AN APPROACH TOWARDS ADVANCED SCM SYSTEM THROUGH AI AND INDUSTRY 4.0

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:03/Issue:03/March-2021

Impact Factor- 5.354

www.irjmets.com

AN APPROACH TOWARDS ADVANCED SCM SYSTEM THROUGH AI AND INDUSTRY 4.0 Pushpit Tiwari *1, Prof. Dr. Dinesh Shringi *2 *1Research

Scholar, Department of Mechanical Engineering, M.B.M. Engineering College, Jodhpur, Rajasthan, India. *2Professor and Head, Department of Mechanical Engineering, M.B.M. Engineering College, Jodhpur, Rajasthan, India.

ABSTRACT This research paper focuses on the study of the present utilization along with the technological implementation gap of AI and Industry 4.0 in SCM, by going through this research work we will be able to determine the potential of AI techniques that can enhance both the study and practice of SCM. This research paper will help us to understand the relation between Supply Chain 4.0 and Industry 4.0 along with the impact of their implementation on companies’ performance. This work explains the various concepts and definitions of SCM around global platforms, this also includes the discussion about the digitization of SCM process, use of AI, and Industry 4.0 concepts in all different sectors of industries. This provides us brief information about the first industrial revolution. The research methodology includes pilot research, Locating studies, and Study selection and evaluation. We also emphasize the concept of AI and Machine Learning in the field of supply chain management which will lead to improved productivity and responsiveness of the process.

Keywords: SCM, AI, Industry 4.0, Supply Chain 4.0, Industrial Revolution, Artificial Intelligence.

I.

INTRODUCTION

SC 4.0 or Supply Chain 4.0 is the re-arrangement of design and planning, production process, distribution process, consumption, and reverse logistics in supply chains with the help of technologies that comes under the umbrella of “Industry 4.0”. Technologies, which emerged in the 21st century such as automation, big data, AI, etc are largely implemented by firms that prominent in terms of supply chain management in high-income countries.

Recent developments such as big data, the Internet of Things (IoT), Industry 4.0, Artificial Intelligence (AI), and other digital technologies are transforming Supply Chains, allowing them to operate based on autonomous decisions analyzing collected data in real-time modus. Thus, granting access to previously inaccessible software solutions and new levels of automation. All major industries are pushed to explore new ways to digitize their operations due to the current situation in the world because of the pandemic of the covid-19 virus; this digital transformation is considered as a strategic weapon, Industry 4.0 and Smart Factory are the terms that are being used for digital transformation [1].

Industry 4.0 is the source of these new technologies, it includes additive manufacturing, advanced robotics, and robots, artificial intelligence, augmented reality, human-machine interfaces, machine-to-machine communication, blockchain, internet of thing, cloud-stored data, internet of services, digital transformation, autonomous vehicles, drones, etc [2].

The Industries which deals with automation and use automated industrial processes plays a important role in the growth and advancement of technological development and its application in various fields, despite that, there is requirement of more advanced research literature, only limited researches and writings that deal with SC 4.0 in this industry [3].

Real benefits are given to the plants where the industry 4.0 technologies are implemented, such as performance, improvement, costs, and delay reductions, most of these technologies could be implemented in the automotive factories and affect all of the processes within this type of industry such as production logistics, engineering industries, management, etc. [2].

The researches on SCM 4.0 is more Europe-centric at the moment this concept is researched in developed rather than emerging countries, the author concluded that the countries which are aware of the importance of www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[391]

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:03/Issue:03/March-2021

Impact Factor- 5.354

www.irjmets.com

the digital transformation will most likely are going to conduct and publish studies about the concept of SCM 4.0, and it is a reality that at the same time many underdeveloped and developing countries are having lack of interest for the same.[3]

