Forecasting Software in Practice: Use, Satisfaction, and Performance

Sanders, Nada R.; Manrodt, Karl

doi:10.1287/inte.33.5.90.19251

Cited by 66 publications

(34 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This also positions future work on FDSS within the context of adaptive systems where the complexity of a time series could be used to design systems that restrict or guide forecaster actions. This of course is critical as about 60% of forecasters, in some manner, adjust forecasts from their FDSS and, in the process, often end up compromising forecast accuracy (Sanders and Manrodt, 2003). Complex series might benefit from greater guidance or might require that some features of an FDSS be made "unavailable" to forecasters.…”

Section: Introductionmentioning

confidence: 99%

Replication and Extension of a Forecasting Decision Support System: An Empirical Examination of the Time Series Complexity Scoring Technique

Adya¹,

Lusk²

2018

AIS TRR

View full text Add to dashboard Cite

Abstract:This study presents a conceptual replication of Adya and Lusk's (2016) forecasting decision support system (FDSS) that identifies the complexity or simplicity of a time series. Prior studies in forecasting have argued convincingly that the design of FDSS should incorporate the complexity of the forecasting task. Yet, there existed no formal way of determining time series complexity until this FDSS, referred to as the Complexity Scoring Technique (CST). The CST uses characteristics of the time series to trigger 12 rules that score the complexity of a time series and classify it along the binary dimension of Simple or Complex. The CST was originally validated using statistical forecasts of a small set of 54 time series as well as judgmental forecasts from 14 representative participants to confirm that the FDSS successfully distinguished Simple series from Complex ones. In this study, we (a) replicate the CST on a much larger set of data from both statistical and judgmental forecasting methods, and (b) extend and validate the series classification categories from the binary Simple-Complex used in the original CST to Very Simple, Simple, Complex, and Very Complex thus adding an ordinal link between the two previous binary designations. Findings suggest that both the replication and extension of the CST further validate it, thereby greatly enhancing its use in the practice of forecasting. Implications for research and practice are discussed.

show abstract

Section: Introductionmentioning

confidence: 99%

Replication and Extension of a Forecasting Decision Support System: An Empirical Examination of the Time Series Complexity Scoring Technique

Adya¹,

Lusk²

2018

AIS TRR

View full text Add to dashboard Cite

show abstract

“…The negative effects of unsuccessful sales forecasting have been documented widely (see e.g., Kerkkänen et al, 2009). While a number of surveys indicate that quantitative sales forecasting methods may not be preferred (Sanders and Mandrodt, 2003), there seems nonetheless to be an upward trend for using quantitative modelling (see e.g., Fotr et al, 2015), including the use of PA systems.…”

Section: Introductionmentioning

confidence: 99%

The role of a predictive analytics project initiator in the integration of financial and operational forecasts

Huikku

Hyvönen

Järvinen

2017

BJM

View full text Add to dashboard Cite

Purpose -This paper investigates the initiation of accounting information system projects. Specifically, it examines the role of the predictive analytics (PA) project initiator in the integration of financial and operational sales forecasts.Design/Methodology/Approach -The study uses a field study method to address the studied phenomenon in 8 Finnish companies that have recently adopted PA systems. The data is primarily based on 19 interviews in the companies and 5 interviews with the PA consultants.Findings -We found that initiators appear to play a major role regarding the degree of integration of financial and operational sales forecasts. The initiators from an accounting function have a tendency to pay more attention to the integration than the representatives from other functions, such as operations and sales.Practical Implications -Our study also makes a practical contribution to companies in showing and discussing the important role of the accounting department as an initiator of a project if the target is to achieve a tight coupling of financial and operational forecast figures, i.e., "one set of numbers." Originality/Value -Even though companies have increasingly adopted PA systems in recent years, we still know little about how the initiation affects the design of accounting information systems overall.

show abstract

“…For instance, in a survey of 240 US firms, only 11% used forecasting decision support systems (FDSSs) and, within this sub-group, over 60% judgmentally adjusted software-generated forecasts. 2 Although best practices around judgmental techniques have been rapidly accumulating, many aspects still require further research. In this study, we address one such aspect -time series complexity for decision support and FDSS design.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a rule-based time series complexity scoring technique to support design of adaptive forecasting DSS

Adya

Lusk

2016

Decision Support Systems

View full text Add to dashboard Cite

Abstract:Evidence from forecasting research gives reason to believe that understanding time series complexity can enable design of adaptive forecasting decision support systems (FDSSs) to positively support forecasting behaviors and accuracy of outcomes. Yet, such FDSS design capabilities have not been formally explored because there exists no systematic approach to identifying series complexity. This study describes the NOT THE PUBLISHED VERSION; this is the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation at the bottom of the page.Decision Support Systems, Vol 83 (March 2016): pg. 70-82. DOI. This article is © Elsevier and permission has been granted for this version to appear in e-Publications@Marquette. Elsevier does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Elsevier.2 development and validation of a rule-based complexity scoring technique (CST) that generates a complexity score for time series using 12 rules that rely on 14 features of series. The rule-based schema was developed on 74 series and validated on 52 holdback series using well-accepted forecasting methods as benchmarks. A supporting experimental validation was conducted with 14 participants who generated 336 structured judgmental forecasts for sets of series classified as simple or complex by the CST. Benchmark comparisons validated the CST by confirming, as hypothesized, that forecasting accuracy was lower for series scored by the technique as complex when compared to the accuracy of those scored as simple. The study concludes with a comprehensive framework for design of FDSS that can integrate the CST to adaptively support forecasters under varied conditions of series complexity. The framework is founded on the concepts of restrictiveness and guidance and offers specific recommendations on how these elements can be built in FDSS to support complexity.

show abstract

Forecasting Software in Practice: Use, Satisfaction, and Performance

Cited by 66 publications

References 16 publications

Replication and Extension of a Forecasting Decision Support System: An Empirical Examination of the Time Series Complexity Scoring Technique

Replication and Extension of a Forecasting Decision Support System: An Empirical Examination of the Time Series Complexity Scoring Technique

The role of a predictive analytics project initiator in the integration of financial and operational forecasts

Development and validation of a rule-based time series complexity scoring technique to support design of adaptive forecasting DSS

Contact Info

Product

Resources

About