility (). Smart factories that incorporate CPS are considered to be the main facilities that create value in Industry 4.0. In this context, () envisage that the future production systems, generation of energy, health systems, transport and communication will be largely shaped by smart factories that use intelligent technical systems, big data analytic, cloud-based design, cloud computing and extrapolative manufacturing. Currently Germany is a forerunner in the Fourth Industrial Revolution and has designed the ‘High-Tech Strategy 2020’ initiative based on the concept of Industry 4.0 since 2011.

Hermann et al (2015) defined Industry 4.0 as a combined term used for technologies and concepts associated with value chain organisation. Within the realm of the modular structured Industry 4.0 Smart Factories, cyber-physical systems is used to monitor the physical processes, design a virtual replica of the physical world and take decisions that are decentralised. Using the IoT technology, CPS not only interacts but collaborates with each other as well as with humans in real time. By means of the IoT, both internal and cross-organisational services are provided to and utilised by the users of the value chain.
According to this definition, the key constituents of Industry 4.0 are identified to be IoT, CPS, IoS, and Smart Factory (SF). Hermann et al (2015) also came up with a set of design principles that are applicable to one or more of the Industry 4.0 constituents. These include interoperability (between humans and CPS relevant to all the constituents), Decentralisation (where CPS makes autonomous decisions Smart Factory), Virtualisation (through which the CPS conducts monitoring of the SF production), Service orientation (relates to IoS and provides personalised products), Real-time capability (evaluate the production data at the Smart Factory), and Modularity (flexible adaptation with the changing requirements and applicable to IoS). In this context, (aa) focuses on the 6C system of big data analytics that play a significant role in employing the principles of Industry 4.0 design. The 6C system incorporates Cloud (computing and data), Connection (networks and sensors), Cyber (memory and model), Community (collaboration and sharing), context (meaning and correlation), and customization (personalization and creation of value).

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Industry 4.0 makes possible real-time-capable vertical and horizontal connectedness based on internet technology, for individuals, machines, and objects (). It also makes use of information and communication technology (ICT) for managing the complicated set of business processes in a dynamic way. Industry 4.0 also helps to overcome the persistent challenges associated with the volatile markets, intense competition, sudden and unpredictable consumer demands, and the need for customization and innovation.

3. Recognition that some elements of industry 4.0 is applicable to the warehousing:

The industry 4.0 shows some distinct trends where the use of data and its use to automate systems in the production process is inevitable. While the structure is moving towards cyber physical systems, the use of elements like IoT (internet of things), cloud computing, Bigdata comes into the picture when warehousing is considered. The technologies mentioned here are critical as it is likely to bring a phenomenal change in the way existing systems work, if these are implemented. ( ) stated that small and medium scale industries are aware about efficiency y of each technology that improves production metrics, while the very aspect of human machine interface in the logistics and transportation has been seamless, reducing the time to wait and identification, which translates to firm level profits. The ability to do digital physical transfer is reducing the time for prototyping and lesser time to enter a market. For a warehouse function, the technologies cited above is able to enable the products, services and information to be exchanged in seamless flow of streams, in manufacturing context. It shows that technology and systems would help to make the network of manufacturing sites, warehouse to be synched that requires lesser human intervention, with speed and accuracy maintained for the mission critical applications. However, the design of the entire warehouse system needs a redo as the multiple technologies need machines, sensors, and goods passing through them to show the interoperability factor. The whole system when implemented in a wider space can seek Internet of people (IoP) to connect as a community, which is the scalability issue of the technology to a higher level. To make it worthwhile the information technology transparency is needed and the systems without human intervention is capability of the higher assistance. It supports the physical side of human but the accuracy of the systems and data (report) generated shows it is too exhausting. The context of warehousing management and its importance has been multi-fold now, as the traditional business model is giving way to e-commerce model. The ability of the systems which assist in the seeking data converting into knowledge, shows that the concept of informed decisions for humans is essential and when it is extracted in a format that is wanted makes the convenience factor to emerge. Warehousing of data in ecommerce firms unlike the physical warehouses is smarter as it is providing insights of combination of machines, devices (RIFD), sensors to streamline the process of warehousing. It is decentralising the warehousing function as data driven decision making model is enabling huge inventories (millions) to be managed efficiently. Industry 4.0 therefore offers customisable, and shift from mass marketing and production to be managed using bots for picking items for an order. Therefore the shift of man-machine interface in a new age warehouse is also integrating the digital applications to influence its physical model. The production, supply chain and warehouses are also disrupted towards smart warehouses, as it has multi robot collaboration to detect accurately, the human activity recognition using seamless flow of information and sensors placed at strategic places. The smart warehouses of ecommerce giant www.amazon.com, uses RFID, NFC (near field communication), wifi AP (access points), BLE (Bluetooth energy beacons), cameras to be integrated into a system. It is enabling the warehousing function, associated functions of supply chain and logistics to be efficiently scheduled in LAN, where time efficient scheduling mechanisms is helping to this sector to move towards unmanned model.

