Relational Approaches Related to Digital Supply Chain Management Consolidation
Abstract
:1. Introduction
2. The General Theoretical Framework of the Research
2.1. The Importance of Digital Supply Chain Management
- “Visibility” reflects the established exchange of information, in a digitalized system, between the partners within the supply–delivery chain;
- “Trust” reveals strict exchanges of information and data to achieve levels of digital relationships and interaction that are necessary to achieve common goals within the business system;
- “Sustainability” highlights the viability and fairness of transactions in the digital system of the supply chain, for all partner companies;
- “Efficiency and standards” reveal the continuous observance of the principles imposed by standards, transparency and business ethics in the structure of the digital supply chain, to ensure the necessary collaboration, full functioning and performance [37].
- Connection, i.e., adequate communications between the system components, as well as between them and the external environment, by ensuring all digital devices and interconnected processes;
- Information, through a complex informational system that facilitates inputs, processing, timely capitalization and outputs of information to the beneficiaries of the microenvironment and macroenvironment of supply chain management;
- Smart mechanism, which allows capabilities and rapid actions of advanced analysis for decision-making and real-time business process management;
- Automation using robots and other advanced technological systems to gain a competitive advantage over competitors by increasing productivity and reducing costs in the reference market or markets [39].
2.2. Actions That Are Required for the Evolution and Functional Consolidation of Digital Supply Chain Management
- The existence of digital technical systems, supported by a small number of well-motivated professionals, is insufficient for the execution of complicated (digital) profile procedures in line with the constraints imposed by the business’s complexity;
- The evolved digitalization only of some important functional segments from the staff of the partner companies of a supply–delivery chain, which did not determine an efficient holistic digital operation for the entire business system;
- The use of external (digitized) services for the creation of complicated SCM processes, which prohibited additional investments in digitization, training and skills development by field-based employees;
- Manifestation of deficiencies in business planning within SCM, due to the non-existence of a high-performance digital infrastructure;
- Despite the availability of modern digital infrastructure, some or all digitized business operations were carried out inefficiently, necessitating significant changes in the future;
- The absence of constant communication between the structures responsible for digital integration within the SCM components led to the non-optimal attainment of the efficiency metrics set by digitalization within the relevant business systems [41].
2.3. Determinations and Classifications of High-Performing Organizations Using Digital Supply Chain Management
- Achieving the objectives appropriate to its own digitization;
- Achieving the necessary parameters of agility and resilience to face any risks and challenges in business;
- Investments in new technologies for innovation and sustainability [46].
3. Materials and Methods
3.1. Research Methods
3.2. Research Results
3.2.1. Relational Considerations Regarding the Consolidation of the Digital Supply Chain Management
3.2.2. Considerations Regarding Appropriate Information Management within Consolidated Digital SCM
3.2.3. Proposed Mathematical Relationships to Strengthen Digital Supply Chain Management
- Digital business facilities achieved through digital consolidated SCM (Fa.; for example, the surplus (value) resulting in turnover determined in a certain period of time, as a result of digital transformation);
- The average value level of the digitization achieved within the SCM, resulting by summing the value levels of each component (Dav; represents a constant resulting from the ratio of the two value states of the system, ie: high digitized SCM/lower digitized SCM; it reflects the expenses (efforts) made in order to strengthen the digital SCM);
- The value level of losses related to all risks manifested in the operation of the digital consolidated SCM in local, zonal or global environments (Ll; for example, some interruptions in the operation of new digital systems, with an impact on the performance of digitally consolidated SCM).
- The income obtained in 2021, Ic1/2022 = 1.4 billion EUR (the digital transformation of SCM takes place);
- The income obtained in 2022, Ic2/2022 = 2.1 billion EUR;
- Value of technological systems/previous digitization, Vts1 = 130 million EUR;
- Value of technological systems/digital consolidation, Vts2 = 270 million EUR;
- Associated risks, Ll = 0.
