Demand Forecasting and Production Planning Free Essays

string(51) " bottleneck stage due to its long processing time\." ScienceAsia 27 (2001) : 271-278 Demand Forecasting and Production Planning for Highly Seasonal Demand Situations: Case Study of a Pressure Container Factory Pisal Yenradeea,*, Anulark Pinnoib and Amnaj Charoenthavornyingb a Industrial Engineering Program, Sirindhorn International Institute of Technology, Thammasat University, Patumtani 12121, Thailand. b Industrial Systems Engineering Program, School of Advanced Technologies, Asian Institute of Technology, P. O. We will write a custom essay sample on Demand Forecasting and Production Planning or any similar topic only for you Order Now Box 4, Klong Luang, Patumtani 12120, Thailand. * Corresponding author, E-mail: pisal@siit. tu. ac. th Received 24 May 2001 Accepted 27 Jul 2001 ABSTRACT This paper addresses demand forecasting and production planning for a pressure container factory in Thailand, where the demand patterns of individual product groups are highly seasonal. Three forecasting models, namely, Winter’s, decomposition, and Auto-Regressive Integrated Moving Average (ARIMA), are applied to forecast the product demands. The results are compared with those obtained by subjective and intuitive judgements (which is the current practice). It is found that the decomposition and ARIMA models provide lower forecast errors in all product groups. As a result, the safety stock calculated based on the errors of these two models is considerably less than that of the current practice. The forecasted demand and safety stock are subsequently used as inputs to determine the production plan that minimizes the total overtime and inventory holding costs based on a fixed workforce level and an available overtime. The production planning problem is formulated as a linear programming model whose decision variables include production quantities, inventory levels, and overtime requirements. The results reveal that the total costs could be reduced by 13. % when appropriate forecasting models are applied in place of the current practice. KEYWORDS: demand forecasting, highly seasonal demand, ARIMA method, production planning, linear programming, pressure container factory. INTRODUCTION Most manufacturing companies in developing countries determine product demand forecasts and production plans using subjective and intuitive judgments. This may be one factor that leads to production inefficiency. An accuracy of the demand forecast significantly affects safety stock and inventory levels, inventory holding costs, and customer service levels. When the demand is highly seasonal, it is unlikely that an accurate forecast can be obtained without the use of an appropriate forecasting model. The demand forecast is one among several critical inputs of a production planning process. When the forecast is inaccurate, the obtained production plan will be unreliable, and may result in over- or understock problems. To avoid them, a suitable amount of safety stock must be provided, which requires additional investment in inventory and results in an increased inventory holding costs. In order to solve the above-mentioned problems, systematic demand forecasting and production planning methods are proposed in this paper. A case study of a pressure container factory in Thailand is presented to demonstrate how the methods can be developed and implemented. This study illustrates that an improvement of demand forecasts and a reduction of total production costs can be achieved when the systematic demand forecasting and production planning methods are applied. The demand forecasting and production planning methods are proposed in the next section. The background of the case study, including, products, production process, and the forecasting and production planning procedures being used in the factory, are briefly described in Section 3. The detailed analyses of the forecasting methods and the production planning method are explained in Section 4 and Section 5, respectively. Finally, the discussion and conclusion are presented in Section 6. 272 ScienceAsia 27 (2001) P ROPOSED D EMAND F ORECASTING PRODUCTION PLANNING METHODS AND The proposed demand forecasting and production planning methods are depicted in a step-by-step fashion in Fig. . Most factories produce a variety of products that can be categorized into product groups or families. Individual products in the same product group generally have some common characteristics. For example, they may have the same demand pattern and a relatively stable product mix. As a result, it is possible to forecast the aggregate demand of the product group first, and then disaggregate it in to the demand of individual products. Since the forecast of the aggregate demand is more accurate than that of the individual demand1, it is initially determined in Step 1. Then the demands of individual products are determined in Step 2 by multiplying the aggregate demand with the corresponding product mix that is normally known and quite constant. Since the demand forecasts are always subject to forecast errors, safety stocks are provided to avoid stock-out problems. Based on the standard deviation of the forecast errors and the required service level, the safety stocks for individual products are determined in Step 3. Production planning decisions are so complicated and important that they should not be subjectively and intuitively made. Consequently, an appropriate production planning model should be formulated to determine the optimal decisions. With this model, its parameters, eg, demand forecasts, safety stocks, holding cost, overtime cost, machine capacity, inventory capacity, and available regular time and overtime, are entered or updated (Step 4). In step 5, the optimal decisions regarding the production quantities, inventory levels, and regular production time and overtime for each product in each production stage are obtained by solving the production planning model. Step 6 indicates that only the optimal production plan of the current month will be implemented. After one month has elapsed, the demand forecasts and the production plan will be revised (by repeating Steps 1 to 5) according to a rolling horizon concept. BACKGROUND OF THE CASE STUDY The pressure container factory manufactures 15 products, ranging from 1. 