Robust Supply Chain Design for Highly-Customized Manufacturing
Bibliographic record
Abstract
Customers in today's evolving markets are seeking options that best suit their specific needs; consequently, the demand for highly-customized and personalized products has been growing steadily.In order to facilitate customized manufacturing, the structure of the underlying supply chain (SC) needs to be enhanced in terms of flexibility and resilience.In this study, we consider an SC comprising a main manufacturer that produces custom-designed modularstructured products, featured with different design complexity levels.The products have a bill-of-material (BOM) that can be altered in terms of the design of a subset of sub-assemblies and components.It is further assumed that the company collaborates with a group of manufacturers and part suppliers (upstream SC entities), differentiated in terms of capacity, technological capabilities, and cost, along with a group of logistics carriers (downstream entities) distinguished in the sense of their cost and lead time.In other words, in addition to involving customers in the product design, the company also provides different modes of product delivery (e.g., fast, regular, and slow) offered by different logistics carriers.We also incorporate the uncertainty involved in the manufacturing of subassemblies and parts featured with complex designs.More specifically, to reflect the amount of effort required to manufacture complex designs, the production and procurement costs of (highly or moderately complex) items are considered as piece-wise linear functions of their order quantity.Furthermore, the manufacturing capability of upstream entities for producing complex items are assumed uncertain, and modeled as scenarios.More precisely, under some scenarios, the producers/suppliers will not be capable of fulfilling the order of highly/moderately complex items within the promised production lead time due to technological limitations.To obtain the optimal configuration of the above-mentioned SC, we first develop a deterministic mixed-integer programming (MIP) model that seeks the optimal choice of sub-assembly producers, part suppliers, and logistics carriers in addition to the optimal quantity of procurement, production, and transportation at different echelons.The objective is to minimize the total manufacturing, transportation, and lost-sale cost.Afterward, in order to incorporate the uncertain technological capabilities of producers/suppliers, the above MIP model is reformulated as a two-stage stochastic program (2-SP).In this model, three types of corrective (recourse) actions are considered in order to compensate for the incapability of producers/suppliers in the manufacturing of complex products.These actions correspond to resorting to backup suppliers/producers; purchasing sub-assemblies and components from the open market; and lost-sale, in case none of the first two actions are feasible.Obviously, the cost of all recourse actions mentioned above is substantially higher than the cost of the initial assignment of producers/suppliers that must be set prior to receive full insight in terms of suppliers' capabilities.We explore a sample average approximation (SAA) scheme to solve the 2-stage stochastic MIP model under different scenario sets.In-depth computational experiments are conducted to validate the proposed models and solution algorithm while showcasing the value of incorporating uncertainty into the SC design problem.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".