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A New Vendor-Managed Inventory Model by Applying Blockchain Technology and Considering Environmental Problems

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One of the most common and successful approaches to integrated supply chain management (SCM) is vendor-managed inventory (VMI). One of the technologies that has been widely used recently to share information in the VMI is blockchain technology (BT). Given that many factors, such as scalability and adoption cost, play a role in obtaining the optimal number of transactions, it is of vital importance to account for them in the VMI. Since the VMI strategy aims for a long-term relationship between the vendor and the retailer and highly affects the supply chain’s total cost, the vendor must pay more attention in selecting retailers. Another contribution of this study is to consider the issue of environmental pollution generated by inventory holding, ordering, set-up, and transportation operations, which has not been thoroughly investigated in the existing literature. This study uses a green two-echelon multi-product, multiple-vendor, and multiple-retailer supply chain with a hybrid of multi-criteria decision-making (MCDM) methods, and a multi-objective programming under the VMI policy is developed. This paper examines BT for supply chain management by accounting for the most impactful criteria of BT implementation in retailer selection and optimization. For this purpose, this paper applies the Bayesian best–worst approach (BWM) as one of the MCDM techniques. The obtained weights are then plugged into the model as the inputs of the proposed model. Finally, the efficiency of the presented method is verified through a case study with actual data collected from the electronic supply chain.

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Data Availability

All data generated or analyzed during this study are included in this published article (and its Appendix information section).


  • Astanti RD, Daryanto Y, Dewa PK (2022) Low-carbon supply chain model under a vendor-managed inventory partnership and carbon cap-and-trade policy. J Open Innov: Technol, Market, Complexity 8(1):30.

  • Bafandegan Emroozi V, Modares A, Mohemi Z (2022) Presenting a model for diagnosing the implementation of total quality management based on performance expansion model (Case: Simorgh Rail Transportation Company). Road.

  • Bafandegan Emroozi V, Fakoor A (2023) A new approach to human error assessment in financial service based on the modified CREAM and DANP. J Ind Sys Eng 14(4):95–120.

  • Bafandegan Emroozi V, Faezian A, Seffati K, Ebrahimi H, Dadakhani B (2023a) Evaluation commercialization challenges and resolutions in SMEs using ML-FCM (case study: Sanat Prozheh Toos). J Syst Thinking Pract 2(1).

  • Bafandegan Emroozi V, Roozkhosh P, Modares A, Roozkhosh F (2023b). Selecting green suppliers by considering the internet of things and CMCDM approach. Process Integr Optim Sustain.

  • Bai Q, Jin M, Xu X (2019) Effects of carbon emission reduction on supply chain coordination with vendor-managed deteriorating product inventory. Int J Prod Econ 208:83–99.

  • Bieniek M (2021) The ubiquitous nature of inventory: vendor managed consignment inventory in adverse market conditions. Eur J Oper Res 291(2):411–420.

    Article  MathSciNet  MATH  Google Scholar 

  • Chang AC (2019) Blockchain adoption and design for supply chain management. (Doctoral dissertation, Rutgers University-Graduate School-Newark).

  • Chiou HK, Tzeng GH (2003) An extended approach of multicriteria optimization for MODM problems. In: Multi-objective programming and goal programming. Advances in Soft Computing, vol 21. Springer, Berlin, Heidelberg.

  • Choudhary D, Shankar R (2015) The value of VMI beyond information sharing in a single supplier multiple retailers supply chain under a non-stationary (Rn, Sn) policy. Omega 51:59–70.

    Article  Google Scholar 

  • Dasaklis T, Casino F (2019) Improving vendor-managed inventory strategy based on Internet of Things (IoT) applications and blockchain technology. In 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (pp. 50–55). IEEE.

  • Gharaei A, Karimi M, Shekarabi SAH (2019) An integrated multi-product, multi-buyer supply chain under penalty, green, and quality control polices and a vendor managed inventory with consignment stock agreement: the outer approximation with equality relaxation and augmented penalty algorithm. Appl Math Model 69:223–254.

    Article  MathSciNet  MATH  Google Scholar 

  • Goyal SK (1988) “A joint economic-lot-size model for purchaser and vendor”: a comment. Decis Sci 19(1):236–241

    Article  Google Scholar 

  • Hsiao SJ, Sung WT (2022) Blockchain-based supply chain information sharing mechanism. IEEE Access 10:78875–78886.

