27 Nov OM Simulation Descriptions and Implementation Tips. After completing the simulation, capture a screen image of your final simulation results including the rubric evaluation metrics (i.e
- carefully review the simulation's introductory information and instructions, as well as the information in the OM Simulation Descriptions and Implementation Tips. After completing the simulation, capture a screen image of your final simulation results including the rubric evaluation metrics (i.e. MAPE), which are to be included in your Critical Thinking Assignment.
- The Operations Management Forecasting content, paper or presentation option, must include the following sections:
- 3.1 Introduction: Explain the purpose or thesis of the paper and explain how the body of the paper is arranged to support the purpose of the paper.
- 3.2 Provide a brief yet substantive definition of operations management forecasting and identify why it is important in an organization's operations.
- 3.3 Provide a brief overview of the Forecasting Simulation including the targeted goals of the simulation.
- 3.4 Describe specifics about the model or approach used as the basis for your strategy in performing the Forecasting Simulation; in an appendix, include an illustrated (worked-out) example of a formula, calculation, or technique developed as a central part of your Forecasting Simulation strategy. In approach, typically you will describe how you used combination of quantitative and qualitative methods
- 3.5 Describe at least three operations management forecasting methods, principles, or techniques experienced in the Forecasting Simulation. Clearly describe hypothesis/rationale as to why you chose those methods and how exactly you used them in the simulation. Do not describe methods/principles/techniques that you did not use in the simulation.
- 3.6 Clearly describe your simulation results and indicate how well they met the targeted simulation goals.
- 3.7 Itemize at least three lessons learned from the Forecasting Simulation and describe how this understanding is important for a career in operations management. Here you can describe lessons such as (but not limited to only these) any surprises, how you accommodated seasonal variability, how you can improve MAPE further, what method was effective, was there any cause and effect relationship you observed, were your hypothesis worked the way you thought, etc.
- 3.8 Conclusion should present a recap of key points and summary of main emphasis without repeating verbatim and exclusive of new information.
- Your written Operations Management Forecasting paper must contain the sections outlined in the instructions.
- Don't forget to include a screenshot of your final simulation results.
Submit your Critical Thinking Assignment document(s) in the submission area established for this purpose. Per the assignment rubric, a portion of your evaluation is based on your simulation results
· It should be 3-4 pages in length not including the required cover and references pages.
· It should be with at least 2 peer-reviewed or professionally published sources Use current sources, not older than 5 years.
· Format: APA guidelines
Operations Management Forecasting
Holly Crosley
Colorado State University Global
OPS510: Operations Management
Dr. Parimal Kopardekar
November 28, 2021
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Operations Management Forecasting
Forecasting is taking historical data to make future predictive assumptions. Managers can
use forecasting to estimate orders for future goods and services. Accurate forecasts help
businesses reduce expenditures for raw materials. Proper scheduling of staff is an additional
benefit. (Heizer, et al., 2020) It can be considered an artistic science (Trevidi, 2017).
Makridakis, et al. (2020) make it clear when forecasting there is no way for anyone to know with
absolute certainty a predictive outcome. Over the years many different forecasting methods and
techniques have been developed and tested. Regardless of which method or technique is
employed, the analyst should remember past results are not necessarily an indicator of future
events (Seroney, et al., 2019).
Many events can affect the efficiency, quality, profitability, and customer experience
adversely. Supply chain interruptions can be due to supply, demand, transport, volatility of the
market, and political unrest. The petroleum industry is a large and complex industry where quick
and accurate forecasting is incredibly important lends itself to demand forecasting. (Seroney, et
al., 2019)
PetroPlex is a fictional company in a simulation from Pearson as part of the Operations
Management book by Heizer, Render, and Munson (2020). The simulation scenario results will
be discussed as they relate to forecasting. There were different forecasting methods and
techniques utilized to make the forecast. Following the simulation, upon reflection, lessons
learned were applied to the author’s analysis.
Forecasting Simulation
The forecasting simulation was 24 month simulation testing demand predictions for
gasoline. The premise of the simulation was:
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You have taken up the role of an Operations Consultant and just signed a 2 year contract
with a new client, PetroPlex, which is a gas station in your area. As part of your role, you
will provide monthly forecasts for PetroPlex to match customer demand. PetroPlex sells
3 types of gas – Regular, Midgrade, and Premium. You will need to provide accurate
forecasts of the demand for the three types of gasoline at the beginning of each month.
Your performance will be based on the collective Mean Average Percentage Error
(MAPE) of the three types of gas. The final MAPE value should be less than 25%.
The simulation began with emails, texts, and voice mail messages from the owner and
subject matter experts setting up the scenario and giving background data about historical
gasoline sales. An expert in gasoline sales forecasting offers his service 3 times over the course
of 24 months. The simulation requires a decision to be made about volume of gasoline for the
next month of sales. Every month following a new email or text would come in and every few
months, the owner would leave a voice mail with either praise or reprimands.
In performing the simulation forecasts, I utilized a hybrid approach. The demand forecast
with naïve forecasting utilizing trend projections and sales force composite. In order to make my
monthly forecasts, I looked at the monthly communication from the SMEs and analyzed the data
they suggested. The messages varied from local information, national information, and global
information. All three had different ramifications in regards to the forecast for that month.
Sometimes the information complemented the trend data and sometimes it was in direct contrast.
The demand trend data was the second item I looked at when considering my monthly forecast.
The table showed the monthly demand from the previous 12 months. This historical data was
helpful to see the purchasing trends of consumers. The other item I looked at and considered was
the small trend picture that was located on the main page of the simulation. Finally, I had to
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consider whether to use the expert or not. All of this data was reviewed to make a decision for
the monthly forecast (see Appendix B).
