Evolutions of Omni‐Channel Fulfillment Performance: An In‐Depth Case Study in Grocery Retailing
Bibliographic record
Abstract
ABSTRACT The rapid adoption of omni‐channel strategies has prompted grocery retailers to reconfigure their back‐end fulfillment operations to efficiently and effectively meet the demands of online and offline retail channels. Viewing back‐end fulfillment operations in omni‐channel grocery retail as a complex adaptive system, we present an eight‐year multi‐method case study of the UK operations of a leading global grocery retailer. Over this period the share of online sales significantly grew as proportion of overall sales. We observe four evolutions in the back‐end fulfillment complex adaptive system to respond to the operational demands associated with increasing online sales. Complex adaptive systems theory suggests that such evolutions should eventually lead to a state of equilibrium, where the system is reconfigured to effectively and efficiently respond to the market. However, we observe that this equilibrium was never achieved and propose this results from two opposing and irreconcilable environmental energies preventing optimal adaptation. Drawing on both in‐depth interviews and a proprietary fulfillment dataset from the organization, we expose the implications of conflicting energies being imported from the environment, and propose three strategies, drawn from paradox theory, for reconciling these energies within a complex adaptive system.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".