25 to 50 kg of the capacity of pressurized gas. The products are divided into eight product groups, namely, Group 1 to Group 8. The first six groups have only two components, â€Å"head† and â€Å"bottom†, while the last two groups have three components, â€Å"head†, â€Å"bottom†, and â€Å"body†. The production process can be divided into five stages as shown in Fig. 2. Stage 3 is only required to produce the products having three components (ie, those in Groups 7 and 8). Stage 4, the circumference welding, is found to be a bottleneck stage due to its long processing time. You read "Demand Forecasting and Production Planning" in category "Papers" Presently monthly demand forecasts are subjectively determined by the Marketing Department based on past sales and expected future market conditions. No systematic method is used in forecasting. Using these forecasts and other constraints, such as availability of raw materials, equipment, and production capacity, the monthly production plan for a three-month period is intuitively determined without considering any cost factor. This results in inaccurate demand forecasts and, subsequently, an inefficient production plan. Stage 1 Blanking 1) Forecast the monthly demands of each product group throughout the planning horizon of 12 months 2) Determine the demand for each individual product 3) Determine the safety stock for each individual product Stage 2 Forming of bottom and head Stage 3 Forming of body 4) Update the parameters in the production planning model Stage 4 Circumference welding 5) Run the planning model to obtain the optimal planning dicisions ) Roll the plan by repeating Steps 1 to 5 after one month has elapsed Stage 5 Finishing Fig 1. Proposed forecasting and planning steps. Fig 2. The production process to manufacture a pressure container. ScienceAsia 27 (2001) 273 FORECASTING METHODS Steps 1, 2, and 3 of the proposed forecasting and planning process are discussed in detail in this section. Firstly, the aggregate demand forecasts of eight product groups throughout the planning horizon of 12 mont hs will be determined. Secondly, the demand forecasts of the product groups will be disaggregated into those of individual product. Thirdly, the safety stocks of individual product will be calculated based on the forecast error. Aggregate Demand Forecasts of Product Groups The typical demand pattern of each product group is seasonal. As an example, Fig. 3 shows the demand pattern of Product Group 3. Thus, three forecasting models that are suitable for making seasonal demand forecasts are considered. They are Winter’s, decomposition and Auto-Regressive Integrated Moving Average (ARIMA) models. 2-5 Because of their simplicity, the Winter’s and decomposition models are initially used to forecast the aggregate demand of each product group. If the Winter’s and decomposition models are inadequate (ie, the forecast errors are not random), the ARIMA model which is more complicated and perhaps more efficient will be applied. The Winter’s model has three smoothing parameters that significantly affect the accuracy of the forecasts. These parameters are varied at many levels using a computer program to determine a set of parameters that give the least forecast errors. There are two types of the decomposition model, namely, multiplicative and additive types. The former is selected since the demand pattern shows that the trend and seasonal components are dependent. The forecast errors of the Winter’s and decomposition models are presented in Table 1. Based on the calculated mean square error (MSE) and the mean absolute percentage error (MAPE), it is seen that the decomposition model has lower Original Series (x 1000) 16 forecast errors in all product groups than the Winter’s model. Thus, it is reasonable to conclude that the decomposition model provides better demand forecasts than the other. One way to check whether the forecasting model is adequate is to evaluate the randomness of the forecast errors. The auto-correlation coefficient functions (ACFs) of the errors from the decomposition model for several time lags at the significant level of 0. 05 of each product group are determined. The ACFs of Groups 1 and 3 are presented as examples in Fig. 4 and 5, respectively. The ACFs of Groups 4, 5, 6, 7, and 8 are similar to those of Group 1 in Table 1. Forecast errors of the Winter’s and decomposition models. MSE Products MAPE (%) Winter’s Decomposition Winter’s Decomposition 9,879,330 4,363,290 2,227,592 4,507,990 10,039,690 574,108 636,755 883,811 36. 14 48. 94 24. 25 30. 08 18. 80 53. 86 61. 99 46. 52 26. 97 31. 86 15. 97 23. 4 13. 14 34. 80 34. 45 28. 76 Group 1 16,855,149 Group 2 8,485,892 Group 3 5,433,666 Group 4 6,035,466 Group 5 23,030,657 Group 6 1,690,763 Group 7 2,034,917 Group 8 1,884,353 Estimated Autocorrelations 1 0. 5 coefficient 0 -0. 5 -1 0 4 8 lag 12 16 20 Fig 4. ACFs of the residuals from the decomposition model for Group 1. Estimated Autocorrel ations 1 0. 5 16 demand 3 coefficient 0 8 -0. 5 4 -1 0 0 10 20 30 time index 40 50 60 0 4 8 lag 12 16 20 Fig 3. Actual demand of Group 3. Fig 5. ACFs of the residuals from the decomposition model for Group 3. 274 ScienceAsia 27 (2001) Fig 4, while those of Groups 2 and 3 are similar. It can be seen from Fig. 4 that the ACFs of all lags are within the upper and lower limits, meaning that the errors are random. However, the ACF of lag 1 in Fig. 5 exceeds the upper limit. This indicates that auto-correlations do exist in the errors and that the errors are not random. From the ACFs, we can conclude that the decomposition model is adequate for forecasting the demands of Groups 1, 4, 5, 6, 7, and 8, but inadequate for forecasting those of Groups 2 and 3. Therefore, the ARIMA model is applied to Groups 2 and 3. From the original time series of the demand of Group 3 (in Fig. 3), and the ACFs of its original series (in Fig. ), it can be interpreted that the original series has a trend, and a high value of ACF of lag 12 indicates the existence of seasonality. 2 Hence, a non-seasonal first-difference to remove the trend and a seasonal first-difference to remove the strong seasonal spikes in the ACFs are tested. Fig. 7 shows the ACFs of the ARIMA (p,1,q)(P,1,Q) 12 model afte r applying the first difference. The nonseasonal plot indicates that there is an exponential decay and one significant ACF of lag 2. Thus, the AR(1) and MA(1) process denoted by ARIMA (1,1,1)(0,1,0)12 is identified. The ACFs of the residuals after applying this ARIMA model shown in Fig. reveals that there is a high value of ACF of lag 12. Therefore, the AR(1) and MA(1) process for the seasonal part or ARIMA (1,1,1)(1,1,1)12 can be identified. The ACFs of the residuals generated from this model are shown in Fig. 9. Since all ACFs are within the two significant limits, the ARIMA (1,1,1)(1,1,1)12 model is adequate. Using the Statgraphic program, the model coefficients can be determined. The demand forecast for Group 3 is presented in Eq. 1. Ft = 1. 197 X t ? 1 ? 0. 197 X t ? 2 + 0. 54408 X t ? 12 ? 0. 65126 X t ? 13 + 0. 10718 X t ? 14 + 0. 45592 X t ? 24 ? 0. 54574 X t ? 25 + 0. 08982 X t ? 26 ? 1. 6699et ? 1 ? 0. 7154et ? 12 + 0. 76332et ? 13 + 29. 34781 (1) where Ft is the demand fo recast for period t Xt is the actual demand for period t et is the forecast error for period t Similarly, the forecasting model for Group 2 is ARIMA (3,0,0)(3,0,0). 12 The demand forecast of Group 2 is presented in Eq. 2. Estimated Autocorrelations for Original Series 1 Estimated Residual ACF 1 0. 5 0. 5 coefficient coefficient 0 0 -0. 5 -0. 