DOI : 10.17577/IJERTCONV14IS090003- Open Access

- Authors : Manmeet Singh Soni, Dr. Kanwarpreet Singh, Dr. Inderpreet Singh Ahuja
- Paper ID : IJERTCONV14IS090003
- Volume & Issue : Volume 14, Issue 09, RTMSE-2026
- Published (First Online) : 15-09-2026
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License:
This work is licensed under a Creative Commons Attribution 4.0 International License
Paradigm Shift from Lean Towards Agile Supply Chain Management for Business Performance Enhancement in Indian Manufacturing Industry
Manmeet Singh Soni Research Scholar
Dept of Mechanical Engineering Punjabi University
Patiala
Dr. Kanwarpreet Singh Assistant Professor
Dept of Mechanical Engineering Punjabi University
Patiala
Dr. Inderpreet Singh Ahuja Professor
Dept of Mechanical Engineering Punjabi University
Patiala
Abstract – Post pandemic, the markets have become highly turbulent and volatile due to uncertainty and global economic and competitive pressures. The prolonged and sluggish logistics channels have become unsustainable, thus warranting inevitable changes in the structure and management of supply chains. This research work highlights key vital factors required for survival in these circumstances by providing insights into vital factors required for imbibing agility in an organization. The basic purpose of this paper is to comprehensively review the literature on agile supply chain management (ASCM), keeping in view the objective of a thematic examination of the literature to elucidate the primary factors of ASCM.This work is a novel effort to use various descriptive analysis techniques using pivot charts and tables to analyse the key factors regarding the application of agile ASCM in manufacturing industries.
Keywords Agile Supply Chain Management, Uncertainty in the environment, Operations management, Manufacturing industries, India.
-
INTRODUCTION
The coronavirus disease 2019 (COVID-19), first started in China towards the end of 2019 and has spread worldwide, thus causing current pandemic condition (Statista, 2020). This pandemic situation has caused grave economic and social disturbance worldwide leading to a major global downturn (Wheelock, 2020). Since the start of its major outburst in early months of 2020, the unexpected demand for certain products led to complications in handling Supply Chain Management (SCM) for all industries due to limitations of the lockdown (Statista, 2020). As this pandemic was of unprecedented dimension, there were no particular comparisons and past research in this field. SCM is a network of various representatives responsible for accomplishing various critical functions and synchronizing key conclusions with further pertinent representatives. Functional representatives and information representatives are two types of representatives who control activities in the supply chain and support other representatives by giving the relevant information respectively.
-
SUPPLY CHAIN MANAGEMENT (SCM) AND LOGISTICS
Lambert et al. (1998) stated that various business processes need to be integrated i.e., from end-user to supplier. This includes raw material, finished products, and services by optimally using information available from various sources thus adding value for end-users. Earlier, the products were simple (Fulkerson, 1997; Cao and Zhang, 2011), therefore, companies manufactured them independently (Kumar, 2004; Burgess et al., 2006). However, over the period of time with the advent of globalization, the majority of the components were started being outsourced. Hence the suppliers became key stakeholders in the supply chain process (Chen and Paulraj, 2004; Gripsrud et al., 2006). The schematic diagram on SCM given by Fox et al. (1993) highlights the overall view of the supply chain in the organization (Figure 1). Li (2014) reveals that in the early 1960s, an MIT professor, Jay Forrester, worked on various parameters related to suppliers with customers and realized that the physical separation of the inventories from the customers adversely affected the supply chain leading to excess inventory or orders not completed. By adjusting to the flexibility to cater to market requirements, the supply chain can effectively provide products and services. The business competition globally is organization driven, and key role is played by their supply chains (Kuei et al., 2001; Li et al. 2005).
Logistics
Order Acquisition
Information Agent
Transportation Management
Resource Management
Information Agent
Scheduling
Dispatching
Figure 1 Basic SCM Model (Fox et al. 1993)
3 LEAN SUPPLY CHAIN MANAGEMENT (LSCM) AND TRANSFORMATION FROM LEAN TOWARDS AGILE SCM
Data foundation
Data analysis within the supply chain by creating a digital network Effective Supplier relationship
Change management
Dynamically modifying supply chain as per market requirement.
