EVALUATION OF LOGISTICS RISK FACTORS WITH BWM METHOD; SIVAS PROVINCE EXAMPLE

Ölmez, Umur; Ulutas, Alptekin; Üre, Salim; Yürüyen, Ali Aygün; Meidute-Kavaliauskiene, Ieva

doi:10.1590/0101-7438.2023.043.00286745

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ARTICLES • Pesqui. Oper. 44 • 2024 • https://doi.org/10.1590/0101-7438.2023.043.00286745 linkcopy

EVALUATION OF LOGISTICS RISK FACTORS WITH BWM METHOD; SIVAS PROVINCE EXAMPLE

Authorship SCIMAGO INSTITUTIONS RANKINGS

This study investigates the logistics risk factors impacting Sivas, a strategically significant region in Turkey’s logistics network, especially within the Belt and Road Initiative (BRI) framework. Using the Best-Worst Method (BWM), the study analyzes nine main criteria and twenty-seven sub-criteria that can cause delays or disruptions in logistics activities. Data were collected from 15 logistics experts in Sivas through questionnaires and analyzed using Lingo 20.0 software. The analysis reveals that transportation risk is the most critical factor affecting logistics activities in Sivas, while organizational risks have the least impact. Specifically, accidents during transportation were identified as the most significant sub-criterion, whereas worker strikes were deemed the least significant. This study contributes to the literature by offering a detailed categorization and prioritization of logistics risks, providing actionable recommendations for mitigating these risks. Furthermore, it presents a transferable model applicable to other regions with similar logistical and economic profiles, offering valuable insights for policymakers and industry stakeholders.

Keywords:
logistics risk factors; best and worst method (BWM); multi criteria decision making

