Search results

The retail revolution swing from traditional distribution to e-tailing services and unprecedented increase in internet adoption insist practitioners to diversely plan warehousing strategies. More than practically required storage space has been identified as wastes, and also it does not improve performance. An organized framework integrating storage design policies, operational performance and customer value improvement for retail-distribution management is lacking. Therefore, the purpose of this paper is to develop broad guidelines to design the “just-right” amount of forward area, i.e., “lean buffer” answering the following questions: “What should be lean buffer size? How effective the forward area is? As per demand variations, which storage waste (SKU) should be allocated with how much storage space? What is the amount of storage waste (SW)? How smooth the material flow is in between reserve-forward area?” for storage allocation in cosmetics distribution centers.

After forecasting static storage allocation between two planning horizons, if a particular SKU is less or non-moving, then it will cause SW, as the occupied location can be utilized by other competing SKUs, and also it impedes material flow for an instance. A dynamically efficient and self-adaptive, knapsack instance based heuristics is developed in order to make effective storage utilization.

The existing state-of-the-art under study is supported with a distribution center case, and the study investigates the need of a model adopting lean management approach in storage allocation policies along with test results in LINGO. The sensitivity analysis describes the impact of varying demand and buffer size on performance. The results are compared with uniform and exponential distributed demands, and findings reveal that the proposed heuristics improves efficiency and reduce SWs in forward-reserve area.

The presented model demonstrates a novel thinking of lean adoption in designing storage allocation strategy and its performance measures while reducing wastes and improving customer value. Future research issues are highlighted, which may be of great help to the researchers who would like to explore the emerging field of lean adoption for sustainable retail and distribution operations.

The contemporary e-tailing marketplace insists that distribution centers are playing the roles of both wholesalers and retailers which require different storage-handling load sizes due to different product variants. To fulfill piecewise retail orders, a separate small size-fast pick area is design called “forward buffer” wherein pallets are allocated from reserve area. Due to non-uniform pallets, the static allocation policy diminishes forward space utilization and also, more than practically required buffer size has been identified as wastage. Thus, dynamic storage allocation policy is required to design for reducing storage wastage and improving throughput considering non-uniform unit load sizes. The purpose of this paper is to model such policy and develop an e-decision support system assisting enterprise practitioners with real-time decision making.

The research method is developed as a dynamic storage allocation policy and mathematical modeled as knapsack-based heuristics. The execution procedure of policy is explained as an example and tested with case-specific data. The developed model is implemented as a web-based support system and tested with rational data instances, as well as overcoming prejudices against single case findings.

The provided model considers variable size storage-handling unit loads and recommends number of pallets allocations in forward area reducing storage wastes. The algorithm searches and suggests the “just-right” amount of allocations for each product balancing existing forward capacity. It also helps to determine “lean buffer” size for forward area ensuring desired throughput. Sensitivity and buffer performance analysis is carried out for Poisson distributed data sets followed by research synthesis.

Warehouse practitioners can use this model ensuring a desired throughput level with least forward storage wastages. The model driven e-decision support system (DSS) helps for effective real-time decision making under complicated business scenarios wherein products are having different physical dimensions. It assists the researchers who would like to explore the emerging field of “lean” adoption in enterprise information and retail-distribution management.

The paper provides an inventive approach endorsing lean thinking in storage allocation policy design for a forward-reserve model. Also, the developed methodology incorporating features of e-DSS along with quantitative modeling is an inimitable research contribution justifying rational data support.

The assorted piece-wise retail orders in a cosmetics warehouse are fulfilled through a separate fast-picking area called Forward Buffer (FB). This study determines “just-right” size of FB to ensure desired Customer Service Level (CSL) at least storage wastages. It also investigates the impact of FB capacity and demand variations on FB leanness.

A Value Stream Mapping (VSM) tool is applied to analyse the warehouse activities and mathematical model is implemented in MATLAB to quantify the leanness at desired CSL. A comprehensive framework is developed to determine lean FB buffer size for a Retail Distribution Centre (RDC) of a cosmetics industry.

The CSL increases monotonically; however, the results concerning spent efforts towards CSL improvement gets diminished with raised demand variances. The desired CSL can be achieved at least FB capacity and fewer Storage Waste (SW) as it shifts towards more lean system regime. It is not possible to improve Value Added (VA) time beyond certain constraints and therefore, it is recommended to reduce Non-Value Added (NVA) order processing activities to improve leanness.

This study determines “just-right” capacity and investigates the impact of buffer and demand variations on leanness. It helps managers to analyse warehouse processes and design customized distribution policies in food, beverage and retail grocery warehouse.

Proposed buffering model offers customized strategies beyond pre-set CSL by varying it dynamically to reduce wastages. The mathematical model deriving lean sizing and mitigation guidelines are constructive development for managers.

