Charles W. Chase

493 total citations
26 papers, 274 citations indexed

About

Charles W. Chase is a scholar working on Management Information Systems, Management Science and Operations Research and Organic Chemistry. According to data from OpenAlex, Charles W. Chase has authored 26 papers receiving a total of 274 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Management Information Systems, 10 papers in Management Science and Operations Research and 1 paper in Organic Chemistry. Recurrent topics in Charles W. Chase's work include Big Data and Business Intelligence (12 papers), Forecasting Techniques and Applications (9 papers) and Quality and Supply Management (2 papers). Charles W. Chase is often cited by papers focused on Big Data and Business Intelligence (12 papers), Forecasting Techniques and Applications (9 papers) and Quality and Supply Management (2 papers). Charles W. Chase collaborates with scholars based in Japan. Charles W. Chase's co-authors include Kenneth B. Kahn, Francis G. Fang, Bryan M. Lewis, Xiaojie Zhu, Matthew Schnaderbeck, Gordon Wilkie, J.T. Bell, J.L. Collins, Kevin K. Anderson and B.Z. Egan and has published in prestigious journals such as Synlett, Journal of food distribution research and CERN Document Server (European Organization for Nuclear Research).

In The Last Decade

Charles W. Chase

24 papers receiving 256 citations

Peers

Charles W. Chase
Suneel Sharma Australia
Young H. Chun United States
Suk‐Gwon Chang South Korea
Charles W. Chase
Citations per year, relative to Charles W. Chase Charles W. Chase (= 1×) peers Luis Aburto

Countries citing papers authored by Charles W. Chase

Since Specialization
Citations

This map shows the geographic impact of Charles W. Chase's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Charles W. Chase with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charles W. Chase more than expected).

Fields of papers citing papers by Charles W. Chase

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charles W. Chase. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Charles W. Chase. The network helps show where Charles W. Chase may publish in the future.

Co-authorship network of co-authors of Charles W. Chase

This figure shows the co-authorship network connecting the top 25 collaborators of Charles W. Chase. A scholar is included among the top collaborators of Charles W. Chase based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Charles W. Chase. Charles W. Chase is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kahn, Kenneth B. & Charles W. Chase. (2018). The State of New-Product Forecasting. RePEc: Research Papers in Economics. 24–31. 9 indexed citations
2.
Chase, Charles W.. (2015). Using Downstream Data to Improve Forecast Accuracy. 34(1). 21. 1 indexed citations
3.
Chase, Charles W.. (2014). Innovations in Business Forecasting. 33(1). 22. 2 indexed citations
4.
Chase, Charles W.. (2014). Innovations in Business Forecasting: Predictive Analytics. 33(2). 26. 6 indexed citations
5.
Chase, Charles W.. (2013). Putting "M"arketing Back in S&OP. 32(1). 4. 5 indexed citations
6.
Chase, Charles W.. (2013). Using Demand Sensing and Shaping to Improve Demand Forecasting. 32(4). 24. 5 indexed citations
7.
Chase, Charles W.. (2013). Using Big Data to Enhance Demand-Driven Forecasting and Planning. 32(2). 27. 24 indexed citations
8.
Lewis, Bryan M., Charles W. Chase, Francis G. Fang, et al.. (2013). Process Development of Halaven®: Synthesis of the C1-C13 Fragment from d-(-)-Gulono-1,4-lactone. Synlett. 24(3). 323–326. 25 indexed citations
9.
Chase, Charles W.. (2009). Demand-Driven Forecasting: A Structured Approach to Forecasting. CERN Document Server (European Organization for Nuclear Research). 41 indexed citations
10.
Chase, Charles W.. (2004). IMPLEMENTING A FULLY INTEGRATED SALES FORECASTING SOLUTION. 23(1). 2. 1 indexed citations
11.
Chase, Charles W.. (2000). Composite Forecasting: Combining Forecasts for Improved Accuracy. 19(2). 2. 14 indexed citations
12.
Chase, Charles W., et al.. (2000). MODELING THE SUPPLY CHAIN USING MULTI-TIERED CAUSAL ANALYSIS. Journal of food distribution research. 31(1). 9–13. 2 indexed citations
13.
Chase, Charles W.. (1999). Revenue Management: A Review. 18(1). 2. 10 indexed citations
14.
Chase, Charles W.. (1998). Getting People to Use Your Forecasts. 17(1). 2. 1 indexed citations
15.
Chase, Charles W.. (1998). The Role of the Demand Planner in Supply Chain Management. 17(3). 2. 6 indexed citations
16.
Chase, Charles W.. (1997). Selecting the Appropriate Forecasting Method. 16(3). 2. 6 indexed citations
17.
Chase, Charles W.. (1996). What You Need to Know When Building a Sales Forecasting System. 15(3). 2. 1 indexed citations
18.
Chase, Charles W.. (1995). Measuring Forecast Accuracy. 14(3). 2. 38 indexed citations
19.
Chase, Charles W.. (1993). Ways to Improve Sales Forecasts. 12(3). 15. 13 indexed citations
20.
Chase, Charles W.. (1993). Understanding the Gap between Theory and Practice. 12(1). 26. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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