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Key components of the logistics mix are described in an effort to create an understanding of the total logistics concept. Chapters include an introduction to logistics; the strategic role of logistics, customer service levels, channel relationships, facilities location, transport, inventory management, materials handling, interface with production, purchasing and materials management, estimating demand, order processing, systems performance, leadership and team building, business resource management.

The objective of this paper is to propose a methodology based on random demand inventory models and dependence structures for a set of raw materials, referred to as “components”, used by food services that produce food rations referred to as “menus”.

The contribution margins of food services that produce menus are optimised using random dependent demand inventory models. The statistical dependence between the demand for components and/or menus is incorporated into the model through the multivariate Gaussian (or normal) distribution. The contribution margins are optimised by using probabilistic inventory models for each component and stochastic programming with a differential evolution algorithm.

When compared to the non-optimised system previously used by the company, the (average) expected contribution margin increases by 18.32 per cent when using a continuous review inventory model for groceries and uniperiodic models for perishable components (optimised system).

The multivariate modeling can be improved by using (a) other non-Gaussian (marginal) univariate probability distributions, by means of the copula method that considers more complex statistical dependence structures; (b) time-dependence, through autoregressive time-series structures and moving average; (c) random modelling of lead-time; and (d) demands for components with values equal to zero using zero-inflated or adjusted probability distribution.

Professional management of the supply chain allows the users to register data concerning component identification, demand, and stock levels to subsequently be used with the proposed methodology, which must be implemented computationally.

The proposed multivariate methodology allows it to describe demand dependence structures through inventory models applicable to components used to produce menus in food services.

Este trabajo propone una metodología basada en modelos de inventarios con demanda aleatoria y estructura de dependencia para un conjunto de materias primas, denominadas “componentes”, usadas por servicios de alimentación que producen raciones alimenticias denominadas “menús”.

Los margen de contribución de servicios de alimentación que producen menús son optimizados empleando modelos de inventarios con demandas aleatorias dependientes. La dependencia estadística entre demandas de componentes y/o menús es incorporada en el modelado mediante la distribución gaussiana (o normal) multivariada. La optimización de los márgenes de contribución se logra usando modelos de inventarios probabilísticos para cada componente y programación estocástica mediante el algoritmo de evolución diferencial.

El margen de contribución esperado (promedio) aumenta en un 18,32% usando modelos de inventario de revisión continua para abarrotes y modelos uniperiódicos para componentes perecederos (sistema optimizado), en relación al sistema no optimizado usado anteriormente por la compañía.

La metodología multivariada propuesta permite describir estructuras de dependencia de la demanda mediante modelos de inventario aplicables a componentes usados para producir menús en servicios de alimentación.

Una administración profesional de la gestión de la cadena de suministros permite registrar datos de la identificación del componente, su demanda y sus niveles de stock para ser usados posteriormente con la metodología propuesta, la que debe estar implementada computacionalmente.

El modelado multivariado puede ser mejorado (a) utilizando distribuciones probabilísticas univariadas (marginales) distintas a la gaussiana, mediante métodos de cópulas que recojan estructuras de dependencia estadística más complejas; (b) considerando demandas de componentes con valores iguales a cero, mediante distribuciones probabilísticas infladas en cero; (c) usando dependencia temporal, mediante estructuras de series de tiempo autorregresivas y de media móvil, y (d) modelando el lead-time en forma aleatoria.

The impact of product variety decisions on fill rate, inventory and sales performance in a consumer goods company has been examined. From a marketing perspective, it is possible to leverage sales, reach new segments and consequently increase competitiveness when there is a greater product variety on the market. However, operations and logistics professionals indicate potential impacts on the supply chain, such as production, storage and distribution complexity. The nature of the product variety-cost-sales performance relationship is not clear, and empirical evidence about whether and how operations cost and sales performance increases with variety is inconclusive.

The multiple linear regression and the Tobit regression techniques were applied over a seven-year horizon of data from a business intelligence platform of a consumer goods company.

Our results show that sales performance is negatively associated with product variety. The total effect of product variety on sales performance has been examined, including both the direct effect and the indirect effect through inventory and fill rate. Therefore, the findings provide a comprehensive understanding of the impact of product variety on operations and sales performance.

