BUS FPX 4014 Assessment 5 Inventory and Ordering Decisions

BUS FPX 4014 Assessment 5 Inventory and Ordering Decisions

Student Name

Capella University

BUS-FPX4014 Operations Management for Competitive Advantage

Prof. Name:

Date

Inventory and Ordering Decisions

The key operations management issue at hand is the need to reduce the average inventory levels of several products to 50 units in the next six months. To achieve this, it is essential to produce a consistent number of units every month. This is accomplished by first determining the total number of units needed for the entire six-month period. Then, we will use the current inventory and the future orders to calculate the number of units required each month.

Question 1: Monthly Aggregate Production Rate (APR)

To calculate the monthly aggregate production rate (APR), we use the following algebraic equation:

[ (D1 + D2 + D3 + D4 + D5 + EI – SI) = APR \times M ]

Where:

• D1, D2, D3, D4, and D5 represent the demand for each of the five months,
• EI is the ending inventory,
• SI is the starting inventory,
• M is the number of months (6 in this case).

Substituting the provided values:

[ (240 + 225 + 265 + 270 + 260 + 275 + 50 – 150) = 1,435 \quad \text{units} ]

Now, we divide the total units (1,435) by the number of months (6) to calculate the monthly production rate:

[ \frac{1,435}{6} = 239.17 \quad \text{units} \quad \text{(rounded to 240 units)} ]

Thus, the monthly aggregate production rate is 240 units.

Question 2: Number of Workers Needed

To determine the number of workers (W) required to meet the aggregate production rate, we use the following algebraic equation:

[ \left( \frac{\text{HPM}}{\text{HPU}} \right) \times \left( \frac{\text{APR}}{\text{days in a month}} \right) \times (\text{days in a month}) = W ]

Where:

• HPM is the hours per month,
• HPU is the hours per unit,
• APR is the aggregate production rate.

Given the values:

• HPM = 168 hours per month,
• HPU = 3.5 hours per unit,
• APR = 231 units.

BUS FPX 4014 Assessment 5 Inventory and Ordering Decisions

First, calculate the number of units produced per worker per month:

[ \frac{168}{3.5} = 48 \quad \text{units per month per worker} ]

Next, determine the units produced per worker per day:

[ \frac{48}{31} = 1.6 \quad \text{units per day per worker} ]

To meet the total demand of 231 units per month:

[ \frac{231}{31} = 7.45 \quad \text{units per day} ]

Finally, calculate the number of workers needed:

[ \frac{7.45}{1.6} = 4.66 \quad \text{workers per day} ]

Thus, the total number of workers required for 231 units per month is:

[ 4.66 \times 31 = 145 \quad \text{workers} ]

Question 3: Economic Order Quantity (EOQ)

The Economic Order Quantity (EOQ) is calculated using the following algebraic equation:

[ \text{EOQ} = \sqrt{\frac{2 \times AQ \times OC}{UHC}} ]

Where:

• AQ is the annual quantity,
• OC is the ordering cost,
• UHC is the unit holding cost.

Substituting the given values:

[ \text{EOQ} = \sqrt{\frac{2 \times 5400 \times 10}{2}} = \sqrt{54000} = 232 \quad \text{(rounded to the nearest whole number)} ]

Thus, the Economic Order Quantity (EOQ) is 232 units.

Question 4: Reorder Point (RP)

The reorder point (RP) is calculated using the following equation:

[ RP = DQ \times LT ]

Where:

• DQ is the demand quantity,
• LT is the lead time.

Substituting the given values:

[ RP = 22 \times 5 = 110 \quad \text{units} ]

Thus, the reorder point is 110 units.

Question 5: Estimating Techniques

Several estimating techniques can be used for forecasting and decision-making:

• Naïve Forecasting: This method compares the current period’s actual data to that of the past period without any adjustments.

  • Pros: Simple, quick, and useful for benchmarks.
  • Cons: Limited accuracy, and unable to predict turning points.

• Simple Mean: The average of all available data is used to make forecasts.

  • Pros: Easy to calculate and effective for identifying patterns.
  • Cons: It does not provide specific values, only averages.

• Simple Moving Average: This method calculates the average of recent actual values.

  • Pros: Simple to understand and calculate.
  • Cons: Treats all values equally, which may not reflect recent changes.

• Weighted Moving Average: Recent data points are given more weight in this method.

  • Pros: Reflects updated data more accurately.
  • Cons: More complex and requires additional calculations.

• Exponential Smoothing: The most recent data points receive more weight.

  • Pros: Emphasizes updated data.
  • Cons: Potentially includes unnecessary data, affecting the forecast.

• Linear Trend Line: A straight line is used to model data over time.

  • Pros: Allows for the inclusion of multiple statistics.
  • Cons: Requires a lot of data and depends on one independent variable.

References

Muller, M. (2011). Essentials of inventory management (2nd ed.). Saranac Lake, NY: AMACOM Books.

BUS FPX 4014 Assessment 5 Inventory and Ordering Decisions

Reid, R. D., & Sanders, N. R. (2016). Operations management: An integrated approach (6th ed.). Hoboken, NJ: Wiley.