The intention of this project is to demonstrate the function of production planning in a non - artificial environment. Through this simulation we are able to forecast, with a degree of certainty the monthly requirements for end products, subassemblies, parts and raw materials. We are supplied with information that we are to base our decisions on. The manufacturer depicted in this simulation was actually a General Electric facility that produced black and white television sets Syracuse, New York. Unfortunately this plant is no longer operational, it was closed down and the equipment was shipped off to China. One can only wonder if the plant manager would have taken Professor Moily's class in production management the plant still might be running.
Modern production management or operation management (OM) systems first came to prominence in the early half of the twentieth century. Frederick W. Taylor is considered the father of operations management and is credited in the development of the following principles.
a. Scientific laws govern how much a worker can produce in a day.
b. It is the function of management to discover and use these laws in operation of productive systems.
c. It is the function of the worker to carry out management's wishes without question.
Many of today's method's of operation management have elements of the above stated principles. For example, part of Material Requirement Planning system (MRP) is learning how workers to hire, fire, or lay idle. This is because it we realize the a worker can only produce so many widgets a day, can work so many hours a day, and so many days a year.
I will disagree with principle "c" in that the worker should blindly carry out the wishes of management. Successful operations are based upon a two-way flow of thought and suggestions from management to labor. This two-way flow of ideas is incorporated into another modern system of operations management, the Just - In - Time system. Eastman Kodak gives monetary rewards to employees who devises an improvement in a current process or suggests an entirely new process of manufacturing. Often a small suggestion can yield a big reward when applied to a mass-produced item.
In this project we are presented with the following information: bounds for pricing decisions, market share determination, the product explosion matrix, sales history (units per month at average price), unit value, setup man-hours, running man hours, initial workforce, value of inventory, on hand units. We also know that we have eight end products, four subassemblies, eight parts, and four raw materials. The eight end products are comprised entirely from the subassemblies, parts, and raw materials. From this information I was able to determine how many units of each final product, how many units of parts to produce in a month, how many units of raw material to order every month and how to price the final products.
The first step that I took in this project was to develop product structures for each product (please refer to the Appendices for product structures on all eight products, plus new product nine). This information was presented in product explosion matrix. For example, I determined that product one used one subassembly nine and one part thirteen. Part thirteen consisted of raw material twenty-one. Sub-assembly nine consists of part thirteen (which includes raw material twenty-one), raw material twenty one and raw material twenty-four. From this product explosion matrix I have realized that an end product does not just happen; they consist of many subassemblies, parts and raw material.
We also determined the minimal direct costs to each of the eight products. The minimal direct product is the cost of the raw material, plus the price of the amount of labor for the assembly to end product. For product one we have a total of three raw material "twenty-one" which cost ten dollars a piece and one raw material "twenty-four" which cost twenty dollars each. We now have a total of fifty dollars for the price of the parts. Next we calculate the labor that goes into transforming these parts into a viable end product. We get a total of six hours of running man hours/unit and an hourly labor rate of $8.50, which gives us a total of fifty-one dollars. This gives a minimal total cost of $101 to produce product one. This number is useful in determining how much a unit actually cost to manufacture and what we must minimally sell the product for to make a profit. We can than analyze if a product costs to much to make or the sum of the parts is more than the price of the end product. Product eight had the lowest direct minimum cost ($89.50) and four had the highest minimal direct cost.
From a purely economic stand point, it would be beneficial to use as much of raw material twenty-three ($5 unit) and as little of raw material twenty-two ($30 unit). This does not consider that raw material twenty-two may actually be more valuable than raw material twenty-three. Perhaps raw material twenty two may be gold or silver and raw material twenty-three may be sand or glass.
I also converted all information in the sales history per month (figure four of the MANMAN packet). The purpose of this step was so that I could sort and add the sales numbers to chronicle the past twenty four months. Clearly product one was the best-selling apparatus, and product three, four and five where sales laggards.
Entering the information into spreadsheet form was also necessary to present the eight products in graphical form. Of the following graph types that where at my disposal (line, bar, circle) to clearly illustrate the upward and downward trend of each of the eight product I chose the line graph method. A circle graph is good percentage comparisons or comparison of market share. Bar graphs can illustrate a snapshot in time but can distort trend data.
At this point our class gathered into groups to discuss which product to discontinue. Obviously product one was not going to be of the discontinued products, since it was our volume leader. Based on the sales figure for the past twenty-four months my group decided to eliminate products three, four and five. Also, products three, four and five had the highest minimum direct costs as well. Since these products where expensive to manufacture and where our lowest selling products a group decision was made to discontinue these products.
