A joint network design and mulit-echelon inventory optimisation approach for supply chain segmentation

Segmenting large supply chains into lean and agile segments has become a powerful strategy allowing companies to manage different market demands effectively. A current stream of research into supply chain segmentation proposes demand volume and variability as the key segmentation criteria. This literature adequately justifies these criteria and analyses the benefits of segmentation. However, current work fails to provide approaches for allocating products to segments which go beyond simple rules of thumb, such as 80-20 Pareto rules. We propose a joint network and safety stock optimisation model which optimally allocates Stock Keeping Units (SKUs) to segments. We use this model, populated both with synthetic data and data from a real case study and demonstrate that this approach significantly improves cost when compared to using simple rules of thumb alone

Environmental impact of warehousing: a scenario analysis for the United States

In recent years, there has been observed a continued growth of global carbon dioxide emissions, which are considered as a crucial factor for the greenhouse effect and associated with substantial environmental damages. Amongst others, logistic activities in global supply chains have become a major cause of industrial emissions and the progressing environmental pollution. Although a significant amount of logistic-related carbon dioxide emissions is caused by storage and material handling processes in warehouses, prior research mostly focused on the transport elements. The environmental impact of warehousing has received only little attention by research so far. Operating large and highly technological warehouses, however, causes a significant amount of energy consumption due to lighting, heating, cooling and air condition as well as fixed and mobile material handling equipment which induces considerable carbon dioxide emissions. The aim of this paper is to summarise preliminary studies of warehouse-related emissions and to discuss an integrated classification scheme enabling researchers and practitioners to systematically assess the carbon footprint of warehouse operations. Based on the systematic assessment approach containing emissions determinants and aggregates, overall warehouse emissions as well as several strategies for reducing the carbon footprint will be studied at the country level using empirical data of the United States. In addition, a factorial analysis of the warehouse-related carbon dioxide emissions in the United States enables the estimation of future developments and facilitates valuable insights for identifying effective mitigation strategies

Assessing the environmental impact of integrated inventory and warehouse management

There has been considerable research on the environmental impact of supply chains but most of this has concentrated on the transport elements. The environmental impact of warehousing has received relatively little attention except within the context of distribution networks. A high proportion of total warehouse emissions emanate from heating, cooling, air conditioning and lighting and these aspects are largely related to warehouse size. This in turn is greatly influenced by inventory management, affecting stockholding levels, and warehouse design, affecting the footprint required for holding a given amount of stock. Other emissions, such as those caused by material handling equipment, are closely related to warehouse throughput and equipment choice. There is a substantial gap in the literature regarding this interaction between inventory and warehouse management and its environmental impact. The purpose of this paper is to contribute to filling this gap. Therefore, an integrated simulation model has been built to examine this interaction and the results highlight the key effects of inventory management on warehouse-related greenhouse gas emissions. In particular, it is found that decisions on supply lead times, reorder quantities, and storage equipment all have an impact on costs and emissions and therefore this integrated approach will inform practical decision making. Additionally, it is intended that the paper provides a framework for further research in this important area

Demand forecasting for supply processes in consideration of pricing and market information

We develop a dynamic model that can be used to evaluate supply chain process improvements, e.g. different forecast methods. In particular we use for evaluation a bullwhip effect measure, the service level (fill rate) and the average on hold inventory. We define and apply a robustness criterion to enable the comparison of different process alternatives, i.e. the range of observation periods above a certain service level. This criterion can help managers to reduce risks and furthermore variability by applying robust process improvements. Furthermore we are able to demonstrate with our research results that the bullwhip effect is an important but not the only performance measure that should be used to evaluate process improvements

Managing variability in ocean shipping

Purpose – The paper aims to explore the relationship between time-related variables in global ocean transportation networks (GOTNs) and the shipper's inventory management performance. The authors modelled fill rates with daily and weekly sailings, and analysed the impact of variability on these on the shipper's inventory management system.Design/methodology/approach – The authors conducted simulation modelling of the above variables, and supplemented these by means of interviews with executives in a number of liner operators, 3PLs, freight forwarders and a large automotive shipper.Findings – Improvements in variability have different impacts, depending on the source of the variability and the frequency of the shipments. The highest inventory reduction potential arises from a combination of high reliability and improved frequency.Practical implications – The paper demonstrates the potential advantages of reduced variability and improved frequency of sailings. Port-to port (P2P) has been positioned in the context of door-to-door (D2D) supply chain movements.Originality/value – The paper develops clear quantitative analyses of time-based factors in operating GOTNs

The single-period inventory model with spectral risk measures

Inventory management and pricing decisions based on quantitative models both in industrial practice and academic works often rely on minimizing expected cost or maximizing expected revenues or profits, which refers to the concept of risk-neutrality of the decision maker. Although many useful insights in operational problems can be obtained by such an approach, it is well understood that incorporating attitudes toward risk is an important lever for building new theories in other fields such as economics and finance. The level of risk associated with an investment might be as important as the expected gain from the investment. Hence, it is necessary to find appropriate measures of risk and the appropriate objectives related to or including these risk measures for inventory control & pricing problems. After the axiomatic foundation of coherent risk measures the application of risk measures to inventory models such as Conditional Value-at-Risk (CVaR) or convex combinations of mean and CVaR became popular. In our work we apply spectral risk measures to the single-period, single-item, linear cost inventory control & pricing problem (also known as newsvendor problem) and derive optimal policies. By doing so, we are able to unify results obtained so far in the literature under the common concept of spectral risk measures for the case of zero and non-zero shortage penalty cost. In particular, we show convexity results and structural properties for the inventory control and, under some assumptions, unimodality results as well as structural properties for the joint inventory & pricing problem. An extensive numerical analysis illustrates the findings. (author's abstract

The Single-Period Inventory Model with Spectral Risk Measures

Inventory management and pricing decisions based on quantitative models both in industrial practice and academic works often rely on minimizing expected cost, which refers to the concept of risk-neutrality of the decision maker. Although many useful insights in operational problems can be obtained by such an approach, it is well understood that incorporating attitudes toward risk is an important lever for building new theories in other fields such as economics and finance. In this work spectral risk measures are applied to the price-setting newsvendor problem and optimal policies are derived. This allows to unify results obtained so far in the literature under the common concept of spectral risk measures for the case of zero and non-zero shortage penalty cost

The Single-Period Inventory Model with Spectral Risk Measures

Inventory management and pricing decisions based on quantitative models both in industrial practice and academic works often rely on minimizing expected cost, which refers to the concept of risk-neutrality of the decision maker. Although many useful insights in operational problems can be obtained by such an approach, it is well understood that incorporating attitudes toward risk is an important lever for building new theories in other fields such as economics and finance. In this work spectral risk measures are applied to the price-setting newsvendor problem and optimal policies are derived. This allows to unify results obtained so far in the literature under the common concept of spectral risk measures for the case of zero and non-zero shortage penalty cost

Demand forecasting for supply processes in consideration of pricing and market information

We develop a dynamic model that can be used to evaluate supply chain process improvements, e.g. different forecast methods. In particular we use for evaluation a bullwhip effect measure, the service level (fill rate) and the average on hold inventory. We define and apply a robustness criterion to enable the comparison of different process alternatives, i.e. the range of observation periods above a certain service level. This criterion can help managers to reduce risks and furthermore variability by applying robust process improvements. Furthermore we are able to demonstrate with our research results that the bullwhip effect is an important but not the only performance measure that should be used to evaluate process improvements.Demand forecasting Extended price information Supply chain management Performance measurement Bullwhip effect