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공급망 계획 ( SCP: Supply Chain Planning). Contents. 1. 공급망 계획( SCP) 이란? 2. Demand Planning 3. Supply Network Planning 4. Production and Distribution Planning/Scheduling 5. Commercial Software Products 6. New Trends 7. 결론. ERP 핵심개념. Order Information. Released Order. ERP

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공급망 계획 ( SCP: Supply Chain Planning)


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    1. 공급망 계획(SCP: Supply Chain Planning)

    2. Contents 1. 공급망 계획(SCP) 이란? 2.Demand Planning 3. Supply Network Planning 4. Production and Distribution Planning/Scheduling 5. Commercial Software Products 6. New Trends 7. 결론

    3. ERP 핵심개념 Order Information Released Order ERP Master Data Gantt chart

    4. 원자재 재료 재활용 노트북 Oil field 철 판 철광석 자동차 반도체 PDP 모 래 제품 SCM 개념

    5. Supply Chain Management 의 시작은?( J. Forrester, “Industrial Dynamics”, 1961, MIT press) T.C. Jones and D.W. Riley, “Using Inventory for Competitive Advantage through Supply Chain Management,”Int’l Journal of Physical Distribution and Materials Management. v.15 n.1, 1985. “Supply Chain Management” 라는 용어가 처음 사용된 것은1982년, Oliver and Weber공저, "Supply Chain Management: logistics catches up with strategy" 라는 책자에서.

    6. Bull Whip Effect: 10% 수요가 급증하였을 때 Retail, Distribution,Factory수준에서의재고, 주문, 판매량의 변동 추이(J. Forrester저, “Industrial Dynamics”, 1961, MIT press)

    7. Supply Chain(Network)Management Customers Product and material flows Information and financial flows Strategic business units Retailers 1st Tier suppliers Distribution Centers Assembly/Mfg 1st Tier Suppliers 1st Tier Suppliers 2nd Tier Suppliers 2nd Tier Suppliers 2nd Tier Suppliers

    8. SCP(Supply Chain Plan)와 SCE(Supply Chain Execution) • SCM란 ‘공급사슬 상에서 발생하는 모든 활동들을 효과적으로 운영하기 위한 의사결정과정’ • SCC(Supply Chain Council)에서는 Supply Chain의 process를 • 전략적 단계인 Configuration, • 전술적 단계인 Planning, • 작전의 단계인 Execution 으로 분류. • SCP란 결국 Plan on Supply Chain(SC에 대한 계획): • 소비자/소매/도매/물류 센터/공장/공장창고/공급자 에 관한 계획 • 장기적/중기적/단기적 계획 • SCE(Execution)이란 “SCP에 근거한 시행”으로써 Plan에 대한 결과를 보고하고 새로운 Plan을 위한 정보 데이터를 제공.

    9. SCOR(Supply Chain OperationReference-model) version 6.1 Process Category는 Process와 Process Type으로부터 결정됨 MAI Lab. Seminar 14/43

    10. (Customer) Customer Requirements (D1.3, D1.10) Order Backlog, Shipments (EP.3) Planning Data (EP.9) Revised Aggregate Forecast and Projections, Revised Business Assumptions (EP.1) Planning Decision Policies (EP.2) Supply Chain PerformanceImprovement Plan (EP.4) Inventory Strategy P1.1 Identify, Prioritize, and Aggregate Supply-Chain Requirements P1.3 P1.4 Balance Supply-Chain Resources with Supply-Chain Requirements Establish and Communicate Supply-Chain Plans P1.2 Identify, Assess, and Aggregate Supply-Chain Resources Supply Chain Plans • (P2.1, P3.1, P4.1) (Customer) (P2.4) Sourcing Plans (P3.4) Product MAKE Plans (P4.4) Delivery Plans (EP.3) Planning Data (EP.5, EP.6) Projected Internal and External Production Capacity (EP.5, EP.6) Revised Capital Plan (EP.5, EP.6) Outsource Plan (EP.8) Regulatory Requirements (Customer) Inventory P1. Plan Supply Chain(SCOR6.1)

