Logistics in the Supply Chain Track
Abstracts
9:00 AM – 10: 20 AM
a. Trends in Logistics Research
b. Examining Longitudinal Patterns of Motor Carrier Safety Using Latent Transition Analysis c. Agency implications for organizational efficiency: The role of facilitators
10:40 AM – 12:00 PM
a. A Qualitative Exploration of Big Data in International Supply Chain Management: Key Success Factors and a Call for Research
b. Sensitivity of Logistics Costs to The Shape of Lead-Time Distributions in Global Supply Chains
c. Exploring the Impact of Logistics Service Quality on Customer Satisfaction & Loyalty in an Omni-Channel Environment
1:30 PM – 2:50 PM
a. Innovation in Logistics Outsourcing Relationships: Investigating the Form and Function of Innovation Alignment
b. Bootstrapping Inventory Parameter Estimates in the Presence of order crossing With Positively Skewed Bimodal Distributions
c. Achieving Flexibility and Performance in the Service Parts Supply Chain: An Empirical Assessment of Lateral Transshipment Strategies
Trends in Logistics Research
Examining Longitudinal Patterns of Motor Carrier Safety Using Latent Transition Analysis
Abstract:
Since December 2010 the Federal Motor Carrier Safety Administration (FMCSA) has publically disclosed motor carriers’ safety performance on dimensions including unsafe driving,
noncompliance with hour-of-service rules, and unsatisfactory maintenance. One feature of this disclosure is a 0/1 indicator that signals whether a carrier has been “flagged” for poor safety performance vis-à-vis its peers. Research indicates that carriers receiving one or more safety flags have higher accident rates. Moreover, shippers and brokers have been keen to
incorporate these indicators into carrier selection decisions. As such, developing an
understanding regarding the likelihood that carriers will transition from one safety status to another has important theoretical and managerial implications. Using data on large for-hire large motor carriers, I investigate the probability that carriers transition from one safety status to another using latent transition analysis, a statistical technique designed to model stage-sequential development. My findings shed light on the complex dynamics that underlie motor carriers’ safety changes following the start of the CSA program.
Agency implications for organizational efficiency: The role of facilitators
Saif Mir, University of Arkansas David Hyatt, University of Arkansas
Abstract
Improving truck fuel performance has become increasingly important to firms under pressure to reduce costs and carbon emissions. It is well known in industry that drivers’ habits factor heavily into such efforts, particularly with respect to reducing idling and increasing fuel
efficiency. In this study, we examine the impact of driver behaviors on a firm’s goals to reduce idling and improve fuel performance. Unlike mechanized processes, which may be tuned precisely to achieve desired organizational outcomes, drivers are human beings with
idiosyncrasies that may not be attuned precisely to organizational aims. Worse, drivers may often have incentives at odds with those of the organization, such as maintaining personal comfort on long hauls, a classic multi-task principal-agent problem. Using two years of driver performance data from a private shipping fleet, we use the lens of multitask agency theory to explore the roles of drivers and managers in producing sustainability and operational
performance outcomes for the fleet owner. We find that driver use of auxiliary power units (alternative power source) provides a solution to this dilemma by mitigating the consequences of idling while providing personal comfort.
A Qualitative Exploration of Big Data in International Supply Chain Management: Key Success Factors and a Call for Research
R. Glenn Richey, Jr, Auburn University Tyler R. Morgan, Iowa State University
Abstract:
“Big Data” is one of industry’s hottest buzzwords. Current investments suggest that Big Data is important as “IDC predicts that the market for Big Data will reach $125 billion in 2015, growing six times faster than the overall IT market” (Press 2014). Still, conservative companies, top executives, and supply chain managers suggest that Big Data’s value is exaggerated and that it is little more than an extension of forecasting or business intelligence (Chen et al. 2012; Provost and Fawcett, 2013). Unfortunately, supply chain research regarding Big Data is lacking. “There is very little management scholarship that tackles the challenges of using such tools – or better yet, that explores the promise and opportunities for new theories and practices that big data might bring about” (George et al. 2014, p. 321). Supply chain managers remain confused about where to source Big Data and who creates it. In fact, there is almost no consensus on what Big Data is, its purpose, or its role in the future of supply chain management.
