Application of the sales and operations planning (S&OP) process at Douglas Pharmaceuticals Limited : a thesis presented in partial fulfillment of the requirements for the degree of Masters in Applied Science in Logistics and Supply Chain Management at Massey University

To be successful in today's fast paced, demanding markets, companies must be poised to support changeable market demand while maintaining operational efficiencies. Recognising the need to coordinate and communicate details of supply and demand across multiple divisions, successful companies have adopted a process that has become widely known as sales and operations planning (S&OP). When implemented effectively, S&OP can provide many benefits including improved customer service, stability in production plans, improved forecast accuracy and reduced inventories. This report analyses S&OP processes operating at three successful companies and outlines the benefits these companies are achieving with S&OP. The report identifies the critical success factors in S&OP and how S&OP can be operated effectively. The report also presents a generic executive S&OP meeting format based on the formats operating at these companies and includes key performance metrics that should be presented as part of the S&OP process. The report analyses the S&OP process that has been operating at Douglas Pharmaceuticals Ltd since May 2000 and finds it to be lacking in several key areas. The report concludes that the main barriers to successful implementation of S&OP at Douglas were a lack of knowledge about the process at middle management level and a lack of buy-in and participation at senior management level. As a consequence, the current S&OP process at Douglas Pharmaceuticals is limited. There are major shortfalls in the reports used, the key performance metrics presented and accountability for key metrics such as forecast accuracy results. This report provides detailed recommendations on how Douglas Pharmaceuticals can substantially improve its S&OP process

Weather forecasting for weather derivatives : [revised version: January 2, 2004]

We take a simple time-series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time-series modeling reveals conditional mean dynamics, and crucially, strong conditional variance dynamics, in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. As we argue, it also holds promise for producing the long-horizon predictive densities crucial for pricing weather derivatives, so that additional inquiry into time-series weather forecasting methods will likely prove useful in weather derivatives contexts

Stock returns and expected business conditions : half a century of direct evidence

We explore the macro/finance interface in the context of equity markets. In particular, using half a century of Livingston expected business conditions data we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a statistically and economically significant counter-cyclical fashion: depressed expected business conditions are associated with high expected excess returns. Moreover, inclusion of expected business conditions in otherwise standard predictive return regressions substantially reduces the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium, while simultaneously increasing R2. Expected business conditions retain predictive power even after controlling for an important and recently introduced non-financial predictor, the generalized consumption/wealth ratio, which accords with the view that expected business conditions play a role in asset pricing different from and complementary to that of the consumption/wealth ratio. We argue that time-varying expected business conditions likely capture time-varying risk, while time-varying consumption/wealth may capture time-varying risk aversion. Klassifikation: G1

Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence

We explore the macro/finance interface in the context of equity markets. In particular, using half a century of Livingston expected business conditions data we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a statistically and economically significant counter-cyclical fashion: depressed expected business conditions are associated with high expected excess returns. Moreover, inclusion of expected business conditions in otherwise standard predictive return regressions substantially reduces the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium, while simultaneously increasing. Expected business conditions retain predictive power even after controlling for an important and recently introduced non-financial predictor, the generalized consumption/wealth ratio, which accords with the view that expected business conditions play a role in asset pricing different from and complementary to that of the consumption/wealth ratio. We argue that time-varying expected business conditions likely capture time-varying risk, while time-varying consumption/wealth may capture time-varying risk aversion.Business cycle, expected equity returns, prediction, Livingston survey, risk aversion, equity premium, risk premium

Weather Forecasting for Weather Derivatives

We take a simple time-series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time-series modeling reveals conditional mean dynamics, and crucially, strong conditional variance dynamics, in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. As we argue, it also holds promise for producing the long-horizon predictive densities crucial for pricing weather derivatives, so that additional inquiry into time-series weather forecasting methods will likely prove useful in weather derivatives contexts.Risk management; hedging; insurance; seasonality; temperature; financial derivatives

Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence

We explore the macro/finance interface in the context of equity markets. In particular, using half a century of Livingston expected business conditions data we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a statistically and economically significant counter-cyclical fashion: depressed expected business conditions are associated with high expected excess returns. Moreover, inclusion of expected business conditions in otherwise standard predictive return regressions substantially reduces the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium, while simultaneously increasing R2. Expected business conditions retain predictive power even after controlling for an important and recently introduced non-financial predictor, the generalized consumption/wealth ratio, which accords with the view that expected business conditions play a role in asset pricing different from and complementary to that of the consumption/wealth ratio. We argue that time-varying expected business conditions likely capture time-varying risk, while time-varying consumption/wealth may capture time-varying risk aversion.Business Cycle, Expected Equity Returns, Prediction, Livingston Survey, Risk Aversion, Equity Premium, Risk Premium

Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence

We explore the macro/finance interface in the context of equity markets. In particular, using half a century of Livingston expected business conditions data we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a statistically and economically significant counter-cyclical fashion: depressed expected business conditions are associated with high expected excess returns. Moreover, inclusion of expected business conditions in otherwisestandard predictive return regressions substantially reduces the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium, while simultaneously increasing R-squared. Expected business conditions retain predictive power even after controlling for an important and recently introduced non-financial predictor, the generalized consumption/wealth ratio, which accords with the view that expected business conditions play a role in asset pricing different from and complementary to that of the consumption/wealth ratio. We argue that time-varying expected business conditions likely capture time-varying risk, while time-varying consumption/wealth may capture time-varying risk aversion.