    11. P2. Plan Source(SCOR6.1) P2.1 Identify, Prioritize, and Aggregate Product Requirements P2.3 P2.4 Balance Product Resources with Product Requirements Establish Sourcing Plans P2.2 Sourcing Plans (P1.2, P4.2, P5.1, S1.1, S2.1, S3.1, S3.3, D1.3, D2.3) Identify, Assess, and Aggregate Product Resources

    12. P3.1 Identify, Prioritize, and Aggregate Production Requirements P1.3 P1.4 Balance Supply-Chain Resources with Production Requirements Establish Production Plans P1.2 Identify, Assess, and Aggregate Production Resources Production Plans P3. Plan Make(SCOR6.1)

    13. P4.1 Identify, Prioritize, and Aggregate Delivery Requirements P4.3 P4.4 Balance Delivery Resources with Delivery Requirements Establish Delivery Plans P4.2 Identify, Assess, and Aggregate Delivery Resources Delivery Plans Stocking Requirements P4. Plan Deliver(SCOR6.1)

    14. P5.1 Identify, Prioritize, and Aggregate Return Requirements P5.3 P5.4 Balance Return Resources with Return Requirements Establish and Communicate Return Plans P5.2 Identify, Assess, and Aggregate Return Resources Source Return Requirement Return Production Requirements Return Plans P5. Plan Return(SCOR6.1)

    15. SCP의 구성요소 • 크게 4부문 정도로 나눌 수 있음: • Demand Planning • Supply Network Configuration/Planning • Production/OperationPlanning and Scheduling* • Distribution/Return Planning (*)APS(Advanced Planning Scheduling)라고도 불림. 기본적으로ATP(Available To Promise), CTP(Capable To Promise)를 포함. 이들은 Order Fulfillment라고도 칭함. • 일반적으로 SCM 소프트웨어들은 SCP에 관한 것: • “SAP AG”의 “APO:Advanced Planner and Optimizer)” • “i2 Technologies” 의 “Business Optimization Service” • “Oracle”의 “Supply Chain Planning 11i.10” • “SSA Global”의 “APS;WM;TM;Event&Performance Mgt” • …

    16. 2. Demand Planning • 주요기능: • Top-down/Bottom-up/Consensus-based Forecasting • Multi-tier/Multi-facet Forecasting • Promotional Planning • Causal Analysis • Life-cycle Management • What-if Simulation • Data Management(Multi-facet, Multi-level, Multi-source) • Function Libraries

    17. 3. Supply Network Planning • Supply Chain의 목표: • 완제품과 원자재 조달비의 절감 • 고객 서비스 증진 • 재고수준의 절감 • 모든 사내 자원의 활용 • SNP: • SC의 목표에 가장 큰 영향을 미치는 중장기 계획 수립 • 조달기간이 길고 병목자원과 관련된 핵심제품의 BOM(Bill of Material)를 포함 • 무리한 자원동원 없는 적재, 적량, 적시 공급을 목표로 함

    18. Customer Distribution center Plant

    19. Customer Distribution center Plant

    20. Customer Distribution center Plant

    21. Customer Distribution center Plant

    22. SNP 의 기능(SAP사의 APO의 예) • Supply Network Planning Strategies • Heuristic : infinite planning • Capable-to-Match (CTM) : Rules-based finite planning • Optimization : integrated finite planning • Deployment (short-term Replenishment Planning) • Deployment adjusts the stock transfers for short-term changes on the supply and demand side. • Transport Load Builder • Plan for optimal use of transportation method.