- The elements in formula (1) presented above have the following configuration:
- Important improvement of the parameters of functional agility (speed and flexibility) (Ifa) within the SCM processes;
- Adequate increase in the visibility of the operations (IVO) specific to the efficient functioning of the SCM components;
- The functional association (high-performance and complementary) of digitalization with the robotization of artificial intelligence and the Internet of Things (FA), all technologically integrated within the evolved processes, specific to the SCM components;
- The possibility of avoiding or diminishing the effects of disturbing factors (Pa/ddf) on SCM components;
- Rapid recovery of some value losses (because of the action of some disturbing factors) and the return in a short period of time to the initial functional performances (Rrl) of SCM. Based on the presented, we highlight the following relationship:
- Ab–the abilities of the staff in the management and execution subsystems, both at the higher level of the digital SCM and at the level of each component company. In turn, the mentioned abilities can be expressed as follows:Ab = Q × (K + S + Exp)The elements of the relationship represent Q-qualification; K-knowledge; S-skills; Exp–the experience. The relationship has the expected effect in the conditions where the objectives and performance indicators are adequately described in the job description. However, this can be achieved when: the qualification is based on a relevant formative side accompanied by multi-qualification; the knowledge must be in line with the requirements of the position and continuously updated; the skills involve skills, clarity and positive and innovative justification of the effort made with efficiency to carry out the assigned tasks; experience denotes successful practices in previous functions and the accumulation of added value in professional activities, which allows new high-performance developments in one or more functions that will be occupied later. We believe that the role and importance of abilities (Ab), as a fundamental element within mentioned formula for determining performance (P), is given by the individual value of the acquired level of the component sub-elements. Therefore, within the consolidated digital SCM, the weighting of skills (Q) with the group resulting from the combination of knowledge (K) with skills (S) and experience (Exp) determines, for an employee, a beneficial action power resulting from an individual professional aspect characterized by: efficiency and effectiveness; adequate collaboration—internal and external; intelligent, proactive, innovative and productive thinking; prolonged effort in situations imposed by the performance of certain work duties (natural; urgent; anticipatory etc.); smart management and leadership that continuously engages a mix of resources (human, material, financial, informational) etc.
- M-the pecuniary and non-pecuniary motivation of the management and execution staff within the digital SCM.
- R-the resources committed for the efficient operation of SCM in win-win terms, for the digital SCM components and for the clients of this business system.
3.2.4. Case Study on the Selection and Implementation of the Optimal and Timely Solution, Needed to Strengthen the Digital Supply Chain Management
- (a)
- Initial details
- (b)
- Data for analysis, comparison and decision
- (c)
- Requirements set to be addressed
- V1–V3, are the three decisional variants;
- R1–R3, are the risk nodes, where some random events take place (favorable, average or unfavorable conditions), determined by the costs of acquisition, installation and verification of the operation of the technological components of the digital intelligent system;
- D1–D9, decisional nodes, where the intervention of the deciding manager will take place, who will opt for one of the three decisional variants (V1–V3);
- E1–E18, represent final nodes in which the costs of acquisition and functional implementation of the elements of the intelligent digital system are measured.
4. Findings
5. Limits of Research
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- European Commission; Directorate-General for Research and Innovation; Breque, M.; De Nul, L.; Petridis, A. Industry 5.0: Towards a Sustainable, Human-Centric and Resilient European Industry; Publications Office: Luxembourg, 2021. [Google Scholar] [CrossRef]
- Guilherme, F.F. From Supply Chain 4.0 to Supply Chain 5.0: Findings from a Systematic Literature Review and Research Directions. Logistics 2021, 5, 49. [Google Scholar] [CrossRef]
- European Commission; Directorate-General for Research and Innovation; Müller, J. Enabling Technologies for Industry 5.0: Results of a Workshop with Europe’s Technology Leaders; Publications Office: Luxembourg, 2020. [Google Scholar] [CrossRef]
- International Telecommunication Union. Economic Impact of COVID-19 on Digital Infrastructure; Report of an Economic Experts Roundtable organized by ITU; International Telecommunication Union: Geneva, Switzerland, 2020; pp. 7–11. Available online: www.itu.int/dms_pub/itu-d/opb/pref/D-PREF-EF.COV_ECO_IMPACT-2020-PDF-E.pdf (accessed on 6 May 2022).
- Pyun, J.; Rha, J.S. Review of Research on Digital Supply Chain Management Using Network Text Analysis. Sustainability 2021, 13, 9929. [Google Scholar] [CrossRef]
- Nasiri, M.; Rantala, T. Managing the digital supply chain: The role of smart technologies. Technovation 2020, 96–97, 102121. [Google Scholar] [CrossRef]
- Tseng, M.-L.; Bui, T.-D.; Lim, M.K.; Lewi, S. A Cause and Effect Model for Digital Sustainable Supply Chain Competitiveness under Uncertainties: Enhancing Digital Platform. Sustainability 2021, 13, 10150. [Google Scholar] [CrossRef]
- Alicke, K.; Rachor, Y.; Seyfert, A. Supply Chain 4.0-the Next-Generation Digital Supply Chain; McKinsey & Company: Atlanta, GA, USA, 2016; p. 12. Available online: https://www.mckinsey.com/business-functions/operations/our-insights/supply-chain-40--the-next-generation-digital-supply-chain (accessed on 8 May 2022).
