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Fast fulfilment is a key performance measure in online retail, and some retailers have achieved faster times by adopting new designs in their order fulfilment infrastructure. This research empirically confirms and quantifies the fulfilment time advantage that Amazon has achieved, relative to other online retailers. The purpose of this paper is to investigate three research questions: what is the overall mean fulfilment time difference between the new logistics designs of Amazon and the alternative designs of other retailers? For each order what is the distribution of the fulfilment time difference? What is the difference in fulfilment time by product category, price and size?

This research uses an empirical method to evaluate the fulfilment time performance of consumer orders made through the Amazon website and one or more competing online retailers. For 1,000 different products two fulfilment times, one at Amazon and another at a competing omnichannel retailer, are recorded. The analysis is then focused on the comparison between this paired data.

The research confirms that the new logistics methods, including physical facilities, distribution networks and intelligent order processing methods, have resulted in faster order fulfilment times. The performance, though, is not universally dominant and for 33 per cent of orders, the difference is 1 day or less. The fulfilment time difference varied by product, category, price or size.

The ongoing transformation of fulfilment and logistics operations at online retailers has generated several new research questions. This includes the need to confirm the fulfilment efficiency of the new designs and specify time targets. This paper identifies the fulfilment time gap between new and traditional operations. The results suggest that store-based or distribution centre-based fulfilment strategies may not match the new designs.

The study provides a quantitative analysis of the fulfilment time differentials in online retailing. The critical role of fulfilment logistics in the rapidly growing online retail industry can now be better modelled and studied. The survey method representing a single buyer allows for order pair equivalency and eliminates order bias. The results suggest that new warehousing and logistics designs can lead to significantly faster fulfilment times.

Resource allocation is essential to infrastructure management. The purpose of this study is to develop a methodological framework for resource allocation that takes interdependencies among infrastructure systems into consideration to minimize the overall impact of infrastructure network disruptions due to extreme events.

Taking advantage of agent-based modeling techniques, the proposed methodology estimates the interdependent effects of a given infrastructure failure which are then used to optimize resource allocation such that the network-level resilience is maximized.

The findings of the study show that allocating resources with the proposed methodology, where optimal infrastructure reinforcement interventions are implemented, can improve the resilience of infrastructure networks with respect to both direct and interdependent risks of extreme events. These findings are also verified by the results of two case studies.

As the two case studies have shown, the proposed methodological framework can be applied to the resource allocation process in asset management practices.

The proposed methodology improves the resilience of the infrastructure network, which can alleviate the social and economic impact of extreme events on communities.

Capitalizing on the combination of agent-based modeling and simulation-based optimization techniques, this study fulfills a critical gap in infrastructure asset management by incorporating infrastructure interdependence and resilience concepts into the resource allocation process.

The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.

Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.

The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.

The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.

The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.