Develop An Inventory Plan For MPBC To Meet Service

Develop an inventory plan for MPBC to meet service

Martin-Pullin Bicycle Corp. (MPBC), located in Dallas, is a wholesale distributor of bicycles and bicycle parts. The company primarily supplies retail outlets within a 400-mile radius, providing prompt deliveries for in-stock items but losing business when unable to fulfill non-stocked orders. MPBC's best-selling model is the AirWing, sourced from an overseas manufacturer with a 4-week lead time and an order cost of $85 per shipment. The purchase price per bicycle is 60% of its retail price of $210, so $126, and the inventory holding cost is 24% annually of this purchase price.

MPBC aims to develop an effective inventory management plan for 2015, aiming to maintain a 95% service level to reduce lost sales. They have forecasted sales for 2014 to inform their planning—although demand fluctuates monthly, a forecasted total demand of 439 units for 2014 is provided. To establish an inventory plan, several key parameters need to be analyzed, including economic order quantity (EOQ), reorder points (ROP), and adjustments for demand variability.

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To develop an effective inventory plan for MPBC, it is essential to calculate the optimal order quantity, determine appropriate reorder points considering lead time and demand variability, and address seasonal or fluctuating demand patterns. The goal is to balance ordering and holding costs while ensuring a high service level, reducing lost sales, and maintaining operational efficiency.

Forecasting and Base Data

The forecasted annual demand for the AirWing model is 439 units, averaging approximately 36.58 units per month. The purchase price per bicycle is calculated as 60% of retail, that is $126.00, and the annual holding cost per unit is 24%, which amounts to roughly $30.24 per year. The ordering cost per shipment is $85, and the retail price to customers is $210 per bicycle. The target service level of 95% associates with a Z-score of 1.645, indicating the level of safety stock required to meet demand fluctuations.

Economic Order Quantity (EOQ)

The EOQ model suggests the ideal order quantity that minimizes total inventory costs by balancing ordering and holding costs. Using the classical EOQ formula:

EOQ = sqrt((2  D  S) / H)

Where:

• D = Annual demand = 439 units
• S = Ordering cost = $85
• H = Annual holding cost per unit = $30.24

Calculating:

EOQ = sqrt((2  439  85) / 30.24) ≈ sqrt(74,530 / 30.24) ≈ sqrt(2466.49) ≈ 49.66 units

Rounding to the nearest whole number, MPBC should order approximately 50 units per order to minimize costs (Chopra & Meindl, 2016). This quantity balances the trade-off between order costs and holding costs, ensuring efficiency in operations.

Reorder Point (ROP) Calculation

Since the lead time is four weeks, the expected demand during this period is:

Average weekly demand = 36.58 / 4.345 ≈ 8.42 units
Expected demand during lead time = 8.42 * 4 ≈ 33.68 units

The standard deviation of demand per week can be estimated from historical data; however, assuming demand variability is significant, safety stock must be incorporated. The safety stock (SS) is calculated as:

SS = Z * σL

Where:

• Z = 1.645 (for 95% service level)
• σL = standard deviation of demand during lead time = standard deviation per week multiplied by √lead time

Given the variance of demand is not precisely provided, a conservative estimate might take the standard deviation of monthly demand (24.58) as a proxy, adjusting for weekly demand variations. Assuming a standard deviation per week of approximately 4 units:

SS = 1.645  4  sqrt(4 weeks) = 1.645  4  2 = 13.16 units

The ROP thus becomes:

ROP = expected demand during lead time + safety stock ≈ 34 + 13 ≈ 47 units

Since the ROP (47 units) exceeds the EOQ (50 units), MPBC should adjust ordering policies to prevent overstocking or stockouts, perhaps ordering slightly more than EOQ per cycle or staggering orders.

Handling Larger Reorder Point Than EOQ

When the ROP exceeds EOQ, it indicates that demand variability and safety stock considerations require placing orders more frequently with smaller quantities or increasing the order size to meet demand fluctuations adequately. It also suggests a need for closer monitoring of demand trends and flexible replenishment strategies, such as dynamic safety stock adjustments based on real-time sales data.

Addressing Demand Variability and Planning Horizon

Demand fluctuations over different months necessitate adaptive planning strategies. For example, seasonality can be identified by analyzing historical data, allowing MPBC to increase safety stock in peak months and reduce during off-peak periods. Additionally, shortening the planning horizon and applying rolling forecasts can improve responsiveness. Implementing a periodic review system instead of continuous review might further refine stock levels, aligning inventory with actual demand trends (Nahm, 2016).

Furthermore, adopting a more sophisticated approach such as demand smoothing or probabilistic modeling can help better capture demand uncertainty and variability. By integrating these methods, MPBC can develop a more resilient inventory system that balances costs with customer service levels, accommodating fluctuations effectively (Silver, Pyke, & Peterson, 2016).

In summary, MPBC should order approximately 50 units per cycle based on EOQ, maintain safety stock of around 13 units to meet 95% service level during lead time, and adapt their reorder points dynamically based on ongoing demand data. Recognizing demand seasonality and varying lead times can further enhance inventory efficiency and customer satisfaction.

References

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• Nahm, R. (2016). Inventory management in practice. Journal of Business Logistics, 37(2), 120-137.
• Silver, E. A., Pyke, D. F., & Peterson, R. (2016). Inventory Management and Production Planning and Scheduling. Wiley.
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