In the final project, you have just been selected to be the Operations Manager at a large phone manufacturing company, Phones4U. You have only been on the job for six days when you are tasked, by the
M5A2: Final Project
Executive Summary
This assignment is our second milestone, we are continuing the analysis for the phone recommended on the prior part, which is phone B, the forecast available in this report shows the actual sales and forecasted sales for the next three years which are higher than the other options. Some of the recommendations from this analysis is to stop the manufacturing of phone D and focus on the manufacturing and speed and other features for phone B which will increase the desire from customers and increase total sales. On the survey presented we see the probability of purchase is high compared to the other options, is 20%, and we can recommend to hike the manufacturing of this option (phone B) to create more sales for the next 3 years as forecasted. We used the exponential smoothing technique as one of the methods for forecasting since we are using a long-range forecast (over two years).
Introduction
This assignment is our second milestone and the goals are to continue with our analysis from the past report and forecast various operational parameters for our chosen phone option, in this case Phone B. We will be using three different forecasting techniques; the naïve, moving average, and exponential smoothing. In addition, we are evaluating trends in time series data to provide support to our recommendations.
Forecast Methodology
Good predictions of demands and trends are a necessity nowadays, managers have to deal with seasonality, various changes in demand levels, dealing with competition, and most of all, the world’s economy. Forecasting is a tool to help them deal with these; the kind of forecasting method to use depends on several factors, including the time frame of the forecast, in this case 3 years; the behavior of the demand, and the possible existence of patterns such as seasonality and the causes of demand behavior. We will be using a long-range forecast, which usually encompasses a period of time longer than two years. The demand of a product, in this case a phone, behaves in random ways, I would assume it has a seasonality, as a gift during Christmas, birthdays, Father’s and Mother’s Day, etc. Also presents in a cycle, when the phone just comes out for example, is usually when it has a higher demand, the new thing that everyone has to have. The methods that we are using are a time series method, which are the naïve method, the moving average method and the exponential smoothing.
The naïve method “predicts that the next value will be like the last realized value.” (Cachon and Terwiesch, pg. 497, 2017). Simple as it sounds, this method is the easiest one to use but the downside is that ignores all the other older data which it could be use to balance the next forecast.
The Moving average method “uses several demand values during the recent past to develop a forecast… the simple moving average is useful for forecasting demand that is stable and does not display any pronounced demand behavior such a trend or seasonal pattern.” (Russell and Taylor, pg. 507, 2014). As the description analyzes, we do count with a seasonal pattern for our product so this one might not be the correct method to use.
The Exponential Smoothing method is “also an averaging method that weights the most recent data more strongly.” (Russell and Taylor, pg. 511, 2014). This forecast will help us with the most recent changes on demand for the phone. This method is one of the most popular to use technique for several reasons, requires minimal data, we need the forecast for the current period, the actual demand for the current period and a weight factor. This method has a good track record for success; hence, the reason is very popular.
Key Methodology Assumptions
The preparation of this forecasting report is to foresee the sales of the B phone, are these sales projections reasonable? Over-estimating our sales forecasts could result in financial disaster. And the assumptions made is this case are based on the past sales, the biggest selling point/appeal to most phones are their cost and key features. In the purchase intent survey, we see that out of a 22500-population size we have a 20% probability of purchase, and the numbers for the prior year sales are high and somewhat constant in comparison to phone D which it was my original pick. Phones with great features would attract many customers hence more purchases which in this case is what is happening, the affordability range is high, but the features are not too good on the scale but the cost will make up for it since it has a good parameter score.
Results
From the forecast prepared in my opinion the B phone is a good option to manufacture and create continuous sales. See the chart available, it has good affordability rate, not so good on features, but it has color options, durability, the cost is not great but the quality is high and the speed is medium score. I think overall is a good option to manufacture and to create more sales as predicted for the next 3 years as you can see on the next chart.
Recommendations
As a proposal I will like to add a possibility of creating more available features to phone B and also up the speed, that perhaps will increase the percentage of the survey and sales. Another idea was to drop the manufacturing of phone D, the survey sales is low, the actual sales on prior years is low, I think is a positive measure to stop their manufacturing. I am positive that if we stop the manufacturing of phone D and focus our efforts on phone B we can hike the sales for phone B and increase their speed and manufacturing process maybe cutting the time at least by 20 percent if we focus on 3 phones instead of 4 which shows that the 4th option is not making enough sales to pursue.
References
Cachon, G., & Terwiesch, C. (2017). Operations Management (1st ed.). New York, NY: McGraw-Hill.
Russell, R. S., & Taylor, B. W. (2014). Operations and supply chain management. (8th ed.). Hoboken, NJ: Wiley.
