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Fashion retail companies typically exhibit short life-cycles, high volatility and low predictability. Therefore, their success is largely determined by the organisation’s flexibility and responsiveness. The purpose of this paper is to present a methodology to facilitate inventory control to minimise both shortages and excess inventory for a multi-product, multi-period finite time horizon inventory problem by using statistical and stochastic analysis.

The proposed methodology operates in two phases: the first phase consists on determining an aggregate plan (AP) that will be used for monitoring the behaviour of the items during the time horizon. This plan is obtained by statistically analysing historical data related to sales and inventory shortages and is used to determine a demand forecast during the time horizon that allows to handle with potential disruptions derived from real demand variations. Finally, supply replenishment policies are defined to facilitate the monitoring process during the second phase. For the second phase, the behaviour of real demand for every item is captured into a database and compared against its projected demand (from the AP). If needed, adjustments are made in the procurement of future deliveries to reduce the probability of having shortages and/or excess inventory.

A case study in a Mexican fashion retail company was conducted to assess the performance of the methodology. Results indicate that shortage in early periods can be reduced totally for certain products while, for others, the reduction is about 90.5 per cent. In addition, the incomes of the company were increased over 57 per cent.

Even when the success of the methodology has been shown, cultural and behavioural factors were not considered. An extensive study is suggested to determine if these factors should be included to enhance the performance of the methodology.

A case study of a Mexican fashion retail company was conducted to assess the performance of the proposed methodology. The methodology is easy to implement and effectively and quickly responds to disruptions in the demand and it also significantly reduces the level of shortages while increasing sales and revenue for the company.

This paper proposes a methodology that is able to anticipate product’s behaviour from early weeks. Additionally, replenishment policies allow to quickly adjust future orders to guarantee the availability of items and minimise overstock.

Inventory carrying costs represent one of the highest costs of distribution. Although they are a necessary input to the design of logistical systems, such costs are ignored by many companies and when they are used usually represent estimates or industry benchmarks. The authors present a methodology designed to provide managers with a practical framework for determining the costs of carrying inventory.

Inventory carrying costs, the costs associated with the quantity and value of inventory stored, represent a large portion of the expense attached to distribution activities. These costs also are influenced by the configuration of the physical distribution system utilised. Therefore, accurate assessment of inventory carrying costs is essential to not only controlling the cost area, but also for the analysis of the cost effectiveness of different physical distribution systems. Traditionally, managers who do consider the cost of holding inventory use estimates, industry benchmarks that range from 12 to 35 per cent of the value of the inventory, or textbook figures of 25 per cent. Many sources have quoted the 25 per cent figure and have not changed it in the last 30 years, thus ignoring the impact of inflation on the cost of capital. Also, it is believed that many firms do not even consider inventory carrying costs.

A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand rate is unknown. That is, optimal inventory levels are decided using these two approaches at consecutive time intervals. Simulations were conducted to compare the total inventory cost using a Bayesian approach and a non-Bayesian approach to a theoretical minimum cost over a variety of demand rate conditions including the challenging slow moving or intermittent type of spare parts. Although Bayesian approaches are often recommended, this study’s results reveal that under conditions of large variability across the demand rates of spare parts, the inventory cost using the Bayes model was not superior to that using the non-Bayesian approach. For spare parts with homogeneous demand rates, the inventory cost using the Bayes model for forecasting was generally lower than that of the non-Bayesian model. Practitioners may still opt to use the non-Bayesian model since a prior distribution for the demand does not need to be identified.

The purpose of this paper is to present a review of the origins and development of national inventories in Scotland and Wales from paper-based records to digital databases. The impact of digital technologies on data management and dissemination is considered.

Exploring the evolution of historic environment inventories in Scotland and Wales highlights the common issues faced and solutions adopted. In considering the longue durée, the durability and flexibility of information is highlighted.

Inventories may combine locational, descriptive and visual material to help document a fragile, finite and non-renewable resource. They provide the evidence base for decision making and stewardship in managing change, stimulating interest in and valuing the historic environment. Contribution to recognised inventories should be a requirement for activities documenting the historic environment. Digital technologies shape the expectations of a modern inventory and associated digital archives, with emphasis placed on the repurposing of inventory contents so that they can better contribute to an information network serving multiple audiences.

Transformation from paper records to digital data has radically enhanced and democratised access to knowledge about the historic environment. Digital delivery helps place heritage data within mainstream societal activities and fosters public interest and engagement in the historic environment.

Inventories are the foundation building blocks of informed decision making. Digital technologies have transformed these resources to help place the historic environment within mainstream societal activities and interest.

Urban areas are responsible for significant amounts of greenhouse gas (GHG) emissions, but cities can have quite different values of carbon footprints. The purpose of this paper is to identify the determinants of urban GHG emissions in order to explain these differences.

Seven global cities – Bangkok, Chicago, London, Madrid, Mexico City, Milan and New York City – have been included in the paper's sample. These cases have been chosen in order to represent a variety of characteristics and contexts of developed and developing countries and according to data availability. A first level of analysis regards local GHG emission inventories, which attribute emissions to activity sectors. Sectorial emissions are then evaluated to quantify the relevance of several determinants: climate conditions, urban form, economic activities in place, state of technology, mobility and housing infrastructures and costs, and income and life style.