- Lee, S.-Y. Sustainable Supply Chain Management, Digital-Based Supply Chain Integration and Firm Performance: A Cross-Country Empirical Comparison between South Korea and Vietnam. Sustainability 2021, 13, 7315. [Google Scholar] [CrossRef]
- Stute, M.; Sardesai, S.; Parlings, M.; Senna, P.P.; Fornasiero, R.; Balech, S. Technology Scouting to Accelerate Innovation in Supply Chain. In Next Generation Supply Chains; Lecture Notes in Management and Industrial Engineering; Fornasiero, R., Sardesai, S., Barros, A.C., Matopoulos, A., Eds.; Springer: Cham, Switzerland, 2021. [Google Scholar] [CrossRef]
- Tsipoulanidis, A.; Nanos, I. Contemporary Potentials and Challenges of Digital Logistics and Supply Chain Management. Int. J. Innov. Technol. Manag. 2022, 19, 2241003. [Google Scholar] [CrossRef]
- Wang, M.; Teng, W. Digital Innovation and Firm Environmental Performance: The Mediating Role of Supply Chain Management Capabilities. Front. Psychol. 2022, 13, 897080. [Google Scholar] [CrossRef]
- Barykin, S.Y.; Bochkarev, A.A.; Kalinina, O.V.; Yadykin, V.K. Concept for a Supply Chain Digital Twin. Int. J. Math. Eng. Manag. Sci. 2020, 5, 1498–1515. [Google Scholar] [CrossRef]
- Wang, L.; Deng, T.H.; Shen, Z.J.M.; Hu, H.; Qi, Y.Z. Digital twin-driven smart supply chain. Front. Eng. Manag. 2022, 9, 56–70. [Google Scholar] [CrossRef]
- Sanders, N.R.; Swink, M. How to Build a Digital Supply Chain: Focus on Capabilities. Association for Supply Chain Management. 2020. Available online: www.ascm.org/contentassets/68a05e01e80b4747a31281fa055fb5be/final-research-report---how-to-build-a-digital-supply-chain.pdf (accessed on 9 May 2022).
- Japan Business Federation (Ministry of Economy, Trade and Industry, Japan). Supply Chains of the Society 5.0 Era: Toward Digitalization of Commercial Distribution and the Money Flow. September 2020. Available online: https://www.meti.go.jp/english/report/data/wp2021/pdf/2-1-4.pdf (accessed on 10 May 2022).
- Sahara, R.C.; Paluluh, E.D.J.; Aamer, M.A. Exploring the Key Factor Categories for the Digital Supply Chain. In Proceedings of the 9th International Conference on Operations and Supply Chain Management, Ho Chi Minh City, Vietnam, 15–18 December 2019; pp. 1–3. [Google Scholar]
- Rasool, F.; Greco, M.; Grimaldi, M.; Schiuma, G. Digital Supply Chain Performance Metrics—A Literature Review. In Proceedings of the 15th International Forum on Knowledge Asset Dynamics (IFKAD)—Knowledge in Digital Age, Matera, Italy, 9–11 September 2020; pp. 1648–1664. [Google Scholar]
- Ukko, J.; Saunila, M.; Rantala, T. Connecting relational mechanisms to performance measurement in a digital service supply chain. Prod. Plan. Control 2020, 31, 233–244. [Google Scholar] [CrossRef]
- Agrawal, P.; Narain, R.; Ullah, I. Analysis of barriers in implementation of digital transformation of supply chain using interpretive structural modelling approach. J. Model. Manag. 2020, 15, 297–317. [Google Scholar] [CrossRef]
- Irfan, I.; Sumbal, M.S.U.K.; Khurshid, F.; Chan, F.T.S. Toward a resilient supply chain model: Critical role of knowledge management and dynamic capabilities. Ind. Manag. Data Syst. 2022, 122, 1153–1182. [Google Scholar] [CrossRef]
- Ivanov, D. Digital Supply Chain Management and Technology to Enhance Resilience by Building and Using End-to-End Visibility During the COVID-19 Pandemic. IEEE Trans. Eng. Manag. 2021, 1–11. [Google Scholar] [CrossRef]
- Ho, W.R.; Tsolakis, N.; Dawes, T.; Dora, M.; Kumar, M. A Digital Strategy Development Framework for Supply Chains. IEEE Trans. Eng. Manag. 2022, 1–14. [Google Scholar] [CrossRef]
- Shan, W.; Wang, J. Mapping the Landscape and Evolutions of Green Supply Chain Management. Sustainability 2018, 10, 597. [Google Scholar] [CrossRef]