5 -1 0 5 10 lag 15 20 25 -1 0 5 10 lag 15 20 25 Fig 6. ACFs of the actual demand for Group 3. Fig 8. ACFs of the residuals of ARIMA (1,1,1)(0,1,0)12 model for Group 3. Estimated Residual ACF 1 Estimated Autocorrelations for 1 Nonseasonal Differences 1 Seasonal Differences 1 0. 5 0. 5 coefficient coefficient 0 0 -0. 5 -0. 5 -1 0 5 10 lag 15 20 25 -1 0 5 10 lag 15 20 25 Fig 7. ACFs after first differencing for Group 3. Fig 9. ACFs of the residuals of ARIMA (1,1,1)(1,1,1)12 model for Group 3. ScienceAsia 27 (2001) 275 Ft = 0. 36951X t? 1 + 0. 30695X t? 2 – 0. 18213X t? 3 + 0. 20132 X t? 12 ? 0. 07439 X t? 13 ? 0. 06180 X 14 + 0. 03667 X t? 15 ? 0. 03325X t? 24 + 0. 01228 X t? 25 + 0. 01021X t? 26 ? 0. 00606 X t? 27 + 0. 68660 X t? 36 ? 0. 25371X t? 37 ? 0. 21075X t? 38 + 0. 12505X t? 39 + 354. 4515 2) The forecast errors of the decomposition and ARIMA models for Groups 2 and 3 are presented in Table 2. It reveals that the ARIMA model has lower Table 2. Forecast errors of the decomposition and ARIMA models. MSE Products Group 2 Group 3 Decomposition ARIMA 4,363,290 2,227,592 3,112,974 1,235,788 MAPE (%) Decomposition ARIMA 31. 86 15. 97 29. 05 13. 18 MSE and MAPE than t he decomposition model. Therefore, the ARIMA model should be used to forecast the aggregate demands of Groups 2 and 3. For other product groups, however, the decomposition model should be used because it is more simple yet still adequate. The comparison of the demand forecast errors obtained from the forecasting models and those from the current practice of the marketing department (as presented in Table 3) indicates that the errors of the forecasting models are substantially lower than those of the current practice. Demand Forecasts of Individual Products The demand forecast of product i for period t, dit, is obtained by multiplying the aggregate demand forecast of the product group (obtained from the previous steps) by the corresponding product mix (as presented in Table 4). Table 3. Forecast errors of the current practice, decomposition, and ARIMA models. MSE Product Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Group 7 Group 8 Current practice Decomposition 16,672,342 4,394,693 4,988,962 4,754,572 19,787,102 795,621 849,420 1,060,301 9,879,330 4,507,990 10,039,690 574,108 636,755 883,811 ARIMA 3,112,974 1,235,788 MAPE (%) Current practice Decomposition 30. 58 34. 68 23. 50 25. 73 17. 54 42. 70 38. 36 37. 93 26. 97 23. 24 13. 14 34. 80 34. 45 28. 76 ARIMA 29. 05 13. 18 – Table 4. Product mix. Product group Product 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 0. 17 0. 20 0. 26 0. 23 0. 14 1. 0 0. 53 0. 47 0. 65 0. 35 1. 0 1. 0 1. 0. 3 0. 7 2 3 4 5 6 7 8 276 ScienceAsia 27 (2001) Calculation of Safety Stock The safety stocks of finished products must be provided to protect against stock-out problems due to inaccurate demand forecasts. Based on the forecast errors obtained from the demand forecasting models, the amount of the safety stock is calculated using the following formula. 12 SSit = sf * ? j * ? ij (3) PRODUCTION PLANNING METHO D The production planning model is developed by initially defining decision variables and parameters, and then mathematically formulating the production planning model. Step 4 of the method requires that the model parameters be estimated and entered into the model. The model is solved for the optimal solution (Step 5). Step 6 recommends that the model parameters are updated, and the model is solved again after one planning period has passed. The production planning problem of the factory under consideration belongs to the class of multistage, multi-item, capacitated production planning model. The models in this class have been discussed extensively in. 6-11 They differ in assumptions, objectives, constraints, and solution methods. Our production planning model is a modification of the multi-stage, multi-product model discussed in Johnson and Montgomery. 6 Its objective is to minimize the total overtime and inventory holding costs. Costs of laying off and rehiring are not considered because laying off and rehiring are not allowed according to the labor union regulation. Since the production cost is time-invariant and all demands must be satisfied, the regular time production cost is thus not included in the objective function. Relevant parameters and decision variables are defined as follows: Parameters : hik = Holding cost per unit of product i at stage k (baht/unit/period) co = Cost per man-hour of overtime labor (baht/man-hour) dit = Demand forecast of product i for period t (units) aik = Processing time for one unit of product i at stage k (hours/unit) (rm)kt = Total available regular time excluding preventive maintenance and festival days at stage k for period t (man-hours) (om)kt = Total available overtime excluding preventive maintenance and festival days at stage k for period t (man-hours) W = Warehouse capacity (units) SSit = Safety stock of product i for period t (units) Iik0 = Initial inventory of product i at stage k (units) N = Total number of products (15 products) T = Total number of periods in the planning horizon (12 periods) K = Total number of stages (5 stages) where SSit = Required safety stock level of product i for period t sf = Safety factor = 1. 64 for a required service level of 95 % of the standard normal distribution ? j = Standard deviation of forecast errors of Group j. ?ij = Product mix of Product i in Group j. Since the errors of the recommended demand forecasting models are lower than those of the current practice, it is clear that SSit based on the use of the models must be lower than that determined from the current practice (assuming that the service levels of both cases are the same). Table 5 presents the required safety stocks of the current practice and the recommended forecasting models at 95 % service level. Table 5. Required safety stock of current practice and of recommended forecasting models. Safety stock (units) Product 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Current practice 1,138 1,339 1,741 1,540 937 3,438 1,941 1,722 2,324 1,252 7,295 1,463 1,511 507 1,182 Recommended forecasting models 887 1,043 1,356 1,200 730 2,905 979 868 2,274 1,224 5,258 1,245 1,323 460 1,072 ScienceAsia 27 (2001) 277 Decision variables: Xikt = Quantity of product i to be produced at stage k in period t (units) Iikt = Inventory of product i at stage k at the end of period t (units) Rkt = Regular time used at stage k during period t (man-hours) Okt = Overtime used at stage k during period t (man-hours) LP model: Minimize Z = ? ? ? hik Iikt + ? ? co Okt , (4) i =1 k =1 t =1 k =1 t =1 N K T K T Eq. 7 represents the material balance constraint in Stage 3, which produces the body of threecomponent products, for Products 13, 14, and 15. Constraint (13) must be included since the finished products are very bulky and require significant warehouse space that is quite limited. Work-inprocess inventory does not require significant storage space because it can be stacked. The