Timing of the Effects: A Dynamic Analysis of Pack‐Size Variety, Demand, and Cost

• Date: 01 September 2016
• Published date: 01 September 2016
• Author: Xiang Wan
• DOI: http://doi.org/10.1111/jbl.12132

Timing of the Effects: A Dynamic Analysis of Pack-Size Variety,

Demand, and Cost

Xiang Wan

The Ohio State University

As an importation logistics factor, pack-size variety (the number of different pack sizes, such as packs of 6 units and 12 units of products)

is involved in a dynamic system. This system includes the forward impacts of the present pack-size variety on the present demand and

the present cost, as well as the reverse influences of past demand and past cost on present pack-size variety decisions. Any static analysis of a

segment of this system misses an important factor of these effects: the timing. In this study, I unveil the timing of these dynamic relationships

among pack-size variety, demand, and cost. The findings suggest that the effect of past demand on present pack-size variety follows a short-

delayed and long-lasting pattern, while the effect of past cost on present pack-size variety follows a long-delayed and short-lasting pattern.

These findings offer important implications for pack-size variety decisions in industrial practices.

Keywords: pack-size variety; timing effect; cost; dynamic analysis

INTRODUCTION

The variations in unit quantities provided in a pack of products are

represented by pack-size variety (various quantities of units in a

pack). For example, Honey Nut Cheerios cereals are sold in a pack

of one unit at Wal-Mart and in a pack of two units at Costco. Deci-

sion making in product pack-size variety is challenging because it

affects both demand and cost outcomes and is affected by past

demand and past cost. In this study, I examine the effects among

pack-size variety, demand, and cost by investigating the timing of

these effects. By doing so, I provide an in-depth understanding of

dynamic decision making related to the pack-size variety.

Consumer products are commonly stored, transported, and sold

in various packs of different units. However, it is not a simple

decision to make on pack-size variety because the pack-size vari-

ety decisions are involved in a dynamic system, which includes

the forward effects (impacts of present pack-size variety on pre-

sent demand and present cost) and reverse effects (influences of

past demand and cost on present pack-size variety decisions)

(Bayus and Putsis 1999; Koenigsberg et al. 2010). These effects

do not necessarily exist in the same time period, but may have

some time lags. A static investigation on one piece of these

dynamic relationships is likely to yield an incomplete understand-

ing of pack-size variety decisions and provide biased implications

that lead to poor decisions regarding new product development

and innovative packing. Without a clear understanding of the tim-

ing of these effects, managers may fail to consider the past

demand and past cost outcomes in a proper time frame when

making decisions on pack-size variety.

Within the dynamic system, the forward effects have been well

recognized in the literature. On the demand side, the present deci-

sions on pack quantity help firms gain the market share and raise

sales in the present time period (Waller et al. 2008, 2010). While

pack quantity in some articles refers to the number of product

units in a retail shipping case (Eroglu et al. 2011; Wen et al.

2012), pack-size variety in this study indicates the number of pro-

duct units included in a consumer pack. Both case pack quantities

and pack-size variety create the variety in product packing in sup-

ply chains. On the cost side, increased present pack-size variety

results in the present operational and logistics difficulties (such as

truck utilization, pallet dimensions, packaging capability, and

warehouse storage) and raises costs in the present time period

(Campo et al. 2000; Ramdas 2003; Wan et al. 2014). These stud-

ies suggest that the forward effects of pack-size variety on demand

and cost take effective in the present time frame without time lag.

Nevertheless, the reverse effects, especially the timing of the

reverse effects, have been overlooked in the literature. The orga-

nizational learning theory implies that the past performance of an

organization may affect its present strategy (Huber 1991; Van

Ryzin and Mahajan 1999). Firms can change their future product

decisions based on past performance. The balance between

demand and costs should affect future decisions on pack-size

variety. Little empirical research applies the organizational learn-

ing theory in search of the timing of these reverse effects of

demand and cost on pack-size variety.

This study aims to fill the gap in the literature by investigating

the timing of effects in this dynamic system. I collected and

developed a balanced panel data set of 3,666 observations over

three years from the soft-drink distribution network in the United

States and developed an empirical dynamic model. This model

captures the influences of pack-size variety on demand and costs

as well as how past demand and cost performance impacts pre-

sent pack-size variety decisions.

The main contribution of this article is to examine the timing

of the effects among pack-size variety, demand, and cost. I

explore the curvilinear effects of pack-size variety on demand

and cost in the present time frame and, for the first time to the

best of my knowledge, investigate the timing of reverse impacts

of the past demand and past cost on present pack-size variety

decisions. The results discover the short-delayed and long-lasting

effect of demand and the long-delayed and short-lasting effect of

cost on pack-size variety decisions.

The remainder of this article is organized as follows. The sec-

ond section provides a literature review and proposes hypothe-

ses-based theories. In the third section, I develop the empirical

Corresponding author:

Xiang Wan, Fisher College of Business, The Ohio State University,

2100 Neil Ave, Columbus, OH 43210, USA; E-mail: wan.207@osu.edu

Journal of Business Logistics, 2016, 37(3): 271–283 doi: 10.1111/jbl.12132

© Council of Supply Chain Management Professionals