Paired Assignment Results
I am listing your article summaries here for the Tehcawiboonwong & Yenradee Article:
“Aggregate Production Planning Using Spreadsheet Solver: Model and Case Study” explains the aggregate production planning (APP) process, as well as guides the reader through solving the model using the spreadsheet solver. The APP provides useful information, such as production quantity and inventory level. The article explains the pertinent portions of the plan, such as the parameters, objective function, constraints, etc. Once finished reading the article, the reader should have a clear understanding of the components of the model, as well as the steps to solving it. The guideline for using the spreadsheet solver recommends using four steps to generate the aggregate production plan: data collection, formulate APP model in the spreadsheet format, evaluate the obtained solutions, and implement the APP. The case study given to help the reader better understand the process, should be carefully read to understand what each data set represents. Using the case study, the reader can clearly determine understand how the data from Step 1 (Data Collection) is used within the spreadsheet solver. During Step 3, the APP is presented to determine whether it meets the objective.
[The] article was VERY complex it kind of took me by surprise but eventually I finally figured out the point of all the detailed formulas that was given in it. The article talked about aggregate production planning. It was first described as minimum cost workforce and production plan to meet customer demands. This at first meant nothing to me but during the article I found out the true reason about why having a aggregate production plan is good for a company. Since I want to get the the point Ill explain why first. The reason this model is important is to manage human labor. This activity is usually done by the human resources department and having a aggregate plan would make things much easy. This plan would help HR determine when to hire permanent and temporary workers. The hiring of these workers have many factors such as a season when demand is high like during Christmas. Scheduling is a important factor towards having an effective amount of production during the right times so you don't build up too much inventory which would cost a company money. The APP model is not used by a lot of companies because they are stupid and don't have the right engineers to use/interpret the math that is involved in these models. The rest of the article is basically formulas that I really hope we don't have to memorize for this class. If you had the formulas on hand and had to put the information into you're spreadsheet, it wouldn't be that hard though.
In aggregate planning there are many techniques used to solve
aggregate planning problems. Some of them give optimum results, while others
yield only acceptable results. The spreadsheet solver approach is found to
be the most applicable to industries. Spreadsheet solver is a powerful tool
for developing an aggregate planning production "since it has a good user
interface and optimization capability." This software allows users many
options and features which enhances itself over other types of software. It
was developed with the requirements of the situations and general characteristics
of industries. It offers many parameters and constraints that can be used to
receive optimum results. It is also very easy to use. In only 4 steps, the
aggregate planning can begin: Step 1. Data Collection, Step 2. Formulate APP
model in the spreadsheet format. 3. Evaluate the Obtained Solutions, 4. Implement
the aggregate production plan.
In a recent case study, a medium-sized manufacturer of air conditioning units
located in Thailand was used to demonstrate the validity of the outline. After
its trial use, the company found that it takes about one day to manually generate
an acceptable plan. This saves the company a significant amount of time. The
information was relatively easy to apply into the spreadsheet and it was fairly
easy to interpret the results of the outcome. The hopes of this spreadsheet
solver is to reduce production costs and to increase competitive advantage.
This aggregate planning model is best when used in industries that allow production
quantity per period to be adjusted. This aggregate planning model, however,
can not be applicable to process industries unless a new APP model can be constructed
to handle the restraints that must be applied.
The article discusses a way to develop an optimal Aggregate Production Plan (APP) that can be modified to fit a variety of companies in different industries. It lists a range of different techniques that can solve an APP issue, such as linear and non-linear programming, linear decision rule, trial and error, and simulation search, but ultimately considers the spreadsheet software, solver as the best tool to use. The reason for using this tool is the spreadsheet software’s user-friendly interface, optimization capabilities, multiple regressions, its ability to provide sensitivity analysis and forecasting, and that it does not require broad mathematical knowledge. Also, the program is inexpensive due to the fact that it is an add-in for spreadsheet software such as Microsoft Excel, which most likely a company already owns. These features make it appealing to any company considering on using software for creating an APP model to find an optimal solution. The purpose of this study is to offer an Aggregate Production Plan that consists of general industry requirements and constraints, suggest guidelines to develop an optimal APP for an industry using the solver software, and evaluate whether the optimal solution is acceptable before implementing the results. The suggested guidelines to develop an APP are to 1) collect data and all parameters that are relevant, 2) formulate the model using the spreadsheet software, 3) evaluate the optimal solution obtained by using solver and discuss with relevant departments to determine if the solution is acceptable, and lastly implement the changes that need to be made in the company. When implementing the solution, it needs to be updated periodically to keep up with changing parameters. A case study was then created to show an example of the process for a company to create an APP model using the guidelines as well as evaluating it to see if the optimal solution is acceptable and ready to be implemented.
The purpose behind this article is showing how using the spreadsheet solver approach can develop an optimal aggregate production plan. In the article, they use a case study involving manufacturing demonstrating how the guideline is to be applied. The Aggregate Production Plan that is developed is evaluated in terms of being satisfactory, as well as whether or not it can lead to immediate implementation. Aggregate Production Planning helps determine minimum cost workforce and production plans to meet customer demands; it is also aimed at aggregately determining the production quantity and inventory level. Usually monthly or quarterly, the data in an APP has a strong need when a demand pattern is highly seasonal. Some of the problems found in APP are trial-and-error, linear and nonlinear programming, linear decision rule, and simulation search. One of the best and most powerful tools for using and developing APP is spreadsheet software because it since it has a good user interface and optimization capability. This is a basic, simplified version of the article, explaining the basic idea behind the utilization and application of Aggregate Production Planning and its features.