non-negativity constraint (16) ensures that shortages of work-inprocess inventory do not occur. Input Parameters The initial inventory of product i at stage k, Iik0, is collected from real data of work-in-process or finished good inventories on the factory floor at the beginning of the planning horizon. The inventory holding cost of product i at stage k, hik, is estimated by assuming that the annual inventory holding cost is 25% of the cost per unit of the product at the respective production stage. Since the cost per unit is constant over the planning horizon, the annual inventory holding cost is time-invariant. The factory has enough space in the warehouse to store not more than 40,000 units of finished products. The total available regular time, (rm)kt, is estimated based on the fact that the factory is normally operated 16 hours a day and six days a week, and the total available overtime, (om)kt, is calculated by assuming that the overtime could not be more than six hours a day. The overtime cost, co, is assumed to be constant throughout the planning horizon, and is estimated to be 60 Baht per man-hour. After all related parameters have been estimated and entered into the planning model, the optimal values of all decision variables are calculated using the LINGO software. The computation time takes less than one minute on a Pentium PC. Results of the Production Planning Models with Different Levels of Safety Stock In this section, two production planning models with different safety stock levels (as shown in Table 5) are solved to determine the total cost savings when the recommended forecasting models are applied in place of the current practice. The inventory holding, overtime, and total costs of both models are presented in Table 6. Based on the optimal total cost of the current practice (4,078,746 Baht per year) and the optimal total cost of the recommended forecasting models (3,541,772 Baht per year), the total cost saving is 536,974 Baht per year, or 13. 2 %. It can be also seen Subject to – Finished product requirement constraints I i 5,t? 1 + X i 5t ? I i 5t = dit – ? i, t ; k = 5, (5) Material balance between stages constraints ? i, t ; k = 4, (6) (7) ? i, t ; k = 2, (8) ? i, t ; k = 1, (9) I i 4 ,t? 1 + X i 4 t ? I i 4 t = X i 5t I i 3,t? 1 + X i 3t ? I i 3t = X i 4 t ?t ; i = 13, 14, 15; k = 3, I i 2,t? 1 + X i 2t ? I i 2t = X i 4 t I i1,t? 1 + X i1t ? I i1t = X i 2t Capacity constraints ? aik X ikt ? Rkt + Okt i= 1 N ?k , t , (10) – Available regular and overtime constraints. Rkt ? (rm) kt Okt ? ( om) kt ?k , t , ? k , t , (11) (12) – Inventory capacity of finished product constraints. ? I ikt ? W i= 1 N ?t ; k = 5, (13) – Safety stock of finished product constraints. I ikt ? SS it ?i, t ; k = 5, (14) – Non-negativity conditions X ikt ? 0 I ikt ? 0 ?i, k , t , ? i, t ; k = 1, 2, 3, 4 (15) (16) 278 ScienceAsia 27 (2001) Table 6. Comparison of the optimal costs of production planning models. Optimal costs (Baht/year) Model based on the current practice Inventory holding cost Overtime cost Total cost 2,117,051 1,961,695 4,078,746 Model based on recommended forecasting models 1,775,552 1,766,220 3,541,772 REFERENCES 1. Nahmias S (1993) Production and Operations Analysis, 2nd ed, Irwin, New York. 2. Vandaele W (1983) Applied Time Series and Box-Jenkins Models, Academic Press, New York. 3. Winters PR (1960) Forecasting Sales by Exponentially Weighted Moving Average. Management Science 6(4), 324-42. 4. Box GE and Jenkins GM (1970) Time Series Analysis, Forecasting, and Control, Holden-Day, San Francisco. 5. Makridakis S Wheelwright SC and McGee VE (1983) Forecasting Methods and Applications, 2nd ed, John Wiley Sons, New York. 6. Johnson LA and Montgomery DC (1974) Operations Research in Production Planning, Scheduling, and Inventory Control, John Wiley Sons, New York. 7. Bullington P McClain J and Thomas J (1983) Mathematical Programming Approaches to Capacity Constrained MRP Systems: Review, Formulation, and Problem Reduction. Management Science 29(10). 8. Gabbay H (1979) Multi-Stage Production Planning. Management Science 25(11), 1138-48. 9. Zahorik A Thomas J and Trigeiro W (1984) Network Programming Models for Production Scheduling in MultiStage, Multi-Item Capacitated Systems. Management Science 30(3), 308-25. 10. Lanzanuer V (1970) Production and Employment Scheduling in Multi-Stage Production Systems. Naval Research Logistics Quarterly 17(2), 193-8. 11. Schwarz LB (ed) (1981) Multi-level Production and Inventory Control Systems: Theory and Practice, North-Holland, New York. 12. Tersine RJ (1994) Principles of Inventory and Materials Management, 4th ed, Prentice Hall, New Jersey. that the optimal inventory holding cost and overtime cost in the production planning model based on the recommended forecasting models are almost equal which indicates that the model can efficiently achieve a tradeoff between both costs. Normally, the optimal decisions in the first planning period will be implemented. After the first period has passed, the new forecasts will be determined, and the model parameters will be updated. The updated model is solved again to determine the optimal decisions in the current period. This is called a rolling horizon concept. However, the details and results of this step are not shown in this paper. DISCUSSION AND CONCLUSION The ARIMA model provides more reliable demand forecasts but it is more complicated to apply than the decomposition model. Therefore the ARIMA model should be used only when the decomposition model is inadequate. When compared against those of the current practice of the company, the errors of our selected models are considerably lower. This situation can lead to substantial reductions in safety stocks. Consequently, the lower safety stocks result in decreased inventory holding and overtime costs. The results of the production planning model are of great value to the company since the model can determine the optimal overtime work, production quantities, and inventory levels that yield the optimal total overtime and holding costs. The production planning method is more suitable than the existing one that does not consider any cost factors. Moreover, it has been proven that an application of appropriate forecasting techniques can reduce total inventory holding and overtime costs significantly. In conclusion, this paper demonstrates that an improvement in demand forecasting and production planning can be achieved by replacing subjective and intuitive judgments by the systematic methods. 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Bovine Spongiform Encephalopathy Essay Example For Students