Operational Management Forecasting Methods
A demand forecast is one of three main forecasts, along with economic and technological.
Demand forecasts focus on using existing data to quickly respond to what the customer wants
and needs. (Heizer, et al., 2020) The PetroPlex simulation offered lots of historical data and
relevant current event data. And, since the customer request was for a monthly decision in order
to make a quick demand forecast.
A common forecasting model is the time series model. A time series model focuses on the
historic data and trends from that data. (Heizer, et al., 2020) The time series model is the best
model for this PetroPlex simulation based on the plethora of trend data.
One of the qualitative methods used in this simulation was similar to a sales force
composite. A sales force composite is when sales personnel make their projections for a certain
time frame and those projections are combined for one larger forecast. (Heizer, et al., 2020) In
the simulation, it can be presumed all the PetroPlex gas stations send their demand data to
corporate so the complete demand trend can be computed for the total historical trend.
Simulation Results
I completed three total simulations. The first simulation did not go well. My ending
MAPE was around 30%. As I looked at the summary, this was due to a typo around month 12. I
entered 95000 when the forecast was supposed to be 9500. While this was an unfortunate
typographical error, it was a lesson where it could have actual negative consequences if it had
happened in the real world. Thankfully, this was a simulation and the worst thing that happened
is I lost the contract (which would also have happened in real life). It was a great reminder of the
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importance of double-checking data. In this two-year simulation, the remainder of the contract
(about 12 more months) was not enough to overcome the error.
The second simulation performed went well (MAPE was just barely over 10%), but no
bonus was obtained. I remembered to use the expert in this simulation, but the recommendation
was not what I thought it should be based on the email received from the beginning of the month.
I did not use the suggestion and was more correct in my forecast that the expert would have been
in his.
The third time was best, and the bonus was achieved. I did not use the expert in this
simulation since my forecasts were looking accurate across all three gasoline grades (see
Appendix A). Using the historical data predictions and the subject matter experts (SMEs)
enabled me to determine an accurate forecast for PetroPlex. I achieved the bonus by obtaining a
MAPE of 7.58% (see Appendix C).
Lessons Learned
Lessons learned are captured when the positive and the negative experiences are
documented (Project Management Institute, 2017). In the PetroPlex simulation, there were many
lessons learned. There were 3 main lessons that allowed me to be successful in the final
simulation.
The first was making sure to pay attention to the monthly communications from the
SMEs. They directed attention to what was happening globally with regards to the petroleum
industry. While the SMEs provided some general information, it was up to me to determine what
to do with that information.
Second lesson learned was to continually refer back to the past year’s trends. The
monthly demand trend chart was sent in an email communication at the beginning of the
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simulation. This was helpful to see what months the demand was higher and when it would
wane.
Remembering and considering the Expert was the third lesson learned. In my second
simulation, I remembered to ask the expert, but his recommendation was solely based on the
demand trend from the previous year. He did not take the SME email message into his
prediction. I chose not to use his recommendation for that month’s forecast. His recommendation
also made me question whether any of his other forecasts would be accurate and correct, so I
chose not to use him. On my third simulation I was able to perform the entire simulation without
help from the expert. If I had questions or was second guessing my forecasts, I would have asked
him.
Conclusion
“Good forecasts are an essential part of efficient service and manufacturing operations.”
(Heizer, et al., 2020) Forecasting is an important step in Operations Management to make
accurate and effective financial decisions for businesses. The science of forecasting is the
techniques used, while the application of the data is the art of forecasting (Trivedi, 2017). There
are many different methods and techniques to use to forecast data. Managers need to make a
decision on which to apply to come to a conclusion relatively quickly. (Heizer, et al., 2020) The
PetroPlex simulation showed how the naïve forecasting method can work in a real-world
situation, by taking news and historical data to make a current forecast about gasoline usage.
Documenting lessons learned after forecasting is a good best demonstrated practice to assist the
manager and additional stakeholders for future forecasting.
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References
Heizer, J., Render, B., & Munson, C. (2020). Operations management: Sustainability and supply
chain management (13th ed.). Pearson Education, Inc.
Makridakis, S., Hyndman, R. J., & Petropoulos, F. (2020). Forecasting in social settings: The
state of the art. International Journal of Forecasting, 36(1), 15-28.
PETROPLEX, Heizer, J., Render, B., & Munson, C. (2020). Operations management:
Sustainability and supply chain management (13th ed.). Pearson Education, Inc.
Project Management Institute. (2017). A guide to the Project Management Body of Knowledge
(PMBOK guide) (6th ed.). Project Management Institute.
Seroney, J.K., Wanyoike, D.M., Langat, E.K. (2019). Influence of Demand Forecasting on
Supply Chain Performance of Petroleum Marketing Companies in Nakuru County,
Kenya. THE INTERNATIONAL JOURNAL OF BUSINESS MANAGEMENT AND
TECHNOLOGY.
Trivedi, B. (2017, May 3). Demand Forecasting: The Art and Science That Keeps You
Guessing. Arkieva. https://blog.arkieva.com/demand-forecasting-art-science/
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Appendix A
Screen capture from the Pearson OMLab PetroPlex Simulation which shows the
forecasted versus the actual demand for each of the gasoline grades (regular, midgrade, and
premium).
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Appendix B
The decision process for making the demand forecast for the PetroPlex Simulation
The screenshots were taken directly from the PetroPlex simulation.
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Appendix C
Overall MAPE (%) results from PetroPlex Simulation showing previous and current
MAPE (%) from screenshot of Summary on PetroPlex simulation on Pearson MyOMLab.