    23. SNPPlanning Sequence (SAP사의 APO의 예) Set up Master Data and Supply Chain Model Release Demand Plan to SNP Group together loads for Non-assigned Transport orders Perform SNP Heuristic, Optimization, or CTM run TLB run Check plan/ Solve problems Deployment run Finalize SNP plan (available to PP/DS) Release Constraint- Based SN plan to DP

    24. 4. Production and Distribution Planning(연구사례) • 채찍 효과 (Bullwhip Effect) • 수요에 대한 분산이 상위단계로 갈수로 커지는 현상 Main factors of bullwhip effect • Errors in demand forecasting • Batch ordering • Price fluctuation • Inflated orders Batch ordering을 하지 않으면?  LFL?

    25. 연구의 동기 • Traditional manufacturing control framework • Drawback of separate planning [Dudek*, 2005] • No sufficient support for transport and distribution of goods • Plant order are generated with an isolated view of the item in question without taking account of the interdependencies with other items • Independently operated at various facilities based on locally available data, leading to segregated planning processes along the SC Master Production Scheduling Material Requirement Planning Scheduling Shop Floor Control Successive and segregated planning *Lecture notes in economics and mathematical systems 2005

    26. 연구의 동기 • Manufacturing control framework in SC (Supply Chain Planning matrix) • x-axis: the business functions across a supply chain • y-axis: the levels of planning intervals Software modules covering the SC planning matrix (Meyr et al., 2002)

    27. 연구의 동기 • 생산 계획과 연결되지 않은 분배 계획이나 e-Procurement는 큰 의미가 없다 비용 관점에서만 국부적으로 분배계획을 수립하면?

    28. 전체 공급 사슬에서의 발생 비용을 최소화 시키는 생산 및 분배 계획 작성 • 4계층 모델 (고객, 물류센터, 공장, 1차 납품업체) • 고객 사이트에 대한 수요는 확정적 • 복수 제품, 복수공급업체(특정 제품에 대해 복수공급업체 존재) • 생산 또는 수송 관련 준비 비용 존재, 재고 유지 비용 존재 • 최종 고객 수요는 반드시 만족 고객 1차 납품업체 a 고객 공장a 물류창고 a 고객 1차 납품업체 b 물류창고 b 고객 공장b 1차 납품업체 c

    29. Production Planning • Purpose of Production Planning • LFL Production • 잦은 생산 준비 비용 발생 • 생산 용량 조건 위반 • Production Planning • 재고 보유를 통한 생산 횟수 감소 • 생산 용량 조건 고려

    30. Distribution Planning DC A DC A Factory A Factory B Factory A Factory B 요구되는 수요를 공급 사슬내의 관련된 모든 사이트에 분배하여 생산 및 운송 기능을 맡게 한다.

    31. Product Master(MPS Level) • 생산 분배 계획에서는 생산계획을 수립하는 데 필요한 각종 비용요소를 구하기 쉬운 MPS 수준의 제품 (주요 외주 부품, 최종 제품)만을 고려한다. 이를 위해서는 기존의 BOM을 MPS 수준의 BOM으로 향상시키고 계획을 세워주어야 한다. 주요 외주 제품 최종 제품 a A Resource3 b B Resource3 c C

    32. Formulation • 목적식 • 제약식 Minimize {재고유지비용+생산비용+운송비용+생산(입고)준비비용+운송준비비용} 고객의 수요 만족 당기 재고=전기 재고+당기 생산량 – 당기 수요 생산/수송 용량 조건 생산/수송 여부 결정 변수 - 0/1 변수