Bovine Spongiform Encephalopathy Essay Bovine spongiform encephalopathy (BSE) is a relatively new disease foundprimarily in cattle. This disease of the bovine breed was first seen in theUnited Kingdom in November 1986 by histopathological examination of affectedbrains (Kimberlin, 1993) . From the first discovery in 1986 to 1990 thisdisease developed into a large-scale epidemic in most of the United Kingdom,with very serious economic consequences (Moore, 1996). BSE primarily occurs in adult cattle of both male and female genders. The most common age at which cows may be affected is between the ages of fourand five (Blowey, 1991). Due to the fact that BSE is a neurological disease, itis characterized by many distinct symptoms: changes in mental state mad-cow,abnormalities of posture, movement, and sensation (Hunter, 1993). The durationof the clinical disease varies with each case, but most commonly lasts forseveral weeks. BSE continues to progress and is usually considered fatal(Blowey, 1991). After extensive research, the pathology of BSE was finally determined. Microscopic lesions in the central nervous system that consist of a bilaterallysymmetrical, non-inflammatory vacuolation of neuronal perikarya and grey-matterneuropil was the scientists overall conclusion (Stadthalle, 1993). Theselesions are consistent with the diseases of the more common scrapie family. Without further investigation, the conclusion was made that BSE was a new memberof the scrapie family (Westgarth, 1994). Transmission of BSE is rather common throughout the cattle industry. After the incubation period of one to two years, experimental transmission wasfound possible by the injection of brain homogenates from clinical cases(Swanson, 1990). This only confirmed that BSE is caused by a scrapie-likeinfectious agent. How does the transmission become so readily available among the entireUnited Kingdom feedlot population? Studies showed that the mode of infectionwas meat and bone meal that had been incorporated into concentrated feedstuffsas a protein-rich supplement (Glausiusz, 1996). It is thought that the outbreakwas started by a scrapie infection of cattle, but the subsequent course of theepidemic was driven by the recycling of infected cattle material within thecattle population (Lyall, 1996). Although the average rate of infection is verylow, the reason why this led to such a large number of BSE cases is that much ofthe United Kingdom dairy cattle population was exposed for many, continuousyears (Kimberlin, 1993). To help control the outbreak, the British government in 1988 introduceda ban on the feeding of ruminant protein to other ruminant animals (Lacey, 1995). Such knowledge for the pathogenesis of the BSE disease shows precisely theactions that must be taken in order to control and minimize the risk ofinfection in healthy cattle around the world (Darnton, 1996). The appearance of BSE has made a sizable impact throughout much of theworld even though few countries, other than the United Kingdom, have experiencedpositive cases (Burton, 1996). The scare of an outbreak in other countries hasled to a great disruption in the trade economy, as well as other factorsconcerning each of the countrys general welfare. However, a rapid increase inthe understanding of the disease over the last four years leaves few unansweredquestions of major importance (Masood, 1996). BSE has been prevented,controlled and eradicated. As mentioned, BSE was first recognized in the United Kingdom and it isonly there that a large-scale epidemic has occurred (Burton, 1996). By the endof 1990 well over 20,000 cases of BSE had been has been confirmed in England,Scotland, and Wales (Filders, 1990). The deadly epidemic started simultaneouslyin several parts of the country and cases have been distributed over a wide areaever since (Cowell, 1996). Besides the United Kingdom, cases of BSE have occurred in the Republicof Ireland. Some of these cases were associated with the importation of liveanimals, meat, and bone meal from the United Kingdom (Cherfas, 1990). Two cases of BSE have also occurred in cattle from the country of Oman. These animals were thought to be part of a consignment of fourteen pregnantheifers imported from England in 1985. Various cases have also been confirmedin Europe, Switzerland, and France (Patel, 1996). The economic consequences of BSE in the United Kingdom have beenconsiderable. At the beginning, the only losses due to BSE were those directlyassociated with the death or slaughter of BSE infected animals (Cowell, 1996). In August 1988, a slaughter policy with part compensation was introduced to helplessen the burden on individual farmers. As the number of BSE cases increased ,and more farmers were experiencing a second case, full compensation wasintroduced in February 1990 (Moore, 1996). In 1989 alone over 8,000 suspectedand confirmed cases of BSE were slaughtered. The compensation costs for theyear were well over 2.8 million pounds and the slaughter costs amounted to 1.6million pounds (Cockburn, 1996). Kristallnacht EssayUnfortunately, the slaughter of the great majority of affected animalsbecomes necessary at an early stage because of unmanageable behavior and injuryfrom repeated falling and uncontrollable behavior (Cowell, 1996). The durationof the clinical disease, from the earliest signs to death or slaughter, canrange from under two weeks to as long as a year. The average period is aboutone to two months (Lyall, 1996). BSE resembles other members of the scrapie family in not having anygross pathological lesions associated with disease. Characteristichistopathological changes are found in the nervous system (Kimberlin, 1993). Incommon with the other diseases in the scrapie family, BSE has a distinctive non-inflammatory pathology with three main features: -The most important diagnostic lesion is the presence of bilaterally symmetrical neuronal vacuolation, in processes and in soma. -Hypertrophy of astrocytes often accompanies vacuolation. -Cerebral amyloidosis is an inconstant histopathological feature of the scrapie family of diseases. At times, only one of the above will occur in an infected animal, while moreoften a combination of the three will occur (Swanson, 1990). Unfortunately, there are no routine laboratory diagnostic tests toidentify infected cattle before the onset of clinical disease. The diagnosis ofBSE therefore depends on the recognition of clinical signs and confirmation byhistological examination of the central nervous system (Westgarth, 1994). Aclinical diagnosis can also be confirmed by simple electron microscopeobservations, biochemical detection of SAF, or the constituent protein PrP(Hunter, 1996). At present, vaccination is not an appropriate way of preventing any ofthe diseases in the scrapie family. There is no known protective immuneresponse to infection for a vaccine to enhance (Blowey, 1991). However, BSE isobviously not a highly contagious disease and it can be prevented by othersimple means because the epidemiology is also relatively simple: -Restrictions on trade in live cattle -Restrictions on trade in meat and bone meal -Sterilization of meat and bone meal -Restricted use of meat and bone meal -Minimizing exposure of the human population -Minimizing the exposure of other species (Moore, 1996)A great deal of concern, much of it