  • Kamble SS, Gunasekaran A, Kumar V, Belhadi A, Foropon C (2021) A machine learning based approach for predicting blockchain adoption in supply Chain. Technol Forecast Soc Chang 163:120465

    Article  Google Scholar 

  • KarbasiBonab V, YousefiNejadAttari M, Neishabouri E (2018) Presenting a bi-objective vendor managed inventory model with fuzzy demand for multiple vendor. J Decisions Oper Res 2(2):147–168.

    Article  Google Scholar 

  • Karimi M, Niknamfar AH (2017) A vendor-managed inventory system considering the redundancy allocation problem and carbon emissions. Int J Manag Sci Eng Manag 12(4):269–279.

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks 4:1942–1948.

  • Keshavarz-Ghorbani F, Pasandideh SHR (2021) Optimizing a two-level closed-loop supply chain under the vendor managed inventory contract and learning: Fibonacci, GA, IWO, MFO algorithms. Neural Comput & Applic 33(15):9425–9450.

  • Keskin NB, Li C, Song JSJ (2021) The blockchain newsvendor: value of freshness transparency and smart contracts. Available at SSRN 3915358.

  • Kouhizadeh M, Sarkis J (2018) Blockchain practices potentials and perspectives in greening supply chains. Sustainability 10(10):3652.

  • Kumar A (2021) Value and incentives for adoption of blockchain technology for a single supplier multiple retailer networks. J High Technol Manag Res 32(1):100407.

    Article  Google Scholar 

  • Kusuma PD, Kallista M (2022) Collaborative vendor managed inventory model by using multi agent system and continuous review (r, Q) replenishment policy. J Appl Eng Sci 20(1):254–263.

    Article  Google Scholar 

  • Li J, Greenwood D, Kassem M (2019) Blockchain in the built environment and construction industry: A systematic review, conceptual models and practical use cases. Autom Constr 102:288–307

    Article  Google Scholar 

  • Liu W, Ke GY, Chen J, Zhang L (2020) Scheduling the distribution of blood products: a vendor-managed inventory routing approach. Transport Res Part E: Log and Trans Rev 140:101964.

  • Lotfi R, Kargar B, Rajabzadeh M, Hesabi F, Özceylan E (2022) Hybrid fuzzy and data-driven robust optimization for resilience and sustainable health care supply chain with vendor-managed inventory approach. Int J Fuzzy Systems 24(2):1216–1231.

  • Malik S, Kanhere SS, Jurdak R (2018) Productchain: scalable blockchain framework to support provenance in supply chains. In 2018 IEEE 17th International Symposium on Network Computing and Applications(NCA) (pp. 1–10). IEEE.

  • Modares A, BafandeganEmroozi V, Mohemmi Z (2021) Evaluate and control the factors affecting the equipment reliability with the approach dynamic systems simulation. Case study: Ghaen Cement Factory. J Qual Eng Manag 11(2):89–106

    Google Scholar 

  • Modares A, MotahariFarimani N, BafandeganEmroozi V (2023c) A new model to design the suppliers portfolio in newsvendor problem based on product reliability. J Ind Manag Optim. 19(6)-4112-4151.

    Article  MathSciNet  Google Scholar 

  • Modares A, MotahariFarimani N, BafandeganEmroozi V (2022) Developing a newsvendor model based on the relative competence of suppliers and probable group decision-making. Ind Manag J 14(1):115–142.

    Article  Google Scholar 

  • Modares A, Motahari Farimani N, Bafandegan Emroozi V (2023a) Applying a multi-criteria group decision-making method in a probabilistic environment for supplier selection (Case study: Urban railway in Iran). J Optim Ind Eng 16(1):129–140.

  • Modares A, Kazemi M, Bafandegan Emroozi V, Roozkhosh P (2023b) A new supply chain design to solve supplier selection based on internet of things and delivery reliability. J Ind Manag Optim 19(11):7993–8028.

  • Modares A, MotahariFarimani N, BafandeganEmroozi V (2023) A vendor-managed inventory model based on optimal retailers selection and reliability of supply chain. J Ind Manag Optim 19(5):3075–3106.

