Collaborative decision-making for hierarchical forecasts: a multi-sector perspective

Sarma, Violina 2020. Collaborative decision-making for hierarchical forecasts: a multi-sector perspective. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

Demand forecasting and planning is never devoid of human judgment. Managers input their opinions at various stages of the organisational decision-making process. The process is generally a combination of statistical forecasts, produced using software packages, and managerial expert judgments. Often this process is broken down into different hierarchies based on product-type, locations, and customer classes. When forecasts are produced in such hierarchical fashion, there is a need to aggregate the forecasts so that consistency is maintained across the organisation. A number of studies have explored how to achieve this forecast consistency using statistical methods. But none of these methods utilise judgmental inputs from managers at different hierarchical levels. Hence, a research gap has been identified to address this topic of hierarchical forecast aggregation using a judgmental reconciliation process. We address this gap in this study by employing a case study design with 6 case organisations, which are spread across industries, sectors and geographies. Exploratory interviews are conducted with case managers to investigate the current hierarchical forecasting process. Four dominant themes of this process are identified as information sharing, time pressure, power, and social value. A strengths, weaknesses, opportunities, and threats (SWOT) analysis on these themes help access their impacts on the forecast decision-making process. Based on this, three theoretical prepositions and a conceptual framework are suggested. A questionnaire is used to gather wider managerial opinions on the SWOT analysis. Using Multiple Attribute Decision Making (MADM) methods, the responses are analysed to confirm the impact of each theme. This validates the prepositions and the suggested framework for collaborative decision-making in hierarchical forecasting. We conclude with research implications and recommendations for future research.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > H Social Sciences (General)
Uncontrolled Keywords:	Forecasting, hierarchical forecasts, judgmental forecasts, forecast aggregation, multi-criteria decision-making, service case study, collaborative decision-making, operations research, behavioural operations management
Date of First Compliant Deposit:	26 February 2021
Date of Acceptance:	25 February 2021
Last Modified:	25 Feb 2022 02:30
URI:	https://orca.cardiff.ac.uk/id/eprint/138973

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