Lean production acquired a key role by 1994-95 and was used as an overriding strategy by the organizations (Karlsson and Ahlstrom, 1996). Womack et al. (1990) have claimed that the philosophies of lean production can facilitate organizations for enhancing performance proportionately.In the current scenario of post-pandemic, SCM strategy for manufacturing industries is predominantly based on external factors, and accordingly, they had to decide between lean and agile (Abdollahi, 2015; Abdallah and Nabass, 2018). Going lean has the inherent disadvantage of lost opportunities as orders could not be materialized due to a shortage of supplies. However, it is more cost-effective than going agile as the later requires major digital transformation (Shashi et al., 2020). With the right digital transformation, an enterprise can quickly move from lean towards agile and vice versa at short notice. The pandemic has made everyone realize that their SCM needs to be robust to cater to unforeseen eventualities (Koh et al., 2007; Shashi et al., 2020). The major steps for the supply chain network to be more resilient and agile are:
There are lots of similarities between the lean and agile SCM. If the entire process is appropriately planned and monitored, lean as well as agile systems can simultaneously exist in the organization, and the transition from Lean to Agile is also easy (Aitken, 2000). ASCM has achieved good attention among academicians in the last few years, and significant research in this field has added to the overall knowledge base. However, the major focus has been on case studies and direct surveys (Rahiminezhad and Helmi, 2016). Very little work has been reported on theory verification and transition from LSCM to ASCM. Table 1 describes the detailed review of literature on ASCM depicting the analysis methods and critical factors considered in various studies. 460 research papers published in major peer reviewed journals over a period of last twenty years have been surveyed, and vital few (55 research papers) were then critically reviewed in order to identify the factors critical for successful agile organizations in managing their supply chains and provide insights about vital factors required for the agility in an
organization. The articles were shortlisted from peer-reviewed journals only, as they follow very stringent review process (Karuppusami and Gandhinathan, 2006). As an initial step, all important factors were recorded from the literature directly. Subsequently, grouping of these factors was done depending on their context, giving new name to each group using "descriptive approach". For instance, all factors depicting Organizational Culture i.e., uncertainty, collaborative relationships, responsiveness to Organizational Culture-related issues responsiveness to external change, competency, flexibility, enterprise integration, decentralized decision making, multi-venturing capabilities, inter-organization coordination, adaptability and information visibility were grouped together.
TABLE I
|
Table I Analysis methods and Critical factors in various ASCM studies |
|||||||
|
Authors |
Country |
Industry |
Analysis method |
Journal |
Database |
Factors |
|
|
Sharp et al. (1999) |
UK |
Multiple |
Descriptive analysis |
International Journal Production Economics |
of |
Elsevier |
Manufacturing agility, Customer related issues, Organization Responsiveness |
|
Meade and Sarkis (1999) |
USA |
Multiple |
Descriptive analysis |
International Journal Production Research |
of |
Taylor & Francis |
Customer related issues, IT and its elements, Human Resource Development (HRD) issues, Organization Responsiveness |
|
Sharifi and Zhang (1999) |
UK |
Multiple |
Comparative analysis |
International Journal Production Economics |
of |
Elsevier |
Strategic Supplier Alignment, Customer related issues |
|
Breu et al. (2002) |
UK |
Multiple |
Principal component and correlation |
Journal Information Technology |
of |
Taylor & Francis |
Administrative issues, IT and its elements |
|
Ren et al. (2003) |
USA |
Multiple |
Artificial Neural Network |
