Go Wide or Go Deep? Assortment Strategy and Order Fulfillment in Online Retail
Bibliographic record
Abstract
Problem definition: Expansions in product assortment by online retailers often engender operational challenges. In undertaking such expansions, retailers exercise a strategic choice between expanding assortment width or depth. Our understanding of how this choice affects the order fulfillment process is limited. Thus, we examine the impact of these dimensions of assortment strategy on order delivery timeliness. Academic/practical relevance: Order delivery timeliness is a critical measure of operational success in online retail. We contribute to theory and practice by adopting a multidimensional perspective of retailer assortment strategy and studying the relative impact of assortment width and depth on order delivery timeliness. Methodology: Employing a data set comprising more than 200 million orders, we study the effects of assortment strategy on delivery timeliness using an instrumental variable approach. We then utilize a two-stage model to estimate the impact of delivery performance on sales. Further, we employ a matched difference-in-differences and a novel Bayesian structural time-series model to confirm this relationship. Results: We find that assortment width has a greater negative impact on order delivery timeliness compared with assortment depth. A one-standard-deviation increase in assortment width increases average delivery times by 0.55 days. Further, we find this effect to be positively moderated (i.e., worsened) by the average size of orders and to be negatively moderated (i.e., improved) by the logistic service provider’s (LSP) experience. Finally, a one-day increase in delivery times for 10% of the orders results in a 2.7% reduction in sales. Managerial implications: Our findings suggest that online retailers focused on ensuring timely deliveries should be wary of widening product assortments, especially when facing larger average order sizes. We also find that experienced logistic service providers can help mitigate the dilatory effects of assortment width expansions. However, the benefits of experienced LSPs are limited for retailers deepening their assortments. History: This paper has been accepted as part of the 2018 MSOM Data Driven Research Challenge. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1156 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".