avoidable, has been expressed overthe possible public health consequences of BSE. This is understanding giventhat the scrapie family of diseases include some that affect human beings (Patel,1996). As a result of research, the circumstances in which BSE might pose arisk to public health can be defined quite precisely, and simple measures havebee n devised to prevent this risk (Kimberlin, 1993). It is important toemphasize that any primary human exposure would still be across a speciesbarrier and there would be no recycling of food-born infection in the humanpopulation, as happened with kuru and with BSE in cattle (Patel, 1996). Thelogical way to address this risk is to make sure that exposure to BSE is kept toa bare minimum. There are two scenarios for the future course of BSE. The first is thatBSE, like TME and kuru, is a dead-end disease. If this is true and meat andbone meal was the sole source of the infection, then removing this source wouldbe sufficient for the eventual eradification of BSE from the United Kingdom(Hager, 1996). The alternative scenario is that there are natural routes oftransmission of BSE and that the outbreak could turn into an endemic infectionof cattle the way scrapie is in sheep (Burton, 1996). To sustain BSE infectionin the cattle population requires that each breeding cow is replaced by at leastone infected female calf, which then transmits infection to at least one of heroffspring. For BSE to become an endemic, the number of infected cattle wouldneed to increase by horizontal spread as seen in scrapie (Masood, 1996). Theessential prerequisite for controlling such a deadly disease is through goodbreeding and movement records which are currently being compiled in the UnitedKi ngdom following recent legislation (Stadthalle, 1993). Meanwhile theprecautionary measures to safeguard other species, including human beings, arealready in place and refined to meet todays needs. Category: Science

Frees Black Success through Hard Work or Af Essay Example For Students

Frees: Black Success through Hard Work or Af Essay firmative Action? affirmative argumentative persuasive Riches through Hard Work or Affirmative Action? In recent years Affirmative Action has become an issue of great interest. Affirmative Action, also known as Preferential Hiring, which was devised to create harmony between the different races and sexes, has divided the lines even more. Supporters on both sides seem fixed in their positions and often refuse to listen to the other groups platform. In this essay, the recipients of preferential hiring will be either black or female, and the position in question will be a professorship on the university level. The hirings in question are cases that involve several candidates, all roughly equal in their qualifications (including experience, education, people skills, etc. ), with the only difference being race and/or sex. What we have here is a case of predetermined preference. The two candidates in question are equal in all ways, except race. The black applicant is selected, not because of skills or qualifications (in that case the white man would have provided the same result), but for his skin color. This seems to be blatant discrimination, but many believe it is justified. Some feel retribution for years of discrimination is reason enough, but that issue will be discussed later. First, lets focus on why this is not a solution to creating an unbiased society. Martin Luther King Jr. had a dream: I have a dream that my four little children will one day live in a nation where they will not be judged by the color of their skin, but by the content of their character. He desired a world without discrimination, without prejudice, and without stereotypes. The fundamental lesson years of discrimination should have taught is that to give anyone preference based on skin color, sex, or religious beliefs is, in one word, wrong. As Martin Luther King Jr. stated, judgment based on skin color must not exist. All preferential hiring does is keep judgments based on skin color alive. Race and sex should not be issues in todays society, yet preferential hiring continues to make these factors issues by treating minorities as a group rather than as individuals. More importantly preferential hiring may actually fuel, rather than extinguish, feelings of racial hostility. Applying the concept of preferential hiring to another situation may help elucidate its shortcomings. A party of white men and a party of black men both arrive at a restaurant at the same time and only one table is free. The headwaiter can only seat one party and must make a decision. According to preferential hiring theory it is necessary to seat the black party first, since historically blacks have been discriminated against when seated in restaurants. In another situation, a white man and a black man are both equidistant from the last seat on the bus. Both men are the same age, have no medical problems, and are equal in all ways except skin color. Should the black man get the seat since in the past black men have been discriminated against? We could continue this practice for several centuries before the debt we owe for depriving blacks of a seat on the bus would be paid. Perhaps these examples are invalid. It could be said that jobs are a different issue. They help define social status and provide economic well-being. They might even boost self-confidence, something that discrimination has stolen. Two points must be considered before moving any further. First, blacks may learn better from a black, and women may learn better from a woman. Second, hiring women and blacks will provide role models for others. .u6a2cfcbc52d70268a081e0a6bc904892 , .u6a2cfcbc52d70268a081e0a6bc904892 .postImageUrl , .u6a2cfcbc52d70268a081e0a6bc904892 .centered-text-area { min-height: 80px; position: relative; } .u6a2cfcbc52d70268a081e0a6bc904892 , .u6a2cfcbc52d70268a081e0a6bc904892:hover , .u6a2cfcbc52d70268a081e0a6bc904892:visited , .u6a2cfcbc52d70268a081e0a6bc904892:active { border:0!important; } .u6a2cfcbc52d70268a081e0a6bc904892 .clearfix:after { content: ""; display: table; clear: both; } .u6a2cfcbc52d70268a081e0a6bc904892 { display: block; transition: background-color 250ms; webkit-transition: background-color 250ms; width: 100%; opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #95A5A6; } .u6a2cfcbc52d70268a081e0a6bc904892:active , .u6a2cfcbc52d70268a081e0a6bc904892:hover { opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #2C3E50; } .u6a2cfcbc52d70268a081e0a6bc904892 .centered-text-area { width: 100%; position: relative ; } .u6a2cfcbc52d70268a081e0a6bc904892 .ctaText { border-bottom: 0 solid #fff; color: #2980B9; font-size: 16px; font-weight: bold; margin: 0; padding: 0; text-decoration: underline; } .u6a2cfcbc52d70268a081e0a6bc904892 .postTitle { color: #FFFFFF; font-size: 16px; font-weight: 600; margin: 0; padding: 0; width: 100%; } .u6a2cfcbc52d70268a081e0a6bc904892 .ctaButton { background-color: #7F8C8D!important; color: #2980B9; border: none; border-radius: 3px; box-shadow: none; font-size: 14px; font-weight: bold; line-height: 26px; moz-border-radius: 3px; text-align: center; text-decoration: none; text-shadow: none; width: 80px; min-height: 80px; background: url(https://artscolumbia.org/wp-content/plugins/intelly-related-posts/assets/images/simple-arrow.png)no-repeat; position: absolute; right: 0; top: 0; } .u6a2cfcbc52d70268a081e0a6bc904892:hover .ctaButton { background-color: #34495E!important; } .u6a2cfcbc52d70268a081e0a6bc904892 .centered-text { display: table; height: 80px; padding-left : 18px; top: 0; } .u6a2cfcbc52d70268a081e0a6bc904892 .u6a2cfcbc52d70268a081e0a6bc904892-content { display: table-cell; margin: 0; padding: 0; padding-right: 108px; position: relative; vertical-align: middle; width: 100%; } .u6a2cfcbc52d70268a081e0a6bc904892:after { content: ""; display: block; clear: both; } READ: Organization Need People or People Need Organization Essay The first point Thomson quickly concedes as likely to be false. Discussion about the second point however is required, and will, in effect, serve to negate the first point as well. First, lets create a character, Bill. Bill is grossly overweight and unattractive. Studies have shown that many employers discriminate (whether subconsciously or not), against both overweight and unattractive individuals. Unfortunately for Bill, he fits into both categories. His inability to land a job reflective of his abilities, coupled with years of public humiliation through jokes made at his expense, has destroyed his self-esteem. This has caused .

Bossa Nova free essay sample

Nova was created in Brazil in the late sass during a period of economical growth and political change, the boost nova has been often described as the music of the Brazilian middle and upper classes. This music style started In the upper class regions along the beaches of the city of Roll De Jeanine and both Its music and lyrics were composed by middle and upper-class musicians and marketed to the same economic group. For this reason, boost nova was criticized by some for emphasizing a carefree way of living that little resembled the life of mostBrazilian, the great majority of which belonged to the working class (3). Indeed, boost nova songs often spoke of love, the beach, and beautiful women and seemed to be a reflection of the authors casual life rather than a story of Brazilian daily life and struggles as usually happened with the samba musical style, a music genre popular among the working class. We will write a custom essay sample on Bossa Nova or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page The Girl from Panama, which became popular outside of Brazil both in its original Portuguese form and in translation, Is a perfect example of the uncommitted quality of boost nova songs. (l)The Girl from Panama Is nothing more than the singers description of a woman walking down towards the beach, the nice way In which she moves and sways, how attractive she Is, finishing with the singers statement that shes the most beautiful thing hes ever seen go by. The musical style of the music on the Boost Nova CD can be related to what we as Americans listen to as lounge music. It is very soothing, carefree and pleasurable. Through most of the CD the piano, string bass, flute, snare drum (played with a light brush) and female singing voice make up most of the function of the CD.It is very orchestral composition, rising and flowing. It follows the European style harmonies of the major and minor keys some are more involved than others. For example The Girl from Panama follows a simple almost predictable riff throughout, not until % of the way Into the song does the sax player deviate from the riff and builds upon to give the song depth. But the song flows nicely and smoothly. Most of the CD Is made up of verse and refrain type melodies. The rhythm follows a nice beat mainly around 60-90 BPML depending on the song.Very easy to dance to with a partner In the style f slow dancing with a little flair added in. I believe the track Wave can speak as an example of the CD the pleasant piano and brushed drum with the muted horn. This tracks adds some strings to the back layer to expound on the depth. This song although does not have any lyrics, but you could almost sing your own lyric about pleasure or beauty with the song. I believe the song gives the listener a sense of laid back relaxation. Just enough of an upbeat to keep the listener interested but not overpowering to take over the foreground of a setting. Also as I stated In a previous paragraph The Girl from Panama also represents the CD well. Its simple riff, yet a clam and flowing melody keeps the listener Interested. The lyrics, sung by both a man and woman flow Just as the melody, following each beat and upturn In the music. I think this adds to the songs appeal and constant memory by the listener (l know I am still singing it in my head).I have enjoyed this CD for exactly the reasons I have explained above. The pleasurable and calming feeling I have while listening to it. The piano, brushed rum, horns and strings remind me of a softer style of the American big band musical style. But the boost nova style feels softer and calming. I have used that reference a couple times throughout this paper, but I do believe that is at the heart of this musical style. The feeling of pleasure and carefreenews culminates in a calming feeling.I feel as if I am sitting along a boulevard, relaxing and taking in the environment at its fullest and warmest. Work Cited (1) http://www. Assortment. Com/all/whiteboards_r]be. Tm (2) http://www. Mallet]jazz. Com/lessons/boost. HTML (3) http://www. Brazil. Mom/cavorted. Tm {this is a great boost nova reference full history and personal bios} The musical style of the Boost Nova was created in Brazil in the late sass during a period of economical growth and political change, the boost nova has been often described as the music of the Brazilian middle and upper classes.This music style started in the upper class regions along the beaches of the city of ROI De Jeanine and both its music and lyrics were composed by middle and upper-class musicians and marketed to the same economic group. For this reason, boost nova was criticized by some for emphasizing a carefree way of living that little resembled the life of most Brazilian, the great majority of which belonged to the working class (3). Translation, is a perfect example of the uncommitted quality of boost nova songs. (l) The Girl from Panama is nothing more than the singers description of a woman walking down towards the beach, the nice way in which she moves and sways, how attractive she is, finishi ng with the singers statement that shes the most beautiful Girl from Panama follows a simple almost predictable riff throughout, not until % of he way into the song does the sax player deviate from the riff and builds upon to 60-90 BPML depending on the song.

