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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.

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.

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.

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.

This purpose of this study is to develop a simple framework for designing a warehouse incorporating lean principles. Multiple objectives like resource planning, material handling, storage, inventory management, including internal and external logistics, are considered.

A design procedure to incorporate lean principles for designing a warehouse for a complex multi-model production line has been proposed. The preferred standards and factors affecting warehouse design, the inputs and outputs of process flow characteristics, are incorporated into the design. Current and future state value stream mappings are drawn to bring out the challenges in the value flow.

The framework for designing a lean warehouse have been implemented and validated in a heavy machinery manufacturer. This framework will ease the work of the future lean-based warehouse designers to apply simple step-by-step processes to achieve the goal with the nearest accuracy. The steps followed can be summarized as defining the lean processes, making the lean process as the design base, collecting inputs like stock-keeping unit master, inventory and space details, and building the lean warehouse design with the step-by-step processes.

Practical tips on warehouse design have been explained focusing on the part volume, quantity handled, inventory and throughput. This will assist the practitioners in designing a lean warehouse and leading to an improved operational performance.

A simplified design procedure for designing a lean warehouse, along with a real-time case study has been enumerated in detail. Effective use of space and resources with lean tools and techniques lead to better storage and picking efficiency resulting in an overall reduction in cost.

This article aims to investigate the most applied lean warehouse practices in Brazilian warehouses.

To perform this research, three phases were conducted: a literature review, a multiple case study, and an analysis of lean warehouses practices implementation by an engineering committee. Thus, both qualitative and quantitative approaches were used. Additionally, the study has an applied nature, with an exploratory and descriptive character.

Results showed that regardless of the type of criterion used, the most implanted practices are those that do not involve investments in technology. On the other hand, practices like RFID and Cross Docking systems were not found in any of the operations, which shows numerous possibilities for improvement.

The main contribution of this article is to initiate a debate about the management and productivity of Brazilian warehouses, a theme still little explored by the academic community despite the importance that the logistic scenario represents for Brazil as an emerging country and leader in Latin America, participating actively in several global supply chains.

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.

The purpose of this study is to apply value stream mapping (VSM) in Malaysian pharmaceutical production warehouse. A current and future state value stream map from the raw material receiving end to the production unit was developed to find out waste and unwanted lead time. It was very much essential to cut down the supply chain lead time at the initial phase as the raw material unloading, sorting, temporary storage and dispatch to production were seen contributing to a huge lead time build-up.

The study was initiated with the selection of a product family, construction of the current state map, identification of various wastes and the development of future state map.

The expected outcomes of the study include the quantification of wastes, improvement in value-added percentage and lead time reduction.

The study was carried out in a single pharmaceutical company. The results of the study are deployable and can be functional in similar production organizations. Contrary to common VSMs that capture core production processes, this study provides strong insights that shall help design lean supply chains, especially in the pharmaceutical domain. This paper has also addressed the viability of the lean in the pharmaceutical warehouse and the reduction in lead time to improve demand forecasting, marketing and sales.

The results of this study have indicated that a significant reduction in pharmaceutical warehouse supply chain lead time is possible as a result of the implementation of VSM from the supply chain’s perspective.

The insights from this study help in understanding the pharmaceutical supply chain risks and their outcomes.

The paper reports a real-time study conducted in a warehouse of a pharmaceutical organization. Hence, the contributions are original.

Lean implementation is vastly incorporated in core manufacturing processes; however, its applicability in the supply chain and service industry is still in its infancy. To acquire performance excellence and thrive in the global competitive market, many firms are adopting newer methodologies. But, there is a stringent need for production simulation systems to analyze supply chains both inbound and outbound. The era of face validation is slowly disappearing. Lean tools and procedures that provide future state assumptions need advanced tools and techniques to measure, quantify, analyze and validate them. The purpose of this study is to enable dynamic quantification and visualization of the future state of a warehouse supply chain value stream map using discrete event simulation (DES) technique.

This study aimed to apply an integrated approach of the value stream mapping (VSM) and DES in a Malaysian pharmaceutical production warehouse. The main focus is diverted towards reducing the warehouse supply chain lead time by initially constructing a supply chain value stream map (both present state and future state) and integrating its data in a DES modelling and simulation software to dynamically visualize the changes in future state value stream map.

The DES simulation was able to mimic the future state lead time reductions successfully, which assists in better decision-making. Improvements were seen related to total lead time, process time, value and non-value-added percentage. Warehouse performance metrics such as receiving, put away and storage rates were substantially improved along with pallet processing time, worker and forklift throughput usage percentage. Detailed findings are clearly stated at the end of this paper.

This study is limited to the warehouse environment and further additional process models and functional upgrades in the DES software systems are very much needed to directly visualize and quantify all the possible Lean assumptions such as radio frequency image identification/Andon (Jidoka), 5S, Kanban, Just-In-Time and Heijunka. However, DES has a leading edge in extracting dynamic characteristics out of a static VSM timeline and capture details on discrete events precisely by picturizing facility modification and lead time related to it.

This paper includes all the fundamental pharmaceutical warehouse supply chain processes and the simulations of the future state VSM in a real-life context by successfully reducing supply chain lead time and allowing managers in inculcating near-optimal decision-making, controlling and coordinating warehouse supply chain activities as a whole.

This integrated approach of DES and VSM can involve managers and top management to support the adoption of anticipated changes. This study also has the potential to engage practitioners, researchers and decision-makers in the warehouse industry.

This study involves a powerful DES software package that can mimic the real situation as a virtual simulation and all the data and model building are based on a real warehouse scenario in the pharmaceutical industry.

This paper focuses on quantifying and ranking the “7-Deadly” Wastes in the warehouse environment. The purpose of this paper is to develop a prioritized road map that would guide the implementation of the “7-Deadly” Wastes and their related waste-reduction practices in a warehouse environment for improving warehouse operational performance.

A four-stage approach was used to achieve the objectives of the study. A Delphi technique was employed to develop indicators of waste-reduction practices at the warehouse activities level and categorize them according to the “7-Deadly” wastes. Whereas categories ranked based on statistical analysis, a regression analysis was used to explore the impacts of this ranking.

The results of the regression analysis showed a significant influence of the ranked “7-Deadly” wastes on warehouse operational performance. The paper develops a road map for implementing waste-reduction practices in the warehouse.

The paper proposes a road map comprising warehouse waste categories. It develops an integrated approach to quantify, rank and prioritize initiatives to adopt the categories of the “7-Deadly” Wastes that would (not) affect warehouse operational performance.

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.

Spaced learning (SL) and experiential learning (EL) have been identified as being more efficient to long-term knowledge retention than other forms of learning. The purpose of this paper is to confirm these benefits of SL and EL in a work-based learning environment.

This case study research monitored changes in learning outcomes of a work-based EL training, the Model Warehouse, when adding SL. The Model Warehouse of the Karlsruher Institute for Technology, Germany intends to educate professionals in lean warehouse logistics. Following a pragmatic standpoint, two groups of students were considered and compared by using multiple-choice question based knowledge tests where one group participated in an additional SL session. The experiences and perceptions of students were assessed by conducting in-depth interviews.

Findings revealed that adding SL to the EL training resulted either in students’ knowledge retention or knowledge improvement. Additionally, participants of the SL session did not perceive it as being required to strengthen understanding of lean warehouse management.

This study recommends considering SL as an effective means to significantly enhance long-term knowledge retention of any work-based or EL training.

This study confirms the benefits of SL and EL drawn from laboratory-based studies in a real business context. Adopting both learning theories in training programmes which converge with realities of the workplace results in a significant improvement of long-term knowledge retention.