- Muafi, M.; Sulistio, J. A Nexus Between Green Intelectual Capital, Supply Chain Integration, Digital Supply Chain, Supply Chain Agility and Business Performance. J. Ind. Eng. Manag. JIEM 2022, 15, 275–295. [Google Scholar] [CrossRef]
- Chen, X.; Jang, E. A Sustainable Supply Chain Network Model Considering Carbon Neutrality and Personalization. Sustainability 2022, 14, 4803. [Google Scholar] [CrossRef]
- Muñoz-Torres, M.J.; Fernández-Izquierdo, M.Á.; Rivera-Lirio, J.M.; Ferrero-Ferrero, I.; Escrig-Olmedo, E.; Gisbert-Navarro, J.V.; Marullo, M.C. An Assessment Tool to Integrate Sustainability Principles into the Global Supply Chain. Sustainability 2018, 10, 535. [Google Scholar] [CrossRef]
- Schniederjans, D.G.; Curado, C.; Khalajhedayati, M. Supply chain digitisation trends: An integration of knowledge management. Int. J. Prod. Econ. 2020, 220, 107439. [Google Scholar] [CrossRef]
- Cantini, A.; Peron, M.; De Carlo, F.; Sgarbossa, F. A decision support system for configuring spare parts supply chains considering different manufacturing technologies. Int. J. Prod. Res. 2022, 1–21. [Google Scholar] [CrossRef]
- Peron, M.; Knofius, N.; Basten, R.; Sgarbossa, F. Impact of Failure Rate Uncertainties on the Implementation of Additive Manufacturing in Spare Parts Supply Chains. In Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology; Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D., Eds.; Springer: Cham, Switzerland, 2021; Volume 634. [Google Scholar] [CrossRef]
- Zekhnini, K.; Cherrafi, A.; Bouhaddou, I.; Benabdellah, A.C.; Bag, S. A model integrating lean and green practices for viable, sustainable and digital supply chain performance. Int. J. Prod. Res. 2021, 1–27. [Google Scholar] [CrossRef]
- Mahroof, K.; Omar, A.; Kucukaltan, B. Sustainable food supply chains: Overcoming key challenges through digital technologies. Int. J. Product. Perform. Manag. 2022, 71, 981–1003. [Google Scholar] [CrossRef]
- Dolgui, A.; Ivanov, D. 5G in digital supply chain and operations management: Fostering flexibility, end-to-end connectivity and real-time visibility through internet-of-everything. Int. J. Prod. Res. 2022, 60, 442–451. [Google Scholar] [CrossRef]
- Melnyk, S.A.; Schoenherr, T.; Speier-Pero, C.; Peters, C.; Chang, J.F.; Friday, D. New challenges in supply chain management: Cybersecurity across the supply chain. Int. J. Prod. Res. 2022, 60, 162–183. [Google Scholar] [CrossRef]
- Tavana, M.; Shaabani, A.; Raeesi Vanani, I.; Kumar Gangadhari, R. A Review of Digital Transformation on Supply Chain Process Management Using Text Mining. Processes 2022, 10, 842. [Google Scholar] [CrossRef]
- Brinker, J.; Haasis, D.H. Power in the Context of SCM and Supply Chain Digitalization: An Overview from a Literature. Logistics 2022, 6, 25. [Google Scholar] [CrossRef]
- International Finance Corporation, IFC. Technology and Digitization in Supply Chain Finance. Washington: Pennsylvania Avenue N.W. 2020, 22–25. Available online: https://www.ifc.org/wps/wcm/connect/f9520505-ff56-4a29-9020-3e3ee17d4c08/Handbook-Digital-Tech-SCF-COMP.pdf?MOD=AJPERES&CVID=nmpQzqP (accessed on 20 May 2022).
- Oliveira-Dias, D.; Moyano-Fuentes, J.; Maqueira-Marín, J.M. Understanding the relationships between information technology and lean and agile supply chain strategies: A systematic literature review. Ann. Oper Res. 2022, 312, 973–1005. [Google Scholar] [CrossRef]
- Zambujal-Oliveira, J. (Ed.) Tools for Supply Chain Management; Operations Management and Research and Decision Sciences Book Series; University of Madeira: Funchal, Portugal, 2019; Volume 4, pp. 71–74. Available online: https://digituma.uma.pt/ (accessed on 6 June 2022).
- Forbes INSIGHTS. Digital Supply Chain. Are You Lessing The Pack? Lessons from Companies That Derive the Most Value from Their Supply Chains. Washington 2017. Available online: https://www.forbes.com/forbesinsights/cognizant_supply_chain/index.html (accessed on 6 May 2022).
- McArthur, S.; Sankur, A.; Shah, K.; Singh, V. Digital Supply-Chain Transformation with a Human Face. McKinsey & Company, Global Editorial Services. 2020, pp. 2–5. Available online: www.mckinsey.com/~/media/McKinsey/Business%20Functions/Operations/Our%20Insights/Digital%20supply%20chain%20transformation%20with%20a%20human%20face/digital-supply-chain-transformation-with-a-human-face.pdf (accessed on 24 May 2022).
- Sharma, M.; Timmermans, M. The Pandemic Put Chief Supply Chain Officers (CSCOs) in the Vanguard of Change and Has Empowered Them to Redefine Resiliency and Lead Transformation in a Responsible and Sustainable Way. Logistics Management, 2021. Available online: https://www.logisticsmgmt.com/article/unbreakable_and_resilient_supply_chain_and_logistics_of_the_future (accessed on 9 June 2022).
- Gezgin, E.; Huang, X.; Samal, P.; Silva, I. Digital Transformation: Raising Supply-Chain Performance to New Levels. McKinsey & Company, Global Editorial Services. 2017, pp. 2–3. Available online: https://www.mckinsey.com/business-functions/operations/our-insights/digital-transformation-raising-supply-chain-performance-to-new-levels (accessed on 1 June 2022).
- Junge, A.L. Conceptualizing and Capturing Digital Transformation‘S Customer Value—A Logistics and Supply Chain Management Perspective. Ph.D. Thesis, Universitätsverlag der TU, Berlin, Germany, 2020; pp. 1–2. [Google Scholar] [CrossRef]
- Griswold, M. The Gartner Supply Chain Top 25 for 2021. Available online: https://www.gartner.com/smarterwithgartner/the-gartner-supply-chain-top-25-for-2021 (accessed on 30 May 2022).
- Seiler, D.; Hanselman, H. McKinsey Global Surveys, 2021: A Year in Review. McKinsey & Company Global Editorial Services: Atlanta, GA, USA, 2021; pp. 16–25. Available online: https://www.mckinsey.com/~/media/mckinsey/featured%20insights/mckinsey%20global%20surveys/mckinsey-global-surveys-2021-a-year-in-review.pdf (accessed on 12 August 2022).
- Kopanaki, E. Conceptualizing Supply Chain Resilience: The Role of Complex IT Infrastructures. Systems 2022, 10, 35. [Google Scholar] [CrossRef]
- Tang, C.S. Perspectives in supply chain risk management. Int. J. Prod. Econ. 2006, 103, 469–475. [Google Scholar] [CrossRef]
- Hippold, S. Gartner Predicts the Future of Supply Chain Technology. Gartner, Inc., USA, 2021: North American Offices. Available online: https://www.gartner.com/smarterwithgartner/gartner-predicts-the-future-of-supply-chain-technology (accessed on 10 June 2022).
- Banker, S. One Multinational’s Supply Chain Transformation Journey, 2022. Available online: https://www.forbes.com/sites/stevebanker/2022/04/01/one-multinationals-supply-chain-transformation-journey/?sh=496f558c6229 (accessed on 10 June 2022).
- Kechagias, E.P.; Miloulis, D.M.; Chatzistelios, G.; Gayialis, S.P.; Papadopoulos, G.A. Applying a system dynamics approach for the pharmaceutical industry: Simulation and optimization of the quality control process. WSEAS Trans. Environ. Dev. 2021, 17, 995. [Google Scholar] [CrossRef]
- Johnston, D. Get Ready for the Next Supply Chain Disruption. Entrep. Innov. Exch. 2021, 1–3. [Google Scholar] [CrossRef]
- Zhu, C.; Guo, X.; Zou, S. Impact of information and communications technology alignment on supply chain performance in the Industry 4.0 era: Mediation effect of supply chain integration. J. Ind. Prod. Eng. 2022, 1–16. [Google Scholar] [CrossRef]
- Tseng, M.L.; Tran, T.P.T.; Ha, H.M.; Bui, T.D.; Lim, M.K. Sustainable industrial and operation engineering trends and challenges Toward Industry 4.0: A data driven analysis. J. Ind. Prod. Eng. 2021, 38, 581–598. [Google Scholar] [CrossRef]
- Jansson, J. Decision Tree Classification of Products Using C5.0 and Prediction of Workload Using Time Series Analysis. Dissertation. KTH, School of Electrical Engineering (EES), 2016. 2016. Available online: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200968</div> (accessed on 11 June 2022).
