Description
You are to create a plan to forecast production output, not future sales. The key is to provide details on how you want the staff to determine the needed future production output for Kibby and Strand in months. You need to explicitly describe the data and quantitative methods you want the staff to use to derive and apply for this forecast plan. Be sure you include any trends or seasonality of the data in the plan. The plan should include the forecasting objectives. Identify at least two advantages and two disadvantages of the chosen forecasting method. Also, identify problems we may encounter by collecting the necessary data for the forecast. This plan will be utilized by the receiving department to ensure we can accommodate the raw material, the production department to ensure we have the resources needed, and to provide shipping with an idea of its workload in future months.
Your target audience for the forecasting plan is the manager of each department. Other departments such as HR and Marketing will also use the plan to inform their decision process.———————————
The document should be prepared consistent with the APA writing style and reflect higher-level cognitive processing (analysis, synthesis, and or evaluation).
consider yourself to be the Production Manager of Kibby and Strand, the company in the scenario.
Create a forecasting plan to forecast production output for Kibby and Strand. The plan should include forecasting objectives, the data to be used in forecasting, and the quantitative methods the staff is to use in creating the production output forecast.
The data you use should be historical production output data, and account for seasonality since the textiles Kibby and Strand produces will change with the seasons. After you determine the data you plan to use, then pick a quantitative method from Chapter 3 of the textbook to turn the data into a forecast value for production output. Recommend you forecast output by the month or quarter
You’re not going to calculate an actual forecast value because you do not have historical data. The purpose of the forecasting plan is to provide direction to the staff so they can create the forecast
Additional Instructions:
Structure of Forecasting Plan
Objective of Plan
Your plan should state the objective is to forecast future production output, and provide details on the data to be used and quantitative method that will turn the data into a forecast. You are creating a forecasting plan to forecast production output, not future sales. There is a difference. Output is what we actually produce and sales is what we hope to produce. Sometimes outages, weather, etc. prevent us from producing what we have contacted to produce.
Historical Data to Use in the Forecast
Since K&S is a seasonal business we have to consider this in the data we select. The best data to use is production output data from quarters of previous years. You may ask why not use the past 6 months, it is more recent? The reason we do not use the past 6 months is because that is Spring and Summer. The textiles we produce during those quarters are much different than the textiles produced in the Fall and Winter quarters. By selecting historical data from previous Fall quarters, we are incorporating seasonality into the forecast to ensure we’re using the most reliable data. Recommend you use at least 3 years of historical data.
Quantitative Method to Create the Forecast
We need to select a quantitative forecasting method from chapter 3 that will turn the data into a forecast.
2. Conduct Research – 3 to 5 peer reviewed sources. The textbook is a peer reviewed source.
Some research questions are:
How to formulate forecasting objective?
What data should I consider using for the forecast?
What quantitative forecasting models should be considered?
Conduct research on what type of data works best for what you want to forecast. Look for articles on forecasting production output. Also, look at the textbook and other articles for how to integrate seasonality into the forecasting plan.
Conduct research on quantitative methods for preparing a forecast. Chapter 3 of the textbook has all the different methods you should need, but you are free to look at other sources. In your research also look for how many data points a particular quantitative method requires so you can state that in the plan.
Look on Google Scholar first to see if you can find good peer reviewed journal articles. If you go to Google Scholar and search “Forecasting textile production” you will get some good hits with free articles. Here’s one: https://www.iksadjournal.com/index.php/iksad/article/view/177/173Links to an external site.
You can download the pdf for free and it has some good information. Especially the five-step forecasting process on page 604.
3. Write the Plan
Use 3rd person, active voice, for writing directives.
Use bold text or underlining to create section headings for the three sections.
You are writing a plan the staff can execute to create a forecast of future production output. It is not possible for the staff to execute
Example of format attached :
Unformatted Attachment Preview
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Kibby and Strand’s Forecasting Plan
Luke Moore
Park University
MBA576DLS1P2024 Operations Management
Dr. Bill Gaught, Ed.D
January 17, 2024
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Kibby and Strand’s Forecasting Plan
Objective
This document aims to forecast productivity output for Kibby and Strand, which is
critical to achieving our growth targets in the upcoming fiscal year. Here at Kibby and Strand,
we are heavily reliant on using historical data to make informed, strategic decisions. Chen and
Lin write, “A high productivity is critical to maintaining a competitive edge in the industry,
which contributes to the sustainability of a factory [13, 29]. For this reason, the productivity of a
factory needs to be evaluated and enhanced. In addition, it is also necessary to forecast the future
productivity of a factory and take actions, such as moving the factory to another region with a
lower wage level [4] or switching to a less expensive supplier [30], to elevate productivity.”