Figure-1: Industrial Revolution The following provides different definitions for industry 4.0 in different scholarly articles: 1. “Industry 4.0 is the vision of smart components and machines which are integrated into a common digital network based on the well proven internet standards.” [4] 2. “Industry 4.0 shall be defined as the embedding of smart products into digital and physical processes. In the concept of Industry 4.0 the Manual and digital processes interact with each other and cross organizational and geographical barriers.”[5] 3. “Basically Industry 4.0 consists of a variety of technologies to propagate the development process of a digital and automated manufacturing environment as well as the digitization of the value chain.” [6] 4. “Industry 4.0 can be considered as the sum of all innovations obtained and implemented in a value chain to address the trends of digitalization, transparency, mobility, modularization, automation and network collaboration and socializing of products and processes.”[7] 5. “The convergence of industrial production and information and communication technologies, called Industry 4.0”. This will also support the concept of smart factories. [8] 6. “Industry 4.0 will involve the technical integration of Cyber-Physical Systems (CPS) into manufacturing and logistics and also having the utilization of the Internet of Things (IoT) and Services in industrial processes. It will lead for value creation, business models, downstream services and work organization.” [9] DEFINATIONS OF SUPPLY CHAIN 4.0: 1. “Smart Logistics” is related to planning and control by tools, means and intelligent methods, and the level of intelligence is directly related to the applications and methods used”.[10] 2. “Supply Chain 4.0 is an modern framework with interconnected processes that grows from detached applications to a wide relationship, managed and effective between phases of the Supply Chain” 3. “Supply Chain 4.0 is the re-arrangement of supply chains: design and planning, production processes, distribution processes, consumption, and reverse logistics with the help technologies that are known as “Industry4.0”. The technologies, which emerged in the 21st century, are broadly implemented by companies in high-income countries”. [11] 4. “Smart logistics is based on the use of technology to obtain information on the flow of material then the treat for monitoring, control and other purposes”.[12]

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[392]

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:03/Issue:03/March-2021

SUPPLIER

Impact Factor- 5.354

DISTRIBUTION

PRODUCTION

www.irjmets.com

CONSUMER

Figure-2: Conventional Supply Chain Model

Figure-3: Supply Chain 4.0 Model

II.

METHODOLOGY

This study adopted an evidence-informed, systematic literature review approach. In the following research work, we implemented the three-phase process consisting of the first phase of pilot search to gain an understanding in depth of the current literature, followed with the construction of the criteria for literature selection and develop or derive the research question and the subsequent steps. Consequently, the systematic review that we employed has three phases, explained below: 1. Pilot research.

2. Locating studies.

3. Study selection and evaluation.

1.1. Pilot Search : As mentioned above, we started our research work by conducting a pilot search as part of the first phase to improve our understanding and basic concepts of the examined field and the available existing literature.

1.2. Locating the studies : To proceed further with the relevant studies, we selected the search engines and the search strings. Having a concept in mind that we require databases consisting of larger access to a multitude of related literature over a specific period, we encircled and selected the five databases having large coverage of the peer-reviewed literature related to our area of research work. We are able to identify databases with the help of search strings specifically, having with the contributions relevant to the topic. As suggested by Rowley and Slack (2004)[13], that it is necessary to be very specific about the search strings. During this study, the search strings were consisting of “artificial intelligence” AND “keyword”. Also, the keywords used were “supply chain”, “production”, “marketing” and “logistics”, which were taken from the definition of SCM.

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[393]

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:03/Issue:03/March-2021

Impact Factor- 5.354

www.irjmets.com

1.3. Study selection and evaluation : The search strings used primarily were comparatively broad to make sure that papers are having different taxonomies are identified. Taking the inclusion and exclusion criteria from the pilot search, we shortlisted several articles. The first criterion is focused around the year of literature, which is majorly between 2012 and 2021, here with the majority of the papers and a large number of new trends and applications related to this topic have emerged during this period. The second criterion focuses on relevance and quality: peer-reviewed journals and conference papers were considered for the review.

III. 

RESEARCH AND ANALYSIS

PERFORMANCE IMPROVEMENT OF SCM WITH THE INFLUENCE OF INDUSTRY 4.0 AND AI: Supply chain process

Performance improvements •Improved production planning and control.

Product development and production

•Improved product design/development and production process.