4. Drivers and enablers of industry 4.0 :

According to () strategic opportunities are real drivers for the real drivers for the implementation of Industry 4.0 in the manufacturing companies. Review of the literature, from a strategic perspective, indicates that Industry 4.0 has a long term implications for business models. This includes transformation in the existing business model as well as upcoming innovative business models (). () stressed on the importance of business model innovation that is designed on the basis of data-centric business logic and digital technology. Research conducted by () identified some of the core aspects associated with Industry 4.0 related business model transformation, such as shift from product centric offerings to system offerings, data-based value creation and suggestion, increased customisation, Information technology and software knowledge as crucial resources, stronger customer relationships, and increasing coalition and co-operation with the stakeholders and key business partners.

Social and environmental elements are key enablers of the implementation of Industry 4.0. As regards to environment, () explains that Industry 4.0 helps to minimise the emission of green house gas (GHG) and carbon footprint control with the aid of data-centred and analytical techniques. Furthermore, Industry 4.0 also helps in better waste management, conservation of energy and other scarce resources that have become inevitable goals of almost every manufacturing company. It is evident that the future of industry 4.0 needs to open up the communication space of the machines and humans, in a large scale format in order to succeed. Therefore, though the technologies are available adapting it to fit the existing warehousing is crucial for the evolution of technology application in the industry. It is evident that the traditional model of warehousing had drawbacks, as the sequence of pickup, deliver, book keeping that was done by storekeeper was time consuming, not scalable for millions at fast speed. The emergence of new age technologies, along with the fast paced e-commerce business models is converging the physical world and digital world together, in processes and systems. ( ) stated that the this change over to the new industry4.0 promises connected machines, work systems, for the business which can control each other and create more value. Hence the ability to predict failure, or deeper changes in the production that is not comprehendible for humans is beneficial for the industry and the business. The high quality automated process, that is able to achieve higher degree of performance using machine-man interface is driving the adoption of IoT. Bigdata is the collection of the huge transactions in the ecommerce firms that is filling up the database, is providing knowledge when processed to depict a meaning or trend. Thus the data sets in real world is nothing, until the technology enables the businesses to realise the value of insightful cues which have a significant impact on the industry. The whole process have appealed to the industries, entrepreneurs, as the end to end implementation of industry 4.0 in true sense shows a bright picture. It leads to optimised use of labour, asset utilisation, better management of inventory, quality of service, supply demand better control, time to market goods, achieve higher sales and services. The above discussion shows a compelling proposition for the industry who are willing to shift to the industry 4.0 as viability of the ROI (return on investment) is high here. Hence, for a perishable item in warehouse, the company would prefer to know the demand flow, current inventory only to place an order as per market based demand. The traditional method of stocking unnecessarily in anticipation of a locked finance in inventory therefore has found a smarter solution with a mix of technologies.

