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This article seeks to present a decision support model for improving supply chain performance. The model aims to provide a holistic view of the supply chain as an integrated system by analyzing inventory options to facilitate the decision‐making process by business partners in the system.

In recent years, organizations have focused on incorporating both internal and external business activities of their supply chain into an integrated system. The goal of integration of all supply chain activities is to maximize total systems performance while minimizing costs. Literature review and professional experience in the field provided the foundation for the model development in this research.

The article demonstrates the usefulness of a decision support model in analyzing and developing a cooperative environment among supply chain members in order to reduce the cost of inventory as well as the cost of goods sold. The effects of utilizing such tools as just‐in‐time and electronic business systems are illustrated and discussed.

The proposed model demonstrates disadvantages of individual optimization in an integrated supply chain system as well as the advantages of collaboration of supply chain members in finding the minimum cost. The model uses one manufacturer with multiple retailers and distributors. Future research in this area could expand the model to allow multiple manufacturers.

The decision support model allows decision makers along the supply chain to employ a series of what‐if analyses to evaluate different scenarios with regard to lowering the cost of products reaching the consumer.

The model developed in this paper provides the foundation for future research as well as support for decision making when various decision makers are involved.

Addresses a number of issues relating to determining whether products should be ordered independently and therefore shipped as a single‐product order, or co‐ordinated and shipped as a group, or multiproduct, order from a single source. Factors which might influence the decision include the level or volume of demand, the distribution of demand across products, the weight of items and the attractiveness of the quantity discount offered. Uses an optimal inventory‐theoretic model, that incorporates transport weight breaks and quantity discounts, to assess when product orders should be combined and what products should be ordered separately. The effects of these decisions on the order interval, the number of order groupings, the proportion of items ordered independently, the proportion of attractive discounts forgone in favour of consolidation, and the relative cost savings, are examined using an extensive set of simulated data that are based on a firm in the automobile industry supply chain.

Why is it important to use computers in physical distribution management (PDM)? The reason is simple. PDM covers almost all of the activities concerned with distributing a product. Some authorities claim that logically the functions of procurement‐and manufacturing should also be considered as part of the overall process of making goods available to customers at the right place, at the right time, and in the right quantity. These activities and functions are inherently complex, involving many stock‐keeping items (SKIs) different in size, colour and other product characteristics. In addition to multiple items, there are multiple options for producing and distributing these items. Think of the many combinations of plant locations, distribution centre locations, choices of transportation modes, inventory levels, and channels of distribution that the physical distribution manager must consider. Imagine having to compare the many possible alternatives by hand calculations, and you can get some feel for the necessity of using modern computers for planning, operating, and controlling physical distribution.

Logisticians are trained to identify trade‐offs and to devise compromising solutions which produce the lowest total costs. For example, the economic batch size is a compromise between setup costs and inventory carrying costs. Presents a problem‐analysis technique which helps the analyst to eliminate the problem so that no compromise is necessary. For example, instead of balancing inventory‐carrying costs and setup costs a solution which produces both lower setup costs and lower inventory‐carrying costs would be sought. Discusses a description of the techniques and several examples of its application to logistical problems.

The purpose of this paper is to explore different aggregate production planning (APP) strategies (inventory levelling, validation of the workforce and flexible production alternatives: overtime and/or outsourcing) by using a system dynamics model in a two-level, multi-product, multi-period manpower intensive supply chain (SC). Therefore, the appropriateness of using systems dynamics as a research method, by focusing on managerial applications, to analyse APP policies is proven. From the combination of systems dynamics and APP, recommendations and action strategies are considered for each scenario to understand how the system performs and to improve decision making on APP in the SC context.

The research design analyses a typical factory setting with representative parameter settings for five different conventional APP policies – inventory levelling, workforce variation, overtime, outsourcing and a combination of overtime and outsourcing – through deterministic systems dynamics-based simulation. In order to validate the simulation model, the results from published APP models were replicated. Then, optimisation is conducted for this deterministic setting to determine the performance of all these typical policies with optimal parameter settings. Next, a Monte Carlo stochastic simulation is used to assess the robustness of such performances in a variety of demand settings. Different aggregate plans are tested and the effect that events like demand variability and production times have on the SC performance results is analysed.

The results support the assertion that the greater the demand variability, the higher the flexibility costs (overtime, outsourcing, inventory levelling, and contracts and firings). As greater inter-month oscillations appear, which must be covered with additional alternatives, the optimum number of employees must be determined by analysing the interchanges and marginal costs between capacity oversizing costs (wages, idle time, storage) and the costs to undersize it (penalties for lowering safety stocks, delayed demand, greater use of overtime and outsourcing). Accordingly, controlling the times to avoid increased costs and penalties incurred by delayed demand becomes an essential important task, but one that also depends on the characteristics of this variability.

