Implementing a Distribution-Network Decision-Support System at Pfizer/Warner-Lambert

Gupta, Vijay; Peters, Emmanuel; Miller, Tan; Blyden, Kelvin

doi:10.1287/inte.32.4.28.54

Cited by 43 publications

(22 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The literature reports on some successful implementations of DSS in either special supply chain planning situations or optimisation models regarding the entire chain. Gupta et al (2002), for example, describe a DSS that helps Pfizer to plan their distribution network. The model is useful in both strategic and operational planning situations.…”

Section: Supply Chain Planningmentioning

confidence: 99%

Centralised supply chain master planning employing advanced planning systems

Rudberg

Thulin²

2009

Production Planning & Control

View full text Add to dashboard Cite

Higher expectations on supply chain performance force organisations to reinvent themselves in order to cut costs and increase customer service, all to gain competitive advantage. Pursuing the best network of manufacturing, supply and distribution facilities relative to the marketplace is therefore on top of many managers' 'most wanted' list concerning supply chain management. Supply chain planners are thus in need of decision support to be able to establish feasible and sufficient plans. This article discusses how decision support through advanced planning systems (APS) can assist tactical supply chain planning. A case study is presented showing how APS can act as an enabler in adapting logistics and supply chain principles, as well as reducing costs through streamlining the supply chain. The purpose of this article is primarily to present findings from a case study regarding supply chain planning with the aid of a master planning APS module. The case study emphasises that APS in the scope of logistics management have several positive effects on supply chain performance.

show abstract

Section: Supply Chain Planningmentioning

confidence: 99%

Centralised supply chain master planning employing advanced planning systems

Rudberg

Thulin²

2009

Production Planning & Control

View full text Add to dashboard Cite

show abstract

“…However, successful testing proved to be not sufficient condition for successful implementation in sequence number 4 [35,67]. The case studies which were classified in this cluster are [67], [68], [69], [70], [71], and [72].…”

Section: Patternsmentioning

confidence: 99%

Logistics technology transfer process model

Hajri

Hasan

2011

First International Technology Management Conference

View full text Add to dashboard Cite

A consecutive number of studies on the adoption trend of logistics technology since 1988 revealed that logistics organizations are not in the frontier when it comes to adopting new technology and this delayed adoption creates an information gap. Given the integrative nature of logistics technology, failure to implement the technology successfully could result in writing off major investments in developing and implementing the technology or even in abandoning the strategic initiatives underpinned by these innovations. Consequently, the need to employ effective strategies and models to cope with these uncertainties is crucial. This research addresses the aspect of uncertainty in implementation success by process research models. Process research approach focuses on the sequence of events in the technology transfer process that occur over time. Through the use of optimal matching from natural science and cluster analysis, this research revealed four patterns that organizations follow when transferring logistics technology namely, formal minimalist, mutual adaptation, development concerned, and organizational roles dispenser. Analysis of the relationship between these patterns and success pointed to a set of crucial and necessary events which differ from one pattern to another. IntroductionUnlike other business fields, logistics has gone through several major redefinitions of its role in organizations [1-3] starting with a fragmented approach to an integrated to a strategic to a supply chain orientation. Along this line of development, several technologies in the logistics and supply chain area have been developed to satisfy the requirements of each stage. Robeson [4] predicted that in 1995, logistics managers would spend their time dealing with issues related with computers and information processing. Later, Dawe [5] stated that logistics organizations are not in the frontier when it comes to adopting new technology and this delayed adoption creates an information gap. Nilsson [6] found that one of the main sources of uncertainty that faces logistics managers is concerned with technology implementation and understanding. Due to the growing complexity of the developed information systems in the logistics area, implementation of these systems becomes expensive, risky, and difficult to achieve [7]. Consequently, the need to employ effective strategies and models to cope with these uncertainties is crucial. Organizational innovation literature offers three ways to study the adoption and implementation of innovations in organizations; diffusion of an innovation, organizational innovativeness and process theory [8]. Diffusion of an innovation refers to its spread through a

show abstract

“…Their study focused on optimizing the fleet size instead of transporter routing. Applications of optimization techniques for the integrated PDRP without the involvement of transporter routing were reported by Martin, Dent and Eckhart (1993), Gupta, Peters and Miller (2002), and Brown, et al (2001).…”

Section: Transportation Duration Constraintsmentioning

confidence: 99%

On the integrated production, inventory, and distribution routing problem

Liu²,

et al. 2006

View full text Add to dashboard Cite

Abstract. The integrated production, inventory and distribution routing problem (PDRP) isconcerned with coordinating the production, inventory and delivery operations to meet customer demand with an objective to minimize the cost. The particular PDRP that we consider in this study also involves heterogeneous transporters with non-instantaneous traveling times and many customer demand centers each with its own inventory capacities. Optimally solving such an integrated problem is in general not easy due to its combinatorial nature, especially when transporter routing is involved.In this paper, we propose a two-phase solution approach to this problem. Phase I solves a mixed integer programming model which includes all the constraints in the original model except the transporter routings are restricted to direct shipment between facilities and customer demand centers. The resulting optimal solution to the Phase I problem is always feasible to the original model. Phase II solves an associated consolidation problem to handle the potential inefficiency of direct shipment. The delivery consolidation problem is formulated as a capacitated transportation problem with additional constraints and is solved by an efficient heuristic routing algorithm. The main advantage of this proposed approach, over the classical decoupled approach, is its ability to simultaneously optimize the production, inventory and transportation operations (subject to restricted routing/direct shipments) without the needs for aggregating the demand and relaxing the constraints on transportation capacities. We evaluate the performance of this proposed two-phase approach and report its application to a real-life supply network which motivated this study.

show abstract

Implementing a Distribution-Network Decision-Support System at Pfizer/Warner-Lambert

Cited by 43 publications

References 14 publications

Centralised supply chain master planning employing advanced planning systems

Centralised supply chain master planning employing advanced planning systems

Logistics technology transfer process model

On the integrated production, inventory, and distribution routing problem

Contact Info

Product

Resources

About