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In order to display all of the screen components, the following screen shows a module and a loaded data file. In the example, units were demanded and units were produced, and there were a total of unit-months of inventory, unit-months of shortage, and 0 increased or decreased production unit- months. These options can be reset using Help, User Information. When deciding whether to use overtime or subcontracting, the program will always first select the one that is less expensive.
 
 

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Free pom qm for windows v4 download download software at UpdateStar – Free Download Manager makes downloading files and videos easier and faster and. The installations below are only for updating the software. You must have the original, licensed version already installed on your computer for the updates to. POM-QM for Windows, free download. POM-QM for Windows: Prentice-Hall.

 

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This behavior can be adjusted by using Help, User Information and, in addition, if you pom qm for windows software free download the program to automatically prompt you to save the file when you are done entering data, this too can be accomplished through Help, User Information. The Decision Table Model The decision table can be used to find the expected value, the страница minimaxor the maximax minimin when several decision options are available and страница are several scenarios that might occur. A button is available to change the node if this doqnload necessary. The starting inventory источник статьи placed in the far right column towards the bottom.

 
 

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In this example there are three stations, each with a cycle time of 10 seconds, for a total work time of 30 station-seconds. The time needed to make one unit. This is simply the sum of the task times. Idle time. This is the time needed subtracted from the time allocated.

Efficiency is defined as the time needed divided by the time allocated. Balance delay. The balance delay is the percentage of wasted time or percent minus the efficiency. Minimum theoretical number of stations. This is the total time to make 1 unit divided by the cycle time and rounded up to the nearest integer. In this example, 26 seconds are required to make 1 unit divided by a second cycle time for an answer of 2.

In addition, a second window opens that displays the number of stations required using each of the different balancing rules. In this particular case, each rule led to the same number of stations, 3. This is not always the case as shown in Example 4. The precedence graph can be displayed see the end of this section , as well as a bar graph indicating how much time was used at each station. These are shown at the end of this section. In addition, if there is idle time at every station, a note will appear at the top indicating that the balance can be improved by reducing the cycle time.

For example, because there are idle times of 1, 1, and 2 seconds at the three stations, we could reduce the cycle time by 1 second. Example 2: Computing the cycle time Suppose that for the same data a production rate of units in 7. Other Rules Other rules that may be used are mentioned although the results are not displayed. Please note that this is one of the modules where if you change the method using the drop-down box from the solution screen, the problem will immediately be resolved.

That is, you do not need to use the EDIT button and return to the data. Most Following Tasks A common way to choose tasks is by using the task with the most following tasks. Notice from the diagram at the beginning of the section that a has three tasks following it, and b also has two tasks following it.

Therefore, there is a tie for the first task. If Task a is chosen then the next task chosen will be Task b because Task b has 3 following tasks whereas Task c has only one.

The task with the largest weight is scheduled first if it will fit in the remaining time. Notice that e has a higher ranked positional weight than c.

Least Number of Followers The last rule that is available is the least number of followers. Example 3: What to do if longest operation time will not fit Some books and some software do not apply the longest operation time rule properly. If the task with the longest time will not fit into the station, the task with the second longest time should be placed in the station if it will fit. In the following screen data is presented for eight tasks. Notice that Tasks b, c, e, and f immediately follow Task a.

The balance appears in the following screen for a cycle time of 5 seconds. After Task a is completed, tasks b, c, d, and e are ready. Task b is longest but will not fit in the 4 seconds that remain at Station 1. Therefore, Task c is inserted into the balance.

Example 4: Splitting tasks If the cycle time is less than the amount of time to perform a specific task, there is a problem. We perform what is termed task splitting but which in reality is actually duplication. For example, suppose that the cycle time is 2 minutes and some task takes 5 minutes.

The task is performed 3 times by three people at three machines independent of one another. The effect is that 3 units will be done every 5 minutes, which is equivalent to 1 unit every 1. Now, the actual way that the three people work may vary. Although other programs will split tasks, the assumptions vary from program to program. Rather than making assumptions, you should split the tasks by dividing the task time appropriately.

Suppose that in Example 1 a cycle time of 5 seconds was used. Then it is necessary to replicate both Tasks d and f because they will not fit in the cycle time. The approach to use is to solve the problem by dividing the task times by 2, because this replication is needed. The results are presented in the following screen. Notice that different rules lead to different minimum numbers of stations!

