1506. Proactive transhipment strategies for an omnichannel retailer with partial-lost sales
Invited abstract in session MD-47: Retail Supply Chain Management, stream Retail Operations.
Monday, 14:30-16:00Room: Parkinson B08
Authors (first author is the speaker)
| 1. | Ben Lowery
|
| Department of Mathematics and Statistics, Lancaster University | |
| 2. | Anna-Lena Sachs
|
| Management Science, Lancaster University | |
| 3. | Idris Eckley
|
| Mathematics and Statistics, Lancaster University | |
| 4. | Louise Lloyd
|
| Tesco Mobile |
Abstract
We consider a retailer who operates a complex network of integrated online and offline storefronts, where items can be shipped between them. When an order is unavailable at the store, the customer can choose to have their order delivered to their home from the warehouse instead. Such a mechanism is useful for stores facing a surge in demand. However, scenarios arise whereby stores suffer from the opposite problem, with little to no demand for products; in these cases, it may be beneficial to proactively redistribute this stock to other stores via transshipment policies.
We expand on previous development of a divergent, periodic-review model with partial lost sales, to allow for lateral transshipments. We evaluate the effectiveness of transshipment policies, both established in the literature and novel, for this setting. We focus on joint replenishment and transshipment decisions, formulating the problem as a Markov Decision Processes. Due to the curse of dimensionality, only small instances are solved to optimality. Therefore, we consider a capped base-stock policy coupled with equalization or expected shortage reduction for transshipments. To allow for more robust decision making, we propose an integrated replenishment and transshipment method. We compare these methods in a numerical study as well as real data by analysing different demand characteristics, determining decision rules as to when including the option of transshipments provides tangible operational benefits.
Keywords
- Inventory
- Supply Chain Management
- Stochastic Models
Status: accepted
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