MGT-530: Operation Management

CT-1 (Operation Management)

Forecasting (100 points)

Read through the Case Study entitled “Highline Financial Services, Ltd.” in Chapter 3 of your textbook (ATTACHED BELOW). Examine the historical trends this company has experienced for the three products (A, B, C) discussed over the 2 years shown.

Address the following requirements:

  • Prepare demand forecasts for the next four quarters for all three products, describe the forecasting method you chose and explain why that forecasting method is best suited to the scenario.
  • Explain why you did, or did not, choose the same forecasting method for each product.
  • What are the benefits of using a formalized approach to forecasting these products?

Directions:

  • Your essay is required to be four to five pages in length, which does not include the title page and reference pages, which are never a part of the content minimum requirements.
  • Support your submission with course material concepts, principles, and theories from the textbook and at least three scholarly, peer-reviewed journal articles. Use the Saudi Digital Library to find your resources.
  • Use APA style guidelines.

Introduction

Demand forecasting is an essential tool that businesses and organizations rely on to make their decisions. This pivotal tool enables businesses to maintain correct levels in their inventories, but the proper price tags on their products and enhances their revenue generation. This paper aims to look at forecasting by looking at the case of “Highline Financial Services, Ltd” and working to develop a sales forecast for their third year, and discussing the forecasting methods used.  

Demand Forecasting for Products

Services
YearQuarterABC
31 2 3 482 61 122 9590 80 89 5898 83 110 95

The forecasting method used in the scenario is historical trend analysis. Through this approach, we assume that data from the past sales records of the company offers information that indicates possible future trends (Kowal, 2010). This approach was utilized because having a simple extrapolation to determine trends gave the best fit in coming up with a potential future trend.

 The approach was deemed as the most appropriate for this scenario because even though the data provided was minimal, covering only the past two financial years, it formed a basis that could be used to base the predictions. Further, an individual could use the available information and intuition to develop a reasonable demand estimate for the organization’s product/services.  As Soyiri and Reidpath (2012) indicate, it requires information, data, and advanced knowledge.

Justification for the Forecasting Method

Different approaches were used to forecast the future demand of products A, B, and C. The method used for product A differed from the approach used to determine the probable value for products B and C for the four quarters.

 For product A, a linear trend method was used to determine the next potential level of demand. This is based on the fact that, unlike the other product, A registered continual growth when the first year’s sales level was compared with those of the second year throughout all the four quarters.  Although the level fluctuated between quarter one and quarter two, the rest registered an average increase above ten units.  Further, given that the rise in demand from the other three quarters was not uniform, it was essential to get a uniform number by calculating the average increase between all the quarters and using the number obtained to predict the future demand level.

 The formula used to calculate the average demand level for each of the four quarters in year three was degerming the difference between quarter one in year one and quarter one in year two. This was calculated for all the quarters and summed to obtain the average number.  The figure from the third year was thus obtained by adding the average increase to the sales demand experienced in year two.

The linear approach used to forecast this product was utilized based on its ability to show a straight and steady trend in an observed increase between the first two years.  Further, this approach was instrumental in attaining an upward trend that would help the firm predict the demand for its products in the short term.

Product B and C differed in their demand progression when compared to A. While A had an upward trend in their demand, these two products indicated a complex performance pattern. There were certain times when the products had an upward trend and other times that it experienced a negative performance.  For example, for product B, the first two quarters increased the demand by ten units. However, when looking at the last two quarters, there was a drop in demand by eight and 15 units between the first two years. The scenario is also replicated in product C, which had a demand increase in the first quarter with nine units. However, there was a decline in the second quarter with 15 units, and the third quarter had no change in the level of demand while the fourth quarter had an increase of 10 units. It was not easy to determine the trend that the product would follow going into the future by simply looking at the data. I relied on intuitive forecasting to obtain a reliable future forecast, which provided extended capabilities to generate demand forecasts. Therefore, to determine the third year forecast, I took the demand forecast for year one, summed it up with the demand for year two, and used the average of the two as the potential level of demand for year three.  The formula for product b was as follows.