The first is a precedence graph, as shown in the following figure. Please note that there may be several different ways to draw a precedence graph. The second graph not displayed here is of time used at each station. In a perfect world these would all be the same a perfect balance. The model is a special case of the transportation method. In order to generate an assignment problem, it is necessary to provide the number of jobs and machines and to indicate whether the problem is a minimization or maximization problem.

The number of jobs and number of machines do not have to be equal but usually they are. Objective function. The objective can be to minimize or to maximize. This is set at the creation screen but can be changed in the data screen. Example 1 The following table shows data for a 7-by-7 assignment problem. The goal is to assign each salesperson to a territory at minimum total cost. There must be exactly one salesperson per territory and exactly one territory per salesperson.

The data structure is nearly identical to the structure for the transportation model. The basic difference is that the assignment model does not display supplies and demands because they are all equal to one.

Note: To try to preclude an assignment from being made, such as Bruce to Pennsylvania in this example, enter a very large cost.

The assignments can also be given in list form, as shown in the following screen. The marginal costs can be displayed also. Cost-volume analysis is used to find the point of indifference between two options based on fixed and variable costs.

A breakeven point is computed in terms of units or dollars. Breakeven analysis is simply a special case of cost-volume analysis where there is one fixed cost, one variable cost, and revenue- per-unit. Cost-Volume Analysis In cost-volume analysis, two or more options are compared to determine what option is least costly at any volume. The costs consist of two types – fixed costs and variable costs, but there may be several individual costs that comprise the fixed costs or the variable costs.

In the example that follows, there are five different individual costs and two options. Data Cost type. Each type of cost must be identified as either a fixed cost or a variable cost. The default is that the first cost in the list is fixed and that all other costs are variable.

These values can be changed by using the drop-down box in that cell. The specific costs for each option are listed in the two right columns in the table. If a volume analysis is desired, enter the volume at which this analysis should be performed. If the volume is 0, no volume analysis will be performed other than for the breakeven point. Volume analysis is at units. In the preceding screen there are five costs with some fixed and some variable.

The program displays the following results: Total fixed costs. For each of the two options, the program takes the fixed costs, sums them, and lists them in the table. Total variable costs. The program identifies the variable costs, sums them, and lists them. Breakeven point in units. The breakeven point is the difference between the fixed costs divided by the difference between the variable costs, and this is displayed in units.

In the example, it is units. Breakeven point in dollars. The breakeven point can also be expressed in dollars. A volume analysis has been performed for a volume of units. The total fixed costs and total variable costs have been computed for each option and these have been summed to yield the total cost for each option.

A graph is available, as follows. Data entry for this option is slightly different in that the program creates a column for costs and a column for revenues. The fixed and variable costs get entered in the cost column and the revenue per unit is placed in the revenue column. This model requires exactly three inputs. This example could also have been solved using the cost-volume submodel.

Select two options and let one be the costs and one be the revenues. Place the fixed costs and variable costs in their obvious cells; use no fixed cost for the revenue and use the revenue per unit as a variable cost, displayed as follows. The following screen demonstrates the output for a three-option breakeven. The screen indicates that there are three breakeven points as it makes comparisons for Computer 1 versus Computer 2, Computer 1 versus Computer 3, and Computer 2 versus Computer 3.

Of course, even though there are three breakeven points, only two of them are relevant. This is seen a little more easily by looking at the following breakeven graph. The breakeven point at 40, units does not matter because at 40, units the two computers that break even have higher costs than the Computer 2 option.

The data for this example consist of a stream of inflows and a stream of outflows. In addition, for finding the net present value an interest rate must be given. Net Present Value Consider the following example. The company would like to know the net present value using an interest rate of 10 percent. The data screen follows. The screen has two columns for data. One column is labeled Inflow and the other column is labeled Outflow. At the time of problem creation a six-period problem was created and the data table includes the six periods plus the current period 0.

The six savings in the second column are inflows, and they are placed in the inflow column for Periods 1 through 6. The salvage value could be handled two ways, and we have chosen the way that we think gives a better display. Instead, it is represented as a negative outflow. This keeps the meaning of the numbers clearer. The last item to be entered is the interest rate in the text box above the data.