•Enhanced production efficiency and productivity.

•Improved planning and control.

•Improved distribution.

•Effective order fulfillment management.

Fulfillment, procurement and logistics

•Reduced bullwhip effect.

•Improved procurement and supplier relationship management.

•Effective purchasing.

•Improved product distribution and delivery.

Inventory management

•Accurate inventory planning and control.

•Increased operational efficiency.

•Improved operational efficiency and productivity.

Retailing

•Enhanced forecasting and planning.

•Improved responsiveness and revenue growth.

AI-Based Assembly-to-Order Supply Chain:

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[394]

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:03/Issue:03/March-2021

Impact Factor- 5.354

www.irjmets.com

Figure-4: AI-Based Assembly-to-Order Supply Chain In the age of information technology and increasingly complex technical and industrial processes, agile and efficient logistics processes play a central role. There are larger logistic essentials such as reliability, transparency, and flexibility in combination with optimal economic conditions that form the very beginning for a successful supply chain (SC). Continuously or Dynamically changing processes are required with a technology that is capable to coordinate with the increasing complexity of supply chains.

The increase in data volume and diversity has led to larger data sets than ever before. Processing with conventional, practical management tools is often inefficient or impossible. To manage and evaluate these new and potentially valuable data sets, new methods and applications have been developed in the form of predictive analytics [14].

Machine learning (ML) is one of the predictive analytics methods. The success of this process is achieved through the development of sophisticated ML models, along with the availability of large data sets which are also called "big data" and the use of hardware architectures such as GPUs. 

Machine Learning Application Areas in Supply Chain Management :

This paper presents practical applications of ML by using three examples. Each of these examples represents one of the three main tasks of the SCM Model and is intended to show the reader how to use ML in practice. The three applications are selected as each of them represents a concise structure, a comprehensible explanation, and gives the first impression of ML application.

1.

Selecting Supply Chain Partners:

In the field of SCD, one of the major challenges remains to find suitable business partners such as customers or suppliers to exploit new opportunities. This is especially valid regarding globalization and the fast development of technology. To simplify finding new plausible business partners, use ML techniques based on company profiles and transaction relationships.

A Support Vector Machine (SVM) was applied to model the existing relationships by using features inherent to the company, such as a number of employees and capital, as well as features defined by the inter-linkage of firms, like customers of a supplier and common Industrial categories. The accuracy of the SVM-model reached up to 85%.

2.

Demand forecast for DM:

uses machine learning algorithms to predict future demands. The distribution centers have to ensure that the incoming goods of the industry partners are arranged for the individual stores in such a way that there is no product deficit and customer satisfaction remains high.

As a result of the artificial intelligence algorithms, the demand forecasts became so precise over a period that a significant improvement in forecast quality could be achieved and industrial partners are now able to plan www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[395]

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:03/Issue:03/March-2021

Impact Factor- 5.354

www.irjmets.com

much earlier. Delivery reliability and product availability can thus be significantly optimized, which is supported by an automated information flow of future requirements to industrial partners.

3.

Detecting False-Positive RFID Tag Reads in Transport Management :

One of SC's key processes is the shipment of goods from distribution centers. An error-free shipment depends on several factors, be it the picking process or the assignment of pallets to the allotted truck. To integrate a control mechanism, the METRO Group Cash & Carry implemented RFID portals to check on outgoing pallets. In its distribution center in Unna, Germany, loading ramps are equipped with RFID portals to automatically detect goods leaving the warehouse. For the detection of a pallet, it carries a transponder. As soon as the pallets pass the portals, the reading device records goods leaving the warehouse and automatically adjusts the available inventory via direct communication with the warehouse management system. The RFID identification supports the shipment of goods and the automatic inventory adjustment. 

