Examining the balance between efficiency and resilience in closed-loop supply chains.

We study a hybrid system where the demand of customers can be satisfied by both manufacturing new products and remanufacturing used products. To manage the serviceable inventory, we implement a proportional order-up-to (POUT) replenishment policy. In this context, we first analyse the system efficiency by assessing its capacity to meet customer demand in a cost-effective manner. To this end, we consider both inventory performance (i.e., the balance between inventory holding and stock-out costs) and production smoothness (by measuring the Bullwhip effect in the supply chain). Second, we investigate the system resilience to demand volatility. In particular, we explore the impact of demand shocks on the inventory and production of the closed-loop system. Interestingly, we find that tuning the POUT controller to optimise efficiency may be problematic in terms of resilience to demand shocks. In this sense, a key trade-off exists that needs to be carefully considered by supply chain managers. Linking efficiency to resilience in such supply chains thus becomes essential to strengthen the transition towards more circular economic models. All in all, our analysis, via control-theoretic and simulation techniques, provides professionals with valuable insights into how to identify the appropriate 'formula' for building both efficient and resilient closed-loop supply chains.

The effects of quantity discounts on supply chain performance: Looking through the Bullwhip lens.

Quantity discounts are a common pricing mechanism to stimulate large orders. We explore their impact on the dynamic behaviour of production and distribution systems by studying key operational and economic metrics. In a three-echelon supply chain, we observe that the discount generally increases the Bullwhip Effect, which especially harms the manufacturer. The discount also reduces the retailer's purchase costs, but increases its inventory- and capacity-related costs. A key trade-off thus emerges, which manifests itself through a U-shaped relationship between the total cost and the discount acceptance parameter. In the light of this trade-off, we discuss how key factors should affect the retailer's willingness to pursue the discount. We observe that managers that need to deal with tougher environmental conditions, such as high demand uncertainty and long lead times, should be less reluctant to increase orders up to the discount quantity. We also discuss in detail other valuable insights for professionals, both from the perspective of sellers and buyers.

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions.

OBJECTIVES: We develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment. Through three structured fuzzy inference systems (FISs), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of the risk. METHODS AND MATERIALS: Three FISs, based on the Mamdani model, determine the level of innovation risk of large pharmaceutical laboratories according to their strategic choices. Two subsystems measure different aspects of innovation risk while the third one builds on the results of the previous two. In all of them, both the partitions of the variables and the rules of the knowledge base are agreed through an innovative 2-tuple-based method. With the aid of experts, we have embedded knowledge into the FIS and later validated the model. RESULTS: In an empirical application of the proposed methodology, we evaluate a sample of 31 large pharmaceutical laboratories in the period 2008-2013. Depending on the relative weight of the two subsystems in the first layer (capturing the financial and the pipeline sides of innovation risk), we estimate the overall risk. Comparisons across laboratories are made and graphical surfaces are analyzed in order to interpret our results. We have also run regressions to better understand the implications of our results. CONCLUSIONS: The main contribution of this work is the development of an innovative fuzzy evaluation model that is useful for analyzing the innovation risk characteristics of large pharmaceutical laboratories given their strategic choices. The methodology is valid for carrying out a systematic analysis of the potential for developing new drugs over time and in a stable manner while managing the risks involved. We provide all the necessary tools and datasets to facilitate the replication of our system, which also may be easily applied to other settings.

Measuring the efficiency of large pharmaceutical companies: an industry analysis.

This paper evaluates the relative efficiency of a sample of 37 large pharmaceutical laboratories in the period 2008-2013 using a data envelopment analysis (DEA) approach. We describe in detail the procedure followed to select and construct relevant inputs and outputs that characterize the production and innovation activity of these pharmaceutical firms. Models are estimated with financial information from Datastream, including R&D investment, and the number of new drugs authorized by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) considering the time effect. The relative performances of these firms-taking into consideration the strategic importance of R&D-suggest that the pharmaceutical industry is a highly competitive sector given that there are many laboratories at the efficient frontier and many inefficient laboratories close to this border. Additionally, we use data from S&P Capital IQ to analyze 2071 financial transactions announced by our sample of laboratories as an alternative way to gain access to new drugs, and we link these transactions with R&D investment and DEA efficiency. We find that efficient laboratories make on average more financial transactions, and the relative size of each transaction is larger. However, pharmaceutical companies that simultaneously are more efficient and invest more internally in R&D announce smaller transactions relative to total assets.