From a relationships perspective, it is nearly impossible for managers to work collaboratively without consistent terminology. Yet, industry and academic research have not provided a complete and specific definition for Big Data. Popular literature discusses dimensions of volume, velocity, and variety (Laney, 2001) while others also add veracity (Jewell et al. 2014), variability (Hopkins 2011), or complexity (SAS.com). However, limited definitions for the origins of each are given. Thus, our first step was to develop a managerial understanding of what Big Data is from a process-based perspective. Only after this working and grounded definition is confirmed can accurate future research be performed.
Following this basic conceptualization (or perhaps re-conceptualization) of the definition of Big Data, we sought to develop an initial understanding of Big Data’s future in supply chain
relationships. From a best practices perspective, managers often ask about the key success factors to making relationship-based processes work (Fawcett and Magnan 2002; Richey et al. 2010). At the onset of new supply chain initiatives, managers are asked to assess existing capabilities and options. Therefore, the next task is uncovering Big Data-related performance-based key success factors going directly to the managers in charge of these data-connected supply chain relationships.
Thanks to a grant from the Council of Supply Chain Management Professional, we are in pursuit of answers to questions relating to how supply chain partners are employing Big Data in supply chain strategy, Big Data’s potential to influence supply chain performance, and the obstacles to developing its potential. Categorizing, assessing quality, and identifying the impact of Big Data is very new to management in general (George et al. 2014). Therefore, the core of our study sought answers to these questions:
1. What is Big Data in a supply chain relationships context?
Sensitivity of Logistics Costs to The Shape of Lead-Time Distributions in Global Supply Chains
John P. Saldanha, West Virginia University John E. Tyworth, Penn State University
Abstract:
Significant research effort has been expended on the importance of the shape of lead time and demand parameters for modeling and setting inventory policy decisions. Namely the relevance of the Normal approximation in setting inventory policies. The discussion continues with two competing hypothesis. On the one hand one argument is that the marginal value of time is very low, which means that the change in the unit inventory cost per unit change in lead time amounts to <1% per unit cost per week. A manifestation of this low value of time is the offshoring of US manufacturing that has led to global supply chains with longer lead times. On the other hand the second argues that that incorrectly accounting for uncertainty in lead time, which is manifested in the shape of lead time distributions will lead to severe cost penalties when using the conventional Hadley-Whitin Normal approximation for setting inventory policies. This argument stems from the operational nature of global supply chains employing ocean shipping where lead time distributions are often positive skewed and bimodal. The primary research question we pose concerns the effects of asymmetric lead-time distribution shapes (bimodal and right skewed) on total logistics costs, which raises the perennial issue of the relevance of the Normal approximation approach. We ground the analysis in a real world setting employing product attributes from trade data and benchmark ocean container freight rate data allowing us to use a landed costs approach across a wide range of product profiles from various industry settings. ). The findings from our results show that the bimodality and right skew of the distributions do influence the accuracy of safety stocks when employing the Normal approximation. However, these differences are trivial (<1%) in expected annual
Exploring the Impact of Logistics Service Quality on Customer Satisfaction & Loyalty in an Omni-Channel Environment
Christopher Boone, Georgia Southern University Monique L. Murfield, Miami University Paige Rutner, Georgia Southern University Rodney W. Thomas, Texas Tech University
Abstract:
“The omnichannel consumer has forever changed retail, and there is no going back.” – Retail System Research (Baird & Kilcourse, 2011)
The introductory quote reflects the reality of modern retail and impact of the omnichannel consumer’s demand for increased retailer engagement and improved purchase experience. Unfortunately, a recent survey of more than 400 retail executives from eight countries found only 16% of firms are profitably meeting omnichannel demands. Logistics related activities such as returns and shipping were reported has having the greatest negative impact on profitability. As a result, transportation and logistics was identified by more than 88% of the executives as the business area requiring the greatest emphasis and a key to satisfying the demands of the omnichannel consumer.
