A multi-criterion decision analysis (MCDA) approach to pallet material selection for varying supply chain networks
Date
2019
Authors
Chama, Chama
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Abstract
Pallets maintain a pivotal role in the storage and transportation of products
within a supply chain. Although pallets have been traditionally constructed
from wood, there has been a shift towards plastic and other composite
materials for pallet construction. Subsequently, debate has inundated the
industry with questions pertaining to the identification of “the best” pallet
material type in relation to a supply chain, which could be highly subjective.
As such, the aim of this research is to provide insights into the impact that
these materials of construction (MOC), of a pallet, have on a supply chain
network and examine the limitations and drawbacks as well as strengths
and benefits associated with pallets of a particular MOC. The goal is to
assist the end consumer come to an informed decision when making the
choice between pallets of different MOC. In order to achieve this, the main
objective of the research was to develop a decision support tool that can be
used by industry professionals when deciding between alternative pallets
for their supply chain.
Four main facets were used in order to conduct the study namely: (I) a
theoretical framework and market analysis, which laid the foundation of the
study; (II) a material analysis phase which looked at the material properties
of the selected pallets with the incorporation of modelling; (III) a macro study
phase which looked at the “big” picture in the form of a case study; and (IV)
a final phase which culminated the findings of the study and presented the
backdrop for the formation of a decision support tool. A mixture of qualitative
and quantitative research methods, feature in this study in order to fully
examine the research objectives.
In addition to the literature review, surveys were circulated to prominent
pallet manufacturers and consumers within the local South African (SA)
market. This was the method of choice for data collection in phase I. Within
phase II, the materials used for pallet construction were characterized by
scanning electron microscopy (SEM) and atomic force microscopy (AFM)
for surface morphology and topology characteristics respectively. Fourier
transform infrared spectroscopy (FTIR) analysis was used to confirm the
chemical structure of the MOC and assess if major additives were present
that could affect pallet performance. Lastly tensile testing was carried out
as a means by which to assess mechanical properties of the pallet MOC
and provide useful data for the simulation models. A case study was the
main feature of phase III.
Pallets of wood and plastic were introduced into 3 different supply chain
types namely: i) a cold chain loop in the frozen vegetable sector, ii) an
ambient goods chain primarily featuring household cleaning agents and iii)
a perishable foods chain. This then presented the opportunity to
substantiate test results (from phase II) with real world data. In the final
phase, a multi-criteria decision analysis (MCDA) tool is formulated using
analytic hierarchy process techniques.
From the first phase of the study, a survey was used to evaluate the local
landscape and market perception of pallets from a consumer and
manufacturer perspective. The survey results confirmed the dominance of
wood as a MOC in the SA marketplace with 86 % of the total pallet market
being wood, as per respondents from pallet manufacturers and poolers
alike. With 85.71 % of respondents asserting that they supplied fast moving
consumer goods (FMCG) industries, the decision to conduct the case study
in this field also proved justified.
The characterization of the MOCs provided critical material data for use in
the pallet modelling and provided insight into the chemical and mechanical
properties that the materials possess. In terms of pallet tests, all the pallets
performed well in the stacking and racking tests. Average deflections of
5.7 mm, 3.7 mm and 5.0 mm in the racking tests were recorded for the wood
(Pallet B – SA Pine), plastic (Pallet A – HDPE + PP) and plastic (Pallet C –
PP) pallets respectively. In terms of the stacking tests, deflection rates were
4.0 mm, 3.5 mm and 4.8 mm for these pallets as well.
The case study results indicated that each pallet had areas of strength and
weakness with respect to a particular supply chain network or operating
environment. The scorecards for each pallet were specified and derived
from site observations (based on performance within supply chains). The
plastic (Pallet C – PP) pallet was however the best performer overall (as an
aggregate across all 3 supply chains was considered) with an average
rating of 4.81/5 and closely followed by wood (Pallet B) at 4.70/5 and lastly
4.54/5 for the Pallet A plastic pallet. These results stem from a comparison
of the pallets across a number of criteria with a focus on the various stages
along the supply chain.
As a consequence, the study also proved that deciding on a pallet type for
a particular supply chain is an arduous task due to the complexity of
assessing pallets in different scenarios and the varied performance
attributes presented and requires consideration of multiple criteria.
Therefore, the proposed solution may have practical implications for all
users of pallets in that the formulated tool can support in optimising the
consumer’s supply chains by choosing the best pallet solution to meet their
needs. The resulting MCDA tool along with pertinent media from the case
study is hosted on the following web address and can be accessed at:
https://pallet-mcda.firebaseapp.com. Here the tool will perform the relevant
calculations on the basis of customer requirements and output a suggested
pallet solution.
Description
A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering
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Citation
Chama, Chama. (2919). A multi-criterion decision analysis (MCDA) approach to pallet material selection for varying supply chain networks. University of the Witwatersrand, https://hdl.handle.net/10539/29377