Purpose – The purpose of this paper is to represent a unique combined Real time Delphi (RTD) – analytic network process (ANP) approach considering efficient decision making with practical validation. Design/methodology/approach – An ANP model encounters invisible relationship and interdependency among qualitative and quantitative criteria for assessment. RTD supports continuous assessment and improvement in team building, modeling, developing, implementing and validating the procedure. To illustrate practical validation of the model, the authors apply it in a manufacturing firm. A case illustrating the model, finds improved results and judgments followed by conclusion. Findings – A case illustrating the model, finds improved results and judgments. This model improves warehouse performance by integrating lean and people issues. The outcome results in an efficient decision making and consensus judgments. It also fosters high trust and coordination level among people in warehouse. Originality/value – Previous studies have assessed leanness either at enterprise or manufacturing level. As lean transformation and assessment both are continuous and long-term procedure, first the concept should apply to single function and should lead toward enterprise level. A web-based approach and multi criteria decision-making techniques like analytic hierarchy process, and ANP had been applied individually to measure leanness at enterprise level. Because of the warehouse contributing significantly to the total wastes and costs for an organization, such operations are considered presently.

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Purpose – A hybrid storage assignment (combination class-volume-based) framework considering quality proximity, customer and material categorization are key distinguished contents of this paper. In spite of using individual storage allocation approach, the hybrid allocation policy performs better under certain environment. The paper aims to discuss these issues. Design/methodology/approach – Although it has been proved that every storage assignment policy has their advantages and limitations, one or more storage assignment policies with combination of zoning and layout design can be used together for further improvement. The authors have conducted this study at warehouse of a manufacturing firm that produce only single product with varieties of material and quality criteria. Picking optimization includes elimination of non-value-added activities like unwanted forklift and package movements, time and distance traveled for retrieval as well as storage. Other allied operations with respect to customer acceptance level and resource utilization are also considered. Findings – The time and distance from manufacturing point to storage location are accountable as it also contributes to picking performance. Originality/value – Quality-based cluster analysis is carried out to find out closeness among customers, which is used to propose algorithm with new layout design, zoning and storage allocation policy.

The Covid-19 has shifted the face of many markets including e-commerce and online business with many bottlenecks to be cleared. The last-mile delivery project has the greatest effect on all types of e-commerce companies because it has many consumer touchpoints as well as the Covid-19 pain points. Due to these interconnected issues, the delivery projects itself requires modern solutions. The purpose of this paper is to identify, analyse and categorize the major factors that affect the last mile delivery projects in e-commerce, food sector, retail sector and so on using total ınterpretive structural modelling approach during the Covid-19. Ten major factors are identified from literature review, and expert opinions are collected from multiple organizations that are involved in the last mile delivery projects. The results indicate that types of goods, achieving routing efficiency and meeting fulfilment timeline are the key factors for last mile delivery projects during the time of Covid-19. This study helps the managers to identify the key factors and to focus on these factors for the successful implementation of last mile delivery project.

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Purpose 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. Design/methodology/approach 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. 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. Practical implications 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. Originality/value 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.

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