    Article  MathSciNet  MATH  Google Scholar 

  • Mishra D, Gunasekaran A, Papadopoulos T, Hazen B (2017) Green supply chain performance measures: A review and bibliometric analysis. Sustain Prod Consum 10:85–99

    Article  Google Scholar 

  • Mohammadi M, Rezaei J (2020) Bayesian best-worst method: a probabilistic group decision making model. Omega 96:102075.

  • MotahariFarimani N, Ghanbarzade J, Modares A (2022) A new approach for pricing based on passengers’ satisfaction. Transp J 61(2):123–150.

    Article  Google Scholar 

  • Najafnejhad E, TavassoliRoodsari M, Sepahrom S, Jenabzadeh M (2021) A mathematical inventory model for a single-vendor multi-retailer supply chain based on the vendor management inventory Policy. Int J Syst Assur Eng Manag 12(3):579–586.

    Article  Google Scholar 

  • Olawumi TO, Ojo S, Chan DWM, Yam MCH (2021) Factors influencing the adoption of blockchain technology in the construction industry: a system dynamics approach. In: Proceedings of the 25th international symposium on advancement of construction management and real estate. Springer Singapore Singapore 1235–1249

  • Omar IA, Jayaraman R, Salah K, Debe M, Omar M (2020) Enhancing vendor managed inventory supply chain operations using blockchain smart contracts. IEEE Access 8:182704–182719

    Article  Google Scholar 

  • Poursoltan L, Mohammad Seyedhosseini S, Jabbarzadeh A (2021) A two-level closed-loop supply chain under the constract of vendor managed inventory with learning: A novel hybrid algorithm. J Ind Prod Eng 38(4):254–270.

  • Ramachandran N (2000) Taguchi method as a tool for reducing costs. Management System Engineering, Virginia Polytechnic Institute, and State University,

  • Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57.

    Article  Google Scholar 

  • Rajabi S, Roozkhosh P, Farimani NM (2022) MLP-based Learnable Window Size for Bitcoin price prediction. Appl Soft Comput 129:109584.

    Article  Google Scholar 

  • Ramazanian MA, Modares A (2011) Application of particle swarm optimization algorithm to aggregate production planning. Asian J Bus Manag Studies 2(2):44–54

    Google Scholar 

  • Roozkhosh P, Pooya A, Agarwal R (2022) Blockchain acceptance rate prediction in the resilient supply chain with hybrid system dynamics and machine learning approach. Oper Manag Res 1–21.

  • Sadeghi J, Mousavi SM, Niaki STA, Sadeghi S (2013) Optimizing a multi-vendor multi-retailer vendor managed inventory problem: two tuned meta-heuristic algorithms. Knowl-Based Syst 50:159–170.

    Article  Google Scholar 

  • Sadeghi J, Mousavi SM, Niaki STA, Sadeghi S (2014) Optimizing a bi-objective inventory model of a three-echelon supply chain using a tuned hybrid bat algorithm. Transp Res Part E: Logist Transp Rev 70:274–292.

    Article  Google Scholar 

  • Saghih AMF, Modares A (2022) A new dynamic model to optimize the reliability of the series-parallel systems under warm standby components. J Ind Manag Optim 19(1):376–401.

    Article  MathSciNet  MATH  Google Scholar 

  • Sevkli M (2010) An application of the fuzzy ELECTRE method for supplier selection. Int J Prod Res 48(12):3393–3405

    Article  MATH  Google Scholar 

  • Shang X, Zhang G, Jia B, Almanaseer M (2022) The healthcare supply location-inventory routing problem: a robust approach. Transformation Research Part E: Logistics and Transportation Review 158:102588.

  • Stellingwerf HM, Kanellopoulos A, Cruijssen FCAM, Bloemhof JM (2019) Fair gain allocation in eco-efficient vendor-managed inventory cooperation. J Clean Prod 231:746–755.

    Article  Google Scholar 

  • Taudes A, Tian F (2018) An information system for food safety monitoring in supply chains based on HACCP, blockchain, and Internet of things. Ph.D. Thesis, WU Vienna University of Economics and Business, Vienna, Austria; pp. 63–102,

  • Tipmontian J, Alcover JC, Rajmohan M (2020) Impact of blockchain adoption for safe food supply chain management through system dynamics approach from management perspectives in Thailand. Multidiscip Dig Publ Inst Proc 39(1):14

  • Wei Q, Zhang J, Zhu G, Dai R, Zhang S (2020) Retailer vs. vendor managed inventory with considering stochastic learning effect. J Oper Res Soc 71(4):628–646.