    33. Notation

    34. Notation

    35. Decision Variable

    36. 유전자 알고리즘이란? • Selection(선별) • 현재 임의의 해 집단이 있을 때 이중 n개를 새로 뽑는 작업을 선별이라고 한다. 이때 우수한 해는 열등한 해에 비해 뽑힐 확률이 높도록 선별작업을 계속한다면 초기 해의 가장 우수한 해만 n개 남을 것이다. 이렇게 새로운 해 집단이 생성되는 과정을 generation(세대)라고 한다. • Crossover(교배) • 임의의 두 해를 교배하여 새로운 해를 만드는 것이다. 선별과정을 반복하면 그 전에 비하여 우수한 해들로 세대가 구성되어진다. 우수한 해들은 최적해의 특징 스키마를 가지고 있다.세대가 진행됨에 따라 초기 해 집단에서의 교배와 달리 우수한 해들끼리의 교배가 빈번해진다. 우수한 해들의 교배는 최적해의 특징 스키마를 가지는 새로운 해를 생성한다. • Mutation(변이) • 단순히 교배 연산만을 하여 세대를 진행시킬 경우 최적해에 근접하지 못하고 해 집단의 모든 해가 특정 해로만 구성되어질 수 있다. 임의로 gene값의 변화를 주어 새로운 해를 만드는 것을 변이라고 한다.

    37. Genetic Algorithm • Genetic algorithm is an algorithm hinted from natural evolution and can be seen as an optimization method through probability search. Decoding New Generation Evaluation & Selection Encoding Creating new population Initial Population Genetic operation Applying the rule to satisfy capacity constraint Meta Heuristic

    38. Handling Item Chromosome Site Time Handling Item Chromosome Network Time Genetic Algorithm • Expression of solution i Representation of solutions V t,s,i = N t,n,i = s 1 If production decision is made 0 or not t i 1 If network is linked 0 or not n t

    39. Genetic Algorithm Example of crossover

    40. Genetic Algorithm • How to meet capacity constraints at sites Forward Method Backward Method Modified Mutation Method LFL based creation of initial population

    41. Experiment • The purpose of the experiment • To see if the proposed algorithm can find a solution to the problem of the given size and if so how fast? • To findhow effective the LFL-based creation of initial population and a modified mutation are in meeting production capacity constraints SCM Solver Ver 1.0

    42. Experiment I • Optimal solution and comparative experiment • Experiment model (3 models)

    43. Experiment I • Optimal solution and comparative experiment

    44. Experiment II • Evaluation of techniques used to help meet production capacity constraints • Experiment model • 22 models (Altering the problem size : M1 ~ M2) • The number of entities • Time period

    45. Experiment II • Evaluation of techniques used to help meet production capacity constraints

    46. Another Model – Multi-level Lot Scheduling Problem • Multi-facility production planning final item component • Demands for the final products of the supply chain were deterministically determined. • No external demand for components is allowed. • No backlog is allowed for all component parts and final products. • Neither positive initial inventories nor scheduled receipts are introduced. • No more than two facilities produce the same item. Assumptions

    47. Setup Cost Inventory Holding Cost Production Cost Transportation Cost Model - Formulation • Formulation subject to Parameters for the problem ................. (1) ................. (2) ................. (3) ................. (4) ................. (5)

    48. MA - Memetic Algorithm • Memetic Algorithm • first proposed by Moscato in 1989 • hinted from the cultural evolution. • crucial feature of memetic algorithm  inclusion of local refinement procedure • also commonly known as hybrid evolution algorithm and genetic local search General procedures of memetic algorithm

    49. Local refinement procedure for multi-facility production plan Single level local refinement procedure Wagner-Whitin (optimal solution) Silver-meal (near optimal solution) Multi-facility production plan The application of a single level optimization technique in higher level products may lead to unfavorable dependent demand for its immediate lower level products A MA – Local refinement procedure Cost Inventory Holding Cost Setup Cost # of setup Multi-facility production plan ? ? Production plan for item A in multi-facility environment How to modify the multi-facility production plan in order to improve the solution quality?

    50. A MA - Local refinement procedure • The local refinement procedure based on the benchmark Inventory holding cost Setup cost Agent 2 Local refinement procedure Cost Agent 1 Agent 3 Local refinement procedure Local refinement procedure The memetic agent which shows the best performance in population The best Agent Mutation Recombination Total cost occurred in the production plan of item A Agent 3 Integrated Production Plan # of setup <local refinement procedure for the production plan of the item A>