This research provides an inventive approach of VSM model and Mathematical algorithm endorsing lean thinking to design effective buffering policies in a forward warehouse.

In order to achieve competitive advantage over the physical marketplace, the e-retailers are insisted on endowing with lenient return policies. The piece-wise returns-and-reordering process incurs excessive buffering and unwanted logistics costs which raises overall fulfillment charges. The objective of this study is to re-design e-retail distribution policy by providing temporal storage at logistics service provides' (LSP) location. The impact of recurrent returns on pricing and profit margins are also investigated over time continuum.

A framework is developed to reduce the non-value added (NVA) storage and distribution efforts by providing collaborative buffering between LSP and e-retailer. The knapsack based buffering approach is tested and compared with traditional e-retail distribution practices. The revenue sharing concept is mathematically modelled and implemented in GAMS, which finally validated through multiple return scenarios.

The proposed model outperforms the existing one under all scenarios with different configuration settings of re-ordering, profit margins, and buffer time windows. The distribution cost is found, linearly related to the necessary product buffering space. The findings help to re-design sustainable return policies for individual products so that maximum customer value can be yield with minimum costs.

This study helps to determine the NVA efforts incurred while storing and delivering multi-time returned products to ensure desired service levels. The revenue sharing model provides pricing strategies for e-retail practitioners deciding which product should store in what quantity for how much time at the shipping agency location so that it fulfils the re-ordering at least waiting and sufficient buffering.

The proposed model extends the role of LPSs as temporary buffer providers to reduce returns-and-reordering fulfilment efforts in the e-retail network. This Collaborative framework offers an opportunity to amend the distribution contracts and policies time by time that enhances e-retailer's performance and customer satisfaction.

The paper explores how social networks influence Cameroonian consumers' buying behavior. Then, the authors examine customers' advertising perceptions and psychological dispositions to explain their purchase intention and behavioral consumption.

The research framework is developed based on Nelson's theory of advertising by studying advertising perceptions, consumer psychological dispositions associated with social network characteristics and behavioral consumption. Using partial least squares structural equation modeling (PLS-SEM), the validation takes support from 231 responses collected with an online questionnaire from Cameroun.

The study reveals three critical results: (1) consumers' perceptions of advertising significantly influence their psychological disposition, (2) consumers' psychological dispositions and the social network significantly influence their intention to purchase and (3) consumers' intention to purchase significantly impacts their behavioral consumption.

The proposed and validated model contributes to understanding the influence of social network communication on customers' buying behavior on social s-Commerce platforms of developing country enterprises.

This study aims to highlight the system-wide potential relationships between forms of human bias, selected Lean tools and types of waste in a manufacturing process.

A longitudinal single-site ethnographic case study using digital processing to make a material receiving process Lean was adopted. An inherent knowledge process with internal stakeholders in a stimulated situation alongside process requirements was performed to achieve quality data collection. The results of the narrative analysis and process observation, combined with a literature review identified widely used Lean tools, wastes and biases that produced a model for the relationships.

The study established the relationships between bias, Lean tools and wastes which enabled 97.6% error reduction, improved on-time accounting and eliminated three working hours per day. These savings resulted in seven employees being redeployed to new areas with delivery time for products reduced by seven days.

The single site case study with a supporting literature survey underpinning the model would benefit from testing the model in application to different industries and locations.

Application of the model can identify potential relationships between a group of human biases, 25 Lean tools and 10 types of wastes in Lean manufacturing processes that support decision makers and line managers in productivity improvement. The model can be used to identify potential relationships between forms of human biases, Lean tools and types of wastes in Lean manufacturing processes and take suitable remedial actions. The influence of biases and the model could be used as a basis to counter implementation barriers and reduce system-wide wastes.

To the best of the authors’ knowledge, this is the first study that connects the cognitive perspectives of Lean business processes with waste production and human biases. As part of the process, a relationship model is derived.

Recent literature recognizes the role of warehouses in enhancing the overall logistics performance. Thus, lean thinking has recently found its way in supporting warehouse and distribution centre operations. The purpose of this paper is to examine the relationships among warehouse waste reduction practices, warehouse operational performance, distribution performance and business performance.

A two-stage study was conducted. First, a Delphi technique was adopted to develop a relevant questionnaire. Second, this questionnaire was used to measure the degree of waste reduction in the different warehouse activities and to test the developed research hypotheses. The authors test the hypotheses with a sample of Middle Eastern warehouse operators.

There exists a positive relationship between warehouse waste reduction level and both warehouse operational performance and distribution performance. There was no direct relationship between warehouse waste reduction level and business performance. However, results revealed that the relationship between warehouse waste reduction level and business performance was mediated by warehouse operational performance and distribution performance.

