Scenario Planning vs Time-Series Analysis - How to Make Better Forecasts in Volatile Industries: -

Scenario Planning vs Time-Series Analysis - How to Make Better Forecasts in Volatile Industries

Marinkov, Fabióla

4. term

Operations and Innovation Management (cand.tech.), Master

2019

2019-06-03

57

Dette speciale undersøger, hvordan man kan lave bedre prognoser i hurtigt skiftende brancher ved at kombinere statistiske metoder med strategisk tænkning. Tidsrækkeanalyse er brugen af historiske data til at finde mønstre over tid og lave numeriske prognoser. Scenarieplanlægning er en struktureret måde at forestille sig flere plausible fremtider og teste, hvordan organisationer kan reagere. Da scenarieplanlægning mangler en stærk teoretisk forankring, er dens udvikling i praksis også begrænset (Chermack, 2004). Tidligere studier forbinder strategi og tal, men er mest beskrivende og sjældent sammenlignende. Dette arbejde undersøger, om en kombineret tilgang er gavnlig, og hvilke udfordringer der skal håndteres for at omsætte den til prognoser af højere kvalitet. Resultaterne peger på, at en blandet metode, der kobler tidsrækkeanalyse og scenarieplanlægning, giver værdifuld indsigt i mulige fremtider, og at læringen kan være større end ved at bruge teknikkerne hver for sig.

This thesis explores how to make better forecasts in fast-changing industries by combining statistical methods with strategic thinking. Time-series analysis uses historical data to find patterns over time and produce numerical forecasts. Scenario planning is a structured way to imagine multiple plausible futures and test how organizations might respond. Because scenario planning lacks a strong theoretical foundation, its development in practice has been limited (Chermack, 2004). Prior work links strategy and numbers but is mostly descriptive and rarely comparative. This study examines whether a combined approach is beneficial and, if so, which challenges must be addressed to turn it into higher-quality forecasts. The findings indicate that a mixed method that joins time-series analysis and scenario planning provides valuable insights into possible futures, and that the learning can be greater than when the techniques are used separately.

[This abstract was generated with the help of AI]

Statistical forecasting, forecasting, scenario planning, storytelling, strategic thinking, future uncertainties, trends, big data analytics, energy industry, accuracy, renewable energy, wind power industry, time series analysis

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