Integrated Manufacturing systems |
Emerald |
Resource optimization, Manufacturing agility, Administrative issues, Customer related issues |
|
|
Guisinger and Ghorashi (2004) |
USA |
Chemical industry |
Comparative analysis |
International Journal Operations Production Management |
of and |
Emerald |
HRD issues, Manufacturing agility |
|
Devadasan et al. (2005) |
India |
Pump Industry |
Taguchi methods |
Journal Manufacturing Technology Management |
of |
Emerald |
Resource optimization, Flexible/hybrid engineering |
|
Dowlatshahi and Cao (2006) |
USA |
Multiple |
Structural Equation Modelling |
European Journal of Operational Research |
Elsevier |
Collaborative relationships, Administrative issues, Flexibility, Logistic integration |
|
|
Lin et al. (2006) |
Taiwan |
Casting |
Descriptive analysis |
International Journal of Production Economics |
Elsevier |
Resource optimization, Process integration, Administrative issues, IT and its elements, Flexibility |
|
Vazquez- Bustelo et al. (2007) |
Spain |
Multiple |
Exploratory factor analysis |
International Journal of Operations and Production Management |
Emerald |
Flexibility Responsiveness.IT and its elements |
|
Ramesh and Devadasan (2007) |
India |
Pump Industry |
Descriptive analysis |
International Journal of Mass Customization |
Inderscience |
Customer related issues, Strategic Supplier Alignment |
|
Eshlaghy et al. (2010) |
Iran |
Multiple |
Exploratory factor analysis |
International Journal of Production Research |
Taylor & Francis |
IT and its elements, Knowledge optimisation |
|
Zhang (2011) |
UK |
Multiple |
Content and Cross-case analysis |
International Journal of Production Economics |
Elsevier |
Administrative issues, Flexibility |
|
Sreenivasa et al. (2012) |
India |
Pneumatic Products |
Descriptive analysis |
International Journal of Services and Operations Management |
Inderscience |
IT and its elements |
|
AL-Tahat and Batainesh (2012) |
Jordan |
Multiple |
Structural Equation Modelling |
Mathematical Problems in Engineering |
Hindawi Publishing Corporation |
Flexibility, Operational performance |
|
Mishra et al. (2011) |
India |
Automobile |
Fuzzy logic |
International Journal of Logistics Systems and Management |
Inderscience |
Flexibility, Customer related issues, Administrative issues |
|
Aravindraj and Vinodh (2014) |
India |
Relays Manufacturin g Industry |
Fuzzy logic |
Journal of Engineering, Design and Technology |
Emerald |
Customer related issues, IT and its elements, Knowledge optimisation |
|
Samantra et al. (2015) |
India |
Automobile |
Fuzzy logic |
International Journal of Industrial and Systems Engineering |
Inderscience |
IT and its elements, Organization Responsiveness, Cost efficiency |
|
Dubey and Gunasekaran (2015) |
India |
Multiple |
Confirmatory factor analysis |
International Journal of Advanced Manufacturing Technology |
Springer |
Administrative issues, Customer related issues |
|
Routroy et al. (2015) |
India |
Boiler manufacturing industry |
Fuzzy logic |
Measuring Business Excellence |
Emerald |
Cost management, IT and its elements |
|
Dev and Kumar (2016) |
India |
Equipment manufacturing industry |
Analytical Hierarchy Process (AHP) |
Chinese Journal of Mechanical Engineering |
Springer |
HRD issues, Business process reengineering |
|
Leite and Braz (2016) |
Portugal |
Multiple |
Content analysis |
Journal of Manufacturing Technology Management |
Emerald |
Flexible/hybrid engineering |
|
Sindhwani and Malhotra (2016) |
India |
Multiple |
Descriptive analysis |
Process Management and Benchmarking |
Inderscience |
IT and its elements, Network efficiency |
|
Kumar et al. (2017) |
India |
Automobile |
Interpretive structural modelling |
International Journal of System Assurance Engineering and Management |
Springer |
HRD issues, Cost efficiency |
|
Sindhwani and Malhotra (2017) |
India |
Multiple |
Total interpretive structural modelling (TISM) and MICMAC analysis |
Benchmarking: An International Journal |
Emerald |
Flexibility, IT and its elements |
|
Mittal et al. (2017) |
India |
Multiple |