The developed instrument provides a guide for logistics managers as to understand how to reduce waste in each warehousing activity. The results also inform logistics managers of how distribution performance can be improved through lean warehousing. The resulting performance improvements in the distribution operations will ultimately be reflected in the logistics performance of downstream retailers.

The study develops an original instrument for measuring waste reduction in warehouses, and provides insights into the evolving lean warehousing research area. This is the first scholarly work to uncover the relationships among warehouse waste reduction practices, warehouse operational performance, distribution performance and business performance.

The purpose of this paper is to investigate the costs/benefits of implementing the cross-docking strategy in a retail supply chain context using a cost model. In particular, the effects of using different typologies of cross-docking compared to traditional warehousing are investigated, taking into consideration an actual case study of a fast-moving consumer goods (FMCG) company and a major French retailer.

The research is based on a case study of an FMCG company and a major French retailer. The case study is used to develop a cost model and to identify the main cost parameters impacted by implementing the cross-docking strategy. Based on the cost model, a comparison of the main cost factors characterizing four different configurations is made. The configurations studied are, the traditional warehousing strategy (AS-IS configuration, the reference configuration for comparison), where both retailers and suppliers keep inventory in their warehouses; the cross-docking pick-by-line strategy, where inventory is removed from the retailer warehouse and the allocation and sorting are performed at the retailer distribution centre (DC) level (TO-BE1 configuration); the cross-docking pick-by-store strategy, where the allocation and sorting are done at the supplier DC level (TO-BE2 configuration); and finally a combination of cross-docking pick-by-line strategy and traditional warehousing strategy (TO-BE3 configuration).

The case study provides three main observations. First, compared to traditional warehousing, cross-docking with sorting and allocation done at the supplier level increases the entire supply chain cost by 5.3 per cent. Second, cross-docking with allocation and sorting of the products done at the retailer level is more economical than traditional warehousing: a 1 per cent reduction of the cost. Third, combining cross-docking and traditional warehousing reduces the supply chain cost by 6.4 per cent.

A quantitative case study may not be highly generalisable; however, the findings form a foundation for further understanding of the reconfiguration of a retail supply chain.

This paper fills a gap by proposing a cost analysis based on a real case study and by investigating the costs and benefits of implementing different configurations in the retail supply chain context. Furthermore, the cost model may be used to help managers choose the right distribution strategy for their supply chain.

Current online business development redistributes last-mile logistics (LML) from consumer to retailer and producer. This paper identifies how empirical LML research has used and defined logistic performance measures for key grocery industry actors. Using a multi-actor perspective on logistic performance, the authors discuss coordination issues important for optimising LML at system level.

A semi-systematic literature review of 85 publications was conducted to analyse performance measurements used for effectiveness and efficiency, and for which actors.

Few empirical LML studies exist examining coordination between key actors or on system level. Most studies focus on logistic performance measurements for retailers and/or consumers, not producers. Key goals and resource utilisations lack research, including all key actors and system-level coordination.

Current LML performance research implies a risk for sub-optimisation. Through expanding on efficiency and effectiveness interplay at system level and introducing new research perspectives, the review highlights the need to revaluate single-actor, single-measurement studies.

No established scientific guidelines exist for solving LML optimisation in the grocery industry. For managers, it is important to thoroughly consider efficiency and effectiveness in LML execution, coordination and collaboration among key actors, avoiding sub-optimisations for business and sustainability.

The study contributes to current knowledge by reviewing empirical research on LML performance in the grocery sector, showing how previous research disregards the importance of multiple actors and coordination of actors, efficiency and effectiveness.

The objective of this paper is to examine the impact of cross-docking on the retail out of stock (OOS).

The research is based on a three-phase Delphi study consisting of a seeding/literature review phase, a pre-testing phase and a three-round Delphi study. The Delphi study used in this paper brings together leading supply chain management experts with leading academics.

The findings of the paper show that cross-docking may impact the retailers OOS drivers positively or negatively. The study demonstrates that cross-docking has a negative impact on ordering, placement, delivery, handling, DC handling and receipt. On the other hand, cross-docking has a positive effect on supplier ordering. Finally, academics and supply chain managers disagreed on the effect of cross-docking on the promotions driver. Academics consider that cross-docking has a positive impact on promotions OOS driver, while supply chain managers believe the opposite.

The Delphi study was administrated to supply chain managers from a single major FMCG company, which is a supplier of grocery retailers. By including supply chain managers from the retailers' side, more perspectives on the impact of cross-docking on the OOS drivers can be investigated.

The study develops an original instrument to investigate the impact of cross-docking on OOS drivers. This is the first scholarly work to investigate the relationship between a distribution strategy and the OOS drivers.