Author(s)	Year	Aim	Method (s)
Moeinzadeh et al.	2009	Identifying supply chain risks of the power, oil and gas industry.	ANP and Fuzzy VIKOR
Samvedi et al.	2013	To identify risks in the supply chain in the Indian textile and steel industry.	Fuzzy AHP and Fuzzy TOPSIS
Jovic	2014	To determine the internal and external supply chain risks of a food company operating in Istanbul.	DEMATEL
Rajesh et al.	2015	Reducing electronics industry supply chain risks.	Grey DEMATEL
Sun et al.	2015	Assessing supply chain risks of a Chinese manufacturer.	Fuzzy TOPSIS
Venkatesh et al.	2015	To assess supply chain risks in the apparel industry in India.	Interpretative Structural Modeling (ISM) and Fuzzy MICMAC
Mangla et al.	2015	To identify green supply chain risks of a poly plastic manufacturing company.	Fuzzy AHP and IRP
Chand et al.	2015	To assess supply chain risks in the Indian manufacturing industry.	ANP and MOORA
Mangla et al.	2016	Assessing green supply chain risks of a plastic manufacturer in India in turbid environment.	Fault-tree analysis(FTA) and Fuzzy AHP
El Mokrini et al.	2016	Evaluating outsourcing logistics risks in the pharmaceutical industry.	Fuzzy AHP-PROMETHEE
Wang et al.	2016	To assess supply chain risks of fresh agricultural products.	The improved TOPSIS method
Govindan et al.	2016a	To identify risks related to the customers of the cement manufacturing company’s third-party logistics service providers.	DEMATEL
Govindan et al.	2016b	To evaluate the supply chain risks of the original equipment manufacturer of brake shoes.	ELECTRE TRI-C
Khemiri et al.	2017	Managing performance and risk in integrated supply-production planning.	Fuzzy TOPSIS
Song et al.	2017	To identify supply chain risk factors in the telecommunications industry in China.	DEMATEL
Rostamzadeh et al.	2018	To assess petrochemical industry supply chain risks in Iran.	Fuzzy TOPSIS and Fuzzy CRITIC
Danışman	2019	Selecting the warehouse location by evaluating the risk factors in hazardous material logistics.	AHP, TOPSIS and WASPAS
Chowdhury et al.	2019	To assess supply chain risks for the garment industry in Bangladesh.	ISM and MICMAC
Zubayer et al.	2019	Assessing supply chain risks in the ceramics industry.	Fuzzy TOPSIS
Abdel-Basset et al.	2019	Assessing risks in the supply chain.	Neutrosophic AHP and Neutrosophic TOPSIS
Ali et al.	2019	Identifying risks in the supply chain in the food industry.	Grey DEMATEL
Mzougui et al.	2020	Assessing supply chain risks in the automotive industry.	Failure Mode, Effects and Criticality Analysis (FMECA), AHP and DEMATEL
Abdel-Basset et al.	2020	To assess Telecommunications Equipment Company’s supply chain risks.	TOPSIS and CRITIC
Benabdallah et al.	2020	Assessing agri-food supply chain risks in Tunisia.	Rough DEMATEL
Çalık	2021	To evaluate the green supply chain risks of a furniture products manufacturing business in Turkey.	Fuzzy TOPSIS, Fuzzy VIKOR and Fuzzy ARAS
Shahed et al.	2021	Reducing supply chain risks during the COVID-19 pandemic.	Genetic Algorithm(GA)
Moktadir et al.	2021	Assessing supply chain risks in the leather industry.	Pareto Analysis and BWM
Thakkar et al.	2021	To assess supply chain risks in the oil sector in India.	DEMATEL and ANP
Khan et al.	2022a	To identify supply chain risks in public sector agricultural development projects in Bangladesh.	Delphi and AHP
Khan et al.	2022b	To identify supply chain risks in public sector agricultural development projects in Bangladesh.	Delphi, BWM, ISM and MICMAC
Bhalaji et al.	2022	To assess risks in the collaborative supply chain of a medical equipment manufacturing industry in India.	Grey DEMATEL
Yılmaz et al.	2022	To determine the risk factors of logistics management in hospitals.	DEMATEL
Raian et al.	2022	Assessing textile industry supply chain risks.	Fuzzy synthetic evaluation (FSE)
Raihan et al.	2022	To assess soft drink industry supply chain risks.	Fuzzy comprehensive evaluation method(FCEM) and Fuzzy AHP
Yalçınkaya et al.	2022	To assess supply chain risks in the pharmaceutical industry and analyze the risk performance of 3PL businesses.	Pythagorean fuzzy AHP and Pythagorean fuzzy WASPAS
Sutrisno et al.	2023	To evaluate the small and medium enterprise(SME) supply chain risks of small and medium-sized enterprises producing handicrafts.	PSI and Shannon Entropy
Korucuk et al.	2023	To list the success criteria of agile supply chain management and determine the risk reduction strategy in businesses that produce cleaning and cleaning products.	Bipolar Neutrosophic SWARA and Bipolar Neutrosophic TOPSIS
Khan et al.	2023	Assessing supply chain risks for international development agriculture projects.	FGDFuzzy Delphi
Silva et al.	2023	To assess supply chain risks in Sri Lanka’s spice sector and create mitigation strategies.	AHP and TOPSIS
Das et al.	2023	To assess food grain supply chain risks in India.	Fuzzy AHP and Fuzzy TOPSIS
Krstić et al.	2024a	Assessing agri-food supply chain risks.	Grey BWM and Grey COBRA
Krstić et al.	2024b	Assessing circular supply chain risks.	Fuzzy Delphi, Fuzzy AHP and Fuzzy ADAM
Hosseinpour et al.	2024	Assessing healthcare supply chain risks.	Fuzzy BWM and Fuzzy DEMATEL