MOORA method and VIKOR analysis |
Procedia CIRP |
Science Direct |
IT and its elements, Knowledge optimisation |
|
Patel et al. (2017) |
India |
Multiple |
Fuzzy logic |
Journal of Manufacturing Technology Management |
Emerald |
Quality management, Manufacturing agility, IT and its elements, Strategic Supplier Alignment, Manufacturing management |
|
Goswami and Kumar (2018) |
India |
Automobile |
Structural Equation Modelling |
Measuring Business Excellence |
Emerald |
Manufacturing management, Flexible/hybrid engineering, Product complexity |
|
Khatri et al. (2018) |
India |
Multiple |
Correlation analysis, regression analysis, principal component analysis |
International Journal of Business Information Systems |
Inderscience |
Collaborative relationships. Administrative issues. Flexibility |
|
Nejatian et al. (2018) |
Iran |
Food industry |
Fuzzy logic |
Benchmarking: An International Journal |
Emerald |
IT and its elements, Organization Responsiveness |
|
Christopher (2000) |
UK |
Multiple |
Descriptive analysis |
Industrial Marketing Management |
Elsevier |
IT and its elements, Network efficiency |
|
Yusuf et al. (1999) |
UK |
Manufacturin g Industry |
Descriptive analysis |
International Journal of Production Economics |
Elsevier |
Organization Responsiveness, Manufacturing management |
|
Agarwal et al. (2006) |
India |
Multiple |
Descriptive analysis |
European Journal of Operational Research |
Elsevier |
HRD issues |
|
Braunscheide l and Suresh (2009) |
USA |
Multiple |
Structural Equation Modelling |
Journal of Operations Management |
Elsevier |
HRD issues, Flexibility |
|
Overby et al. (2006) |
USA |
IT |
Exploratory factor analysis |
European Journal of Information Systems |
Springer |
Process integration, IT and its elements |
|
Swafford et al. (2006) |
USA |
Multiple |
empirical study/modellin g |
International Journal of Production Economics |
Elsevier |
Customer related issues, Organization Responsiveness |
|
van Hoek, Harrison, and Christopher (2001) |
UK |
IT |
Descriptive analysis |
International Journal of Operations and Production Management |
Emerald |
Quality management, HRD issues, Flexibility, Cost efficiency |
|
Power, Sohal, and Rahman (2001) |
Australia |
Manufacturin g Industry |
empirical study/modellin g |
International Journal of Physical Distribution & Logistics Management |
Emerald |
Market sensitivity, Administrative issues, Flexibility, Cost efficiency, Operational performance |
|
Swafford, Ghosh, and Murthy (2008) |
USA |
IT |
Descriptive analysis |
International Journal of Production Economics |
Elsevier |
Business process reengineering, Organization Responsiveness, IT and its elements |
|
Mason-Jones and Towill (1997) |
UK |
Fashion Industry |
Simulation |
International Journal of Production Economics |
Elsevier |
Flexible/hybrid engineering |
|
Inman, Sale, Jr, and Whitten (2011) |
USA |
Manufacturin g Industry |
Structural Equation Modelling |
Journal of Operations Management |
Elsevier |
Strategic Supplier Alignment, Customer related issues |
|
Tsourveloudi s and Valavanis (2002) |
Greece |
Multiple |
Correlation analysis, regression analysis, principal component analysis |
Journal of Intelligent and Robotic Systems |
Springer |
Customer related issues, HRD issues |
|
Elnagar et al. (2018) |
UK |
Manufacturin g Industry |
Triangulation research approach |
Journal of Open Innovation: Technology, Market, and Complexity |
MDPI |
Flexibility, Organization Responsiveness, Market sensitivity |
|
De Vass et al. (2021) |
Australia |
IT |
IOT |
Australian Journal of Information Systems |
Springer |
Process integration, IT and its elements |
|
Geng et al. (2020) |
China |
Marine Industry |
QFD, Fuzzy and Evaluation Laboratory (DEMATEL) |
Journal of Marine Science and Engineering |
MDPI |
Knowledge optimisation issues, Flexibility |
|
Kumar et al. (2017), |
India |
Multiple |
Descriptive analysis |
Journal of Management Information and Decision Sciences |
Elsevier |
Supply chain optimisation, Flexible/hybrid engineering |
|
Sahu, et.al, (2016) |
India |
Multiple |
Analytical Hierarchy Process (AHP) |
