Management of supply chain: an alternative modelling technique for forecasting
Name
Forecasting and Risk paper in Journal of Operations Research.pdf
Size
425.37 KB
Format
Adobe PDF
Checksum (MD5)
0b70f6ffc676139d2127072b79ec9e56
Author(s)
Datta, Shoumen
Granger, C W J
Barari, M
Gibbs, T
Date Issued
May 23, 2007
Publisher
Journal of the Operational Research Society
Citation
JORS
Abstract
Forecasting is a necessity almost in any operation. However, the tools of forecasting are still primitive in view
of the great strides made by research and the increasing abundance of data made possible by automatic identification
technologies, such as radio frequency identification (RFID). The relationship of various parameters
that may change and impact decisions are so abundant that any credible attempt to drive meaningful associations
are in demand to deliver the value from acquired data. This paper proposes some modifications to adapt
an advanced forecasting technique (GARCH) with the aim to develop it as a decision support tool applicable
to a wide variety of operations including supply chain management (SCM). We have made an attempt to coalesce
a few different ideas toward a ‘solutions’ approach aimed to model volatility and in the process, perhaps,
better manage risk. It is possible that industry, governments, corporations, businesses, security organizations,
consulting firms and academics with deep knowledge in one or more fields, may spend the next few decades
striving to synthesize one or more models of effective modus operandi to combine these ideas with other
emerging concepts, tools, technologies and standards to collectively better understand, analyse and respond
to uncertainty. However, the inclination to reject deep-rooted ideas based on inconclusive results from pilot
projects is a detrimental trend and begs to ask the question whether one can aspire to build an elephant using
mouse as a model.
Description
The tools of forecasting (software) in general business use are still primitive in view of the strides made by research. Hence, promoting advances in forecasting to aid
predictive analytics is deemed a worthwhile endeavour and is the purpose of this paper. Such tools may further reduce uncertainty and volatility characteristic of global trade. The relationship of various business parameters that may change and impact decisions are so abundant that any credible attempt to drive meaningful associations are in demand by global businesses. This paper proposes some modifications to adapt an already advanced forecasting technique (GARCH) with the aim to develop it as a decision support tool applicable to a wide variety of operations including supply chain management.
Subjects
forecasting; supply chain management; multivariate GARCH; risk analysis; intelligent decision systems
MIT Department
Massachusetts Institute of Technology. Auto-ID Laboratory
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