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The shortcomings of the traditional warehousing and inventory management took a lot of time and inefficiencies have led to the industry looking towards adopting data centric technology that are mixing the datasets to elicit a correlation in the variables. ( ) argued that the whole concept has become more penetrative in terms of operations accuracy, that removes the decision making uncertainty, ( ) stated it as not multi dimensional but high dimensional. The implications of the IoT design and network in a warehouse therefore can have huge impact on the bottom line. The type of the data in a localised area, that is adding value to the existing business process hence is most wanted, as rising costs need to be compensated by technology which is a better value for the organisations. Data driven design to value and prediction are primarily business intelligence that the firms want to design their operations for the basic warehousing functions. Companies are seeking lean practices and real time monitoring to have greater control over their decisions. Thus the real time supply chain visibility and optimisation is also affecting the warehousing part as they are a part of an extended management system that serves the customer at the end. There are however other drivers like internet based technologies such as cloud, community (community and collaboration) to find new opportunities, customisation (mass to personalised) that is attracting owners towards smarter factory management.

5. Critique of application of technology and the barriers faced [650 words]

The above discussion shows that Industry 4.0 does cast a huge positive impact on the manufacturing sector. Operational perspective however, is challenging as new technology and a mix of series of interconnectivity between devices enables process optimisation much before the realisation of value in actual practice. However, at hardware and software integration level failure in design and interconnectivity does not help at all. This is primarily because of the vertical simulation of the activities of production or the total supply chain need to match the warehouse and inventory at machine and physical level. This aspect of technology roadmap for Industry 4.0 hence is a challenging part at the design and implementation phase.
The horizontal and vertical connections make possible accelerated time-to-market as the lead time gets shorter and shorter (). The manufacturing firms are in a better position to respond faster and amenably the unpredictable market demand or sudden changes in the client’s orders ().The flexibility provided by Industry 4.0 helps manufacturing firms to produce according to the specific customer needs and thereby control wastage and optimise resource. The make-to-order value creation and accessibility of accurate data in real time across the complete value chain process, helps in coordination of materials in alignment with demand and thus the stock levels minimise (). Based on above discussion, the challenge is to do digital modelling, then fabrication as per the physical process maps which results into operational opportunities of new Industry 4.0. Closely linked to the manufacturing technologies and processes of production, is the service4.0 as much of it depends on how machine to machine data and information flow happens to make a positive impact on the implementation of this concept. However, the technology implementation also have a bigger challenge of the intelligent maintenance system as the aspect of core functions is done by manufacturing robots, while its failure leads to a complete stop. Ability to detect the failure of machines using Bigdata is a highlevel technology, while the old concept of retrofitting of machineries, and the re-use of tools and resources () are time taking propositions.

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Moreover, additive manufacturing is identified as a core technology in the age of Industry 4.0, as it helps to optimise the logistics processes and physical transportation. Few examples comprise of closed value-creation networks, recycling, crowd sourcing that is opening new opportunities for firms using networks. From the social perspective, Industry 4.0 plays a crucial role in maintaining flexibility and fostering transparency in the managerial processes, communication, collaboration, and smarter decision making (). However, any wrong computation of data set in Bigdata can lead to wrong output and decisions that can mismatch the business requirements. It also enables the availability of precise data in real time across the supply chain process. On the basis of digitalised correlation of corporate functions, tele-work processes, and home-office co-ordination, it is expected that working time models become more flexible, pliable, and susceptible to personalisation (). However it is easier in theories, and depends on the consumer trend of adoption of smart devices, gadgets, and robot assistance mechanisms in the work places. Thus there needs user end and producer end adoption of technology to make it worthwhile. In physically hazardous and ergonomically unfavourable tasks, safeguarding the health requires industry 4.0 to devise a solution. Smart and independent production systems, also check the frequency of repetitive and monotonous task, however the challenge is making it more systematic and less time consuming, thereby resulting in higher customer satisfaction (). Therefore, socio-technical factors, are challengers for Industry 4.0, as investment made in technology need to show viability of its purpose, failing which leads to a situation. hence, integrating multiple technologies at hardware, software, in contemporary organisations, particularly in the manufacturing, supply chain, logistics and transportation, and the daily operational processes and procedure is a big challenge.

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