Author(s)	Year	Problem(s)
Rezaei	2015	Mobile phone selection problem
Rezaei	2016	Evaluation of the criteria for car purchase
Ahmadi et al.	2017	Evaluation the social sustainability of supply chains
Salimi et al.	2018	Evaluation of companies’ R&D performance
Askarifar et al.	2018	Evaluation of investment opportunities
Rezaei et al.	2018	Evaluation of the weights of logistics performance index criteria
Norouzi et al.	2019	Evaluation of risk factors threatening mega projects
Malek et al.	2019	Measuring the weights of risk factors affecting production factors
Khanmohammadi et al.	2019	Criteria evaluation for motorcycle wheel
Demir and Bircan	2020	Evaluation of the support programs provided by KOSGEB
Yıldırım	2020	Determining the importance levels of the supports provided by KOSGEB
Ali et al.	2020	Robot selection problem
Ulutaş	2020	Vehicle selection problem of cargo companies
Arslanhan and Tosun	2021	The transport mode selection problem of the logistics company
Topal et al.	2021	Evaluation of the criteria used in the selection of transportation vehicles by cargo companies
Ulutaş	2021	The supplier selection problem of the furniture workshop
Aksakal et al.	2022	Insulation material evaluation
Aghaloo et al.	2023	Optimal site choosing for the solar-wind renewable energy systems
Khan et al.	2023	Evaluation of barriers of blockchain technology in food supply chain
Altay et al.	2023	Location selection of e-scooter sharing stations
Badi et al.	2023	Wind farm site selection

Numerical Equivalent	Linguistic Equivalent
1	Equally Important
2	Equally Important - Moderately Important
3	Moderately Important
4	Moderately Important - Highly Important
5	Highly Importan
6	Highly Important - Very Highly Important
7	Very Highly Important
8	Very Highly Important - Utterly Important
9	Utterly Important

Main Criteria	Sub-Criteria	Author(s)
Macro Risk Criteria(MRC)	Natural Disasters (ND)	Chopra & Sodhi (2004)
	War and Terrorism (WT)	Chopra & Sodhi (2004)
	External Legal Issues (ELI)	Wu, Blackhurst & Chidambaram (2006)
Transport Risk Criteria(TRC)	Accidents (AC)	Wu, Blackhurst & Chidambaram (2006)
	Packaging Risk (PR)	Korucuk & Erdal (2018)
	Risk of Disruption on the Road (RDR)	Sodhi et al. (2012)
Supply Risk Criteria(SRC)	Inability to Handle Volume Demand Changes (IHVDC)	Zsidisin & Ellram (2003)
	Capacity and Responsiveness of Alternative Suppliers (CRAS)	Zsidisin & Ellram (2003)
	Unable to Provide Competitive Pricing(UPCP)	Zsidisin & Ellram (2003)
Production Risk Criteria(PRC)	Capacity Cost (CC)	Zsidisin & Ellram (2003)
	Manufacturing Yield (MY)	Cucchiella &Gastaldi (2006)
	Capacity Flexibility (CF)	Zsidisin & Ellram (2003)
Information Risk Criteria(IRC)	Information Infrastructure Breakdown(IIB)	Chopra &Sodhi (2004)
	System Integration and Closed System	Chopra & Sodhi (2004)
	Networking (SICSN)	Wu, Blackhurst, & Chidambaram (2006)
	Internet Security (IS)
Financial Risk Criteria(FRC)	Low Profit Margin (LPM)	Wu, Blackhurst, & Chidambaram (2006)
	Price Fluctuations (PF)	Cucchiella & Gastaldi (2006)
	Exchange Rate (ER)	Chopra & Sodhi (2004)
Technology Risk Criteria(TCRC)	Not Keeping Up with Technology (NKT)	Tuncel & Alpan (2010)
	Lack of Expert Personnel and Equipment (LEPE)	Tuncel & Alpan (2010)
	Technologically Staying Behind Competitors (TSBC)	Zsidisin & Ellram (2003)
Organizational Risk Criteria (ORC)	Workers’ Strikes (WS)	Tuncel & Alpan (2010)
	Lack of Experience or Education (LEE)	Tuncel & Alpan (2010)
	Staff Shortage (SS)	Tuncel & Alpan (2010)
Inventory Risk Criteria(IVRC)	Risks Caused by the Material Flow System (RCMFS)	Jüttner et al. (2003)
	Stock Keeping Risk (SKR)	Chopra & Sodhi (2004)
	Product Obsolescence Rate (POR)	Zsidisin & Ellram (2003)