To the right of this, the inflows and outflows are multiplied by these present value factors, and the far right column contains the present values for the net inflow inflow minus outflow on a period-by-period basis.

Internal Rate of Return The computation of the internal rate of return is very simple. The data is set up the same way but the method box is changed from net present value to internal rate of return.

The results appear as follows. You can see that the internal rate of return for the same data is The Decision Table Model The decision table can be used to find the expected value, the maximin minimax , or the maximax minimin when several decision options are available and there are several scenarios that might occur.

Also, the expected value under certainty, the expected value of perfect information, and the regret opportunity cost can be computed. The general framework for decision tables is given by the number of options or alternatives that are available to the decision maker and the number of scenarios or states of nature that might occur.

In addition, the objective can be set to either maximize profits or to minimize costs. Scenario probabilities. For each scenario it is possible but not required to enter a probability. The expected value measures expected monetary value, expected value under certainty, and expected value of perfect information require probabilities, whereas the maximin minimax and maximax minimin do not.

Profits or costs. The profit cost for each combination of options and scenarios is to be given. Hurwicz alpha. The Hurwicz value is used to give a weighted average of the best and worst outcomes for each strategy row.

Please note that the Hurwicz value is not in every textbook. The possible scenarios states of nature are that demand will be low, normal, or high; or that there will be a strike or a work slowdown. The table contains profits as indicated. The first row in the table represents the probability that each of these states will occur. The remaining three rows represent the profit that we accrue if we make that decision and the state of nature occurs. For example, if we select to use overtime and there is high demand, the profit will be Solution The results screen that follows contains both the data and the results for this example.

Expected values. Row minimum. For each row, the minimum element has been found and listed. This element is used to find the maximin or minimin. Row maximum. For each row, the maximum element in the row has been found and listed.

This number is used for determining the maximax or minimax. These represent 40 percent multiplied by the best outcome plus 60 percent multiplied by the worst outcome for each row. For example, for subcontracting the Hurwicz is. Maximum expected value. Because this is a profit problem finding the maximum values is of importance. The maximum expected value is the largest number in the expected value column, which in this example is In this example, the maximin is The maximax is the largest value in the table or the largest value in the maximum column.

In this example, it is Perfect Information A second screen of results presents the computations for the expected value of perfect information as follows. Perfect information.

In this row, the best outcome for each column is listed. For example, for the low demand scenario the best outcome is the given by using overtime. The expected value under certainty is computed as the sum of the products of the probabilities multiplied by the best outcomes. Expected value of perfect information.

The expected value of perfect information EVPI is the difference between the best expected value Table values. The values in the table are computed for each column as the cell value subtracted from the best value in the column in the data.

For example, under low demand the best outcome is The two columns on the right yield two sets of results. There also is a window not displayed in this manual that yields Hurwicz values for alpha ranging from 0 to 1 by.

Decision Trees Decision trees are used when sequences of decisions are to be made. The trees consist of branches that connect either decision points, points representing chance, or final outcomes. The probabilities and profits or costs are entered, and the decisions that should be made and the values of each node are computed. All decision tables can be put in the form of a decision tree. The converse is not true.

The first model has tabular data entry whereas the second model is easier to use because it has graphical data entry. The first model has been maintained in the software for consistency with previous versions.

Example 2: A Decision Tree – non graphical The general framework for decision trees is given by the number of branches or the number of nodes in the tree. The number of branches is always one less than the number of nodes. Each node always has exactly one branch going into it. The number of branches going out of any node can be 0, 1, 2, or so on.

The nodes are of three types. There are decision nodes, chance nodes, and final nodes. Typically, the decision nodes are represented by rectangles, and the chance nodes are represented by circles.

The following example is given by a typical decision tree diagram. The figure has 12 branches. Profits are to the right of the terminal nodes. In order to use the decision tree module, two things must occur. First, nodes must be added to the right of the ending branches. Technically, it is illegal to draw a tree that ends with branches rather than nodes. Second, the nodes must be numbered. The figure that follows shows the added nodes and the fact that all nodes have been given numbers.

The most convenient way to number the nodes is from left to right and top to bottom. The following screen contains both the data and the solution. Start and end node. Branches are characterized by their start and end nodes. The node values are shown in the far right column. Branching probabilities. These occur in column 4 and are the probability of going from the start node on the branch to the end node. The probabilities out of an individual chance branch should sum to 1.

The profit cost for each ending node that is terminal is to be entered. In addition, it is possible to enter a profit or cost for any branch. In the example, choose node 1- node3 rather than node 1- node 2.

For example, if you get to node 6, select node 6 — node 9 rather than node 6- node 8. However, there is no guarantee that you will get to node 6 due to the probabilistic nature of the decision tree. The last type of branch is one that should be selected if you get there, but you should not get there. If you get to node 8, you should use this branch.

However, because node 1 to node3 will be selected at the beginning, you should not end up at node 4. Ending node. The ending node is repeated to make the output easier to read. Ending node type. For each ending node, the program identifies it as either a final node, a decision node, or a chance node. Expected value. The expected value for each node is listed.

For final nodes, the expected value is identical to the input. For chance nodes, the expected value is the weighted combination of the values of the nodes that follow. For decision nodes, the expected value is the best value available from that branch.

Both chance nodes and decision nodes will have any costs subtracted from the node values. A graph of the tree structure can be displayed by the program. Example 3: A decision tree — Graphical user interface One of the models allows for decision trees to be entered graphically rather than in the table as given previously. This model can be used to examine the same example just completed. After selecting the model, the interface will be displayed as follows.

This is the only model in the software that has an input interface that is not the usual data table interface. In the beginning, there is only one node. The next step is to add two event nodes to node 1. The tool on the right is set to node 1. The default for node 1 is that it is a decision node as needed in this case. A button is available to change the node if this becomes necessary.

The new tree appears as follows. Notice that two branches have been added. The current node is node 2, which is indicated by both the fact that the node number in the upper right is node 2 and by the fact that the branch to node 2 is highlighted in a different color. The default is to add decision branches to events and vice versa.

The type of node can always be changed later. This yields the following diagram. Complete the data input by adding decision branches and data at nodes 4 and 6 and an event at node After all data has been entered, click on the Solve button on the toolbar. The data is in black and the solution is in blue as usual. Notice that branches that should be used are indicated in blue. In the past, an airline has observed a demand for meals that are sold on a plane as given in the following table.

How many meals should the airplane stock per flight? Meals Probability The program is requesting three profits as well as the obvious demands and probabilities.

Profit per unit. This is the normal profit for units bought and sold. Profit per unit excess. This is the profit for units that are overordered. In some cases, where there is a salvage value that exceeds the cost of the unit this will be a profit whereas in other cases this will be a loss. This is the profit for units when not enough units are ordered. It will be a profit if you can purchase units to sell after the fact at a cost less than the selling price.

Otherwise it will be a 0 or possibly a loss. If there were no voucher there would be no profit or loss for units for the demands that could not be satisfied.

Demands and probabilities. Enter the list of demands and their associated probabilities. The solution follows. The airline should order 20 meals to maximize its expected profit.

The first type of model is when past data sales are used to predict the future demand. This is termed time series analysis, which includes the naive method, moving averages, weighted moving averages, exponential smoothing, exponential smoothing with trend, trend analysis, linear regression, multiplicative decomposition, and additive decomposition.

The second model is for situations where one variable demand is a function of one or more other variables. This is termed multiple regression. There is overlap between the two models in that simple one independent variable linear regression can be performed with either of the two submodels.

In addition, this package contains a third model that enables the creation forecasts given a particular regression model, and a fourth model that enables the computation of errors given demands and forecasts. Time Series The input to time series analysis is a series of numbers representing data over the most recent n time periods.

Although the major result is always the forecast for the next period, additional results presented vary according to the technique that is chosen. When using trend analysis or seasonal decomposition, forecasts can be made for more than one period into the future. The summary measures include the traditional error measures of bias average error , mean squared error, standard error, mean absolute deviation MAD , and mean absolute percent error MAPE.

Note: Different authors compute the standard error in slightly different ways. That is, the denominator in the square root is given by n — 2 by some authors and by n — 1 by others. If you have a Prentice-Hall textbook the denominator should match the one in your text. If not, POM-QM for Windows uses n — 2 in the denominator for simple cases and always displays the denominator in the output.

