Johannes Fichtinger

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 shippers inventory management performance. The authors modelled fill rates with daily and weekly sailings, and analysed the impact of variability on these on the shippers 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 c...

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.

The risk-averse newsvendor problem under spectral risk measures: A classification with extensions

We study the risk-averse newsvendor problem by defining the objective function as a spectral risk measure. We analyze the problem under different types of return formulations, focusing on the impact of risk aversion and cost parameters on the optimal ordering decision. We show that the monotonicity of the return function with respect to random demand determines the structural properties of the problem. When the return function is monotone in demand realization, optimal order quantity does not depend on the return margin but only on the overage and underage costs, and it has a monotone relation to risk aversion. However, if return is non-monotone in demand impact of risk aversion depends on the specific setting and it can also be non-monotone. Additionally, it is non-increasing in the margin which leads to varying impact of selling price under distinct settings.

Single Period Combined Inventory and Pricing Models

We consider a single product, single period inventory problem with stochastic price-dependent demand. The ordering and pricing decision has to be made at the beginning of the period before demand is realized. Unsatisfied demand is lost and excess inventory has to be salvaged. This problem is known in literature as the price-setting newsvendor model.

The Single Period Inventory and Pricing Problem with Spectral Risk Measures

Inventory management 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 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 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 problem. An extensive numerical analysis illustrates the findings.

A Dynamic Forecasting and Inventory Management Evaluation Approach

A common strategy for companies to hedge unpredictable demand and supply variability is to constitute safety stocks as well as safety capacity. However, classical safety stock calculations, often used in practice, assumed demand and lead time to be identical and independent distributed each, which is generally not true when considering empirical data. One cause for this problem can be the misspecification of the demand forecasting model, e. g. if a standard, additive linear regression model is used to describe heteroscedastic demand.While for a stationary demand process the amount of historical data i. e. the number of periods used for estimation of the process variability does not affect the computation, this no longer holds when using empirical data. In this study, we used a two-stage supply chain model to show that in a non-stationary setting the number of observation periods highly influence the supply chain performance in terms of on-hand inventory, fillrate and bullwhip effect. Also, we use the efficiency frontier approach to provide a single performance measure and further analyse our results.