Leveraging risk management in the sales and operations planning process

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references (leaves 71-72).(cont.) Lastly, we visited SemiCo, a leading global supplier of high performance semiconductor products, to gain first-hand insight into the S&OP process of a large multinational company and complete a brief case study about how risk management is currently being utilized within this company's S&OP process. Finally, we synthesized these four sources of information in order to develop a common framework and recommendations that companies can use for understanding the best practices for incorporating risk management into the S&OP process.The objective of this thesis project is to analyze how companies can utilize risk management techniques in their sales and operations planning process (S&OP). S&OP is a strategy used to integrate planning and processes across functional groups within a company, such as sales, operations, and finance. A large body of academic and industry literature already exits, proving that S&OP can integrate people, processes, and technology leading to improved operational performance for a business. However, little research has been done in the area of applying risk management techniques to the S&OP process. When companies use S&OP in order to align their demand, supply, capacity, and production, based on various factors such as history, pricing, promotions, competition, and technology, they rarely factor in uncertainty and risk into the S&OP process. Furthermore, for those companies that do implement risk management in the S&OP process, there is no consensus in the business community about how to do this accurately and effectively. Our basic approach to understanding risk management and its place in the S&OP process will be four-fold. First, we conducted a literature review in order to gain basic S&OP process understanding and current risk management strategies. Next, we conducted thirteen hour-long phone interviews with practitioners and thought leaders in the field of sales and operations planning in order to gain insight into how companies currently discuss, assess, and act upon uncertainty within the S&OP process. Third, we conducted an online survey of various companies and consultants working in the field of S&OP to see how they currently discuss and incorporate uncertainty into their S&OP work.by Yanika Daniels and Timothy Kenny.M.Eng.in Logistic

Product design for supply chain : quantifying the costs of complexity in Hewlett-Packard's retail desktop PC business

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2005.Includes bibliographical references (p. 62-63).Over the past several years, Hewlett-Packard Company's North America Consumer Computing (NACC) division has faced pressures to increase retail product variety in response to growing customer demand. As they pursue incremental revenue and market share to meet corporate milestones, their, product portfolio grows and the overall complexity of the business increases. The holistic effects of this complexity across the supply chain are not fully understood, which can lead to inefficiencies in portfolio management and, ultimately, lower profitability for the division. Faced with this growing problem, the NACC division employed an HP internal consulting group to help quantify the costs and benefits of complexity in their business and to help establish decision-making guidelines to optimize future product cycles. This project involved three main phases: identification of complexity cost drivers in the retail consumer PC business, development of a quantitative model to calculate complexity effects, and suggestion of complexity guidelines for future portfolio planning. Each phase included presentations to senior management and NACC staff as a way to build "complexity consciousness" throughout the organization.(cont.) The results show that complexity affects not only supply chain and operational efficiency, but also marketing costs, internal accounting systems, and organizational performance. By understanding the difference between valuable and non-valuable variety, NACC has improved their portfolio planning and eliminated products that hurt overall profitability.by Aaron Matthew Raphel.M.B.A.S.M

Multi-period supplier selection under price uncertainty

Cataloged from PDF version of article.We consider a problem faced by a procurement manager who needs to purchase a large volume of multiple items over multiple periods from multiple suppliers that provide base prices and discounts. Discounts are contingent on meeting various conditions on total volume or spend, and some are tied to future realizations of random events that can be mutually verified. We formulate a scenario-based multi-stage stochastic optimization model that allows us to consider random events such as a drop in price because of the most favoured customer clauses, a price change in the spot market or a new discount offer. We propose certainty-equivalent heuristics and evaluate the regret of using them. We use our model for three bidding events of a large manufacturing company. The results show that considering most favored customer clauses in supplier offers may create substantial savings that may surpass the savings from regular discount offers

Optimal combined purchasing strategies for a risk-averse manufacturer under price uncertainty