- Xiao, T. A Novel Approach to Evaluating Decision Tree Learning Algorithms. Master’s Thesis, Bishop’s University Canada, Sherbrooke, QC, Canada, August 2020; pp. 6–17. Available online: https://www.ubishops.ca/wp-content/uploads/xiao20200810.pdf (accessed on 11 June 2022).
- Njoku, O.C. Decision Trees and Their Application for Classification and Regression Problems. Master’s Thesis, Missouri State University, Springfield, MO, USA, 2019. Available online: https://bearworks.missouristate.edu/theses/3406 (accessed on 12 June 2022).
- Cosares, S. Models for Decision-Making. CUNY Academic Works. 2018. Available online: https://www.coursehero.com/file/66038205/Models-for-Decision-Makingpdf/ (accessed on 12 June 2022).
Decisional Alternatives (Solutions) | Evaluation Results in Points/Eligibility Criteria | ||||
---|---|---|---|---|---|
Price (Ps; P) | Agility (As; A) | Sustainability (Ss; S) | Robustness (Rbs; Rb) | Resilience (Rss; Rs) | |
The standard evaluation established for each criterion (Ps, As, Ss, Rbs, Rss) according to its importance, in 100 points | 15 | 17 | 18 | 20 | 30 |
V1 | 11 | 15 | 16 | 16 | 22 |
V2 | 13 | 16 | 17 | 18 | 25 |
V3 | 15 | 17 | 18 | 20 | 30 |
Decisional Alternatives (Solutions) | Individual Evaluation Values/Eligibility Criteria and Final Values | |||||
---|---|---|---|---|---|---|
Price (P/Ps) | Agility (A/As) | Sustainability (S/Ss) | Robustness (Rb/Rbs) | Resilience (Rs/Rss) | Final Arithmetic Values of V1, V2, V3 (Pji) | |
V1 | 0.73 | 0.88 | 0.88 | 0.80 | 0.73 | 4.03 |
V2 | 0.86 | 0.94 | 0.94 | 0.90 | 0.83 | 4.47 |
V3 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 5.00 |
Decision Variants (Solutions) (for Technological Components Acquisition of the Intelligent Digital System) | Smart IT Equipment Costs (Million EUR) | Assessment of the Costs Necessary to Make the Decision to Purchase and Implement a Smart Digital System Necessary for the SCM “M” Functional Consolidation (Million EUR) | |||
---|---|---|---|---|---|
Favorable Conditions | Average Conditions | Unfavorable Conditions | High Costs | Low Costs | |
V1 | 160 | 690 | 330 | ||
142 | 590 | 270 | |||
145 | 450 | 190 | |||
V2 | 170 | 850 | 600 | ||
143 | 670 | 350 | |||
139 | 500 | 270 | |||
V3 | 162 | 590 | 340 | ||
145 | 490 | 240 | |||
141 | 350 | 170 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Minculete, G.; Stan, S.E.; Ispas, L.; Virca, I.; Stanciu, L.; Milandru, M.; Mănescu, G.; Bădilă, M.-I. Relational Approaches Related to Digital Supply Chain Management Consolidation. Sustainability 2022, 14, 10727. https://doi.org/10.3390/su141710727
Minculete G, Stan SE, Ispas L, Virca I, Stanciu L, Milandru M, Mănescu G, Bădilă M-I. Relational Approaches Related to Digital Supply Chain Management Consolidation. Sustainability. 2022; 14(17):10727. https://doi.org/10.3390/su141710727
Chicago/Turabian StyleMinculete, Gheorghe, Sebastian Emanuel Stan, Lucian Ispas, Ioan Virca, Leontin Stanciu, Marius Milandru, Gabriel Mănescu, and Mădălina-Ioana Bădilă. 2022. "Relational Approaches Related to Digital Supply Chain Management Consolidation" Sustainability 14, no. 17: 10727. https://doi.org/10.3390/su141710727