International Journal of Physical Distribution & Logistics Management

Pellathy, Daniel A.; In, Joonhwan; Mollenkopf, Diane A.; Stank, Theodore P.

2018 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/ijpdlm-10-2017-0329

The purpose of this paper is to illustrate how a systematic application of middle-range theorizing, which pays particular attention to contexts and mechanisms, can be used to extend current knowledge on logistics customer service (LCS) in a number of critical areas.Design/methodology/approachThe paper applies Stank et al.’s (2017) framework for middle-ranging theorizing in logistics to develop a research framework and agenda that can guide future LCS research. Results are generated through a review of the LCS literature and an application of the main concepts of middle-range theorizing.FindingsThe paper outlines opportunities for middle-range research that would extend LCS knowledge in the areas of human and behavioral factors, time-based competition, supply chain complexity, and digitization and technological innovation.Research limitations/implicationsDescribing the main characteristics of middle-range theorizing and how middle-range theorizing can be fruitfully applied to LCS research should help to stimulate new knowledge creation in this important area of supply chain logistics management.Practical implicationsBy focusing on why and when questions, middle-range theorizing engages with the practical realities of LCS that interest managers and students. Middle-range theorizing moves researchers toward developing a detailed understanding of what actually has to change in order for desired LCS-related outcomes to occur and the contextual factors likely impacting the change process. The paper should, therefore, allow managers to better translate LCS theory into action.Originality/valueMiddle-range theorizing remains new to the supply chain logistics field. The application of middle-range theorizing to LCS research, and logistics research more generally, demands new perspectives on established relationships with the potential to drive original research in areas most relevant to managers.

Kristensen, Jesper; Jonsson, Patrik

2018 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/ijpdlm-11-2017-0352

The purpose of this paper is to describe and categorise how current literature contributes to sales and operations planning (S&OP) research on how contextual variables affect S&OP design and to frame future areas for context-based S&OP research.Design/methodology/approachThe method used was a systematic literature review. Studies for review were obtained through a keyword search of five relevant databases, manual searches of relevant journals and snowballing of citations in relevant papers. In total, 571 papers published between 2000 and 2017 were assessed, and 68 papers were included in the review.FindingsThe review found that S&OP design depends on industry, dynamic complexity, detail complexity and organisational characteristics. The findings of the literature review suggest that future research should study the roles of industry, complexity, system and process and organisational characteristics in S&OP design.Research limitations/implicationsThe findings revealed several gaps in the literature on context-dependent S&OP design. To address these gaps, an agenda for future S&OP contingency research is developed.Practical implicationsThe findings revealed which contextual areas and specific S&OP design issues must be considered when designing and implementing S&OP.Originality/valueThis study focussed on identifying relevant research on S&OP design by analysing the contribution of literature to a research framework inspired by contingency-based research of operations and supply chain management.

Zhu, Suning; Song, Jiahe; Hazen, Benjamin T.; Lee, Kang; Cegielski, Casey

2018 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/ijpdlm-11-2017-0341

The global business environment combined with increasing societal expectations of sustainable business practices challenges firms with a host of emerging risk factors. As such, firms seek to increase supply chain transparency, enabling them to monitor operational activities and manage supply chain risks. Drawing on organizational information processing theory, the purpose of this paper is to examine how supply chain analytics (SCA) capabilities support operational supply chain transparency.Design/methodology/approachUsing data from 477 survey participants, hypotheses are tested using seemingly unrelated regression.FindingsThe results reveal that: analytics capability in support of planning functions indirectly affects organizational supply chain transparency (OSCT) via SCA capabilities in source, make, and deliver functions; SCA capabilities in source, make, and deliver positively influence OSCT; and supply uncertainty moderates the relationship between SCA capabilities in make and OSCT.Research limitations/implicationsThis research suffers from limitations inherent in all survey-based research. Nonetheless, the authors found convincing evidence that suggests firms can employ SCA capabilities to meet transparency requirements.Practical implicationsThe findings inform design of SCA systems, noting the importance of linking planning tools with tools that support source, make, and deliver functions. The research also shows how transparency can be increased via employing SCA capabilities.Originality/valueThis is one of first studies to empirically demonstrate that SCA capabilities can be used to increase supply chain transparency. The research also advances organizational information processing theory by illustrating an analytics capability paradox, where increased levels of certain analytics capabilities can become counterproductive in the face of supplier uncertainty.

Song, Hua; Yu, Kangkang; Lu, Qiang

2018 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/ijpdlm-11-2016-0315

Despite their crucial role in sustaining national economies, small and medium enterprises (SMEs) are beset by the constraint of financing at better conditions. The purpose of this paper is to compare supply chain finance (SCF) solutions provided by commercial banks and financial service providers (FSPs) that help SMEs access financing.Design/methodology/approachThis study looks at multiple case studies using in-depth interviews with focal firms (lenders) to answer the research questions. In-depth interviews were conducted with three Chinese FSPs and three commercial banks providing working capital to the same SMEs. The unit of analysis is SCF solutions that have made the companies competitive in the industry.FindingsThe case studies show that the acquisition of transaction information and business credit in SCF can reduce ex ante information asymmetry. SCF utilizing receivable transfers, closed-loop business, relational embeddedness, and a combination of outcome control and behavioral control can also reduce ex post information asymmetry. For these reasons, compared with commercial bank-dominated SCF, SCF adopted by FSPs in the supply chain can better reduce information asymmetry.Originality/valueThis study contributes to the emerging literature exploring the impact of SCF on SMEs accessing financing. In particular, this study provides supply chain management and operations insights on SCF and their consequent influence. Previous research has focused on the direct dyadic relationship between lenders and borrowers while neglecting supply chain effects. Uniquely, this study explores the different ways commercial banks and FSPs implement SCF solutions.

Kim, Thai Young; Dekker, Rommert; Heij, Christiaan

2018 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/ijpdlm-10-2017-0313

The purpose of this paper is to show that intentional demand forecast bias can improve warehouse capacity planning and labour efficiency. It presents an empirical methodology to detect and implement forecast bias.Design/methodology/approachA forecast model integrates historical demand information and expert forecasts to support active bias management. A non-linear relationship between labour productivity and forecast bias is employed to optimise efficiency. The business analytic methods are illustrated by a case study in a consumer electronics warehouse, supplemented by a survey among 30 warehouses.FindingsResults indicate that warehouse management systematically over-forecasts order sizes. The case study shows that optimal bias for picking and loading is 30-70 per cent with efficiency gains of 5-10 per cent, whereas the labour-intensive packing stage does not benefit from bias. The survey results confirm productivity effects of forecast bias.Research limitations/implicationsWarehouse managers can apply the methodology in their own situation if they systematically register demand forecasts, actual order sizes and labour productivity per warehouse stage. Application is illustrated for a single warehouse, and studies for alternative product categories and labour processes are of interest.Practical implicationsIntentional forecast bias can lead to smoother workflows in warehouses and thus result in higher labour efficiency. Required data include historical data on demand forecasts, order sizes and labour productivity. Implementation depends on labour hiring strategies and cost structures.Originality/valueOperational data support evidence-based warehouse labour management. The case study validates earlier conceptual studies based on artificial data.