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The RA model uses absolute (i.e. total) workload performed in 1 week (acute workload) relative to the 4-week chronic workload (i.e. 4-week average acute workload). This model suggests that each workload in an acute and chronic period is equal. Thus, the RA model considers the relationship between load and injury as linear, and therefore, all workload in a given time period is seen as equivalent.
The most common type of rain in Bangkok is light rain, which accounts for 57% of total rain throughout the year. On average, we experience an average of 20.1 days of rain alone each month in Bangkok. Light rain is usually characterized by small drops of water that falls to the ground in a short period of time (20 seconds or less), without much increase in the rate of intensity. Rain rates are generally at or less than 1 mm/hr. During a rainfall event, we often hear rain drops striking a roof, or the sound of water dripping from the gutter, or the sound of running water. Rain is generally horizontal and unobstructed, and is often accompanied by thunder or lightning. In the rain gauge, the term rain is typically defined by a 10-minute rainfall rate or an accumulation of 10 mm. While there are many rainfall models that exist, the definition of a rain event is generally based on the 10-minute rainfall rate. Also, in using the rain gauge, it is generally recommended that a human collector is used to make observations, as this ensures that the gauge is not disturbed or contaminated, and that the observations are made at approximately the same time of day each day. The RA model uses weekly workload in an acute and chronic period to calculate acute to chronic workload ratio. This model considers the relationship between load and injury as linear, and therefore, all workload in a given time period is seen as equivalent. This model does not account for any decay in fitness, nor does it accurately represent variations in the manner in which loads are accumulated. A potential solution to these limitations of the RA model potentially lie with the EWMA model [13, 14].
The boss has assumed a linear model, a common choice, which says that temperature, cloud cover, and precipitation will have a linear effect on demand. The spreadsheet model’s coefficient values are the same as those that are estimated using the data from the training period.
The boss now must decide how much he is willing to pay for those extra units. He knows that in order to satisfy the demand, the company will have to produce 5,000 units. He also knows that for those 5,000 units, he will have to set aside 5,000 units as spoilage and 1,000 units as airfreight. The boss uses the spreadsheet model to identify the best risk-adjusted expected value for the demand forecast and the inventory forecast.
The boss then selects the number of units that he is willing to pay for a miss. The spreadsheet model will produce a coefficient that represents the amount by which the demand is forecast to fall short of the demand. This coefficient is subtracted from the original coefficient, and the difference is multiplied by the number of units that the company is willing to pay for a miss. This quantity is then subtracted from the cost associated with spoilage and airfreight, and those two costs are added back to the cost associated with a hit. The boss then uses this information to calculate expected profit. 827ec27edc