Read All Questions And Instructions Carefully - The CEO Of B

Read All Questions And Instructions Carefullythe Ceo Of Burke Electro

Read all questions and instructions carefully. The CEO of Burke Electronics, a well-established single store small business in Northern Virginia, is implementing a program to increase its overall top line and enhance the profitability of the concern. BE (Burke electronics) has hired you as a consultant for your expertise in managing multi-product companies and solving their issues through the use of linear programming and related Excel tools. BE currently carries 4 product lines: PCs (Sony, Toshiba, HP etc.), Tablets (Ipad, Nexus etc.), Phones (Both Ios and Android), Chargers & Interface devices. BE’s unique business model allows it to sell all products in each product line at a fixed rate. It sells all PCs at a unit price of $450, Tablets at $540, Phones at $400, and Chargers & Interface devices at $35 each. As with any small business, BE has some constraints. BE cannot offer more than 900 Tablets and Phones combined; it can only offer a maximum of 500 PCs. Additionally, BE must offer at least 1200 Chargers & Interface devices. The total products offered cannot exceed 5000 units or be fewer than 2300 units overall. Furthermore, BE must offer at least 250 Phones.

How can BE determine the optimal product mix to maximize revenue? Additionally, what constraints could be added to make the solution more sustainable and meaningful?

Cost Structure:

- PCs: $375

- Tablets: $250

- Phones: $235

- Chargers & Interface devices: $30

Using the Excel model you develop, propose product mixes that maximize overall profits and reduce total inventory costs, considering the existing constraints and available Excel tools.

Paper For Above instruction

Introduction

The goal of Burke Electronics (BE), a small retail business specializing in multiple electronic product lines, is to increase its overall revenues and profitability through optimal product mix management. This paper develops a linear programming (LP) model using Microsoft Excel to facilitate strategic decision-making by determining the most profitable product combination within existing constraints. The analysis follows a systematic approach, incorporating revenue maximization and cost minimization, alongside potential constraints for sustainability and operational feasibility.

Model Setup and Constraints

The first step involves defining decision variables representing the quantities of each product line—PCs, tablets, phones, chargers & interface devices—that BE plans to stock. Let:

  • xPC = number of PCs
  • xTablet = number of tablets
  • xPhone = number of phones
  • xCharger = number of chargers & interface devices

Revenue per unit and cost per unit are set as constants in the model. The objective function aims to maximize total revenue:

Maximize Z = 450xPC + 540xTablet + 400xPhone + 35xCharger

The constraints are formulated based on business rules:

  • Product availability constraints:
  • xPC ≤ 500
  • xTablet + xPhone ≤ 900
  • xCharger ≥ 1200
  • xCharger + xProduct Lines ≤ 5000
  • xProduct Lines ≥ 2300
  • xPhone ≥ 250

Additional constraints include non-negativity:

xPC, xTablet, xPhone, xCharger ≥ 0

Excel Implementation and Results

Using Excel’s Solver tool, the LP model can be constructed on a dedicated sheet. The model inputs include decision variables, objective function coefficients (selling prices), and constraints. After setting the Solver parameters—'Max' for profit maximization and constraints as formulated—the optimal product mix can be identified.

This approach allows exploration of multiple scenarios by modifying constraint bounds or adjusting prices. For instance, increasing the minimum order of chargers or including sustainability constraints like limiting inventory levels or promoting eco-friendly products could be added. These modifications will reflect in the Solver model, enabling strategic planning aligned with operational goals.

Strategies for Sustainability and Cost Reduction

In addition to maximizing revenue, BE seeks to minimize inventory costs. This is achieved by implementing an LP model where the objective function shifts from revenue to total product costs, subject to the same constraints. By analyzing the optimal solutions under different constraints, the company can identify a product mix that ensures profitability while also controlling inventory expenses.

Furthermore, integrating considerations such as lead times, demand variability, and supplier reliability can enhance the model’s robustness. Strategic constraints like stock rotation policies or environmental standards can also be incorporated, promoting sustainability.

Conclusion

Through the systematic development of an LP model in Excel, Burke Electronics can derive optimal product mixes that maximize profit and reduce costs within operational constraints. The flexibility of the model allows testing various scenarios, including constraints for sustainability and market demands. Ultimately, this approach provides a data-driven framework to inform strategic decisions, fostering growth and operational efficiency in a competitive retail environment.

References

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