This paper has developed a modelling approach for APP in a manpower intensive SC by applying system dynamics. It includes a simulation model, the analysis of several scenarios, the impact on performance caused by variability events in the parameters, and some recommendations and action strategies to be subsequently applied. The modelling methodology proposed can be employed to design-specific models for each SC.

This paper proposes an APP system dynamics approach in a two-level, multi-product, multi-period manpower intensive SC for the first time. This model bridges the gap in the literature relating to simulation, specifically system dynamics and its application for APP. The paper also provides a qualitative description of the various pros and cons of each analysed policy and how they can be combined.

The purpose of this paper is to empirically investigate the impact of global sourcing and exports on US domestic manufacturing inventories and quantify the additional inventory costs associated with global operations.

A panel data set of 19 US manufacturing sectors is constructed over the period 2002‐2005. Data are collected from the US economic census and other government statistics. Fixed and random effects models in both linear and LOG terms are estimated and the estimated coefficients used to calculate the cost to US manufacturing industries of additional inventories attributed to global operations.

Imports and exports have a positive, significant impact on raw materials inventory and finished goods inventory, respectively, in terms of days of supply. Based on estimations using 2005 data, a 10 percentage point increase in the import and export ratios for all US manufacturers is estimated to be accompanied by $3.03 billion additional costs for raw materials inventory and $5.33 billion for finished goods inventory, respectively.

This study is among the first to quantify the impact of global sourcing and exports on US domestic inventories using secondary data. To macroeconomic policy makers and industry managers, the results may serve as a benchmark to how domestic inventories are affected by global outsourcing and exports, and as a reminder that the benefits of global activities may be overestimated if inventory costs are not explicitly taken into consideration.

The conventional Economic Order Quantity (EOQ) lot‐size model ignores the effects of damage costs. While producing an EOQ lot, the inventory may go “out of control”. An extension of the EOQ production model based on damage costs is presented and the relevant costs of damage and their effects on stockholding costs are discussed. A new EOQ formula including damage costs is developed and a comparison between the conventional concept and the new concept based on annual total cost is made. The analysis shows that, if the costs of damage to stock are taken into account, the computed EOQ is smaller, and the conventional EOQ is not the optimum solution.

South Africa's logistics cost measurement was expanded to include externality costs, and scenarios based on the key exogenous risks were developed to inform mitigation strategies. This paper aims to discuss these issues.

The research approach is quantitative, based on a gravity-orientated freight flow model, a road transport cost model, actual transport costs for other modes, a warehousing cost survey, an inventory delay calculation (to inform warehousing cost calculations and inventory financing costs) and an externality cost calculation.

Transport cost pressures are expected to deteriorate due to the increasingly negative outlook for the oil price and the internalisation of externality costs. The nature of these forces compels transport cost challenges to be addressed strategically through collaborative, industry-wide and even nationwide initiatives.

Key limitations are inconsistent commodity classification schemes across information sources, and incomplete container content data. The researchers are collaborating with information providers to address these issues and refine model accuracy and forecasting.

The exogenous risks strengthen the argument for new approaches to South Africa's logistics cost challenges driven by the high densities of corridor freight flows.

The inclusion of externality costs highlighted the negative environmental impact of the current modal configuration and provides impetus for change.

Major advancements to logistics cost modelling were made by incorporating externality costs and developing scenarios for risk mitigation. Freight flow data granularity (in excess of one million records) allows both aggregation to national-level intelligence to inform policies, large-scale infrastructure investments and industrial positioning, and disaggregation to enable practical application.

A conference can range from good to bad. It can be well or poorly organized, comfortably or indifferently housed, a profitable or wasted use of time. If conferences were rated like hotels and stars indicated their merit, the one held in Munich at the end of October should be awarded the maximum number.

To develop and test three different inventory management strategies as applied to the complex emergency in south Sudan.

Quantitative modeling, simulation, and statistics.

This research identified critical system factors that contributed most significantly to inventory system performance, and identified strengths and weaknesses of each inventory management strategy.

This research represents a first step in developing inventory management systems for humanitarian relief. Future work would include modeling correlation among relief items, multiple items, and considering the impact of information.

In a domain that has seen limited application of quantitative models, this work demonstrates the performance benefits of using quantitative methods to manage inventory in a relief setting.

This research has value for relief organizations by providing a real‐world application of quantitative inventory management strategies applied to a complex emergency, and demonstrated performance advantages of quantitative versus ad hoc methods. This research has value for researchers by providing a new application of simulation and mathematical modeling (humanitarian relief).