불확실성하에서의 확률적 기법에 의한 판매 및 실행 계획 최적화 방법론 : 서비스 산업
Optimization Methodology for Sales and Operations Planning by Stochastic Programming under Uncertainty : A Case Study in Service Industry
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
In recent years, business environment is faced with multi uncertainty that have not been suffered in the past. As supply chain is getting expanded and longer, the flow of information, material and production is also being complicated. It is well known that development service industry using application software has various uncertainty in random events such as supply and demand fluctuation of developer’s capcity, project effective date after winning a contract, manpower cost (or revenue), subcontract cost (or purchase), and overrun due to developer’s skill-level. This study intends to social contribution through attempts to optimize enterprise’s goal by supply chain management platform to balance demand and supply and stochastic programming which is basically applied in order to solve uncertainty considering economical and operational risk at solution supplier. In Particular, this study emphasizes to determine allocation of internal and external manpower of developers using S&OP (Sales & Operations Planning) as monthly resource input has constraint on resource’s capability that shared in industry or task. This study is to verify how Stochastic Programming such as Markowitz’s MV (Mean Variance) model or 2-Stage Recourse Model is flexible and efficient than Deterministic Programming in software enterprise field by experiment with process and data from service industry which is manufacturing software and performing projects. In addition, this study is also to analysis how profit and labor input plan according to scope of uncertainty is changed based on Pareto Optimal, then lastly it is to enumerate limitation of the study extracted drawback which can be happened in real business environment and to contribute direction in future research considering another applicable methodology.
Abstract
In recent years, business environment is faced with multi uncertainty that have not been suffered in the past. As supply chain is getting expanded and longer, the flow of information, material and production is also being complicated. It is well known that development service industry using application software has various uncertainty in random events such as supply and demand fluctuation of developer’s capcity, project effective date after winning a contract, manpower cost (or revenue), subcontract cost (or purchase), and overrun due to developer’s skill-level. This study intends to social contribution through attempts to optimize enterprise’s goal by supply chain management platform to balance demand and supply and stochastic programming which is basically applied in order to solve uncertainty considering economical and operational risk at solution supplier. In Particular, this study emphasizes to determine allocation of internal and external manpower of developers using S&OP (Sales & Operations Planning) as monthly resource input has constraint on resource’s capability that shared in industry or task. This study is to verify how Stochastic Programming such as Markowitz’s MV (Mean Variance) model or 2-Stage Recourse Model is flexible and efficient than Deterministic Programming in software enterprise field by experiment with process and data from service industry which is manufacturing software and performing projects. In addition, this study is also to analysis how profit and labor input plan according to scope of uncertainty is changed based on Pareto Optimal, then lastly it is to enumerate limitation of the study extracted drawback which can be happened in real business environment and to contribute direction in future research considering another applicable methodology.
참고문헌 (17)
- Berman, O., "A stochastic optimization model for planning capacity expansion in a service industry under uncertain demand", Naval Research Logistics, 41(4), pp.545~564 (1994)
- Carneiro, M. C., "Risk Management in the Oil Supply Chain : A CVaR Approach", Industry and Engineering Chemistry Research, 49(7), pp.3286~3294 (2010)
- Cook, J., "From raw materials to customers : Supply chain management in the service industry", SAM Advanced Management Journal (2001)
- Figueiredo, J. N., "Optimal product", customer mix selection as a strategic tool for cross-functional integration, 2011, pp.4(1), 51~70
- Higle, J.L., "Tutorials in Operation Research", pp.30~53 (2005)
- 황선민, "Recourse model을 활용한 수요 불확실성하의 추계적 판매생산 계획 - 자동차 산업에서의 사례연구 -", 한국SCM학회지, 15(1), pp.9~20 (2015), 한국SCM학회
- 정욱, "불확실한 수율과 고객이탈행위를 고려한 강건한 뉴스벤더 모델", 품질경영학회지, 40(4), pp.441~452 (2012), 한국품질경영학회[DOI]
- Kim, K.Y., "A Cross-National Comparison of Uncertainty Profile and Software Project Performance", Autumn academic conference on Korea Society of IT Service, pp.279~285 (2002)
- National military strategy based on ICT 2015BIG STEP : The Future Prospect of Bigdata Market, "Kim, M.J.", National Information Society Agency, 8~12 (2015)
- 이진휘, "재무적 가치 기반의 공급사슬 통합 모형에 대한 연구 고찰", 한국SCM학회지, 12(2), pp.25~46 (2012), 한국SCM학회
- Lee, K.T., "Why do small to medium sized software firms stay in strategic partnerships? An empirical investigation of three theoretical perspectives & identification of an emerging theory", pp.2009
- Markowitz, H., "Portfolio Selection", The Journal of Finance, 7(1), pp.77~91 (1952)
- Mulvey, J. M., "Robust optimization of large-scale systems", Operation Research, 43(2), pp.264~281 (1995)
- Schlegel, G. L., "Next Generation of S&OP : Scenario Planning with Predictive Analytics & Digital Modeling", Journal of Business Forecasting, 29(3), pp.20~ (2010)
- 송창용, "채찍효과에 대한 트리즈 분석과 연구현황 고찰", 산업경영시스템학회지, 39(3), pp.109~117 (2016), 한국산업경영시스템학회
- "Service Capacity Competition with Peak Arrivals and Delay Sensitive Customers"[URL]
- Watts, T., "Structural limits of capacity and implications for visibility", Journal of Accounting and Organizational Change, 5(2), pp.294~312 (2009)