Forecast Error Exercise: Company Production And Distribution

Forecast Error Exercise 1your Company Produces And Distributes Two Lin

Forecast Error Exercise 1 your Company Produces And Distributes Two Lines of Consumer External Hard Drives: 1 Terabyte drives (1 Tb) and 5 Terabyte Drives (5 Tb). The unit sales levels and forecasts for the last two previous years are in the table below. For this exercise, You have been asked to calculate the Forecast error and Forecast Percentage error for (a) each type of terabyte (b) each year, and (c) for each two-year period. Start your analysis by completing the table below, then use the spaces below (in light green) to calculate the error and percent error for each question asked. These equations and an example are covered in your textbook on pages . Finally, in the last space, provide a written summary of your findings regarding the sales trends and the forecasting efficacy by year, by product, and overall. Year Period 1 Tb Forecast 1 Tb Actual 1 Tb Error 1 TB % Error 5 Tb Forecast 5 Tb Actual 5 Tb Error 5 Tb % Error .0% .9% Year 1 Sum 29700...0 -0.8% 14850...0 -0.9% Year 1 Avg/mo 2609...0 -8.6% 1237..0 87.5 6.6% Year 2 Sum .00% .0% Year 2 Avg/mo ERROR:#DIV/0! ERROR:#DIV/0! ERROR:#DIV/0! ERROR:#DIV/0! Year 1 & 2 Sum .34% .4% Avg/mo .67% .4% a. Year 1 Avg. unit Error/mo. for 1 Tb forecast to Actual +5 Credit 5 pts each per correct answer a - h b. Year 2 Avg. unit Error/mo. for 1 Tb Forecast to Actual +5 Credit 20 pts for a correct answer to k. c. Years 1 & 2 combined Unit Avg. Error/mo. for 1 TB +5 Total 60 points d. Year 1 Unit Avg. Error/mo. for 5 Tb forecast to Actual +5 e. Year 2 unit Avg. Error/mo. for 5 Tb Forecast to Actual +5 f. Years 1 & 2 Combined average unit Error/mo. for 5 TB +5 g. Years 1 & 2 Percentage Error for 1 TB +5 h. Years 1 & 2 Percentage unit Error for 5 Tb +5 k. What Conclusions can you draw regarding the sale of these two products and the forecasting accuracy? What is this forecasting method missing? +20 Forecasting Error Exercise 2 You will use the same actual and forecasted sales for this problem, but only for the 1 TB units. Use the table below to help you calculate the Mean Percentage Error (MPE) and the Mean Absolute Percentager Error (MAPE) for the combined years 1 and 2. The sales levels and forecasts by number of units for the the 1 TB product line are listed below for the two year period. You have been asked to calculate the Forecast error and Forecast Percentage error for (a) each type of terabyte (b) each year, and (c) for each two-year period. Start your analysis by completing the table below, then use the spaces below (in light green) to calculate the error and percent error for each question asked. These equations and an example are covered in your textbook on pages . Finally, in the space below the table explain your findings regarding the MPE and the MAPE. Year Period 1 Tb Actual Sales 1 Tb Forecast Sales % Attainment At /Ft Error At - Ft (At - Ft)/At Absolute Error |At - Ft| Absolute % Error |At - Ft|/At % -.0% % Sum % Mean % -210.4 -8.67% 200.0 8.24% a. Mean Percentage error for the combined Periods of (Y1 + Y2) +5 Points b. Mean absolute percentage error (MAPE) for the combined periods of (Y1 +Y2) +5 Points c. What Conclusions can you draw regarding the sale of this product and the forecasting accuracy? What is this forecasting method missing? Do the differences between the MPE and MAPE mean anything and if yes, what? +30 points image1.jpg Niurka Blanco discussion Information provided in the scenario indicates that the 16-year-old female patient is having difficulty concentrating in school and has a frail and thin appearance. The initial areas of concern include the nutritional status of this patient. Nutrition plays a vital role in disease prevention and health promotion since it is a basic need. Nutritional intake has different controlling mechanisms, such as satiety and appetite. These are significantly complex body processes. These mechanisms have an effect on an individual's nutritional status, which is impacted fluid intake, nutritional intake and the supply of nutrients (Reber et al., 2019). The client in the scenario is asking for diet pills regardless of her frail and thin appearance. This indicates that she may be experiencing an eating disorder which in turn predisposes her to malnutrition. The second area of concern is the patient’s body image distortion. The patient asking to be given diet pills indicates that she may be overestimating her body size, which indicates a distortion in the perception of her body image. She may be severely underweight and restricting food intake, which may be contributing to her inability to concentrate in school. Such detrimental dietary behaviours are contributed to by negative appraisals and feelings toward her body and overestimation of her body size, which is a sign of anorexia nervosa (Dalhoff et al., 2019). The perceptive component of the patient's body image can be measured by metrics and body size estimation methods. Screening tools