Several studies have researched the impact of product variety on fill rate, inventory and sales performance separately; however, the research of the impact and the relationship of these factors is scarce and limited.

The paper provides a comprehensive overview of the biases in inventory decisions, under the umbrella of behavioural operations, considering research findings that used experimental methods. Research in this field has gained traction but, to the best of our knowledge, review articles that summarize these research findings are scarce. Hence, there is a need to synthesize the anomalies and biases reported in inventory decision-making literature to gain a more integrated understanding.

This study reviews themes relevant to inventory and behavioural operations management from the articles published in recognized top-tier journals during the period between 2000 and 2020 with an aim to build a classification framework. Further, using a systematic review procedure, the relevant research studies are divided into sub-sections and appropriate search strategies are adopted to pleat more information on inventory ordering biases.

This study presents a classification framework by highlighting the factors influencing the biases in inventory decisions and describes the effects of utility preferences on the decision-making behaviour. It highlights the inventory ordering pattern under unconventional settings and also for different supply chain settings. This systematic review helps in identifying the research gap and in giving directions for future researchers.

The study presents a systematic review and detailed analysis of the research in inventory decision making through a behavioural lens. The study shows a clear direction of progress over the years, and implies new directions for looking beyond placing orders and for moving towards a more integrated approach while making supply chain decisions. It will be useful for researchers and practitioners working on newsvendor decisions, supply chain contracts, behavioural economics, behavioural operations management, bounded rationality theory and experimental economics.

This research attempted to establish the underlying dimensions of supply chain management practices, blockchain technology and supply chain performance in the Indian dairy industry. Additionally, the study proposes a conceptual model that shows the mediating effects of blockchain technology in the relationship between supply chain management practices and supply chain performance.

Structural equation modelling (SEM) is incorporated to examine the proposed model using SPSS and AMOS version 24. The study population includes 119 registered Indian dairy processing units operating in Uttar Pradesh and New Delhi (source: Dairy – India). Individual registered dairy processing unit's top four executives, that is Head of the Dairy Processing Plant, Supply Chain head and Marketing Head, and IT head are chosen as the respondents of the study, which renders the sample size of 476. Judgmental sampling based on the organisation's market position and plant production capacity (i.e. one lakh litre per day) has been set as the benchmark for selecting the dairy processing units. The executives are selected as respondents as they are well-versed in the phenomenon of supply chain management practices, blockchain technology and supply chain performance compared to other staff working in the dairy industry. The data was collected from December 2021 to March 2022 through judgmental sampling. The target sample size was 476, but only 286 questionnaires were received in a completed state and were further used for analysis.

Manufacturing practices, information sharing, distribution management, inventory management and blockchain technology have a significant and positive impact on supply chain performance in the Indian dairy industry. Furthermore, the research demonstrates that blockchain technology partially mediates the relationship between supply chain management practices and supply chain performance in the context of the Indian dairy industry.

This research is focused on the Indian dairy industry operating in only two states, namely New Delhi and Uttar Pradesh. More research is needed to determine whether SCM practices and the prospects for blockchain technology among channel members are universally applicable to merchants in non-dairy products. Similar investigations should be carried out on dairy industry operating in various formats and in numerous geographic locations. Further, case studies can be conducted by future researchers to learn how supply chain management methods are deployed, what precisely these practices entail and what costs and time demands are required by these practices in context of small independent retailers across different germane expanse.

While the available literature on the research area is spread out, the influence of blockchain technology in the Indian dairy industry has not yet been sufficiently analysed. Therefore, the research article focused on exploring underlying dimensions of the constructs of supply chain management practices, blockchain technology adoption and supply chain performance in the context of the Indian dairy industry.

For those who like certainty, now is not a good time to be in logistics management ‐ for those who relish challenges, there are plenty to be had. There are challenges not just to the old certainties, but the new certainties which replaced them. Companies have, in recent years, looked to Japan for inspiration, only to find the Japanese economy beginning to falter. Japanese management practices were endorsed by, and imported into, many Western organizations and, when these transplanted practices failed to work, cultural difficulties were cited. It then becomes something of a shock, for example, to see the keiretsu distribution system fall into disrepute, and lean production methods become modified or abandoned by those who developed them.

The purpose of this paper is to consider the use of temporary storage offered by intermodal transshipment points to position some stock of fast moving consumer goods in advance of demand; this floating stock concept combines transport and inventory management. Intermodal transport is compared with direct road transport for a supply chain.

