Operations Management Forecasting Holly Crosley Colorado Sta

Operations Management Forecastingholly Crosleycolorado State Universit

Operations Management Forecasting Holly Crosley Colorado State University Global OPS510: Operations Management Dr. Parimal Kopardekar November 28, 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: 3 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 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 5 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 6 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.â (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.

References

• Heizer, J., Render, B., & Munson, C. (2020). Operations management: Sustainability and supply chain management (13th ed.). Pearson Education.
• 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.
• 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.