Performance evaluation of stochastic systems with dedicated delivery bays and general on-street parking

As freight deliveries in cities increase due to retail fragmentation and e-commerce, parking is becoming a more and more relevant part of transportation. In fact, many freight vehicles in cities spend more time parked than they are moving. Moreover, part of the public parking space is shared with passenger vehicles, especially cars. Both arrival processes and parking and delivery processes are stochastic in nature. In order to develop a framework for analysis, we propose a queueing model for an urban parking system consisting of delivery bays and general on-street parking spaces. Freight vehicles may park both in the dedicated bays and in general on-street parking, while passenger vehicles only make use of general on-street parking. Our model allows us to create parsimonious insights into the behavior of a delivery bay parking stretch as part of a limited length of curbside. We are able to find explicit expressions for the relevant performance measures, and formally prove a number of monotonicity results. We further conduct a series of numerical experiments to show more intricate properties that cannot be shown analytically. The model helps us shed light onto the effects of allocating scarce urban curb space to dedicated unloading bays at the expense of general on-street parking. In particular, we show that allocating more space to dedicated delivery bays can also make passenger cars better off

On-demand last-mile distribution network design with omnichannel inventory

E-commerce delivery deadlines are getting increasingly tight, driven by a growing ‘I-want-it-now’ instant gratification mindset of consumers and the desire of online and omnichannel retailers to capitalize on the growth of on-demand e-commerce. On-demand deliveries with delivery deadlines as tight as one or two hours force companies to rethink their last-mile distribution network, since tight delivery deadlines require decentralization of order picking and inventory holding to ensure close proximity to consumers. This fundamentally changes the strategic design process of last-mile distribution networks. We study the impact of incorporating inventory order-up-to level decisions into the strategic design process of last-mile distribution networks with tight delivery deadlines. We develop an approximate inventory model by including an estimate of the cost of late delivery and additional transportation due to local stock-outs in a newsvendor formulation. Such local stock-outs require an order to be delivered from a more distant facility, which may lead to late delivery and additional transportation cost. We integrate our approximate inventory model and a location-allocation mixed-integer program that determines optimal facility locations, associated order-up-to inventory levels, and fleet composition, into a metamodel simulation-based optimization approach. Our numerical analyses demonstrate that pooling the additional online inventory with brick-and-mortar (B&M) inventories leads to cannibalization by the B&M network and higher B&M service levels. However, the pooling benefits to the online network outweigh the cost of inventory cannibalization. Furthermore, we show under which circumstances omnichannel retailers may have an incentive to consolidate online inventory in specific B&M facilities

Should I endorse a third party? Authorization strategies for brand manufacturers in a refurbishing market

Original equipment manufacturers (OEM) may have little or no control over third-party (3P) refurbishing firms. With the rapid growth of the refurbished market for electronic products, we study whether it is beneficial for an OEM to cooperate with a 3P via authorization schemes that boost an OEM’s brand reputations, increase their sales, and strengthen consumer acceptance of authorized 3P’s refurbished products. We examine the conditions under which both the OEM and the 3P benefit from the authorization strategy, studying the trade-off between the indirect benefit of authorizing a 3P to increase market share and the downside of cannibalizing new-product sales. To estimate our model’s behavioral parameters, we conduct an extensive experiment on MTurk to capture consumer preferences and cannibalization effects. The experimental study examines the price-perceived quality relationship along with brand value, seller identity (OEM, 3P), and product condition; its results show that the discount and seller identity play a large role in consumer choice and that cannibalization is generally linear in price. We subsequently construct a revenue maximizing model that incorporates this linear cannibalization effect, along with the authorization fees. We show that refurbished products offered by authorized 3Ps have higher demand than those that are not authorized and that it is beneficial for 3Ps to participate in these schemes despite the authorization fees. We conclude that authorization can be a win–win strategy for OEMs and 3Ps, especially when low-end consumer demand and average reduction of refurbishing costs are relatively high, and the level of cannibalization is relatively low. To achieve win–win solutions, it is important for OEMs and 3Ps to consider brand recognition, consumer behavior related to refurbished products, and remanufacturable supply.</p

Estimating the benefits of dedicated unloading bays by field experimentation

In most dense urban environments in emerging markets, retail deliveries are very fragmented to thousands of nanostores. It is not uncommon for a delivery route to include more than 60 stops. Unloading bays are often blocked by regular traffic. Due to the complex urban environment, it is difficult to estimate the benefits of making unloading bays available. In this study, we conduct a field experiment in an urban field lab of one square kilometer in the downtown of Querétaro, Mexico. During the treatment period of one week, we obtain help from the local traffic police to keep the unloading bays available for unloading only. Using advanced GPS devices and extensive manual field observations, we are able to capture the change in driver behavior and the direct efficiency increases. We find a high efficiency gain, not only in travel time (39%) but also – remarkably – in the total time parked (17%). Corrected for other effects, we estimate a gain of about 44% in total time per delivery. Apart from the insights on unloading benefits, we also provide insights into the method of field experimentation in such a complex environment