First an analytical comparison is made which shows that the floating stock concept has advantages in inventories over pure road and intermodal transport. Next, a simulation study of a real case is made which quantifies the cost‐differences in detail.

It is found that both storage costs can be lowered and shorter response times be gotten by sending shipments in advance to intermodal terminals. The advance positioning can offset the disadvantage of a longer transit time in intermodal transport.

Demand needs to be somewhat predictable. The pooling effects depend on geographical layout of the customers. The availability of intermodal transport options is based on the situation in Western Europe.

The floating stock concept considers both the transport and inventory issues. By positioning some of the stock at transshipment points close to the customer in anticipation of demand, the concept can yield lower inventory costs as well as a lower customer lead time. The benefit for logistics service providers is a more regular supply chain. Using intermodal transport provides an opportunity to green the supply chain as the environmental impact per ton/kilometer is lower than road transport.

This paper draws on the areas of logistics and inventory management to consider the choice of transport mode; most studies look at these issues in isolation. Considering the holding and storage costs in addition to the distribution strategy enables a more thorough comparison of the transport modes.

We all talk of logistical problems as if they were something which can be left to others ‐ you know, those less creative than ourselves; the slightly dull sorts with computer‐like brains who thrive on just this sort of basically mathematical problem. This may be an overstatement of the case, but how many senior executives regularly meet with the managers responsible for the movement of materials? And how often do the same executives physically visit the scenes of these activities? Often? Sometimes? Never? And how many equate the costs of logistics merely with warehouse rents plus road haulage or rail costs?

Unfortunately, the occurrence of natural disasters is inevitable all over the world. Hence, this paper aims to analyze a scenario-based collaborative problem in a relief supply chain (RSC), where nongovernmental organizations can participate in relief activities with governmental organizations. This study focuses on location-allocation, inventory management and distribution planning under uncertain demand, budget, transportation and holding costs where government and private distribution centers receive relief items from suppliers then send them to affected areas. The performance of the proposed model is surveyed in a real case study in Dorud.

This paper develops a nonlinear mixed-integer programming model that seeks to maximize the coverage of demand points and minimize operating costs and traveled distance. The linear programming-metric technique and grasshopper optimization algorithm are applied to survey the model's applicability and efficiency.

This study compares noncollaborative and collaborative cases in terms of the number of applied distribution centers and RSC's goals, then demonstrates that the collaborative model not only improves the coverage of demand points but also minimizes cost and traveled distance. In fact, the presented approach helps governments efficiently surmount problems created after a disaster, notwithstanding existing uncertainties, by determining a strategic plan for collaboration with nongovernmental organizations for relief activities.

Relief strategies considered in previous research have not been sufficiently examined from the perspective of collaboration of governmental and nongovernmental organizations and provided an approach to develop the coverage of affected areas and reducing costs and traveled distance despite various uncertainties. Hence, the authors aim to manage RSCs better by offering a mathematical model whose performance has been proved in a real case study.

The purpose of this paper is to develop an understanding of the nature of collaborative arrangements that partners in Australian hospital supply chains use to manage inventories.

A case study involving a supply chain network of ten healthcare organisations (three pharmaceutical manufacturers, two wholesalers/distributors and five public hospitals) was studied. Data included 40 semi‐structured interviews, site visits and examination of documents.

This study highlights the existence of a variety of collaborative arrangements amongst supply chain partners such as the “Ward Box” system (a variant of the vender managed inventory system) between wholesalers/distributors and hospitals. The materials management departments were more willing than their pharmacy counterparts to participate in a variety of partial and complete outsourcing arrangements with wholesalers/distributors and other hospitals. Several contingent factors were identified that influenced development of collaborative arrangements.

This study is limited to the Australian healthcare sector. To improve generalisability, this study could be replicated in other industry sectors and countries.

Application of collaborative arrangements between manufacturers and wholesalers/distributors would improve inventory management practices across the supply chains. Also, learning from materials management departments could be transferable to pharmacy departments.

Several contingent variables for the implementation of collaborative inventory management arrangements between healthcare supply chain partners have been identified. Methodologically, data across three echelons in the supply chains (manufacturers, wholesalers/distributors and hospitals) were collected and analysed.