Harry Groenevelt

Areas of Expertise (4)

Quality Management and Analytics

Logistic and Supply Chain Management

Health Care Operations

Service System Management and Design

Social

Biography

Professor Groenevelt has been a consultant on operations management and data analysis issues for numerous manufacturing and service organizations (including hospitals and other health care providers), as well as the city of Amsterdam, the Netherlands.

Education (3)

Columbia University: PhD, Operations Research 1985

Vrije Universiteit: MS, Econometrics 1981

Vrije Universiteit: BS, Econometrics 1979

Selected Articles (3)

Vendor managed inventory contracts – coordinating the supply chain while looking from the vendor’s perspective

European Journal of Operational Research

Harry Groenevelt and Arvind Sainathan

2019-01-01

The paper studies coordination of a supply chain when the inventory is managed by the vendor (VMI). We also provide a general mathematical framework that can be used to analyze contracts under both retailer managed inventory (RMI) and VMI. Using a simple newsvendor scenario with a single vendor and single retailer, we study five popular coordinating supply chain contracts: buyback, quantity flexibility, quantity discount, sales rebate, and revenue sharing contracts. We analyze the ability of these contracts to coordinate the supply chain under VMI when the vendor freely decides the quantity. We find that even though all of them coordinate under RMI, quantity flexibility and sales rebate contracts do not generally coordinate under VMI. Furthermore, buyback and revenue sharing contracts are equivalent. Hence, we propose two new contracts which coordinate under VMI (one of which coordinates under RMI too, provided a well-known assumption holds). Finally, we extend our analysis to consider multiple independent retailers with the vendor incurring linear or convex production cost, and show that our results are qualitatively unchanged.

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Continuous Review Inventory Model with Dynamic Choice of Two Freight Modes with Fixed Costs

Manufacturing & Service Operations Management

Harry Groenevelt, Aditya Jain,We analyze a continuous review (Q, r) stochastic inventory model in which orders placed with a make-to-order manufacturer can be shipped via two alternative freight modes differing in lead time and costs. The costs of placing an order and using each freight mode consist of fixed components and hence exhibit economies of scale. We derive an optimal policy for using the two freight modes for shipping each order. This freight-mode decision is delayed until manufacturing is complete and the optimal policy uses information about the demand incurred in the meantime. Furthermore, given that the two freight modes are used optimally for shipping each order, we solve our model for reorder point and order quantity that minimizes cost. We analyze the cost savings achieved from postponing the freight-mode decision and provide analytical and numerical comparisons between the solutions to our two-freight model and single-freight models. Finally, we illustrate the properties of the solution to our model using an extensive set of numerical examples. and Nils Rudi

2009-06-12

We analyze a continuous review (Q, r) stochastic inventory model in which orders placed with a make-to-order manufacturer can be shipped via two alternative freight modes differing in lead time and costs. The costs of placing an order and using each freight mode consist of fixed components and hence exhibit economies of scale. We derive an optimal policy for using the two freight modes for shipping each order. This freight-mode decision is delayed until manufacturing is complete and the optimal policy uses information about the demand incurred in the meantime. Furthermore, given that the two freight modes are used optimally for shipping each order, we solve our model for reorder point and order quantity that minimizes cost. We analyze the cost savings achieved from postponing the freight-mode decision and provide analytical and numerical comparisons between the solutions to our two-freight model and single-freight models. Finally, we illustrate the properties of the solution to our model using an extensive set of numerical examples.

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Dynamic Revenue Management in Airlines Alliances

Transportation Science

Harry Groenevelt, Christopher P. Wright, and Robert A. Shumsky

2010-02-01

Major airlines are selling increasing numbers of interline itineraries in which flights operated by two or more airlines are combined and sold together. One reason for this increase is the rapid growth of airline alliances, which promote the purchase of interline itineraries and, therefore, virtually extend the reach of each alliance member's network. This practice, however, creates a difficult coordination problem: Each member of the alliance makes revenue management decisions to maximize its own revenue and the resulting behavior may produce suboptimal revenue for the alliance as a whole. Airline industry researchers and consultants have proposed a variety of static and dynamic mechanisms to control revenue management decisions across alliances (a dynamic mechanism adjusts its parameters as the number of available seats in the network changes and time passes). In this paper, we formulate a Markov game model of a two-partner alliance that can be used to analyze the effects of these mechanisms on each partner's behavior. We begin by showing that no Markovian transfer pricing mechanism can coordinate an arbitrary alliance. Next, we examine three dynamic schemes as well as three forms of the static scheme widely used in practice. We derive the equilibrium acceptance policies under each scheme and use analytical techniques as well as numerical analyses of sample alliances to generate fundamental insights about partner behavior under each scheme. The analysis and numerical examples also illustrate how certain transfer price schemes are likely to perform in networks with particular characteristics.

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