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

• Date: 01 October 2016
• Author: Nallan C. Suresh,Torsten Doering
• Published date: 01 October 2016
• DOI: http://doi.org/10.1111/jscm.12111

FORECASTING AND PERFORMANCE:

CONCEPTUALIZING FORECASTING MANAGEMENT

COMPETENCE AS A HIGHER-ORDER CONSTRUCT

TORSTEN DOERING AND NALLAN C. SURESH

State University of New York - Buffalo

This note empirically validates forecasting management competence

(FMC) as a higher-order construct based on four underlying sets of prac-

tices: internal integration, forecasting process quality, effective use of

advanced systems, and evaluation of forecasting. The results from a partial

least squares structural equation model support FMC as a significant dri-

ver for three outcomes: forecast accuracy, cost reduction, and delivery per-

formance, thereby establishing predictive validity. The study provides

insights to the relative effectiveness of the four sets of forecasting practices

constituting FMC. A mediator–moderator analysis shows that forecast

accuracy is not a mediator or a moderator between FMC and the other

two outcome variables of cost reduction and delivery performance. This

indicates that in addition to effective demand forecasting practices, other

processes such as sales and operations planning (S&OP) are also impor-

tant for translating the benefits of effective forecasting to realize favorable

business outcomes. A multigroup analysis shows that the impact of the

four practice elements on the three performance outcomes also differs

based on specific business contexts.

Keywords: forecasting management; human judgment and decision making; supply

chain performance; partial least squares; scale development; survey methods

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 require-

ments, production and capacity planning, and inven-

tory 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 pro-

cesses required to organize the forecasting process for

eliciting information from various external and inter-

nal sources, communicating them among various

business functions, using the right forecasting meth-

ods 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 per-

spective is seen in the framework shown in Table 1,

adapted from the work of Smith (2001). This frame-

work distinguishes between forecasting techniques

and managerial processes on the one hand and fore-

casting 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.

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