Radio Spectrum and the Disruptive Clarity OF Ronald Coase.

In the Federal Communications Commission, Ronald Coase (1959) exposed deep foundations via normative argument buttressed by astute historical observation. The government controlled scarce frequencies, issuing sharply limited use rights. Spillovers were said to be otherwise endemic. Coase saw that Government limited conflicts by restricting uses; property owners perform an analogous function via the "price system." The government solution was inefficient unless the net benefits of the alternative property regime were lower. Coase augured that the price system would outperform the administrative allocation system. His spectrum auction proposal was mocked by communications policy experts, opposed by industry interests, and ridiculed by policy makers. Hence, it took until July 25, 1994 for FCC license sales to commence. Today, some 73 U.S. auctions have been held, 27,484 licenses sold, and $52.6 billion paid. The reform is a textbook example of economic policy success. We examine Coase‘s seminal 1959 paper on two levels. First, we note the importance of its analytical symmetry, comparing administrative to market mechanisms under the assumption of positive transaction costs. This fundamental insight has had enormous influence within the economics profession, yet is often lost in current analyses. This analytical insight had its beginning in his acclaimed early article on the firm (Coase 1937), and continued into his subsequent treatment of social cost (Coase 1960). Second, we investigate why spectrum policies have stopped well short of the property rights regime that Coase advocated, considering rent-seeking dynamics and the emergence of new theories challenging Coase‘s property framework. One conclusion is easily rendered: competitive bidding is now the default tool in wireless license awards. By rule of thumb, about$17 billion in U.S. welfare losses have been averted. Not bad for the first 50 years of this, or any, Article appearing in Volume II of the Journal of Law & Economics.

Decision models for fast-fashion supply and stocking problems in internet fulfillment warehouses

Internet technology is being widely used to transform all aspects of the modern supply chain. Specifically, accelerated product flows and wide spread information sharing across the supply chain have generated new sets of decision problems. This research addresses two such problems. The first focuses on fast fashion supply chains in which inventory and price are managed in real time to maximize retail cycle revenue. The second is concerned with explosive storage policies in Internet Fulfillment Warehouses (IFW). Fashion products are characterized by short product life cycles and market success uncertainty. An unsuccessful product will often require multiple price discounts to clear the inventory. The first topic proposes a switching solution for fast-fashion retailers who have preordered an initial or block inventory, and plan to use channel switching as opposed to multiple discounting steps. The FFS Multi-Channel Switching (MCS) problem then is to monitor real-time demand and store inventory, such that at the optimal period the remaining store inventory is sold at clearance, and the warehouse inventory is switched to the outlet channel. The objective is to maximize the total revenue. With a linear projection of the moving average demand trend, an estimation of the remaining cycle revenue at any time in the cycle is shown to be a concave function of the switching time. Using a set of conditions the objective is further simplified into cases. The Linear Moving Average Trend (LMAT) heuristic then prescribes whether a channel switch should be made in the next period. The LMAT is compared with the optimal policy and the No-Switch and Beta-Switch rules. The LMAT performs very well and the majority of test problems provide a solution within 0.4% of the optimal. This confirms that LMAT can readily and effectively be applied to real time decision making in a FFS. An IFW is a facility built and operated exclusively for online retail, and a key differentiator is the explosive storage policy. Breaking the single stocking location tradition, in an IFW small batches of the same stock keeping unit (SKU) are dispersed across the warehouse. Order fulfillment time performance is then closely related to the storage location decision, that is, for every incoming bulk, what is the specific storage location for each batch. Faster fulfillment is possible when SKUs are clustered such that narrow band picklists can be efficiently generated. Stock location decisions are therefore a function of the demand arrival behavior and correlations with other SKUs. Faster fulfillment is possible when SKUs are clustered such that narrow band picklists can be efficiently generated. Stock location decisions are therefore a function of the demand behavior and correlations with other SKUs. A Joint Item Correlation and Density Oriented (JICDO) Stocking Algorithm is developed and tested. JICDO is formulated to increase the probability that M pick able order items are stocked in a δ band of storage locations. It scans the current inventory dispersion to identify location bands with low SKU density and combines the storage affinity with correlated items. In small problem testing against a MIP formulation and large scale testing in a simulator the JICDO performance is confirmed