Review Of Jobless Turn To Work Helping Others

Review Of Jobless Turn To Work Helping Others Review of "Jobless Turn to Work Helping Others" In a perfect economic situation nobody would face unemployment, but we don't live in a perfect world. In an ever-changing economy, there are good times; and then there are not so good times. If I learned anything about economics from this article, it would be to make the most of the good times; and do what you can in the less than good times. This article describes two twenty-something workers who found high paying jobs when the economy was booming in the late 1990's, and then lost those jobs in recent corporate layoffs. All was not lost because they found jobs where business is still booming.Michael Cagan, age 27, was working for the well known brokerage company Charles Schwab when he got the pink slip last September. Around the same time Hattie Washington, age 26, lost her job as an administrative assistant at TeleCheck International.English: The Frances Perkins Building of the U.S. ...In December they were both hired at Money Managem ent, a non-profit firm that provides nationwide credit counseling. Now they teach others what they had to learn about managing their finances. Cagan had a mortgage payment of $522 and a new salary, which was 40% less than what he was making. Washington had credit card debt of $3,000; she enrolled in Money Management's debt reduction program herself in the hopes of one-day qualifying for a home loan.According to U.S. Department of Labor statistics, about 1.15 workers ages 16 to 24 lost their jobs when employment for that age group was peaked at 20.8 million jobs in December 2000. Over about the same time period the workers age 25 and over suffered a loss of 1.5 million jobs after hitting a peak of 115.3 million jobs in March of 2001. It seems that younger workers have it...

Artificial Demand Creation Essay Example | Topics and Well Written Essays - 1000 words

Artificial Demand Creation - Essay Example In advanced and affluent societies because the intrinsic demands of the consumers have been already met, there is an artificial demand which is generated by the corporate houses. This is done in a race to out do each other and sells their goods which are mainly consumer oriented products; however this statement of Galbraith goes against the theory of consumer demand and behavior. This paper initially examines the classical theory of consumer demand and the intrinsic demands of consumers. It explains the dependence effect as given by Galbraith in his book The Affluent Society and then later looks into the reasons why Galbraith rejected the theory of consumer behavior for affluent markets. Classical Theory of Consumer Demand The theory of consumer demand is defined as the analysis of demand with regard to consumer behavior. The theory predicts the demand under varying factors, such as price, income, and substitute goods. Opportunities and preferences are the two major components of consumer demand. The opportunity is, what the consumer can afford keeping the budget constraint and the preference is, what and how much the consumer likes a product keeping the utility constraint. The classical theory of consumer demand justifies the production of any goods or services on the premise that demand for these goods or services exist. It also goes on to explain that these demands or needs are not limited and their urgency does not appreciably diminish with satisfaction of these demands. The statement that the demands do not become less urgent even when they are satisfied may itself be in contrast to the common sensibilities of many, but it is true because these demands are not the basic physical demands, but these are derived demands which we inherit from the society that we live in and they continuously grow. The Dependence Effect John Kenneth Galbraith (1958) in his book The Affluent Society explains that the needs or demands of consumers in the developed markets are being created by the process that satisfies these demands. It is a known fact that the psychological needs take over, after the physical or the intrinsic needs of food and shelter are satisfied. These psychological needs are also the basis of all the other development which takes place in our societies. The classical theory suggests that the urgency of demands fuel production, however the demands of a sports car or fast food cannot be urgent as they are not the basic demands of an individual. These demands are the derived demands and have been created by the process of over production itself. Advertising and salesmanship act as a catalyst to this entire process and ensure demand creation to dizzying levels. Hence the urgency of demand cannot be used to defend the case of over production. The Dependence Effect therefore is the direct relationship between the production of goods and the demand which is generated for these very goods. In the affluent societies as the basic demands are mostly met the consumer is open to persuasion. The consumer falls prey to a large variety of goods that are made available to him, all of varying quality. As the production of these goods increases so does the expenditure on demand generation. The major methods that are employed for demand generation are advertising

Critical analysis worksheet Essay Example | Topics and Well Written Essays - 500 words

Critical analysis worksheet - Essay Example Article 2: It is ridiculous to say that money does not influence politics these days. The argument that the number of people who believe that money can change the course of politics is comparable to the few people who believe in global warming is not a solid argument. The former Federal Elections Commission chairman Bradley Smith talks about evidence, yet he does not give any. Democracy was founded on the basis that every person had the right to choose government, yet the vast influx of money has completely eroded this process. Campaigns have taken on such importance that they are all politicians ever seem to do, instead of focusing on real policies that will better the country. Article 1: The first question I would ask would be: Are these guest appearances initiated by the guest or the network. The second question would be: Are guests paid for their appearances, and if so is there a budget for this? Article 1: I do agree with the main premise of the reading because I know that big corporate have the money and political influence to be able to secure guest appearances on these cable networks. I have seen it happen many times where a guest commentator refuses to consider the other side of the argument, so they obviously have some sort of bias. Article 2: I do agree that money now seems to control politics in a way like never before. Just last year I saw hundreds of political ads on TV. These ads must have cost millions of dollars, but the reason they were shown so much is because lobbyists wanted to get their candidate elected. Article 1: I think that we should discuss the emergence of these lobby groups and global corporate that are able to influence the voting public so much. Democracy should be about freedom of opinion, but sometimes it seems like we only get to hear one point of view, and it may not necessarily be the best point of view. Article 2: I think that we should discuss how