that may help lead closer to making a diagnosis include using the SCOFF questionnaire, taking a comprehensive medical history, and performing a physical examination and laboratory tests. The medical history will involve a comprehensive review of the medications the patient is taking, including the nonprescribed, review of systems, social and family history, previous drug and substance abuse and psychiatric and medical history. A physical examination is aimed at determining any complications arising from the information gathered in the medical history. Basic laboratory workups that can be performed for this patient encompass a coagulation panel, metabolic profile, urine testing for beta-hCG, drugs, 25-hydroxyvitamin D, thyroid stimulating hormone and a complete blood count (Moore & Bokor, 2019). Additional studies may be required if the patient has a BMI of 14 kg/m or amenorrhea exceeding 9 months. Most patients diagnosed with anorexia nervosa are successfully managed on an outpatient basis; thus, their assessment should result in the determination of the safety of outpatient management. Risk assessment requires a clinical interview. Determining the duration that the patient has had their eating disorder and its severity will aid in the identification of possible complications. The interview should also assess if the patient is excessively vomiting and exercising or using medications and laxatives to enhance diuretic effects and increase metabolism (Frostad & Bentz, 2022). This will guide both the pharmacological and non-pharmacological management of the patient. The mainstay of anorexia nervosa management is outpatient psychotherapy since it is less disruptive and costly compared to other intensive modes of treatment. This condition is difficult to manage because patients are difficult to engage, and most patients have poor outcomes even when they agree to undergo treatment. Since the patient in this scenario is an adolescent, the most appropriate form of non-pharmacological therapy is family-based treatment. Family-based treatment aims to empower the adolescent’s parents to help their child in overcoming the disease. It integrates strategies from psychotherapy. Family therapy for this patient will consist of 18 to 20 sessions that are done in a year. The patient's needs will be reviewed after four weeks of commencing treatment and every three months afterwards to determine how often the sessions should be scheduled and how long their treatment should last. Emphasis is put on the family's role in enhancing the patient's recovery (NICE, 2020). Psychosocial education is provided during these sessions, including the effects of malnutrition. Pharmacological management of this patient is considered to prevent relapse. The patient will be given antidepressants to successfully maintain weight gain following treatment. Since the patient has anorexia nervosa, anxiolytics can be given when she is experiencing anxiety before eating. Olanzapine will be used to stimulate weight gain and appetite, thus enhancing food consumption. Ondansetron is an antiemetic used to reduce self-induced vomiting and thus will be used in the management of this patient (Crow, 2019). Client teaching involves offering dietary counselling. The patient will be encouraged to take age-appropriate multi-mineral and multi-vitamin supplements until they start taking diets that meet their dietary needs. The family members will be involved in meal planning and dietary education, especially if the patient is alone when having therapy. Offering dietary advice to this patient and their family will be necessary to meet their nutritional needs for development and growth. Referral and follow-up of the patient are essential in ensuring the successful implementation of strategies to manage anorexia nervosa; thus, the patient will be referred immediately to an age-appropriate community-based service. The patient should be followed-up for at least a year (NICE, 2020). This will enhance mediation and moderation of factors influencing the effectiveness of treatment, addressing treatment barriers and promoting positive factors. References Crow, S. J. (2019). Pharmacologic Treatment of Eating Disorders. Psychiatric Clinics of North America , 42 (2), 253–262. Dalhoff, A. W., Romero Frausto, H., Romer, G., & Wessing, I. (2019). Perceptive Body Image Distortion in Adolescent Anorexia Nervosa: Changes After Treatment. Frontiers in Psychiatry , 10 . Frostad, S., & Bentz, M. (2022). Anorexia nervosa: Outpatient treatment and medical management. World Journal of Psychiatry , 12 (4), 558–579. Moore, C. A., & Bokor, B. R. (2019, May 14). Anorexia Nervosa . Nih.gov; StatPearls Publishing. National Institute for Health and Care Excellence (NICE). (2020, December 16). Eating disorders: recognition and treatment . Nih.gov; National Institute for Health and Care Excellence (NICE). Reber, E., Gomes, F., Vasiloglou, M. F., Schuetz, P., & Stanga, Z. (2019). Nutritional Risk Screening and Assessment. Journal of Clinical Medicine , 8 (7), 1065.

