Network Planning and Retail Store Segmentation

2013 ◽  
Vol 4 (1) ◽  
pp. 67-79 ◽  
Author(s):  
Philip Bermingham ◽  
Tony Hernandez ◽  
Ian Clarke
Keyword(s):  
Decision Support ◽  
Capital Investment ◽  
Case Study Research ◽  
Network Planning ◽  
Decision Makers ◽  
Support Network ◽  
Decision Support Tools ◽  
Skill Levels ◽  
Support Tools ◽  
Geospatial Tools

Store segmentation aims to divide a network of stores into meaningful groups, typically based on a combination of operational, site and trading environment characteristics. It is an increasingly important component within network planning activities of major retail chains due to the significant capital investment that is physically grounded in their large store networks. The paper outlines findings from case study research that has focused on developing spatial decision support tools that enable decision makers to explore, construct and visualize store segments. An integrated spatial statistical approach to store segmentation is detailed and associated benefits and shortfalls discussed. The paper highlights the potential to develop customised geospatial tools to support network planning decision making activities. It is argued that geospatial decision support tools need to be designed to accommodate the varying GIS skill-levels of potential end-users and that fundamentally more emphasis needs to be placed on creating tools that can be used by decision-makers as opposed to analysts.

Decision Support Tools for Managing Unpredictability in the Supply Chain

2000 ◽  
Author(s):  
Carol Vesier
Keyword(s):  
Supply Chain ◽  
Decision Support ◽  
Inventory Management ◽  
Production Scheduling ◽  
Capital Investment ◽  
Investment Strategy ◽  
Decision Support Tools ◽  
Business Outcomes ◽  
Support Tools

Abstract Effectively managing unpredictability requires decision support tools that can predict the financial and business outcomes of various supply chain strategies. This paper will discuss the role of these decision support tools and their characteristics as well as review a case study. In the case study, decision support tools facilitated development of strategies that increased after tax profit by $140 Million. These strategies included: • Reliability improvement strategy: Identifying the reliability improvements that offered the biggest profitability impact. • Supply chain strategy: Defining inventory management and production scheduling rules that ensured order shipment within two days. • Capital investment strategy: Defining when new capacity should come on line as well as the minimum capital investment.

scholarly journals Communicating with Coastal Decision-Makers and Environmental Educators via Sea Level Rise Decision-Support Tools

2018 ◽  
Vol 17 (03) ◽  
pp. A03 ◽  
Author(s):  
Denise DeLorme ◽  
Sonia Stephens ◽  
Scott Hagen ◽  
Matthew Bilskie
Keyword(s):  
Decision Support ◽  
Focus Groups ◽  
Sea Level Rise ◽  
Sea Level ◽  
Decision Makers ◽  
Decision Support Tools ◽  
Support Tools ◽  
Environmental Educators ◽  
Stakeholder Needs

Communicating about environmental risks requires understanding and addressing stakeholder needs, perspectives, and anticipated uses for communication products and decision-support tools. This paper demonstrates how long-term dialogue between scientists and stakeholders can be facilitated by repeated stakeholder focus groups. We describe a dialogic process for developing science-based decision-support tools as part of a larger sea level rise research project in the Gulf of Mexico. We demonstrate how focus groups can be used effectively in tool development, discuss how stakeholders plan to use tools for decision-making and broader public outreach, and describe features that stakeholders perceive would make products more usable.

Intelligence Density

2011 ◽  
pp. 630-633
Author(s):  
David Sundaram ◽  
Victor Portougal
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Data Warehousing ◽  
Decision Makers ◽  
Decision Support Tools ◽  
Amount Of Information ◽  
Support Tools ◽  
The World

The amount of information that decision makers have to process has been increasing at a tremendous pace. A few years ago it was suggested that information in the world was doubling every 16 months. The very volume has prevented this information from being used effectively. Another problem that compounds the situation is the fact that the information is neither easily accessible nor available in an integrated manner. This has led to the oft-quoted comment that though computers have promised a fount of wisdom they have swamped us with a flood of data. Decision Support Systems (DSS) and related decision support tools like data warehousing and data mining have been used to glean actionable information and nuggets from this flood of data.

Decision Support Tools For Assessing Petroleum Upstream Investments

2020 ◽  
pp. 323
Author(s):  
Nour Elislam Djedaa ◽  
Abderrezak Moulay Lakhdar
Keyword(s):  
Decision Support ◽  
Decision Support Tools ◽  
Support Tools

Elicitation of expert knowledge to develop decision support tools for use in water demand management planning

2007 ◽  
Vol 7 (5-6) ◽  
pp. 53-60
Author(s):  
D. Inman ◽  
D. Simidchiev ◽  
P. Jeffrey
Keyword(s):  
Decision Support ◽  
Water Demand ◽  
Expert Knowledge ◽  
Demand Management ◽  
Decision Processes ◽  
Conservation Strategy ◽  
Influence Diagrams ◽  
Decision Support Tools ◽  
Water Demand Management ◽  
Support Tools

This paper examines the use of influence diagrams (IDs) in water demand management (WDM) strategy planning with the specific objective of exploring how IDs can be used in developing computer-based decision support tools (DSTs) to complement and support existing WDM decision processes. We report the results of an expert consultation carried out in collaboration with water industry specialists in Sofia, Bulgaria. The elicited information is presented as influence diagrams and the discussion looks at their usefulness in WDM strategy design and the specification of suitable modelling techniques. The paper concludes that IDs themselves are useful in developing model structures for use in evidence-based reasoning models such as Bayesian Networks, and this is in keeping with the objectives set out in the introduction of integrating DSTs into existing decision processes. The paper will be of interest to modellers, decision-makers and scientists involved in designing tools to support resource conservation strategy implementation.

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