The Time Series Data Screen Suppose that data is given as in the following table and the forecast for the demand for the week of February 14 and maybe the weeks of February 21, February The preceding example has data for the past six periods weeks , and the forecast for the next period – period 7 February 14 is needed.

Forecasting method. The drop-down method box contains the eight methods that were named at the beginning of this module plus a method for users to enter their own forecasts in order to perform an error analysis.

Of course, the results depend on the forecasting method chosen. A moving average is shown in the preceding screen. Number of periods in the moving average, n. To use the moving average or weighted moving average, the number of periods in the average must be given.

This is some integer between 1 and the number of time periods of data. In the preceding example, 2 periods were chosen, as seen in the extra data area. Demand y or Values for dependent y variable. These are the most important numbers because they represent the data. In most cases, these will simply be the past sales or demands.

The data is in the demand column as , , , , , and For the smoothing techniques of moving averages weighted or unweighted and single exponential smoothing, there is one set of output, whereas for exponential smoothing with trend, there is a slightly different output display. For the regression models, there is another set of output. The first available method is the naive method which simply uses the data for the most recent period as the forecast for the next period.

Begin with the moving averages. The main output is a summary table of results. The computations for all of these results can be seen on the following details window. The first column of output data is the set of forecasts that would be made when using the technique. Notice that because this is a 2-week moving average, the first forecast cannot be made until the third week.

The following three numbers — , Next period forecast. As mentioned in the previous paragraph, the last forecast is below the data and is the forecast for the next period; it is marked as such on the screen. In the example, it is This column begins the error analysis. The difference between the forecast and the demand appears in this column.

The first row to have an entry is the row in which the first forecast takes place. In this example, the first forecast occurs on January 17 row 3 and the forecast was for , which means that the error was 0. In the next week the forecast was for , but the demand was only , so the error was Absolute value of the error. The fifth column contains the absolute value of the error and is used to compute the MAD, or total absolute deviation.

Notice that the in the error column has become a plain, unsigned, positive 10 in this column. Error squared. The sixth column contains the square of each error in order to compute the mean squared error and standard error. The 10 has been squared and is listed as Be aware that when squaring numbers it is quite possible that the numbers will become large and that the display will become a little messy.

This is especially true when printing. Absolute percentage error. The seventh column contains the absolute value of the error divided by the demand. If the demand is 0, then the software will issue a warning regarding the MAPE. The total for the demand and each of the four error columns appears in this row. This row contains the answers to problems in books that rely on the total absolute deviation rather than the mean absolute deviation.

Books using total instead of mean should caution students about unfair comparisons when there are different numbers of periods in the error computation. The averages for each of the four errors appear in this row.

The average error is termed the bias and many books neglect this very useful error measure. The average squared error is termed the mean squared error MSE and is typically associated with regression least squares.

The average of the absolute percentage errors is termed the mean absolute percentage error MAPE. In this example, the bias is 1. Standard error. One more error measure is important. This is the standard error. Different books have different formulas for the standard error. That is, some use n —1 in the denominator, and some use n — 2.

The denominator is displayed in the summary output as shown previously. In this example, the standard error is Note: The Normal distribution calculator can be used to find confidence intervals and address other probabilistic questions related to forecasting. One more screen is available for all of these methods. It is a screen that gives the forecast control tracking signals results. For moving averages there is a summary screen of error measures, versus the n in the moving average.

One of the output displays not shown in this manual presents error measures as a function of n. Also, the moving average graph has a scroll bar that enables you to easily see how the forecasts change as n varies. The far right column is where the weights are to be placed. The weights may be fractions that sum to 1 as in this example. Notes about this free download: This download is potentially unsafe.

This file was last analysed by Softdeluxe 5 months ago. Thank you for choosing us to download QM for Windows, we are glad to know you are among our users. You’re downloading this program version 5. The software is to be downloaded for free. The download is offered as it is, with no corrections or alterations performed on our side. ORG – 7. Free Download Manager makes downloading files and videos easier and faster and helps avoid dreaded broken downloads. It is especially useful for those who are required to download files continually.

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