- [[#0. Managing Material and Information Delay with SCM examples|0. Managing Material and Information Delay with SCM examples]] - [[#1. Intro. to three echelon supply chain|1. Intro. to three echelon supply chain]] - [[#2. Delay|2. Delay]] - [[#3. Forecasting accuracy|3. Forecasting accuracy]] - [[Donohue18_ForecastDecisions.pdf]] - [[#4. Information sharing|4. Information sharing]] - [[#5. Length of delay vs Delay deviation|5. Length of delay vs Delay deviation]] - [[#6. Local vs Global optimization|6. Local vs Global optimization]] - [[#7. Supply chian strategies|7. Supply chian strategies]] - [[#8. Resilient supply chain|8. Resilient supply chain]] - [[#9. Emergency supply chain|9. Emergency supply chain]] - [[#10. Games in supply chian|10. Games in supply chian]] - [[#11. Data Science in supply chain manamegment|11. Data Science in supply chain manamegment]] ### 0. Managing Material and Information Delay with SCM examples a. 제목 어떠신가요? - delay와 uncertainty의 perception이 agent의 behavioral를 결정한다. - 해당 behavior는 bias를 초래한다 [[Behavioral2Prior]] . - aI는 전역최적화를 고려한 prior설계를 자동화해 이런 bias를 최소한다. b. Matching vs managing material (e.g. lead time), info (e.g. expected demand)중 뭐가 더 적확한 표현이라 보시나요? 전 다음과 같이 생각해요. mismatch between inventory and expected demand (observed) are delayed mismatch of supply and demand (latent) which are accumulated mismatch between supply rate and demand rate. ![[Pasted image 20220827233213.png]] c. 언어는 영어인가요? 책쓰면 공부도 자동으로 하게되고 정말 좋은거 같아요! matching expected demand with inventory each of which are delayed cumulation of demand and ### 1. Intro. to three echelon supply chain a. 굳이 3단계를 잡은 이유? 3, 6 order delay를 주로 많이 다룬다던데 관련있나요? b. Tom의 [Bathtub statistics](https://metasd.com/2012/05/bathtub-statistics/)글 중 그림 분석에서 차근히 시작해보는 것도 의미있을듯요. Process noise관련. ![[Mng(Stock)withUncertainty.jpeg]] c. [[Yaman Barlas]]링크 중 1,2,high order system (유추) 목차에 매료돼 유사형태로 써보고 싶어요. Systems Science and Engineering (IE 350) $\begin{aligned} &\text { WEEK }\\ &\begin{array}{ll} \hline 1 & \text { Course Objective, Organization and Overview } \\ 1 & \text { "Systems" concepts, philosophy and history } \\ 2 & \text { Systems analogies: } 1^{\text {st }} \text { order dynamic systems } \\ 2,3 & \text { Electrical-hydraulic-mechanical analogies } \\ 3 & \text { Industrial, socio-economic, managerial analogies } \\ 4 & \text { Systems analogies: 2 nd order dynamic systems } \\ 4,5 & \text { Electrical-hydraulic-mechanical analogies } \\ 5 & \text { Industrial, socio-economic, managerial examples } \\ 6 & \text { Higher order linear dynamic systems } \\ 6 & \text { Midterm exam 1 (April 29, at 18:00) } \\ 7 & \text { Non-linear systems and limits of mathematical analysis } \\ 7,8 & \text { Simulation method and software } \\ 8,9 & \text { Equilibrium and stability analysis } \\ 10 & \text { Typical non-linear structures and formulations } \\ 11 & \text { Midterm exam 2 (June 04, at 18:00) } \\ 11 & \text { Time delays in dynamic systems } \\ 12 & \text { Formulation principles for large-scale socio-technical systems } \\ 13 & \text { Large scale stock-flow modeling examples } \\ 13 & \text { Systems Science and Systems Approach in a Complex World } \end{array} \end{aligned}$ ## 2. Delay a. oscilation, uncertainty, resilience..무슨 내용을, 어떤 예시를 다루면 좋을까요? ## 3. Forecasting accuracy a. 다음 발표 참고할만한가요? - [lecture](https://www.youtube.com/watch?v=YsjsqkogNhU&ab_channel=BurakKandemir)for Burak Kandemir's work on analyzing the impact of forecasting and demand pattenrs in SCM ([[Kandemir22_ForecastingDmdPattern.pdf]]) with summary: - The simulation results indicate; - Accuracy of forecasts have a direct impact on supply chain performance in terms of inventory and service levels - Inventory policies try to stabilize the supply chain system even in low forecast accuracy - Stock adjustment takes more time in demand decrease scenarios ![[Pasted image 20220827212144.png]] ## 4. Information sharing a. pooling이라 부르나요? 전통적인 재고관린에서 전통적으로 pooling이 공급과 수요의 불확실성 중 뭘 대상으로 할까요? ## 5. Length of delay vs Delay deviation a. supply side of "uncertainty from delay (information)" affecting the flow ## 6. Local vs Global optimization (tbc) ## 7. Supply chian strategies ## 8. Resilient supply chain ## 9. Emergency supply chain ## 10. Games in supply chian ## 11. Data Science in supply chain manamegment - [Supply chain optimization with python](https://towardsdatascience.com/supply-chain-optimization-with-python-23ae9b28fd0b) uses lp-based optimization - a. AI와 Data science 중 뭐가 나을까요? [[2_workflow]]의 b. 전체적이 excellent로 번역되는 이유는? 왜 이 맥락에서 꼭 전체적 공급사슬이어야하나요? --- Demand-Supply (DS) <img width="1210" alt="image" src="https://user-images.githubusercontent.com/30194633/182761909-83a6c6dd-c683-4fec-9cf8-5f302be4209c.png"> DS has 8 parameters to estimate and 5 parameters that is assumed 8 = 6 (2 * 3) stock-related - `latent_state_init`, `msr_error_scale` for each stock `Supply Line`, `Inventory`, `Backlog` plus 2 estimated params - `supply_lead_time`, `shipment_lead_time`, and 5 `assumed params` - const: `supply_line_adj_time`, `inventory_adj_time`, `backlog_adj_time` , `demand_adj_time` - series: `demand`