Determinants show different weights in influencing behaviours at city level, and ultimately depend on economic, technical, social and cultural factors. Beside a significant role of climate conditions, urban density appears as the main determinant in shaping residential emissions from direct fuel consumption, whereas, for electricity, consumption patterns and technological features of power generation play a major role. For ground transport, urban form affecting mobility patterns and technological features of the vehicle stock stand out as the most significant determinants.

The paper provides a deep insight into urban GHG emission values, making use of a comprehensive set of urban data and highlighting several areas which could possibly be targeted in cities' GHG reduction policies. An enhanced and widened set of data could improve the paper's results in a significant way.

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 cost of providing healthcare is spiralling up in recent times. On the one hand, patients expect the highest quality of service, while on the other hand, the managers of the healthcare services want to minimise the total operating expenses. Hence, healthcare organisations implement lean thinking (LT) to achieve these twin objectives. LT reduces the eight wastes that are prevalent in the healthcare processes and functions. In particular, if the wasteful inventories related to expensive medical supplies are reduced, the resulting cost savings can help in providing affordable and accessible healthcare.

Hence, in this paper, a case study of a hospital is presented where LT is implemented. One of the projects was related to inventory reduction in the store of the catheterisation laboratory (cath lab). A hybrid methodology called multi-unit selective inventory control (MUSIC) that combined these three dimensions (3D), namely, consumption value, criticality and lead time or ease of availability was used to classify the medical supplies into different categories.

Based on the results obtained, various inventory systems and the associated tools and techniques of LT were proposed. For example, a deep dive into the A-class items revealed that some of the medical supplies fell under both vital and scarce categories. Hence, it was recommended that the case hospital should follow the economic order quantity (EOQ) with safety stock approach as these items were to be shipped from other states in India. Subsequently, the focus should be on developing a local supplier and attempts should be made to establish a kanban system with adequate information sharing.

This study demonstrates the step-by-step methodology of MUSIC-3D which would guide the procurement managers to apply the same in their organisation. It also helps them in identifying appropriate elements of LT for inventory reduction before the actual deployment.

None of the papers has utilised the MUSIC-3D methodology as a precursor for inventory reduction, specifically within the domain of LT. Similarly, identifying and proposing different type of inventory systems and various LT practices based on this unique method is a novel attempt.

The purpose of this paper is to illustrate the relevance of cultural heritage inventories and documentation as critical tools for heritage conservation and management in historic cities, through the case study of the historic city of Ahmadabad, India.

The paper discusses the creation of inventories as part of the comprehensive documentation undertaken for preparation of the World Heritage nomination dossier. In this context, the goal of preserving the outstanding universal value of the historic city formed the basis for selection and assessment of the assets to be included in the inventory. Standard processes and methodology employed for creation of the database involved archival research and planning; field surveys for mapping, photo documentation and data collection; data registration and processing using CAD, Ms Office and geographic information system (GIS)-based digital platform. Optimal data acquisition while maintaining cost and time efficiency was an important consideration for the fieldwork.

While an inventory remains a critical conservation tool, the heritage assets are integrally linked to and influenced by their surroundings in an urban context. A comprehensive documentation approach that provides a broader understanding of the city’s natural features, built environment, heritage resources, cultural practices and socio-economic networks is therefore necessary for creation of a suitable management plan for historic cities. Traditional field surveys based on external visual observation are time and cost effective, and can generate adequate information on identification and assessment of built heritage assets for creation of inventory along with documentation of their context. However, the scale of investigation and the intended role of an inventory as an urban management tool calls for creation of integrated, accessible and updatable data management systems.

At the moment, the database created is only used for the limited purpose of monitoring and managing the conservation of listed properties. The importance of the inventory as a critical conservation and urban management tool can be further validated once the GIS database of the historic city is integrated within the urban management and planning system for the city.

The inventory created for the historic city of Ahmadabad offers one of the few examples of heritage documentation at such a large scale. It covers an area of 543 ha with a detailed inventory of more than 6,000 properties. The description of, and the reflections on the process employed provide an overview of usual challenges encountered in creation, presentation and management of heritage inventories for historic cities which may offer useful guidelines to practitioners planning similar exercises and help them establish efficient work processes.

The purpose of this paper is to provide an approach for determining inventory levels that result in a minimum cost customer service level for specific products based on their demand characteristics and profit margin.

The paper uses logistic regression to quantify the relationship between customer service level and inventory on‐hand in relation to forecasted demand, as well to estimate the impact of factors such as forecast accuracy, customer lead‐times, and demand variability on this relationship. It then performs financial analysis in order to associate a cost with customer service level.

Empirical results based on data from a semiconductor manufacturer indicate significant cost‐savings can be achieved by applying the proposed method over the organization's current ad hoc practices.

The minimum cost customer service level identified via the methodology is based on values of dynamic factors that are specific to the time when data were collected. Therefore, frequent updating is necessary to ensure the customer service level remains close to the minimum cost. Future research could identify the ideal frequency for updating inventory levels based on cost minimization and production stability.

This research presents an inventory management methodology for organizations with variable, non‐stationary demand. In contrast to much of the current inventory modeling literature, in which service level goals are selected in an ad hoc or a priori manner, this research determines an ideal (minimum cost) customer service level from the supplier's perspective based on products' unique characteristics.