In many production environments, a fixed network of capacity is shared flexibly between multiple products with random demands. What is the best way to configure the capacity of the production network and to allocate the available capacity to meet predetermined fill rate requirements? We develop a new approach for network capacity configuration and allocation and characterize the relationship between the capacity of the network and the attainable fill rate levels for the products, taking into account the flexibility structure of the network. This builds on a new randomized allocation mechanism to deliver the desired services. We use this theory to investigate the connection between the flexibility structure and capacity configuration. We provide a new perspective to the well-known phenomenon that “long chain is almost as good as the fully flexible network”: for given target fill rates, the required capacity level in a long-chain network is close to that in a fully flexible network and is much lower than a dedicated system. We apply these insights and techniques on problems arising in the design of last-mile delivery operations and in semiconductor production planning, using real data from two companies. This paper was accepted by Terry Taylor, operations management.
Problem definition: The Singapore government has recently proposed the concept of “Locker Alliance” (LA), an interoperable network of public lockers in residential areas and hot spots in community, to improve the efficiency of last mile parcel delivery operations. This is to complement the existing infrastructure, composed mainly of proprietary lockers and collection points in commercial areas set up by large delivery companies. How do we determine the density and coverage of the LA network to promote adoption of locker pickup in Singapore? What will be the impact on the delivery profile in the central business district, far from the residential areas? Academic/practical relevance: We discuss the operational challenges associated with the problem of public locker installation in a city, following a new smart nation initiative in Singapore. We used data analytics to address the following questions: What are the chances that a customer will choose to pick up the parcel from a locker, over home or office deliveries, based on walking distance (to lockers) and a variety of other features? Without knowing the transit routes of the customers, how do we design the LA network to ensure that the lockers will be well utilized? Methodology: We use a set of locker usage data from a commercial courier company to calibrate a locker choice model to determine the impact of walking distance on locker pickup intentions. We use the current (observed) parcel delivery profile to develop a facility location model for the LA network. We use this model to extrapolate and approximate the true adoption and new delivery profiles when the LA network is built. Results: Contrary to conventional wisdom, our model does not always place lockers near areas with peak parcel volume (in preexisting data) because the LA lockers provide another option for customers to pick up from lockers near residential areas. Furthermore, the model suggests that a coverage of 250 meters is appropriate for the LA network in Singapore. Managerial implications: Commercial parcel locker installation has traditionally focused on hot spots in the transit routes of the citizens in the city. The LA network is the first attempt in Singapore to allow public lockers in residential areas. This paper develops an analytical method to determine network density and coverage based on a locker choice model and argues how useful insights can be gleaned from the model, despite not having the full transit route information of all citizens in the city.
With dual‐channel choices, E‐retailers fulfill their demands by either the inventory stored in third‐party distribution centers, or by in‐house inventory. In this article, using data from a wedding gown E‐retailer in China, we analyze the differences between two fulfillment choices—fulfillment by Amazon (FBA) and fulfillment by seller (FBS). In particular, we want to understand the impact of FBA that will bring to sales and profit, compared to FBS, and how the impact is related to product features such as sizes and colors. We develop a risk‐adjusted fulfillment model to address this problem, where the E‐retailer's risk attitude to FBA is incorporated. We denote the profit gaps between FBA and FBS as the rewards for this E‐retailer fulfilling products using FBA, our goal is to maximize the E‐retailer's total rewards using predictive analytics. We adopt the generalized linear model to predict the expected rewards, while controlling for the variability of the reward distribution. We apply our model on a set of real data, and develop an explicit decision rule that can be easily implemented in practice. The numerical experiments show that our interpretable decision rule can improve the E‐retailer's total rewards by more than 35%.
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