Understanding Supply Chain 4.0 And Its Potential Impact On Global Value Chains:

Big data and supply chain analytics are running scenarios from a supply chain control tower. New technologies gather prodigious amounts of data. In the last decade, the cost of bandwidth has decreased nearly by 40 times, along with the decrease in processing costs up-to almost 60 times, and many of the sensors used in IoT technology cost no more than 60 cents (CGI 2016) [15]. These data are only useful if they can be reduced to information useful for making decisions actually in real time that lead to creating of business value. Big data analytics thus are about utilizing data sets to drive useful business intelligence, which able to answer the questions such as, “What happened just?”, “Why did it happen?”, and “What can we do next?”. Specific applications of big data analytics include early warning algorithms (are we about to run out of something or hit a bottleneck? Did prices we care about just rise?), predictive algorithms (what is demand likely to look like next spring?), stock-keeping unit (SKU) rationalization (the decision about the optimal set of products, or SKUs, to offer to consumers at any given time), channel assessment (the decision about the optimal way to get product to end market, e.g. e-commerce.

IV. 

RESULTS AND DISCUSSION

IMPACTS OF AI AND INDUSTRY 4.0 ON COMPANIES:

As the modern day industries are understanding the importance and scope of the use of AI and INDUSTRY 4.0 due to which spreading of knowledge and use for the same has increased over the period of time.

There are some of the impacts which have helped the for overall growth for the companies, these are: 

Increased Efficiency: Technologies within Industry 4.0 generate variety of data with continuous analysis which enables to optimize the process. This data helps for self-correction, with seamless integration of people, processes and technology that results in a greater overall efficiency.



Increased Quality: The technologies within Industry 4.0 foresee and detect defect within the quality of the product, or sustainability issues due to low quality that helps to optimize and improve the quality.



Interconnected value chain: RFID technologies enables more visibility within supply chain, that provide it more traceable.



Significant Cost Reduction: Analytic tools with advanced automation, flexible systems with shared information help the industry to increase forecast accuracy and decrease waste.”



Advanced analytical tools: Decision making is refined with data analysis that gain finer forecasting to interpret and meet demand.



Reduced time to market : The technologies within industry 4.0 empower for cheaper and faster technology procedures like 3D printing process and along with additive manufacturing process that makes it an alternative to conventional methods.



Flexibility: Production and service operations are altered to meet varying customer demands. Technology makes it effortless to reframe based on varying demands.

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[396]

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:03/Issue:03/March-2021

Impact Factor- 5.354

www.irjmets.com



Shared information: Digitalization allows sharing of information from sales, resources to production that makes the process work to meet the demand.



Enhanced responsiveness: Analytics and data boost and respond to demand fluctuation and competitors & client technology switch .



Predictive Maintenance: Predictive data analytics provide improved visibility that paves way for predictive maintenance of machinery avoiding the downtime.



Improved Productivity: Real-time data helps the industry to optimize the resources and assist to ensure the process meet the demands without any downtime in the supply chain.



Safety and Sustainability: Industry 4.0 improves operational efficiency by decreased human-machine interaction with automation.

The analysis of the papers resulted in an assignment of the ML use cases to all three main task areas of the SCM model, a number is assigned to each paper. In the area of SCD, all retrieved papers deal with supplier. In the area of SCP, ML topics address the task of demand planning and procurement planning.This paper assigns use cases of machine learning to the task model of Supply Chain Management, resulting in an overview of ML applications within the different supply chain tasks. It was demonstrated that in the SCM task model a single area could have different ML methods applied for a common goal.

V.

CONCLUSION

Through the following research work, we performed the analysis on different impacts of AI and Industry 4.0 on Supply Chain Management (SCM) system when implemented in the industries. We came to a conclusion that on implementation of these advanced technologies and concepts on SCM we achieved improved efficiency, quality, flexibility, responsiveness, maintenance, productivity, safety, and sustainability of the supply chain management process. With the use of the big data concept from Industry 4.0, we also found that it leads to increased communication between companies on a global platform which ultimately provides reduced process time, also the cost of bandwidth has decreased by a factor of nearly 40 times. Therefore we can conclude this research work by saying that with the application of AI and Industry 4.0 we can achieve greater process efficiency and responsiveness which in the future will reduce the cost of the product, increase productivity and, will provide greater coordination between supplier and customer.

VI.