International Journal of Decision Support System Technology |
Emerald |
Organization Responsiveness, Flexibility |
|
Masson et al. (2007) |
UK |
Fashion Industry |
Descriptive analysis |
International Journal of Logistics Management |
Emerald |
IT and its elements |
|
Stefanelli et al. (2019) |
Italy |
Multiple |
Descriptive analysis |
Journal of Supply Chain Management |
Emerald |
Supply chain optimisation, Customer related issues, Organization Responsiveness |
|
Lotfi and Sag hiri (2018) |
UK |
Multiple |
Structural Equation Modelling |
Journal of Manufacturing Technology Management, |
Emerald |
Customer related issues |
|
Moradi et al. (2020) |
Iran |
Multiple |
Integrated FQFD approach |
International Journal of Lean Six Sigma, |
Emerald |
IT and its elements, Administrative issues |
|
Tse et al. (2016) |
UK |
Electronics |
Structural Equation Modelling |
Journal of Supply Chain Management |
Emerald |
Supply chain optimisation, IT and its elements, Organization Responsiveness |
|
Mwangola (2018) |
USA |
Multiple |
Descriptive analysis |
American Journal of Management |
Emerald |
Organization Responsiveness |
|
Abdelilah et al. (2018) |
Morocco |
Multiple |
Descriptive analysis |
Journal of Manufacturing Technology Management |
Emerald |
Flexibility, Customer related issues, IT and its elements issues |
|
Kannan et al. (2025) |
India |
Multiple |
Descriptive Analysis |
IOSR Journal of Business and Management |
Emerald |
Operational Efficiency, Market Responsiveness |
|
Riska and Munjiati Munawaroh( 2024) |
Indonasia |
Multiple |
Descriptive Analysis |
(International Research Journal of Multidisciplinary Scope (IRJMS) |
Emerald |
Supply Chain Strategy Trends,Lean, Agile, Leagile |
The research publications critically scrutinized for the study have been depicted in Figure 2.
Figure 2 Research publications critically scrutinized for the study
There are various analysis method used by the researchers, however most of them are either descriptive analysis or simulation technique. The details of the analysis done in these papers is given in Figure 3.
Figure 3 Analysis method used by the researchers in ASCM
The reseach papers were analysed pan world which have been published in english language only. As the supply chain modalities are affected by the region, hence maximum papers are from India. However, as developed nations have already been working on agility of the supply chain, enough importance was given to the research which has been done in USA and UK. Figure 4 shows the region wise description of counties regarding ASCM applications.
Figure 4 shows the region wise description of counties regarding ASCM applications
35
30
25
20
15
10
5
0
Industry
32
4
4
1
1
1
1
1
2
4
1
1
1
2
1
The research papers for analysis were selected in such a manner that all verticles of the industry are comprehensivily coverd which include automobile, manufacturing, food and also generic papers covering multiple industries. Figure 5 reveals the implementation of ASCM initiatives in various industrial domains.
Figure 5 Sector wise description of ASCM implementation
4. DISCUSSION AND CONCLUSIONS
Though the present research extensively covers the past literature, however, there is need to carry out qualitative analysis to empirically examine the validity and reliability of key vital factors which are necessary prerequisites for the desired transition. However, the KVFs which will be extracted in the study are not generic as they are influenced by the terrestrial factors and type of business and economic conditions. Second, the factors drawn from this work will not be universal and may vary depending on the nature of business, geographical areas and many others. There are various industries that have transitioned from lean to agile SCM, this transition is limited based on overall industry requirements. The further analysis of various KVFs will differentiate between the individual requirements of these industries and based on that their overall level of the transition from lean to agile SCM.
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