Year 3 quarter one demand = (years one quarter one demand +years two quarter one demand)/2

The formula was replicated for the other quarters and for the product to develop an average prediction of the product demand forecast.

 Further, since the company has a limited historical database to lean on and make the necessary forecast, it is risky to use an average demand increase/decrease to make the prediction. As such, through intuition, I was able to determine that an average of the two years’ demand would offer a better projection. Mas Machuca, Sainz, and Martinez Costa (2014) assert that one can predict future sales through an educated opinion. 

The application of the intuitive approach is prompted by the fact that the data used to project demonstrates a complex pattern. Further, it helps to improve the accuracy of the forecasting as well as the planning process. The firm will therefore be able to eliminate the potential challenge of stockouts in their operations.  Harteis and Gruber (2008) support the need to use intuition, indicating that it is part and parcel of professional experience and has the potential of yielding better data, mainly when it relies on experience.

Benefits of using a Formalized Forecasting Approach

Organizations use forecasting methods as they seek to implement various production strategies.  Through forecasting, organizations use different strategies that enable them to estimate and determine the possibility of their future business outcomes (Bass, 2018). Forecasts are instrumental in helping organizations engage in inaccurate planning processes. A formalized forecasting approach is one of the strategies that an organization can adopt when seeking to identify the firm’s future outlook. Unlike other forecasting methods, this approach is beneficial to the organization since it provides a straightforward approach to use on the computer while also quantifying the provided information.

The approach is less formalized, making it possible for the individual undertaking the forecasting to lean on their intuition. Therefore, when an individual is seeking to undertake a forecast for a tiny endeavor, personal intuition can attract some bias as part of the forecast. However, as the forecast requires grows into a significant problem, an individual or organization can’t rely on this less formalized approach. This is based on the intuition used in the small problem that cannot be relied upon to process large quantities.

Other potential benefits of utilizing a formalized approach when forecasting include offering a wide decision range, helping to reduce a factor of uncertainty, and control over stock levels as a result of better inventory management. Therefore, the company will ensure that their stock levels in the market are in line with the potential demand and, therefore, there are no excesses or shortages. Their decision-making will also be based on well-constructed data that will inform their actions and the strategies adopted. Further, the output provided from such an exercise provides the organization with the necessary input for many decisions.

 In conclusion, different approaches can be used in the process of product forecasts. The size of the forecast required, the data available, and the precision required determine the approach adopted. However, when undertaking simple forecast approaches such as formalized approach, which adopt intuition can be adopted.

References

Bass, B. (2018). Advantages and Disadvantages of Forecasting Methods of Production and Operations Management. Retrieved from https://smallbusiness.chron.com/advantages-disadvantages-forecasting-methods-production-operations-management-19309.html

Harteis, C., Gruber, H. (2008) Intuition and Professional Competence: Intuitive Versus Rational Forecasting of the Stock Market. Vocations and Learning 1, 71–85 (2008). https://doi.org/10.1007/s12186-007-9000-z

 Kowal, J. T (2010). Three Simple Methods for new Product sales Forecasting. Retrieved from https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjSua-9xpXsAhV58eAKHdlrCKQQFjARegQIDRAC&url=https%3A%2F%2Fglobalnpsolutions.com%2Fwp-content%2Fuploads%2F2010%2F11%2Fwhite_paper_13_New_Product_Sales-Forecasting.pdf&usg=AOvVaw1duol4EQf8mPxiXnw8nfwx

Mas Machuca, M.; Sainz, M.; Martinez Costa, C.. (2014). A review of forecasting models for new products Intangible Capital, vol. 10, núm. 1, Enero-Marzo, 2014, pp. 1-25Universitat Politècnica de Catalunya Barcelona, España Retrieved from https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiV8sLz-ZXsAhWNERQKHfDGBasQFjAFegQIAxAC&url=https%3A%2F%2Fwww.redalyc.org%2Fpdf%2F549%2F54930453001.pdf&usg=AOvVaw2LI5uMqOyBlOZoRcwZPot6

Soyiri, I. N., & Reidpath, D. D. (2012). Evolving forecasting classifications and applications in health forecasting. International journal of general medicine, 5, 381–389. https://doi.org/10.2147/IJGM.S31079

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