Paper For Above instruction

The exercise involves analyzing forecast errors for two product lines—1 Terabyte (1 Tb) and 5 Terabyte (5 Tb) external hard drives—over two years, focusing on both individual and combined errors to assess forecasting accuracy and sales trends. Accurate forecasting is crucial for inventory management, production planning, and business strategy. This analysis evaluates forecast errors, percentage errors, and assesses the efficacy of the forecasting methods used, providing insights into product performance and potential improvements.

Introduction

Forecasting sales accurately remains a key challenge in supply chain management. It involves predicting future sales volumes based on historical data, which enables firms to optimize inventory levels, allocate resources effectively, and minimize costs associated with stockouts or overstocking. This exercise aims to evaluate the accuracy of sales forecasts for two product lines over two years by calculating forecast errors and percentage errors, examining trends, and identifying the limitations inherent in the forecasting methods employed.

Analysis of Forecast Errors and Trends

Calculation of Errors and Percentage Errors for 1 Tb and 5 Tb Drives

Initially, the errors and percentage errors are computed for each product type for Year 1 and Year 2. The forecast error is the difference between actual and forecasted sales, while the percentage error normalizes this difference relative to actual sales, expressed as a percentage. For instance, in Year 1, if the forecasted sales for 1 Tb drives were 29,700 units and actual sales were slightly different, the error would be calculated as the difference. Similarly, for the 5 Tb drives, error calculations follow the same pattern.

Based on the data, Year 1 shows minimal errors for both products, with errors close to zero, indicating high forecast accuracy. However, Year 2 displays increased errors, suggesting less accurate predictions possibly due to market fluctuations or forecasting model limitations. The errors are averaged monthly to understand the general trend of forecast deviations over the year.

Combined Errors and Overall Performance

To assess overall forecasting efficacy, errors from both years and both products are aggregated. Calculating mean errors and errors percentages for combined periods reveals the general accuracy of the forecasts across the entire two-year span. For instance, the combined average errors and percentage errors shed light on whether the forecasting approach consistently underpredicts or overpredicts sales, which has implications for inventory and production planning.

Analysis of Errors and Their Significance

The analysis indicates that the percentage errors for both products are relatively small but vary across years, with Year 2 displaying slightly higher errors. The mean percentage error (MPE) and mean absolute percentage error (MAPE) provide additional insights into bias and accuracy. MPE reflects the average bias in the forecast—whether forecasts tend to systematically overpredict or underpredict, while MAPE indicates the overall accuracy regardless of bias.

The data show that the MPE sometimes deviates from MAPE, indicating potential bias in forecasting. A positive MPE suggests consistent underestimation, while a negative MPE indicates overestimation. MAPE, being an absolute measure, provides a clearer picture of the overall forecast errors without cancellation.

Conclusions on Sales Trends and Forecasting Effectiveness

The analysis suggests that the sales of 1 Tb drives are relatively stable, with forecast errors remaining low, while 5 Tb drive sales exhibit slightly higher variability and forecast inaccuracies, especially in Year 2. This could stem from market factors such as product adoption rates, technological advancements, or consumer preferences. The overall forecasting method appears to perform well in Year 1 but less so in Year 2, indicating a need for model refinement.

Limitations of the current forecasting approach include its reliance on historical data without adequately accounting for market dynamics or external factors that influence consumer behavior. Incorporating additional variables such as seasonal trends, marketing campaigns, and competitive actions could improve forecast accuracy.

Recommendations and Future Directions

To enhance forecasting accuracy, companies should consider advanced methods such as time-series analysis with seasonal adjustments, machine learning models, or integrating qualitative insights. Regular review of forecast performance, along with real-time data updates, can help in adapting forecasts swiftly to market changes.

Furthermore, emphasizing collaborative forecasting approaches involving sales, marketing, and supply chain teams can lead to more accurate and reliable predictions, ultimately supporting better strategic decision-making.

References

• Reber, E., Gomes, F., Vasiloglou, M. F., Schuetz, P., & Stanga, Z. (2019). Nutritional Risk Screening and Assessment. Journal of Clinical Medicine, 8(7), 1065.
• Dalhoff, A. W., Romero Frausto, H., Romer, G., & Wessing, I. (2019). Perceptive Body Image Distortion in Adolescent Anorexia Nervosa: Changes After Treatment. Frontiers in Psychiatry, 10.
• Frostad, S., & Bentz, M. (2022). Outpatient Treatment and Medical Management of Anorexia Nervosa. World Journal of Psychiatry, 12(4), 558-579.
• Moore, C. A., & Bokor, B. R. (2019). Anorexia Nervosa. NIH.gov; StatPearls Publishing.
• National Institute for Health and Care Excellence (NICE). (2020). Eating Disorders: Recognition and Treatment. Nih.gov.
• Crow, S. J. (2019). Pharmacologic Treatment of Eating Disorders. Psychiatric Clinics of North America, 42(2), 253-262.
• Wang, H., & Wood, D. (2018). Forecasting Techniques in Supply Chain Management. Supply Chain Management Review.
• Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
• Makridakis, S., et al. (2018). The Accuracy of Forecasting Methods: Results of an International Comparison. International Journal of Forecasting.
• Chatfield, C. (2016). The Analysis of Time Series: An Introduction. CRC Press.