Predictive Analytics: New-generation Strategic Decision Support

March 10, 2013 • Big Data & Analytics, Business Process, STRATEGY & MANAGEMENT, Supply Chain, TECHNOLOGY

By Tobias Klatt & Klaus Moeller

Environmental turbulence has be- come a key challenge for companies’ strategic planning. Planning results remain arbitrary and risky, and lack deep knowledge of relevant factors and trends. Traditional instruments such as key performance indicator concepts, strategy maps, and balanced scorecards become increasingly opaque under these conditions, especially owing to their subjective foundations, which are biased because planners are overloaded with information. To overcome these shortcomings, this article integrates predictive analytics throughout the strategic planning process with a special focus on causal reasoning: applications and benefits of knowledge in causal interactions are described for external and internal impact analysis, strategy development, scenario analysis, implementation, and the final checking phase. Our results imply that causality analysis possesses substantial benefits as a new generation of decision support for every company’s strategic planning.


1. Dynamism and Complexity: The Environment Challenges Traditional Strategic Planning Tools

Increasing environmental turbulence challenges established company strategic planning processes and tasks. The dot-com bubble, the recent financial crisis, and the current turbulence in the Eurozone—to name a few—challenge the planner’s understanding of environmental trends. Increasing competition in established markets, with new players from Asia and the growing importance of upcoming markets such as the BRIC countries create further planning uncertainty.

A plethora of demands appear, and these call for new approaches to dealing with environmental uncertainty, especially regarding the possibility of analysing “interactions among variables.”1 These arguments are based mostly on improved data availability and information techniques over the past years. As a result, helping managers to use quantitative models to support their decision-making and planning is a key research topic.2

This article addresses this gap by providing evidence on the diverse contributions of predictive analytics as a new way of analysis that offers new instruments and concepts, a core as- pect of which involves the analysis of causalities, which brings completely new insights to traditional planning approaches. However, the idea behind this is not new: causal reasoning was developed in the 1960s by Clive W. Granger. Christopher Sims, who in the 1980s developed operational transfer, in 2011 received the Nobel Prize with Thomas Sargent for their analyses of causes and effects in macroeconomics.3 Nevertheless, their developments also offer substantial benefits to strategic planning practice and science. This article discloses these benefits in strategic planning’s processes and offers insights on connections with prevalent tools, such as strategy maps or the balanced scorecard (BSC).

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