ACKNOWLEDMENT

It gives me immense pleasure in presenting my research work on the subject entitled “AN APPROACH TOWARDS ADVANCED SCM SYSTEM THROUGH AI AND INDUSTRY 4.0 ”.

First and foremost, I would like to express my deepest gratitude and special thanks to respectable Dr. Dinesh Shringi Sir, Professor & Head of Department Mechanical Engineering, M.B.M. Engineering College, Jodhpur. Who supported and guided me throughout the entire process of this research work. The accomplishment of this work became possible by the timely enlightenment by respected professor.

VII. [1]

[2] [3]

[4] [5]

REFERENCES

S. Xue-Feng, W. LIU, and Y. Liu, « Multistage implementation framework for smart supply chain management under industry 4.0 ». Technological Forecasting and Social Change, 2020, vol. 162, p.

120354.

K. Markov and P. Vitliemov, « Logistics 4.0 and supply chain 4.0 in the automotive industry ». In: IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2020. p. 1-7.

K. Zekhnini, A. Cherrafi, I. Bouhaddou, Y. Benghabrit, and J. A. Garza-Reyes, « Supply chain management 4.0: a literature review and research framework ». Benchmarking: An International Journal, 2020. (In press).

D. Kolberg, J. Knolbloch and D. Zühlke, « Towards a lean automation interface for workstations ».

International journal of production research, 2017. vol. 55, no 10, p. 2845-2856.

R. Schmid, M. Möhring, R. C. Härting, C. Reichstein, P. Neumaier, and P. Jozinović « Industry 4.0 potentials for creating smart products: empirical research results ». In: International Conference on Business Information Systems. Springer, Cham, 2015. p. 16-27.

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[397]

e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:03/Issue:03/March-2021 [6]

[7] [8] [9]

[10]

[11] [12]

[13] [14]

[15]

Impact Factor- 5.354

www.irjmets.com

T. D. Oesterreichet and F. Teuteberg, « Understanding the implications of digitisation and automation in the context of Industry 4.0: a triangulation approach and elements of a research agenda for the construction industry ». Computers in industry, 2016. vol. 83, p. 121-139.

H. C. Pfohl, B. Yahsi and T. Kurnaz « The Impact of Industry Supply Chain ». In: Innovations and Strategies for Logistics and Supply Chains. 2015. ISSN (online): 2635-5070.

M. Hermann, T. Pentek, and B. Otto, « Design principles for industrie 4.0 scenarios ». In: 2016 49th Hawaii international conference on system sciences (HICSS). IEEE, 2016. p. 3928-3937.

H. Kagermann, J. Helbig, A. Hellinger, and W. Wahlster, « Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry ». final report of the Industrie 4.0 Working Group. Forschungsunion: Berlin, Germany, 2013.

K. Douaioui, M. FRI, and C. Mabroukki, « The interaction between industry 4.0 and smart logistics: concepts and perspectives. ». In: 2018 international colloquiom on logistics and supply chain management (LOGSTIQUA). IEEE, 2018. p. 128-132.

M. J. Ferrantino et E. E. Koten, « Understanding Supply Chain 4.0 and its potential impact on global value chains ». GLOBAL VALUE CHAIN DEVELOPMENT REPORT 2019.

K. Douaioui, M. FRI, and C. Mabroukki, « The interaction between industry 4.0 and smart logistics: concepts and perspectives. ». In: 2018 international colloquiom on logistics and supply chain management (LOGSTIQUA). IEEE, 2018. p. 128-132.

Rowley, J., & Slack, F. (2004). Conducting a literature review. Manag. Res. News, 27, 31–39.

Waller, M. A. and Fawcett, S. E., 2013. Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics,(34(2)),pp.77–84. <https://pdfs.semanticscholar.org/9c1b/9598f82f9ed7d75ef1a9e627496759aa2387.pdf>.

Citigroup (2016), Technology at Work v2.0: The Future Is Not What It Used to Be. Citi GPS: Global Perspectives & Solutions, Joint Report with Oxford Martin School.

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[398]