Academic journal article Journal of Supply Chain Management

Forecasting and Performance: Conceptualizing Forecasting Management Competence as a Higher-Order Construct

Academic journal article Journal of Supply Chain Management

Forecasting and Performance: Conceptualizing Forecasting Management Competence as a Higher-Order Construct

Article excerpt

INTRODUCTION

Many organizations are still striving to make demand forecasting more effective, despite years of effort on the part of researchers and practitioners. It is widely known that demand forecasting drives strategic and operational decisions such as identification of market opportunities, planning of financial requirements, production and capacity planning, and inventory management (Fildes, Nikolopoulos, Crone & Syntetos, 2008; Makridakis, Wheelwright & Hyndman, 1998; Moon, Mentzer & Smith, 2003). Given this importance, forecasting methods have been studied extensively (Dalrymple, 1987; Kahn, 2002; Klassen & Flores, 2001; Sanders & Manrodt, 1994), yielding many useful insights. For instance, it has been found that simple forecasting methods such as exponential smoothing can outperform more sophisticated ones; the relative ranking of the performance of different methods depends on the accuracy measure being used; and the accuracy of combined methods is greater than that of individual methods on average (Armstrong, 2001; Makridakis & Hibon, 2000).

Despite such valuable insights, the managerial processes required to organize the forecasting process for eliciting information from various external and internal sources, communicating them among various business functions, using the right forecasting methods for the right contexts, and translating them via the sales and operations planning process into bottom-line advantages for the firm are yet to be understood fully.

More recently, forecasting has received attention at the supply chain level, highlighting the benefits of sharing forecasts among partners via collaborative planning, forecasting, and replenishment (CPFR), and vendor managed inventory (VMI). This broader perspective is seen in the framework shown in Table 1, adapted from the work of Smith (2001). This framework distinguishes between forecasting techniques and managerial processes on the one hand and forecasting as an intrafirm or interfirm (supply chain) process on the other. But even as forecasting is now being addressed at the supply chain level, forecasting processes within firms continue to be problematic. Thus, the focus of our study is on quadrant II in Table 1.

Several contributions are made by this study. First, we advance the notion of forecasting management as a competence, in the form of a higher-order construct. In past studies, four underlying groups of forecasting practices have been prescribed as essential components of forecasting management (Mentzer & Moon, 2005; Moon et al., 2003). We empirically assess the construct validity of these prescribed practices as a reflective-formative hierarchical component model (HCM) (Becker, Klein & Wetzels, 2012). We draw from resource advantage (R-A) theory to substantiate the proposed forecasting management competence (FMC). A survey-based investigation is conducted to establish the construct and predictive validity of FMC. Given many intervening organizational processes between forecasting and operational outcomes, we then investigate if forecasting accuracy acts as a mediator or moderator between FMC and cost reduction and delivery performance. In addition, we explore contingency effects through multigroup analyses to better understand the relationships between forecasting practices and performance outcomes.

THEORETICAL DEVELOPMENT

Underlying Practices of Forecasting Processes

Four groups of practices have emerged from past studies as being essential for successful forecasting management (Mentzer & Moon, 2005; Moon et al., 2003). These four elements are as follows; (1) internal integration among marketing, production, and other relevant functions; (2) forecasting methods and process quality; (3) systems employed for forecasting (e.g., hardware and software); and (4) performance measurement (i.e., evaluation of forecast accuracy and updating). Based on a substantial number of case studies, Mentzer and Moon (2005) also categorized the level of sophistication on each of these four practices into four stages. …

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