How can we improve the performance of supply chain contracts? An experimental Study

Although optimal forms of supply chain contracts have been widely studied in the literature, it has also been observed that decision makers fail to make optimal decisions in these contract setups. In this research, we propose different approaches to improve the performance of supply chain contracts in practice. We consider revenue sharing and buyback contracts between a rational supplier and a retailer who, unlike the supplier, is susceptible to decision errors. We propose five approaches to improve the retailer’s decisions which are in response to contract terms offered by the supplier. Through laboratory experiments, we examine the effectiveness of each approach. Among the proposed approaches, we observe that offering free items can bring the retailer’s effective order quantity close to the optimal level. We also observe that the retailer’s learning trend can be improved by providing him with collective feedbacks on the profits associated with his decisions

An Integrated Outsourcing Framework: Analyzing Boeing’s Outsourcing Program for Dreamliner (B787)

This paper analyzes the outsourcing model which Boeing devised to develop its latest commercial airplane model: Dreamliner (B787). The development of this airplane which seemed to be very promising in the beginning turned into the longest delayed program in the history of the company. In this paper, we propose an integrated outsourcing framework through which we try to find the root causes of the delays and the resulted extra costs. The proposed framework shows how the interaction of all influential factors in four outsourcing dimensions (who, what, to whom, and how) determines the performance of an outsourcing program

WHEN STRATEGY DOES NOT MATCH CAPABILITIES: LESSONS FROM THE SUPPLY CHAIN OF BOEING 787

During the early 2000’s the Boeing Company was experiencing a market shrink due to a downturn in the aerospace industry after the 9/11 terrorist attacks, as well as, severe competition from its rival Airbus. To deal with the situation and salvage its market share, Boeing proposed the design of a new aircraft called Boeing 787 or the Dreamliner. This futuristic aircraft was received very well by the airlines. Very soon, it became the fastest selling new airplane in the history of commercial aviation. Nevertheless, after the initial successful launch, the company faced many supply chain related problems, which resulted in repeated delays and huge extra costs. In this research, we investigate the impact of the mismatch between Boeing’s strategy and capabilities for developing the Dreamliner

How can we improve the performance of supply chain contracts? An experimental Study

Although optimal forms of supply chain contracts have been widely studied in the literature, it has also been observed that decision makers fail to make optimal decisions in these contract setups. In this research, we propose different approaches to improve the performance of supply chain contracts in practice. We consider revenue sharing and buyback contracts between a rational supplier and a retailer who, unlike the supplier, is susceptible to decision errors. We propose five approaches to improve the retailer’s decisions which are in response to contract terms offered by the supplier. Through laboratory experiments, we examine the effectiveness of each approach. Among the proposed approaches, we observe that offering free items can bring the retailer’s effective order quantity close to the optimal level. We also observe that the retailer’s learning trend can be improved by providing him with collective feedbacks on the profits associated with his decisions

An Integrated Outsourcing Framework: Analyzing Boeing’s Outsourcing Program for Dreamliner (B787)

This paper analyzes the outsourcing model which Boeing devised to develop its latest commercial airplane model: Dreamliner (B787). The development of this airplane which seemed to be very promising in the beginning turned into the longest delayed program in the history of the company. In this paper, we propose an integrated outsourcing framework through which we try to find the root causes of the delays and the resulted extra costs. The proposed framework shows how the interaction of all influential factors in four outsourcing dimensions (who, what, to whom, and how) determines the performance of an outsourcing program

Correcting Decision Outcomes in a Revenue Sharing Contract

In this paper, we study methods that could improve the performance of a coordinating supply chain contract. Past studies indicate that such contracts as revenue sharing do not necessarily coordinate a supply chain in practice. We propose an approach which could possibly correct this inefficiency by incentivizing the retailer to improve supply chain outcomes. Experimental studies on human subjects are used as the basis to verify our framework in modeling decisions outcomes. Our results show that a revenue sharing contract can still coordinate a supply chain if the retailer is offered additional incentives. We also discuss limitations of our analysis and provide suggestions for further research on how coordinating contracts could be designed to deliver consistent optimal performance

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.