  • Wettasinghe J, Luong HT (2020) A vendor managed inventory policy with emergency orders. J Ind Prod Eng 37(2-3):120–133.

  • Yadav S, Singh SP (2020) Blockchain critical success factors for sustainable supply chain. Resour Conserv Recycl 152104505.

  • Zhang T, Dong P, Chen X, Gong Y (2023) The impacts of blockchain adoption on a dual-channel chain with rish-averse members. Omega (United Kingdom) 114:102747.

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Appendix 1


Table 11 Data related to transactions, ordering costs, and demand

11 and

Table 12 Data related to holding cost, backorder cost, capacity, and production rate


Appendix 2. Tuning the GA and PSO Parameters

To run the GA and PSO, it is essential to effectively tune their parameter values, as the performance of these techniques is affected by modifying their parameters. To tune the parameters of the GA (i.e., the population size (Popsize), the crossover rate \(\left({p}_{c}\right)\), the percent of mutation \(\left({p}_{m}^{2}\right)\), and parameters of PSO: the inertia weights \(\left({\omega }_{i},{\omega }_{f}\right)\), the acceleration coefficients \(\left({c}_{1},{c}_{2}\right)\), and the Taguchi’s experiment design technique are leveraged. To this end, three different levels for each of the parameters are chosen. So, with three parameters in both algorithms, a design is employed with 9 experiments. The Taguchi’s orthogonal design L9 is selected to run the experiments. Table 7 presents various combinations of parameter levels in Taguchi’s experiment. The value S/N is obtained from Eq. (56), where n denotes the number of orthogonal arrays and \({y}_{i}\) denotes the response in the ith experiment, on which the efficiency of the experiments is based. The highest S/N ratio is specific to the optimal level for each factor.

$${~}^{S}\!\left/ \!{~}_{N}\right.=-10\mathrm{log}\left({~}^{1}\!\left/ \!{~}_{n}\right.\sum\limits_{i=1}^{n}{~}^{1}\!\left/ \!{~}_{{y}_{i}^{2}}\right.\right)$$


Table 13 Taguchi’s experimental results on test problem for the GA

13 and

Table 14 Taguchi’s experimental results on the test problem for the PSO

14 show Taguchi’s experimental results on test problems for the GA and PSO. OF in Table 4 represents the objective function.


Fig. 7
figure 7

The relationship between the mean and S/N values for different contours of the objective function

7 shows the relationship between the mean and S/N values for different contours of the objective function. It is clear from Fig. 7 that the objective function increases with the increase of the mean and the decrease of S/N values.

As it is clear from Fig. 

Fig. 8
figure 8

The relationship between the mean and S/N values

8, according to the values of the objective functions obtained in different runs, the mean and S/N values are completely opposite to each other. That is, when S/N is at its maximum value, the mean is at its minimum value. Therefore, there is no need to repeat the experiments, so it can be said that the obtained results have sufficient validity.


Fig. 9
figure 9

The relationship between the objective function and S/N values

9 shows the relationship between the values obtained from different runs of the objective and S/N values.


Fig. 10
figure 10

Mean S/N ratios of the parameters of GA and PSO

10 shows the mean of S/N obtained by the Taguchi’s method on test problems for the GA and PSO, respectively. It is clear from Fig. 10 that the maximum points for factors 2 and 3 in GA are at level 1 and for factors 1 and 4 in GA are at level 3, so they are selected as optimal parameters of GA. Also, the maximum point for factors 1 and 3 in PSO are at level 3, and for factors 2 and 3 in PSO are at level 1, so they are selected as optimal parameters of PSO.


Table 15 Controllable factors and their levels

15 shows the controllable factors and their levels.

Appendix 3. Convergence Diagram of the Best Implementation of the PSO and GA

Here, a convergence diagram of the best implementation of the PSO and GA is given (Fig. 

Fig. 11
figure 11

Convergence diagram of the best implementation of the PSO and GA


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Modares, A., Farimani, N.M. & Dehghanian, F. A New Vendor-Managed Inventory Model by Applying Blockchain Technology and Considering Environmental Problems. Process Integr Optim Sustain 7, 1211–1239 (2023).

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