{"id":18455912,"url":"https://github.com/webintellectual/supply-chain-stability-classifier","last_synced_at":"2025-04-08T04:34:20.319Z","repository":{"id":190797979,"uuid":"683377338","full_name":"webintellectual/Supply-Chain-Stability-Classifier","owner":"webintellectual","description":null,"archived":true,"fork":false,"pushed_at":"2023-08-26T11:31:05.000Z","size":22225,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-16T14:57:36.594Z","etag":null,"topics":["deep-learning","feed-forward-neural-networks","keras-tensorflow","python3"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/webintellectual.png","metadata":{"files":{"readme":"Readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-08-26T11:25:59.000Z","updated_at":"2024-05-18T18:21:46.000Z","dependencies_parsed_at":"2023-08-26T12:59:24.909Z","dependency_job_id":null,"html_url":"https://github.com/webintellectual/Supply-Chain-Stability-Classifier","commit_stats":null,"previous_names":["webintellectual/supply-chain-stability-classifier"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/webintellectual%2FSupply-Chain-Stability-Classifier","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/webintellectual%2FSupply-Chain-Stability-Classifier/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/webintellectual%2FSupply-Chain-Stability-Classifier/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/webintellectual%2FSupply-Chain-Stability-Classifier/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/webintellectual","download_url":"https://codeload.github.com/webintellectual/Supply-Chain-Stability-Classifier/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247779796,"owners_count":20994569,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","feed-forward-neural-networks","keras-tensorflow","python3"],"created_at":"2024-11-06T08:09:27.885Z","updated_at":"2025-04-08T04:34:15.295Z","avatar_url":"https://github.com/webintellectual.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\u003cimg src=\"./Images/neural_network_drawing.png\"\u003e\n\n\u003cbr\u003e\n\n---\nResults:\n\n**Accuracy: 0.9932** \u003cbr\u003e\n**Precision: 0.9932** \u003cbr\u003e\n**Recall: 0.9932** \u003cbr\u003e\n**F1-score: 0.9932** \u003cbr\u003e\nConfusion Matrix: \u003cbr\u003e\n[[21560    20   192    53    38] \u003cbr\u003e\n [    4 44161     0    84     0] \u003cbr\u003e\n [  304     0 48254     1    18] \u003cbr\u003e\n [   22    42     0 24804    10] \u003cbr\u003e\n [  109     1    22    73  6695]] \u003cbr\u003e\n\n---\n\nDataset is of a particular supply chain network. \u003cbr\u003e\n4 echelons of this supply chain are :\nSupplier, Distributor, Manufacturer and Retailer. \u003cbr\u003e\nRisk is associated with each echelon. \u003cbr\u003e\nRisk Index is calculated for each echelon. \u003cbr\u003e\n\nRisk Index for Supplier echelon can be formulated as: \u003cbr\u003e\n$$RI_{supplier} = \\sum_{i=1}^{n} as_{ij} . bs_{ij} . (1 - (1-\\Pi_{j=1}^{m} P(S_{ij}) ) )$$\n\n$i$: i-th supplier \u003cbr\u003e\n$j$: j-th demand \u003cbr\u003e\nn: number of suppliers \u003cbr\u003e\nm: number of demands\n\nWhere $as_{ij}$ is the consequence to the supply chain if the i-th supplier fails,\n$bs_{ij}$ is the percentage of value added to the product by the i-th supplier,\n$P(S_{ij})$ denotes the marginal probability that the i-th supplier fails for j-th demand\n\nSimilary,\n\n$$RI_{distributor} = ad_{risk_i}.bm_i.(1-(1-P(M_j)))$$\n$$RI_{manufacturer} = am_{risk_i}.bm_i.(1-(1-P(M_j)))$$\n$$RI_{retailer} = ar_{risk_i}.br_i.(1-(1-P(R_j)))$$\n\nThe risk fluctuation subjected to the supply chain network is simulated by a sine-wave generator. This adds a dynamic and time-varying aspect to the dataset, enabling the study of how Risk Index values and other attributes change over time.\u003cbr\u003e\nIn real life also some Risk is associated with each echelon which we don't know in advance. Actual risk index can be calculated only after happening. In our dataset Risk Indices and total cost are calculated and recorded at different different time stamps.\n\n---\n\nSCM Stability Category: \u003cbr\u003e\nThe SCM stability category is a discrete classification assigned to different time periods in the dataset. It categorizes the stability of the supply chain based on observed characteristics or metrics. The categories likely range from lower stability (higher risk, higher uncertainty) to higher stability (lower risk, more predictability).\n\n---\n\n\n\n### References:\n\nBanerjee, Heerok; Saparia, Grishma; Ganapathy, Velappa; Garg, Priyanshi; Shenbagaraman, V. M. (2019), [“Time Series Dataset for Risk Assessment in Supply Chain Networks\"](https://data.mendeley.com/datasets/gystn6d3r4/2), Mendeley Data, V2, doi: 10.17632/gystn6d3r4.2\n\nSaparia, Grishma \u0026 Banerjee, Heerok \u0026 Garg, Priyanshi \u0026 Ganapathy, V. \u0026 V M, Shenbagaraman. (2019). [Time-series Dataset for Risk Assessment in Multi-echelon Supply Chain Networks.](https://www.researchgate.net/publication/332343841_Time-series_Dataset_for_Risk_Assessment_in_Multi-echelon_Supply_Chain_Networks) 10.17632/gystn6d3r4.2\". \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwebintellectual%2Fsupply-chain-stability-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwebintellectual%2Fsupply-chain-stability-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwebintellectual%2Fsupply-chain-stability-classifier/lists"}