Criteria analysis for deciding the LTL and FTL modes of transport

Vega, Diego Armando Soto De La; Lemos, Paula Horta; Silva, João Eduardo Azevedo Ramos da; Vieira, José Geraldo Vidal

doi:10.1590/1806-9649-2020v28e5065

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ORIGINAL ARTICLE • Gest. Prod. 28 (2) • 2021 • https://doi.org/10.1590/1806-9649-2020v28e5065 copy

Criteria analysis for deciding the LTL and FTL modes of transport

Análise de critérios para decisão dos modos de transporte LTL e FTL

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract:

Choosing between Less Than Truckload (LTL) and Full Truckload (FTL) is based on a multicriteria decision because it takes into account aspects such as costs and operational efficiency in transportation, handling and stock of goods. The aim of this article is to provide a detailed analysis of the criteria that comprise the LTL and FTL transport decision using a multicriteria methodology that considers decision makers´ preferences. The SMARTS method was applied in a telecommunications company that outsources its logistics operation. Results show that the main criteria for choosing LTL shipment are related to the relationship with the customer and the efficiency in the delivery of the freight (reliability of the delivery time). On the other hand, choosing FTL transport requires a high weight given to the criteria regarding benefits and cost of transit inventory. The contribution of the research is to provide a structured and flexible analysis to choose the types of shipment (FTL and FLT) when taking into account the risk aversion of decision makers using a non-optimal approach, but with approximate solutions encompassing several qualitative and quantitative criteria in this choice.

Keywords:
Supply chain; Distribution problem; Less Than Truckload (LTL) and Full Truckload (FTL); Criteria; Multicriteria Decision Making

Category	Methods	Strong points	Weak points
MCDM	MAUTs	(1) Quick and easy to use	(1) The weightings assigned to the criteria depend on human judgments.
	AHP	(2) They can deal with multiple qualitative or quantitative criteria.	(2) No possibility to include restrictions on the models.
	ANP	(3) They consider the dependence between the criteria.	(3) The construction of comparison matrices and utility curves is a tedious and time-consuming task.
	TOPSIS
	ELETRE
	ISM
	VIKOR
	DEMATEL
	QFD
Statistical approaches	Correlation methods	(1) Allow analysis in large databases.	(1) They do not provide any optimal solution.
	Cluster analysis	(2) They can be applied to complex problems.	(2) No possibility of including constraints in the model (3) Difficulty to define “cluster” when the criteria are highly dependent.
	Binary Logit

Mathematical Programming	LP/NLP	(1) Possibility of introducing constraints in the model.	(1) Difficulty in measuring qualitative criteria.
	MILP	(2) They can optimize desired variables.	(2) Difficulty in analyzing the results, in the case of MOP. (3) Computing the optimal solution can be very time-consuming in NP-Hard problems.
	MOP	(1) The criterion does not necessarily have a common dimension.
	DEA
	DP

Artificial Intelligence	Data mining	(1) They offer a flexible knowledge base.	(1) The pool of knowledge and access to knowledge is difficult. (2) They can be very expensive.
	ANN	(2) They take qualitative factors into account.
	Delphi Method	(3) They allow us to work with complexities and uncertainty, as they are designed to operate similarly to human judgments.
	Inference method

Criteria	Criteria level 2	Criteria level 3
Relevant costs
		Transport costs
		Inventory maintenance cost
		Transit inventory cost
Quality of service/benefit
	Efficiency	Network Lead Time
	Operational	Transit time reliability
		Reliable collection service
		Low delivery error
		Service frequency
	Security in	Cargo security
	Service Provision	Freight transport performance history
		Rapid response in emergency situations
		Geographical coverage of transport and access
		Documentation and billing services
	Customer service	Quality of transport personnel
		Cooperation of transport personnel
		Frequent communication with the carrier
		Information technology (IT)
	Handling Factor	Ease of making changes to shipped freight
	and Handling	Flexible schedules
		Shipment capacity limitations
		Ease of loading and unloading operations
		Shipment tracking
	Customization	Compatibility of strategies
	Factor	Possibility of long-term relationship
		Flexible rates

Utility $f (x)$	Average network lead time $(x)$
0	$x \geq$ 3.0 days
25	2.89 days
50	2.39 days
75	1.78 days
100	$x \leq$ 0.54 days
Approximate function $(0 \leq f (x) \leq 100)$
$f (X) = - 12.103 x^{3} + 50.448 x^{2} - 84.739 x + 133.04$

Criteria	DM1		DM2		DM3
Criteria	Relative Weight	General Weight	Relative Weight	General Weight	Relative Weight	General Weight
Relevant costs	66.7%	66.7%	50.0%	50.0%	66.7%	66.7%
C. Transport	34.5%	23.0%	31.1%	15.5%	33.3%	22.2%
C. Inventory	54.6%	36.5%	49.3%	24.7%	33.3%	22.2%
C. Transit Inv.	10.9%	7.2%	19.6%	9.8%	33.3%	22.2%
Benefit	33.3%	33.3%	50.0%	50.0%	33.3%	33.3%
Operational Efficiency	23.3%	7.8%	47.6%	23.8%	26.1%	8.7%
Lead time	44.5%	3.5%	33.0%	7.8%	22.7%	2.0%
Reliability	31.0%	2.4%	30.3%	7.2%	22.7%	2.0%
Reliable collection	14.2%	1.1%	139%	3.3%	19.7%	1.7%
Delivery error	7.4%	0.6%	20.2%	4.8%	19.7%	1.7%
Frequency	2.9%	0.2%	2.7%	0.6%	15.2%	1.3%
Security in provision	13.2%	4.4%	19.1%	9.6%	32.3%	10.8%
Freight security	8.5%	0.4%	13.6%	1.3%	35.7%	3.8%
History	39.6%	1.7%	19.3%	1.8%	16.1%	1.7%
Responsiveness	19.3%	0.8%	17.5%	1.7%	16.1%	1.7%
Coverage/access	14.3%	0.6%	21.8%	2.1%	17.8%	1.9%
Documentation	18.4%	0,8%	27,8%	2.7%	14.4%	1.5%
Customer service	48.5%	16.2%	23.9%	11.9%	17.4%	5.8%
Quality of Personnel	24.0%	3.9%	24.6%	2.9%	33.3%	1.9%
Cooperation	15.2%	2.5%	21.0%	2.5%	23.5%	1.4%
Communication	8.5%	1.4%	24.6%	2.9%	30.6%	1.8%
Information Technology	52.3%	8.5%	29.8%	3.6%	12.5%	0.7%
Handling factor	7.7%	2.6%	3,4%	1.7%	13.1%	4.4%
Changes	6.0%	0.2%	17.5%	0.3%	25.5%	0.9%
Flexible programs	20.9%	0.5%	16.9%	0.3%	24.2%	1.1%
Capacity	16.7%	0.4%	9.9%	0.2%	20.8%	0.9%
Op. load/unload	9.0%	0.4%	9.4%	0.2%	13.9%	0.6%
Tracking	47.4%	1.2%	46.3%	0.8%	15.5%	0.7%
Personalized factor	7.3%	2.4%	6.1%	3.0%	11.2%	3.7%
Compatibility	24.9%	0.6%	25.0%	0.8%	55.0%	2.0%
LP Relationship	15.7%	0.4%	25.5%	0.8%	21.0%	0.8%
Flexible rates	59.4%	1.4%	50.0%	1.5%	24.0%	0.9%
General inconsistency:		4%		5%		3%

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E-mail: gp@dep.ufscar.br

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