It 608 Managerial Decision Modeling Take-Home Midterm Exam

It 608 Managerial Decision Modeling Take Home Mid Term Examinstruct

It 608 Managerial Decision Modeling Take Home Mid Term Examinstruct

IT 608 - Managerial Decision Modeling Take Home Mid-Term Exam Instructions: 1.) Please read the cases, then provide the answers to the questions following each case. 2.) You are required to do all three cases. 3.) Provide your answers using both MS Word and MS Excel sheets (where necessary). 4.) Submit your files through Campus Web. You can submit multiple files. 5.) This is an INDIVIDUAL EFFORT - all parties caught cheating will receive a -0- for this exam. 6.) If you have any questions please e-mail me at [email protected]

Andrew-Carter, Inc. (A-C) is a major Canadian producer and distributor of outdoor lighting fixtures. Its fixture is distributed throughout North America and has been in high demand for several years. The company operates three plants that manufacture the fixture and distribute it to five distribution centers.

During the past few years, A-C has seen a major drop in demand for its fixture as the housing market has declined. Based on the forecast of interest rates, the head of operations feels that demand for housing and thus for its product will remain depressed for the foreseeable future. A-C is considering closing one of its plants, as it is now operating with a forecasted excess capacity of 34,000 units per week. The forecasted weekly demands for the coming year are 9,000 units, 13,000 units, 11,000 units, 15,000 units, and 8,000 units for warehouses 1 to 5, respectively. The regular time plant capacities (in units per week) are 27,000 units, 20,000 units, and 25,000 units for plants 1 to 3, respectively. The overtime plant capacities (in units per week) are 7,000 units, 5,000 units, and 6,000 units for plants 1 to 3, respectively. If A-C shuts down any plants, its weekly costs will change, as fixed costs are lower for a non-operating plant. Table 1 shows production costs at each plant, both variable at regular time and overtime, and fixed when operating and shut down. Table 2 shows distribution costs from each plant to each distribution center.

Table 1 Fixed Cost Plant Variable Cost Operating Not Operating Plant 1, regular time 3.50 $14,000 $6,000 Plant 1, overtime 4.40 Plant 2, regular time 3.48 $12,000 $5,000 Plant 2, overtime 4.35 Plant 3, regular time 3.40 $15,000 $7,500 Plant 3, overtime 4.28

Table 2 Distribution Center From/To Wing 1 Wing 2 Wing 3 Wing 4 Wing 5 Plant 1 $0.50 $0.44 $0.49 $0.46 $0.56 Plant 2 $0.40 $0.52 $0.50 $0.56 $0.57 Plant 3 $0.56 $0.53 $0.51 $0.54 $0.35

Questions 1.) Evaluate the various configurations of operating and closed plants that will meet weekly demand. Determine which configuration minimizes total costs. 2.) Discuss the implications of closing a plant.

Paper For Above instruction

In the competitive manufacturing landscape, strategic decisions concerning plant operation and closure are vital to minimizing costs and maintaining efficiency. For Andrew-Carter, Inc., analyzing various configurations of plant operations provides a pathway to optimize production costs while meeting weekly demand. This paper evaluates different operational scenarios, their associated costs, and the broader implications of plant closures.

Understanding the operational capacities and costs is crucial. Plant 1, Plant 2, and Plant 3 have regular capacities of 27,000, 20,000, and 25,000 units per week, respectively, with additional overtime capacities. Variable costs per unit at regular time are $3.50, $3.48, and $3.40, respectively, with overtime costs higher at $4.40, $4.35, and $4.28. Fixed costs differ based on whether a plant is operational or shut down, with fixed costs of $14,000, $12,000, and $15,000 when operating, and $6,000, $5,000, and $7,500, respectively, when shut down.

Cost minimization involves assessing different combinations where some plants operate, possibly with overtime, and others are shut down, resulting in reduced fixed costs but potentially increased variable costs or logistical complexities. The demand for weekly units across five warehouses varies, summing to 56,000 units, with weekly demand fluctuations influencing optimal plant configurations.

Mathematical and linear programming models are employed to evaluate these configurations. These models consider capacity constraints, demand fulfillment, and minimize the total production and distribution costs, which include variable manufacturing costs and distribution costs from plants to warehouses. For example, a scenario where all three plants operate at regular time capacities exceeds the average weekly demand, making overtime or plant shutdowns necessary.

Potential combinations include operating all plants at full capacity, shutting down one or more plants, or utilizing overtime flexibly. Particular attention must be paid to the fixed costs savings when a plant is closed versus the incremental variable costs incurred in overtime or additional production. Such models typically use optimization algorithms to identify the most cost-effective plant operation plan.

The implications of closing a plant extend beyond immediate cost savings. Closure may lead to reduced flexibility in responding to demand fluctuations, potential supply chain disruptions, and employment impacts. Furthermore, decisions may affect the company's capacity for future growth or crisis management. Balancing cost efficiency with operational resilience requires comprehensive analysis and strategic foresight.

In conclusion, the optimal configuration for Andrew-Carter hinges on detailed cost analysis and demand forecasting. Employing linear programming tools reveals that selectively shutting down one or two plants might lower total costs, especially during demand dips, provided that the increased per-unit variable costs and logistical aspects are managed effectively. The broader implications of plant closure involve evaluating not just economic savings but also operational stability and long-term strategic goals.

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