Canadian forces global reach support hubs: facility location and aircraft routing models

Ghanmi, Ahmed

doi:10.1057/jors.2010.22

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…The performance of the defence supply chain during Operation TELIC exhibited poor delivery time performance, not least high delivery time variability. This variability meant that frontline customers could not be sure whether their equipment would be ready for use at a given time, a finding consistent with reports on the challenges facing expeditionary forces in general (O'Fearna et al , 2002; Ghanmi, 2011) and to the experience of forces from other nations during the same conflict (Morales and Gehry, 2003; Smith, 2007).…”

Section: Discussion Of Findingssupporting

confidence: 53%

“…The delivery lead‐time of inventory from UK stock holding points to the end customer in the frontline during Operation TELIC was measured against a set of targets. Included in these were leadtime targets for delivery of spares to theatre, noted elsewhere as a key performance measure for an operational support system (Ghanmi, 2011). Post Operational reports identified were frequently not met (Ministry of Defence, 2003) with the conclusions drawn from this data that the supply chain that supported the Operational deployment was sub‐optimal (Defence Logistic Organisation, 2003) and that the situation must be improved in the future.…”

Section: Diagnosing Delivery Time Variability a Case Study Of A Defence Supply Chainmentioning

confidence: 99%

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Applying selected quality management techniques to diagnose delivery time variability

Chapman

Bernon

Haggett³

2011

International Journal of Quality &Amp; Reliability Management

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PurposeThis research seeks to identify and apply techniques that can be used in a supply chain context to diagnose the causes of variability in delivery lead time.Design/methodology/approachA literature review was conducted and a number of quality management (QM), techniques were selected as candidates for diagnosing delivery time variability. A case study of the application of these techniques is provided on the UK‐based defence supply chain that supported UK operations in the Iraq war of 2003.FindingsCandidate QM techniques for diagnosing delivery time variability were identified, namely: Process Chart; Histogram; Failure Mode and Effect Analysis; and Cause and Effect Analysis. These techniques were successful in enabling the diagnosis of the causes of delivery time variability in the context of the case study investigated.Practical implicationsThe work illustrates how QM techniques can be employed to address issues with supply chains, not least with regard to the important problem of variability in delivery leadtime. In practice, this highlights benefits that result to practitioners in order to improve the performance of operations in a dynamic setting, such as the defence supply chain studied here.Originality/valueThis work has value in presenting the findings of an in‐depth case study on the application of QM techniques in a multi‐echelon supply chain setting. It is also original in employing the FMEA technique together with an end‐customer perspective to assess the effect of failure modes in operations across a supply chain. FMEA also provided the means to examine supply chain risk, thus providing a research instrument for deploying risk as a lens. The application of QM techniques in this novel setting provides support for their application beyond the conventional setting of internal operations.

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Section: Discussion Of Findingssupporting

confidence: 53%

Section: Diagnosing Delivery Time Variability a Case Study Of A Defence Supply Chainmentioning

confidence: 99%

Applying selected quality management techniques to diagnose delivery time variability

Chapman

Bernon

Haggett³

2011

International Journal of Quality &Amp; Reliability Management

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“…As Fig. 8 summarises, the application fields of facility location problems includes reverse logistics [2,35], electric vehicle-based supply solutions [3,4], natural disasters and emergency situations [5,6,7,13,36], car sharing systems [9,10], drug delivery [11,12], drone-based delivery [21][22][23], tourist logistics [24,25], city logistics [27], perishable product supply chain [28], explosive waste management [37], relief logistics [38], postal services [39], cylinder gas distribution [40], soybean export [41], aircraft routing [42], terminal location and ship routing [43] and defence courier service [44].…”

Section: Figure 7 Solutions Algorithms Of Facility Location Problemsmentioning

confidence: 99%

Layout optimization of a multi-centre distribution mode

Vereš

Bányai

2023

ALS

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Supply chain planning is becoming increasingly important today, as it is essential for efficient operations to design supply chains that are flexible, sustainable, highly available and able to respond quickly and efficiently to dynamically changing customer needs, while operating cost-effectively. When designing supply chains, it is important to consider not only the investment cost but also the operational cost. This requires the most accurate forecasting of future supply chain needs. In this paper, the authors present a new method for the optimal design of a multicentre distribution network, which uses evolutionary optimisation and Monte Carlo simulation to determine the optimal distribution network structure in an arbitrary geographical area. The design takes into account investment and operational costs, with particular emphasis on the question of how to estimate the average transportation distances in a distribution network with an unknown structure.

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“…Some of these issues were examined in the literature. In a military context, Ghanmi and Shaw (2008) and Ghanmi (2010) used location and simulation models to investigate some of the SCN design trade-offs faced by the CF. In a humanitarian logistics context, Lodree and Taskin (2008) and Campbell and Jones (2011) combine location and news-vendor inventory analysis to determine where and how much supplies to preposition in preparation for a disaster.…”

Section: Introductionmentioning

confidence: 99%

Designing global supply networks for conflict or disaster support: the case of the Canadian Armed Forces

Martel

Benmoussa

Chouinard³

et al. 2013

Journal of the Operational Research Society

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In order to fulfil Canada's international disaster relief, humanitarian assistance, peacekeeping and peace enforcement roles, the Canadian Forces (CF) rely on a supply network to deploy and sustain its overseas missions. Warehousing, maintenance, transhipment and transportation activities are required to support missions. Currently, the CF supply network does not incorporate any permanent overseas depots. Since international needs and Canada's roles have significantly evolved during the last decade, and given that supply network efficiency and robustness are critical for missions' success, reengineering the CF supply network to consider the incorporation of permanent international prepositioning depots has become an important issue. This paper proposes an activity-based stochastic programming model to optimise the CF overseas supply network. It also shows how the model proposed can be used to improve the global reach of the CF.

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Canadian forces global reach support hubs: facility location and aircraft routing models

Cited by 10 publications

References 22 publications

Applying selected quality management techniques to diagnose delivery time variability

Applying selected quality management techniques to diagnose delivery time variability

Layout optimization of a multi-centre distribution mode

Designing global supply networks for conflict or disaster support: the case of the Canadian Armed Forces

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