The discontinued product was then rolled over into a new product, now referred to product nine. Unfortunately, we where unable to decide by the information given if any of the discontinued products was a high margin product, low volume product (IE 50" big screen color Trinitron tube with oak cabinet and stereo sound).
Moving right into our next step we began to analyze our bar charts to make our starting forecast. We viewed sales from each product to see if they fall under one the following situations:
(Base + Trend)
(Base + Trend) * Seasonality
When a product is base the sales alter little each sales period or change erratically with external market signals. An example of a product that would fall under the base model would be sand bags. Sand bags sell at the same level month after month. If a retailer sells a hundred bags in March the will sell a hundred bags in October. But, in a flood plain after terrantiel downpour, the sales of sandbags increase exponentially. This is because many people purchase the sandbags to hold back the rising flood waters. Another example of a product that would emulate the base model is insulin. There is a limited number of people with insulin dependant diabetes. The people with insulin dependant diabetes unfortunately die off, but are replaced with other people who fall ill to the disease. There is very little movement up or down in the sale of insulin.
The base plus trend model illustrates that a product has a trend of upward or downward groth in sales. Products at the begining or ending of their respective product cycles will display this type of performance. Sales of a new product such as Microsoft Windows95(tm) disk operating system will fall into this category. The sales of May are expected to be larger than April, the sales of April will be larger than March and so on. While the sales may decline (or increase) during a particular time frame, a trend of upward or downward growth will be apparent.
Lastly, the base plus trend times seasonality attempts to forecast the swings in demand that are caused by seasonal changes that can be expected to repeat themselves during a single or consecutive time period. For example, florists experience a predictable increases in demand each year, both occur at similiar (or exact) times during the year; Mothers Day and St. Valentines Day. Florists must forecast demand for roses and other flowers so they can meet this predictable demand. If I where to construct a ten year historical graph for a neighborhood florist, there would be clear increase in demand every February and May, in every one of those years. A caveat to the previous example would be that in most lines a business forecasting is never this easy. If it was there would not be a production management class or operations management science!
Some other methods used to forecast demand are: delphi method, historical analogy, simple moving average, box Jenkins type, exponential smoothing and regression analysis. Forecasting falls into four basic types: qualitative, time series analysis, casual relationships and simulation. All of the proceeding have pluses, minuses and degrees of accuraccy. I often depends on the precision of previous data. Also, as is often stated in financial prospectuses "past performance does not guarantee future results".
For product one I used base plus trend. The sales started of at 1246 units and gradually increased to 2146 at the end of twenty four months. There was a slight dip in sales between month nine-teen and month twenty three. This drop can from internal or external variables.
Product two was little more tricky. The swing where eratic and showed no detectable trend. I may have been able to use (Base + Trend) * Seasonality if there was not a decrease in sales from month eight and an increase in sales in month sixteen. For this I had to employ the base or simle method.
While I find it hard to comprehend how television sales can be seasonal, products three, five and six fall under (Base + Trend) * Seasonality models. I was able to replicate the wave in demand with my forecast. Perhaps consumers are buying portable televisions to use at the beach while on vacation, or people are replacing there old televisions to watch the Superbowl championship game or world series. Or maybe even watch the Syracuse Orangemen in the NCAA college basketball championship!
Conceivably, I was reading to much into product six when a decided on base plus trend model. The way I saw it was that none of the upward or downward where that substantial when compared with entire data, and sales from month one (521 units) decreased by almost fifty percent to 242 units.
I felt the same way about product eight that I felt about product two, this product demostrated eratic swings in no particular trend. I forecasted demand using the base or simlple method for this product.
From this point I was able to forecast demand. For the safety stock decision I always tried to error on the side of caution. On average I used a twenty five percent safety stock level. However, when calculating the MRP or labor plans I tried
to have the minimal amount of surplus. This often means that I only had safety stock on hand from period to period.
From this project and from the class lectures I have received an understanding of how how much planning goes into even the most simplest of manufactured goods. Production managers must employ at least one type of forecasting method in order to avoid the everyday stock outs, late deliveries and labor problems that arise. Forecasts are vital to every business organization and for every significant management decision.
I feel that I could have further reduced costs by reducing some of the parts, sub assemblies and outsourcing some of the production. Another situation that I felt was unrealistic was that there was only one source for each part and when that part was unvailable, there was a stock out. Perhaps in future projects there can be allowance for this.