Main criteria	Weight of main criteria	Consistency rate (ξ_L)	Arrangement	Sub-criteria	Sub-criteria weight	Consistency rate (ξ_L)	Combined weight	Rankings
MRC	0,315		1	ND	0,071	0,196	0,022	11
				WT	0,768		0,242	1
				ELI	0,161		0,051	6
TRC	0,128		3	AC	0,806	0,305	0,103	3
				PR	0,056		0,007	20
				RDR	0,139		0,018	13
SRC	0,192		2	IHVDC	0,142	0,331	0,027	10
				CRAS	0,053		0,01	16-17
				UPCP	0,805		0,155	2
PRC	0,027		9	CC	0,154	0,290	0,004	22-23
				MY	0,79		0,021	12
				CF	0,056		0,002	27
IRC	0,055	0,068	7	IIB	0,767	0,233	0,042	8
				SICSN	0,067		0,004	22-23-24-25
				IS	0,167		0,009	18
FRC	0,064		6	LPM	0,791	0,261	0,051	6
				PF	0,059		0,004	22-23-24-25
				ER	0,15		0,01	16-17
TCRC	0,048		8	NKT	0,172	0,265	0,008	19
				LEPE	0,766		0,037	9
				TSBC	0,063		0,003	26
ORC	0,077		5	WS	0,056	0,290	0,004	22-23-24-25
				LEE	0,79		0,061	5
				SS	0,154		0,012	15
IVRC	0,096		4	RCMFS	0,785	0,251	0,075	4
				SKR	0,067		0,006	21
				POR	0,148		0,014	14

Main Criteria	General Combined Weights of Main Criteria	Rankings
MRC	0,087	4
TRC	0,147	1
SRC	0,104	2
PRC	0,084	5
IRC	0,078	6
FRC	0,096	3
TCRC	0,078	6
ORC	0,059	9
IVRC	0,069	8

Sub-Criteria	General Combined Weights	Rankings
ND	0,024	7
WT	0,015	19
ELI	0,014	20-21-22
AC	0,085	1
PR	0,018	14-15-16
RDR	0,017	17-18
IHVDC	0,019	11-12
CRAS	0,018	14-15-16
UPCP	0,033	3
CC	0,019	11
MY	0,026	6
CF	0,014	20-21-22
IIB	0,021	8-9
SICSN	0,001	27
IS	0,021	8-9
LPM	0,019	11-12
PF	0,017	17-18
ER	0,028	4
NKT	0,014	20-21-22
LEPE	0,027	5
TSBC	0,012	23
WS	0,005	26
LEE	0,018	14-15-16
SS	0,02	10
RCMFS	0,034	2
SKR	0,011	24
POR	0,009	25

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[TFN1] Source: (Demir and Bircan 2020: 174).

Step 2: Determining the best and worst criteria
Best: MRC	Worst: PRC
Step 3: Finding vector A _B
	(MRC)	(TRC)	(SRC)	(PRC)	(IRC)	(FRC)	(TCRC)	(ORC)	(IVRC)
(MRC)	1	3	2	9	7	6	8	5	4
Step 4: Finding vector A _W
	(MRC)	(TRC)	(SRC)	(PRC)	(IRC)	(FRC)	(TCRC)	(ORC)	(IVRC)
(PRC)	9	7	8	1	3	4	2	5	6

Step 2: Determining the best and worst sub-criteria
Best: LPM	Worst: PF
Step 3: Finding vector A _B
	(LPM)	(PF)	(ER)
(LPM)	1	9	7
Step 4: Finding vector A _W
	(LPM)	(PF)	(ER)
(PF)	9	1	7