Researchers have long appreciated the impact of logistics service quality on customer satisfaction, as is evidenced by a body of literature spanning several decades. However, recent reviews and analysis of this research have questioned some of the most commonly held relationships, specifically those between logistics service quality, customer satisfaction, and customer loyalty. Similarly, much of the logistics service quality research has emphasized business-to business (B2B) issues at the expense of the business-to-consumer (B2C) perspective, leading some to question whether or not the dimensions of logistics service quality in the B2B domain hold in the B2C domain. Answers to these questions and others related to the role of logistics service quality in an omnichannel context are increasingly important and urgent given the increased importance of logistics highlighted by executives. This research aspires to address these questions using a sequential mixed-method approach which combines preliminary qualitative fieldwork and a survey adapted from existing measures. For the primary data collection effort, Amazon’s Mechanical Turk (MTurk) online labor system
(www.mturk.com) was utilized to collect 508 responses from consumers who had to qualify for the MTurck “task” prior to receiving the survey link by having a recent purchase experience in one of two scenarios: buying a product in the store and having it shipped directly to their home/office (BSSD— n=184), or buying a product online and picking it up in store (BOPS—n=323). Evaluation of the
measures’ psychometric characteristics and hypothesis testing relied on partial-least squares structural equation modeling (PLS-SEM) techniques. This method is appropriate for models with both formative and reflective indicators, to assess the effect of both group and continuous moderators, and is especially appropriate for evaluating complex models as it avoids identification issues sometimes encountered using covariance-based techniques.
Results from this research will contribute to literature in several ways. First, this research addresses the role of logistics service quality in the omnichannel retail environment. To our knowledge, no other research has addressed this gap in extant research. Second, this work answers the call for more consumer-based logistics research. To date, LSQ research has focused on the B2B relationship at the neglect of the B2C relationship. Third, this effort explores the impact of channel type on the
relationship between logistics service quality, customer satisfaction, and customer loyalty. This is important as recent research has suggested that not all channels are equally valuable. Thus, managers need a better understanding of what aspects of LSQ are most valued within each channel. Finally, the use of MTurk to survey consumers is fairly rare in the logistics literature and may motivate future researchers to explore this avenue for data collection.
Innovation in Logistics Outsourcing Relationships: Investigating the Form and Function of Innovation Alignment
Thomas J. Goldsby, Ohio State University A. Michael Knemeyer, Ohio State University
Daniel Taylor, Ohio State University Marzenna Cichosz, Warsaw School of Economics
Abstract:
In the modern business environment, characterized by demanding customers, tough
competition, and uncertainty, innovation is cited as a major contributor to firm success and growth (Grawe et al. 2009, Chapman et al. 2003, Calantone et al. 2002, Kandampully 2002). Indeed, the importance of innovation was highlighted in a 2013 Innovation Survey conducted by Plante Moran, showing that innovation is a strategic priority for 94% of the survey’s C-suite respondents.
This research concentrates on the innovativeness of logistics service providers (LSPs) — companies that perform logistics activities on behalf of others. Estimated as a $142 billion market in the U.S., alone (Armstrong & Associates 2013), LSPs face an increasingly competitive environment (Langley at el. 2014). As emphasized by Flint et al. (2005), the improvement of current processes and the development of new services for their customers will prove to be necessary for LSPs to stay competitive. Results from field research conducted in the logistics service industry by Bellingkrodt and Wallenburg (2013), Raue and Wallenburg (2013),
Daugherty at el. (2011), and Grawe at el. (2009) underscore the strong link between logistics service innovation and a firm’s performance. This situation is contrasted by the findings that the logistics service industry stands at the low end of innovativeness, compared to other industries (Tether and Tajar 2008, Busse 2010).
We propose that LSPs can overcome these limitations by forming innovation alignment (IA) – i.e., cooperating with clients in order to achieve innovation success (incremental service improvements and radical service innovations) – that will allow LSPs to not only strengthen their relationships with existing clients, but also help them to replicate innovation with new clients to fuel business growth. According to organizational behavior literature, the underlying premise of alignment, in itself, is that “organizational performance is a consequence of fit between two or more factors, such as, the organization environment, strategy, structure, system, style, and culture” (Van de Ven and Drazin 1985, p. 334). The concept of IA, however, is not found in the literature. Yet, interviews with logistics professionals reveal that aligning with clients when pursuing something new is a crucial success factor.
