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We value the option of subcontracting to improve Þnancial performance and system coordination by analyzing a competitive stochastic investment game with recourse. The manufacturer and subcontractor decide separately on their capacity investment levels. Then demand uncertainty is resolved and both parties have the option to subcontract when deciding on their production and sales. We analyze and present outsourcing conditions for three contract types:(1) price-only contracts where an ex-ante transfer price is set for each unit supplied by the subcontractor; (2) incomplete contracts, where both parties negotiate over the subcontracting transfer; and (3) state-dependent price-only and incomplete contracts for which we show an equivalence result.While subcontracting with these three contract types can coordinate production decisions in the supply system, only state-dependent contracts can eliminate all decentralization costs and coordinate capacity investment decisions. The minimally sufficient price-only contract that coordinates our supply chain speciÞes transfer prices for a small number (6 in our model) of contingent scenarios. Our game-theoretic model allows the analysis of the role of transfer prices and of the bargaining power of buyer and supplier. We Þnd that sometimes Þrms may be better off leaving some contract parameters unspeciÞed ex-ante and agreeing to negotiate ex-post. Also, a price-focused strategy for managing subcontractors can backÞre because a lower transfer price may decrease the manufacturer's proÞt. Finally, as with Þnancial options, the option value of subcontracting increases as markets are more volatile or more negatively correlated.

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Mashup editors, like Yahoo Pipes and IBM Lotus Mashup Maker, allow non-programmer end-users to "mash-up" information

Academic literature on machine learning modeling fails to address how to make machine learning models work for enterprises. For example, existing machine learning processes cannot address how to define business use cases for an AI application, how to convert business requirements from offering managers into data requirements for data scientists, and how to continuously improve AI applications in term of accuracy and fairness, how to customize general purpose machine learning models with industry, domain, and use case specific data to make them more accurate for specific situations etc. Making AI work for enterprises requires special considerations, tools, methods and processes. In this paper we present a maturity framework for machine learning model lifecycle management for enterprises. Our framework is a re-interpretation of the software Capability Maturity Model (CMM) for machine learning model development process. We present a set of best practices from authors' personal experience of building large scale real-world machine learning models to help organizations achieve higher levels of maturity independent of their starting point.

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In this paper, we present a novel algorithm to compose Web services in the presence of semantic ambiguity by combining semantic matching and AI planning algorithms. Specifically, we use cues from domain-independent and domain-specific ontologies to compute an overall semantic similarity score between ambiguous terms. This semantic similarity score is used by AI planning algorithms to guide the searching process when composing services. Experimental results indicate that planning with semantic matching produces better results than planning or semantic matching alone. The solution is suitable for semi-automated composition tools or directory browsers.