Purpose: The purpose of our paper is to analyze optimal purchasing strategies when a manufacturer can buy raw materials from a long-term contract supplier and a spot market under spot price uncertainty. Design/methodology/approach: This procurement model can be solved by using dynamic programming. First, we maximize the DM’s utility of the second period, obtaining the optimal contract quantity and spot quantity for the second period. Then, maximize the DM’s utility of both periods, obtaining the optimal purchasing strategy for the first period. We use a numerical method to compare the performance level of a pure spot sourcing strategy with that of a mixed strategy. Findings: Our results show that optimal purchasing strategies vary with the trend of contract prices. If the contract price falls, the total quantity purchased in period 1 will decrease in the degree of risk aversion. If the contract price increases, the total quantity purchased in period 1 will increase in the degree of risk aversion. The relationship between the optimal contract quantity and the degree of risk aversion depends on whether the expected spot price or the contract price is larger in period 2. Finally, we compare the performance levels between a combined strategy and a spot sourcing strategy. It shows that a combined strategy is optimal for a risk-averse buyer. Originality/value: It’s challenging to deal with a two-period procurement problem with risk consideration. We have obtained results of a two-period procurement problem with two sourcing options, namely contract procurement and spot purchases. Our model incorporates the buyer’s risk aversion factor and the change of contract prices, which are not addressed in early studies.Peer Reviewe

Multi-period supplier selection under price uncertainty

We consider a problem faced by a procurement manager who needs to purchase a large volume of multiple items over multiple periods from multiple suppliers that provide base prices and discounts. Discounts are contingent on meeting various conditions on total volume or spend, and some are tied to future realizations of random events that can be mutually verified. We formulate a scenario-based multi-stage stochastic optimization model that allows us to consider random events such as a drop in price because o. The most favoured customer clauses, a price change i. The spot market or a new discount offer. We propose certainty-equivalent heuristics and evaluat. The regret of using them. We use our model for three bidding events of a large manufacturing company. The results show that considering most favored customer clauses in supplier offers may create substantial savings that may surpas. The savings from regular discount offers. © 2014 Operational Research Society Ltd. All rights reserved

Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime†models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches.dynamic pricing;trading agent competition;agent-mediated electronic commerce;dynamic markets;economic regimes;enabling technologies;price forecasting;supply-chain

The floating contract between risk-averse supply chain partners in a volatile commodity price environment

In this dissertation, two separate but closely related decision making problems in environments of volatile commodity prices are addressed. In the first problem, a risk-averse commodity user\u27s purchasing policy and his risk-neutral supplier\u27s pricing decision, where the user can purchase his needs through contract with his supplier as well as directly from the spot market, are analyzed. The commodity user is assumed to be the supplier\u27s sole client, and the supplier can always expand capacity, at a cost to the user, to accommodate the user\u27s demand in excess of initially reserved capacity. In the more generalized second problem, both parties (commodity user and supplier) are assumed to be risk averse, and both can directly access the spot market. In addition to making pricing decisions, the supplier is also faced with the challenge of establishing the right combination of in-house production and spot market engagements to manage her risk of exposure to spot price volatility under the contract. While the supplier has a frictionless buy and sell access to the spot market, the user can only access this market for buying purposes and incurs an access fee that is linearly increasing in the purchased volume. In both problems, by adopting the mean-variance criterion to reflect aversion to risk, the decisions of both parties are explicitly characterized. Based on analytical results and numerical studies, managerial insights as to how changes in the model\u27s parameters would affect each party\u27s decisions are offered at length, and the implications of these results to the manager are discussed. A focal point for the dissertation is the consideration of a floating contract, the landing price of which is contingent on the realization of the commodity\u27s spot market price at the time of delivery. It was found that if properly designed, not only can this dynamic pricing arrangement strategically position a long-term supplier against spot market competition, but it also has the added benefit of leading to improved supply chain expected profits compared to a locked-in contract price setting. Another key finding is that when making her pricing decisions, the supplier runs the risk of overestimating the commodity user\u27s vulnerability at higher levels of the user\u27s aversion to risk as well as at higher volatility of spot prices

RISK MANAGEMENT IN GOVERNMENT PURCHASES

Government procurement is exposed to the most diverse risks, such as deserted or failed bids, errors in the technical specification of the object to be acquired and inconsistencies in the market research. In view of this scenario, there is an imperative for the implementation of risk management practices in public procurement, in order to reduce exposure to risks and their consequences. Corroborating this perspective, this study aims to identify the sources of risks and the level of vulnerability existing in the processes of acquisition of goods and services within the scope of the State of Minas Gerais. An exploratory, qualitative research was carried out, which used focus groups to collect the data. The results indicate that most of the identified risks are generated from causes internal to public organizations and result mainly from three sources: (1) human failures, (2) the absence of clear and well-defined organizational processes and (3) inconsistencies in technology tools. The results also demonstrate that public organizations are the main holders of mitigation strategies to reduce the vulnerability found in the acquisition of goods and services

End-of-life supply chain strategy for high-performance servers

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2003.Includes bibliographical references (p. 50).by Don J. Lee.S.M.M.B.A

Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime” models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches