ENG7160 Operations Management Critical Review Assignment Sample : Blackberry Hill Farm
Here’s the ENG7160 Operations Management Critical Review Assignment Sample on Blackberry hills farm written by industry expert.
1. Issues faced by blackberry hill farm
The current business issues faced by Blackberry Hill Farm are that the farm has to be turned into a theme park if the number of visitors increases any more than they already have. The revenue that is collected on the farm is from the revenues from the tourists who visit the farms. As the number of tourists is increasing, the parking space for their cars has to be increased but that will not increase the revenue collected from them as there is already a problem for parking cars during the peak hours for the farm. The owner, Jim wants to keep the farm as a farm and not to turn it into a theme park.
As the farm is expanding more with every new attraction, the maintaining portion of the things is becoming too complex. It is becoming impossible for the co-owners to run the business of the whole park and they are varying themselves thin. This ever-widening spreading of the farm is leaving less to no time for thinking about new directions or to talk to their staff.
As the farm is increasing, it has become tough to maintain the employees as well. Earlier, there was around eighty staff working for the farm.
2. Stable forecasting model
The reason why Blackberry Hill Farm should make a forecasting model is because of the issues that the farm is facing already. They have space for parking the cars of the tourists but they are not enough for the number of cars that come during peak hours. As they have recently started giving tours to the visitors on tractors, it has become costly. Building more car parking space for the increasing number of tourists will be expensive for the farm to bare. They are not able to complete the sales of their recent new farming way due to weather and that costs them a lot of money. Weather is a factor that cannot be controlled or predicted. If it’s raining on the days of the sale, the farm incurs a loss. Bringing in new farm animals for the petting zoo, also, needs a lot of money for taking care of the animals. They need staff to care for those animals. It will be a problem to build a new expansion of this area due to constraints. If the farm’s café and the shop are expanded, they will be able to provide more service to the customers who need to wait in queue during the peak hours (Lim et al., 2020).
3. Forecasting Models
The types of models for forecasting that are available now are huge and always increasing. They range from being fundamental to being very complex in their form. An understanding of the models that are advanced can be made with the help of some study that is extensive, few of the models that are basic can be given by many techniques (Liu et al., 2020). The one that I have chosen is the Quantitative Models and from there I have taken the following:
Forecasting of the Time Series –The time series mode or method of forecasting which are done on the basis of historical data solely. These types of models are used a lot in business scenarios where there is a requirement of making forecasts for a year or so. This sort of a method is basically suited to the sales, marketing, finance, planning of production, etc. They have an advantage because they are simple relatively. This type of forecasting is a way for predicting the events with the help of time. (Karasu et al., 2020).
- This forecasting method is best suited for a forecasting that is short-term (i.e., less than a year).
- This relies on the availability of data that are sufficient from the past and the data represents a quality that is high and is well represented.
- This method is best for situations that is stable relatively. Where the fluctuations are common and substantial and the hidden conditions are always subject to changes that are extreme, then these methods may result poorly.
Some of the examples are:
- Forecast of the yield of potato in tons per state in a year
- Forecast of the rate of unemployment per state in a year
- Forecast of the rate of birth in every city hospital in a year
Basic Forecasting Steps:
A task of Time Series Forecasting, generally, has five steps.
- Defining the problem
- Information gathering
- Analysis done preliminarily
- Models for choosing and fitting
- Use and evaluation of a model for forecasting
Several techniques that are statistical are there for the forecast by time series, however, we found some effective ones. There are as follows:
Simple Moving Average (SMA)
Exponential Smoothing (SES)
Autoregressive Integration Moving Average (ARIMA)
Neutral Network (NN)
The other methods of time series forecasting are as follows:
Projection of Trend – This forecasting method uses the trend that are long-term of the data for the time-series for forecasting the values in the future.
Method of the components of the time and season – This uses the components that are seasonal of the time series and the component of trend.
Method that is casual – This uses the relationship that is cause-and-effect between the forecasting of the variables of which the values are future and the other factors or the values. The method that is common widely is the analysis of the regression, a technique that is statistical is used for developing a model that is mathematical that shows the way the variable set is related.
In several applications that are modern, forecasting of time series are using the techniques with the help of computers, includes:
- Learning via machine
- Networks of artificial neural
- Machines via support vector
- Logic that are fuzzy
- Processes that are gaussian
- Models that are hidden markov
Models used for Time series Forecasting:
Figure 1: Source (Fong)
Interpreting the Forecast Data
Values – The line that has been lined up as the values are just a representation in a graphical format of the reviewed data. The axis X represents the time with a gap of one year. The axis Y represents the value range (Fong et al., 2020).
Bound of the Upper Confidence – This shows that about ninety-five percent of the values in the future will be lower than or will be in the range.
Bound of the Lower Confidence – This shows that ninety-five percent of the future values are going to be more than or above the range.
Combined, the confidence that is upper and the lower tends to find out the explanation of the finding that around ninety-five percent of all the values that are in the future will be lying between the highest and the lowest limits which get carved out by this forecast (Wang et al., 2020).
Forecast – Generally, a collection or the average of the confidence of the bounds that are upper or lower. It takes into consideration the highest and the lowest extremes to be coming up in a line that may fit the best (Elsheikh et al., 2020).
Elsheikh, A.H., Saba, A.I., Abd Elaziz, M., Lu, S., Shanmugan, S., Muthuramalingam, T., Kumar, R., Mosleh, A.O., Essa, F.A. and Shehabeldeen, T.A., 2020. Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia. Process Safety and Environmental Protection.
Fong, S.J., Li, G., Dey, N., Crespo, R.G. and Herrera-Viedma, E., 2020. Finding an accurate early forecasting model from small dataset: A case of 2019-ncov novel coronavirus outbreak. arXiv preprint arXiv:2003.10776.
Karasu, S., Altan, A., Bekiros, S. and Ahmad, W., 2020. A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series. Energy, 212, p.118750.
Lim, J.Y., Safder, U., How, B.S., Ifaei, P. and Yoo, C.K., 2020. Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model. Applied Energy, p.116302.
Liu, H., Song, W., Li, M., Kudreyko, A. and Zio, E., 2020. Fractional Lévy stable motion: Finite difference iterative forecasting model. Chaos, Solitons & Fractals, 133, p.109632.
Liu, Z., Jiang, P., Zhang, L. and Niu, X., 2020. A combined forecasting model for time series: Application to short-term wind speed forecasting. Applied Energy, 259, p.114137.
Sun, X., Liu, M. and Sima, Z., 2020. A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters, 32, p.101084.
Wang, J., Yang, W., Du, P. and Niu, T., 2020. Outlier-robust hybrid electricity price forecasting model for electricity market management. Journal of Cleaner Production, 249, p.119318.
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