You Have Been Hired By A Vendor Preparing For An Upco

You Have Been Hired By A Vendor Who Is Preparing For an Upcoming K-Pop

You have been hired by a vendor who is preparing for an upcoming K-Pop (Korean pop music) concert featuring the singer IU (Lee Ji-eun) in Cincinnati. The concert organizer is allowing vendors to sell IU paraphernalia. You determine that certain one-size-fits-all winter hats featuring IU’s image will be sold. You have four choices for OEM suppliers of the hats. Due to their locations, their lead times differ, along with your costs.

Based on past concerts, the product sales forecast is for 3000 hats, but this estimate is uncertain depending on the concert attendance. Each hat will sell for $32. Once the concert ends, products not sold will be bought by a discounter at a heavily reduced price of $2.40 per hat. Sales forecasts will be more accurate as the concert approaches (forecast CV’s are available based on past forecasting performance). Production quality varies according to location, which is expressed as an expected production yield.

Production costs are adjusted to account for these yields (and shown below as net product cost). Because the products all require careful handling, there will be an inventory holding cost that will apply as soon as the order is placed. However, the annual holding percentage is unknown at this time. The table below summarizes the important information regarding the locations under consideration. Create a spreadsheet that determines, for each manufacturing location, the expected profits and how many units the vendor should order from each manufacturer.

Assume that demand variation is normally distributed. Vary the annual holding cost percentage and show how the expected profits and order quantities for a range of holding rates from 10% to 40%. Show these results in tabular and graphical form. Clearly organize and label the spreadsheet to show inputs and results (i.e., a user may wish to modify inputs later as more information becomes available).

Paper For Above instruction

The provided scenario involves optimizing the procurement and inventory decisions for a vendor preparing for a high-profile K-Pop concert, leveraging multiple manufacturing options, sales forecasts, and cost considerations. This analysis aims to determine the optimal order quantities from four potential suppliers, considering demand uncertainty, manufacturing yields, lead times, costs, and inventory holding costs within a probabilistic framework. The overarching goal is to maximize expected profit while accommodating the variability inherent in sales forecasts and production processes, and to understand how different levels of inventory holding costs influence ordering decisions and profitability.

The critical components of this problem include estimating the optimal order quantities for each supplier, calculating the expected profits associated with these decisions, and analyzing sensitivity to different annual inventory holding cost percentages, ranging from 10% to 40%. The model must account for the probabilistic nature of demand, assuming a normal distribution, and integrate costs such as production, holding, shortfall, and salvage value of unsold inventory.

Introduction

In event-based retail environments like concert merchandise sales, demand forecasting is inherently uncertain, requiring flexible inventory and procurement strategies to minimize costs and maximize revenues. The scenario of selling IU-themed hats at a K-Pop concert in Cincinnati exemplifies this challenge. The vendor’s decision-making process hinges on balancing the costs of overstocking, which leads to discounted salvage sales, against understocking, which results in missed sales opportunities and potential customer dissatisfaction.

Model Overview

The modeling framework involves decision variables representing order quantities from each of the four suppliers, with associated costs, yields, and lead times informing procurement choices. The random demand, modeled as a normal distribution with a mean of 3000 units, introduces uncertainty into the expected profit calculations. The expected profit for each supplier depends on the profit from sold units, lost sales or overstocking costs, salvage value of unsold stock, and procurement costs.

Demand Uncertainty and Inventory Optimization

Demand uncertainty is characterized by a normal distribution with a known mean and variance (based on forecast coefficients of variation). The optimal order quantity can be derived using a newsvendor model, which balances the marginal costs of ordering additional units against the marginal benefits, accounting for the service level determined by the demand distribution and cost parameters.

Cost and Yield Considerations

Each supplier has distinct procurement costs, production yields, and lead times. The yield impacts the net effective cost per unit, as substandard units increase costs. The total cost per unit encompasses production cost adjusted for yield, ordering costs, and inventory holding costs. Inventory holding costs are expressed as an annual percentage of the order value, which influences the order quantity by affecting the critical ratio in the newsvendor model.

Supply Chain and Profitability Analysis

The expected profit calculations consider the revenue from sold hats at full price, the salvage value of unsold units, and the costs incurred, including procurement, holding, and potential lost sales. Sensitivity analysis across different inventory holding cost rates provides insight into how the optimal order quantities and expected profits respond to changes in holding costs.

Spreadsheet Design and Output

The spreadsheet should be organized into input sections where parameters such as demand mean and variance, costs, yields, and holding rates can be modified. The core calculation section computes optimal order quantities and expected profits for each supplier, incorporating demand uncertainty via statistical functions. Results should be summarized in tables and visualized through graphs illustrating the relationship between holding costs and profitability or order quantities.

Conclusion

This analysis provides a comprehensive decision support tool for the vendor, enabling data-driven procurement planning under demand uncertainty and varying inventory costs. The adaptable spreadsheet design ensures that as more information becomes available, updates to input parameters can promptly reflect revised decision recommendations. Ultimately, this approach aims to optimize profitability while managing risks inherent in event-based merchandise sales.

References

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