(2021) In order to properly forecast productivity, we must assume that our demand forecasting is
accurate, and that the previous metrics will be closely replicated in the future. Additionally, it is
critical that every department accurately track our metrics day to day.
Forecasting productivity is seldom 100% accurate. This is due to a multitude of
contributing factors that can alter the potential output. Chen and Lin write, “Forecasting the
future productivity of a factory is a challenging task, because productivity is subject to much
uncertainty caused by unstable product yield [24, 28], changing workforce [32], etc.” (2021).
Therefore, this document will assume that sales data will be consistent from previous years,
staffing will not be a constraint, and there will be no shortages in raw materials. However, with
reasonable due diligence, we at Kibby and Strand can predict the potential performance in the
upcoming year with a +/- 10% degree of accuracy.
Historical Data
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Understanding consumer trends are essential for adequate forecasting. Similar to adjacent
industries, clothing products will ebb and flow based on the time of the year. Oh et al. write,
“Seasonal changes affect consumers’ recognition of the need for clothing. This recognition
results in the creation of information search activities to solve the problem. The purpose of an
information search by a consumer before a purchase is either to enhance the quality of the
purchase outcome or to give pleasure, and heavy searchers spend over twice as much money at
the same time as do light searchers among clothing consumers in a brick-and-mortar store (Bloch
et al., 1986).” (2021). By understanding these phenomena, we can anchor our models to a
relatively fixed set of seasonal behavior. However, end-of-year sales can be influenced by
additional variables that skew the results of the organization. Therefore, we will use a five-year
time horizon in order to nullify the potential for outlier years (Stevenson, 2021). Additionally,
this will allow proper aggregation of the key metrics we hope to predict which are: Production by
clothing type, materials purchased, productivity by season, contracts completed, and sales figures
by clothing type.
Quantitative Forecasting Methods
As discussed in the previous section, the company must establish a reasonable time
horizon to properly assume the output for the upcoming year. One way to incorporate the
seasonal variation would be to incorporate a time-series analysis. Alqatawna et al. write, “Timeseries analysis is a powerful technique used to analyze data patterns and trends and make
predictions based on historical data within a specific time period. Widely employed in business,
economics, and finance, this approach enables forecasting market trends, analyzing financial
data, and predicting future demand.” (2023). This graph represents the seasonal variation of
output.
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Given the fluctuating nature of the company’s sales cycles, one critical forecasting
method Kibby and Strand will use will be a moving average. Stevenson writes, “A moving
average forecast uses a number of the most recent actual data values in generating a forecast.”
(2021). Below is a sample line graph, where the bold lines represent the actual output by product
and the translucent blue line represents the “smoothed out” trend of the product.
Lastly, here at Kibby and Strand, we continually strive to produce the most accurate
forecast data, leveraging all productivity metrics available. The final quantitative analysis the
company will implement will be the “trend and seasonality corrected exponential smoothing”,
also known as “Winter’s Model” (Rakićević and Vujošević, 2015). Similar to the method used
above, this analysis model determines the variation between the studied season and the season
preceding or following. Rakićević and Vujošević write, “Seasonal variations are regular changes
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in the time series, up or down, related to the events that are repeated (e.g. summer or winter
season, holiday season). A seasonal characteristic of variables can be identified based on
increased or decreased values of demand in a certain period of year.” (2015). By leveraging this
analysis model, the company can assign productivity targets by season in order to understand
future performance.
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References
Alqatawna, A., Abu-Salih, B., Obeid, N., & Almiani, M. (2023). Incorporating time-series
forecasting techniques to predict logistics companies’ staffing needs and order volume.
Computation, 11(7), 141. https://doi.org/10.3390/computation11070141
Chen, T., & Lin, Y.-C. (2021). Enhancing the accuracy and precision of forecasting the
productivity of a factory: A fuzzified feedforward neural network approach. Complex &
Intelligent Systems, 7(5), 2317–2327. https://doi.org/10.1007/s40747-021-00416-8
Oh, J., Jo, Y., & Ha, K. (2021). The effect of anomalous weather on the seasonal clothing market
in new york. Meteorological Applications, 28(2). https://doi.org/10.1002/met.1982
Rakicevic, Z., & Vujosevic, M. (2015). Focus forecasting in supply chain: The case study of fast
moving consumer goods company in serbia. Serbian Journal of Management, 10(1), 3–
17. https://doi.org/10.5937/sjm10-7075
Stevenson, W. J. (2021). Operations management (14th ed.). McGraw Hill.
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