We report insights gathered through a focus group, complemented by a thorough review of the literature pertaining to service innovation, in order to frame a two-step survey approach. The first survey is directed to senior leadership of LSPs across a diverse spectrum of firm sizes and logistics service offerings, and seeks to gather firm level data. The second survey, then, is directed to account managers at these LSPs and contains questions that are specific to the account manager’s relationship with a focal account. This two-step approach allows us to
Bootstrapping Inventory Parameter Estimates in the Presence of order crossing With Positively Skewed Bimodal Distributions
John P. Saldanha, West Virginia University Peter F. Swan, Penn State Harrisburg
Abstract:
Global supply chains are the new normal and are extremely reliant on ocean shipping. However, ocean shipping can be susceptible to a wide variety of delays and disruptions
including from port congestion and disruption in port operations that can get exacerbated due to missed Intermodal connections. Consequently, global supply chains employing Intermodal ocean shipping transport experience a high probability of order crossing. Order crossing is the case when orders arrive out of sequence of when they were placed e.g. the order placed at some time t arrives at the replenishment point after the order placed at time t + k. Order crossing presents a problem as it violates a basic assumption of most popular analytical models used to set inventory policy decisions. Namely, violation of the no order crossing assumption can cause significant overestimation of inventory calculated by popular analytic models for setting inventory decisions. While several approaches have been proposed to overcome this problem, they are restricted by their application to singular policy contexts, policy parameters, and distributional forms. Moreover, these methods can also be cumbersome due to the computational requirements necessary and the mathematical intractability of inputs typical of ocean shipping lead times. In this research we intend to explore the efficacy of the distribution free bootstrap estimation procedure as a means to accurately calculate inventory policy decisions when ocean shipping lead times are positive skewed and bimodal. The attraction of this method is threefold: (1) it is distribution free and can be used even with non-standard distribution, (2) it can be used across a wide range of inventory policies, and (3) it is quick and easy to implement in current spreadsheet tools popular with practitioners, such as Microsoft
Achieving Flexibility and Performance in the Service Parts Supply Chain: An Empirical Assessment of Lateral Transshipment Strategies
Christopher Boone, Georgia Southern University Joseph B. Skipper, Georgia Southern University
Benjamin T. Hazen, University of Tennessee Daniel P. Johnstone, USAF – Vandernberg AFB
Abstract:
The effective management of service parts has become increasingly important for customer centric manufacturers and service providers. To remain competitive, service oriented firms must replace failed service parts quickly to minimize disruptive downtimes of the aircraft, machinery, or similar equipment they support. The service market associated with a product can also be up to five times larger than the market for the products themselves. Thus, an approach to inventory management that enhances flexibility and performance especially vital to service parts focused supply chains.
Lateral transshipments add flexibility to an inventory system through the movement of a product or part from one location or echelon within a supply chain to a similar location or echelon (i.e. lateral) in order to fill or prevent a backorder. A review of the literature on lateral transshipments reveals various recommendations on how best to incorporate lateral
transshipments with strategies tending to be based on complete pooling or partial pooling. Complete pooling policies consider all resources from all locations available for transshipment whereas partial pooling systems utilize additional criteria such as location reserve levels, risk associated with stockouts at potential backfill locations, fill-rates, and shipping costs to determine if a transshipment is the most advantageous enterprise option. Complete pooling policies are often preferred over partial pooling policies because complete pooling policies require less active managerial decision-making with regard to, if and from where, a lateral backfill will be sourced. Therefore, managers considering a change in policy must choose whether the potential enterprise-level benefits associated with transitioning to a partial pooling policy are worth the costs associated with managing such a policy. Unfortunately, literature is not clear as to if moving to such a management-intensive policy will serve to evoke benefits. This study investigates the impact of lateral transshipment strategies on measures of flexibility and performance within a service parts supply chain context utilizing data collected from a large Department of Defense (DoD) based initiative considering alternative lateral transshipment policies. The study utilizes two experimental designs, the one group pretest-posttest design and the non-equivalent control group design, to investigate this phenomenon in a field setting and to glean insights of value to both the government and the private sector.
By examining the alternative lateral transshipment strategies in a real-world setting, this research could provide a number of scholarly contributions as well as the evidence managers need and value. For example, this research addresses the general lack of empirical research focused on lateral transshipments and answers the call for the advancement of theory in inventory management. By using resource orchestration theory in a supply chain context, this research also endeavors to expand our understanding of the importance of resource utilization to overall firm performance. Finally, the use of a field based experimental methodology, represents an opportunity to investigate service parts supply chain management in a real life context which enhances the opportunity for this research to have significant managerial
