PRODUCTION PLANNING AND CONTROL TEXTBOOK PDF
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Elements of Production Planning and Control / Samuel Eilon. 2. Modern Production/ operation managements / Baffa & Rakesh Sarin. Production planning and control is a tool available to the management to achieve Thus production planning and control can be defined as the “direction and. The Fundamentals of Production Planning and pixia-club.info - Ebook download as PDF File .pdf), Text File .txt) or read book online.
The forecast for period 5 comes from the average of the demand for periods 2 through 4: The process is called moving average because as time progresses you always move to use the latest demand periods available. Graphically, the process looks as il- lustrated in Figure 2. Three-period moving Period Demand average forecast 1 24 2 26 3 22 4 25 There are two important points that need to be made concerning the graph and the moving average method as well.
First, it is fairly obvious to see that the forecast line is smoother than the demand line, showing the impact of taking an average. The more periods used in computing the moving average, the smoother this effect will be. The reason is that with more periods being used in the average, anyone of the demand points will have less overall influence.
Second, the forecast will always lag behind any actual demand. That is not so obvious in this graph, but suppose we use the same method to graph a demand pattern with an upward trend, as in Table 2. The graph in Figure 2. The implication of this lagging effect is that models such as simple moving averages should normally not be used to forecast demand when the data.
Three-period moving Period Demand average forecast 1 13 2 15 3 18 4 22 It is important to note that forecasting methods should not be arbitrarily selected, but instead should be selected and developed to fit the existing data as closely as possible. Weighted moving averages are basically the same as simple moving aver- ages, with one major exception. With weighted moving averages the weight as- signed to each past demand point used in the calculation can vary.
In this way more influence can be given to some data points, typically the most recent de- mand point. They take the basic form the W stands for the weight:.
In simpler terms, each of the weights is less than one, but the total of all the weights must add to equal 1. Taking the same data points as in the first ex- ample the three-period moving average data point from Table 2. The calculations are again fairly simple. For example, the period 4 forecast is calculated as 0.
Notice this value is smaller than the corresponding period 4 forecast using a simple moving average. The reason is, of course, that a larger weight is being put on the latest demand figure, which also happens to be the smallest of the three demand points being used. Graphically, the data in the table appears in Figure 2. As before, it is obvious that the forecast is smoothed, but also lags actual demand changes. Simple exponential smoothing is another method used to smooth the ran- dom fluctuations in the demand pattern.
There are two commonly used math- ematically equivalent formulas: Three-period weighted Period Demand moving average forecast 1 24 2 26 3 22 4 25 The second form shows that the exponential smoothed forecast incorpo- rated a weighted average of past history [ 1 - ex Ftl Since data from several periods early is still contained in the forecast, and was weighted numerous times as the forecast is developed period by period, one could consider it weighted exponentially, thus the name.
The first form, however, is easiest to explain from the perspective of what the method does from a logical perspec- tive. Essentially the forecast is found by taking the previous period's forecast Ft - i and adding a portion of the previous period's forecast error. The forecast error is, of course, the difference between the actual demand for any period and the forecast for that same period A t - i - Ft - i. The portion of the error term is found by multiplication by ex, which is the Greek letter alpha, and is called a smoothing constant.
The alpha value is always between zero and. It makes the forecast more responsive to actual changes in the demand, but also can equate to more reaction and disruption in the organization as it constantly strives to react to a more er- ratic forecast.
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The impact of the alpha value on the forecast can clearly be seen by taking the same data set used earlier and finding exponentially smoothed forecasts using alpha values first of 0. The table uses simple moving average for the first two periods to develop an initial forecast of 25 units for period 3, after which exponential smoothing can be used to cal- culate the remaining forecasts. When you initially start developing the forecast, however, you do not typically have such an initial forecast.
This implies that you must start the process using another forecasting method, after which the forecast from that method can be used as the initial Ft - I.
The resulting graph showing the demand data and the forecast data is shown in Figure 2. As can be seen, with such a small alpha, there is very little change in the forecast graph line. More responsiveness can be seen when alpha equals 0.
The graph line for the forecast is obviously more responsive than it was for an alpha of 0. Exponential Smoothing with 0. Regression has sometimes been called the "line of best fit. A particu- lar value for regression is to determine trend line equations.
The best way to show how it can be used is to illustrate with an example. In Table 2. We start with a data set that con- tains 2 years of demand data, listed by quarters. Notice that quarters 1 and 5 represent the same season, as do quarters 2 and 6, and so forth.
Placing the demand history in Microsoft Excel or any of the multiple sta- tistical packages that can calculate regressions and applying the regression analysis, it was found the data had an intercept of Quarter Demand 1 2 3 4 5 6 7 8 Quarter Demand Regression forecast 1 Applying this for- mula, we obtain Table 2. As you might expect, a straight-line forecast calculated by using a linear regression model does not show any of the seasonality of the data, as is clearly shown in Figure 2.
In order to pick up the seasonality for the forecast, we need to develop seasonal mUltipliers for each quarter. To do this we first find the ratio of the actual demand to the regression forecast in Table 2. For example, in the first quarter the 0. Now an average for corresponding quarters is calculated.
This means that for the first quarter of the year as represented by quarter 1 and quarter 5 the seasonal multiplier is 0.
Other multipliers are listed in Table 2. Now the seasonal multipliers can be applied to the basic regression fore- cast to develop a seasonally adjusted regression forecast by simply multiplying the seasonal multipliers by the regression forecast, as in Table 2.
Quarter Demand Regression forecast Ratio of demand to forecast 1 Ratio of demand Seasonal Quarter Demand Regression forecast to forecast multipliers 1 Seasonal Seasonally adjusted Quarter Demand Regression forecast multipliers regression forecast 1 Now if we look at the graphical comparison between the actual demand and the seasonally adjusted regression forecast in Figure 2.
In addition, the forecast for period 9 will give us a fair confidence, given how closely other quarters track in fact, on this graph it is very difficult to distinguish that there are in fact two separate lines.
To show how closely they track, Table 2. Before leaving the topic of regression, it may help to clear up any confu- sion that may exist because regression was classified as a forecasting method- ology for both causal forecasting and time series. There is a fundamental difference, even though the mathematical computation of the regression lines is the same. The difference is that the independent variable in time series re- gression is always time, while the independent variable in causal regression is always some other variable, usually a leading indicator from the economy.
It also should be noted that even though the discussion of seasonal in- dexes was presented in the context of time series regression, the concept of developing and applying seasonal indexes can be used for virtually any of the time series models. Quarter Demand Forecast Error Percentage error 1 Since the first rule of forecasting is that the forecast is likely to be incorrect, a critical question is "How incor- rect is it?
Buffer inventory, buffer capacity, or other methods may be needed to be planned to accommodate actual demand that differs from that forecasted. There are several important error calculations used.
Among some of the most useful are included: As the name implies, this term is calculated as the mathematical average forecast error over a specified time period.
The for- mula is: The At - Ft has been encountered earlier in the chapter. It represents the difference between the forecast and the actual demand for any given time pe- riod, also called the forecast error. The MFE involves adding all the individual forecast errors and dividing by the total number of errors. This number is not as important for the actual value of the number, but instead for the sign of the number, whether it is positive or negative in value.
If positive, it implies that over the range of numbers included, the actual demand was larger than the forecast. Another way of putting that is that the forecasting method was bi- ased on the low side.
If negative, of course, it means the forecasts were larger than the demand on average, implying the forecasting method was biased on the high side. For this reason, MFE is often referred to as forecast bias.
There is a very good reason the MFE does not really represent the aver- age forecast error, as can be shown in Table 2.
Adding all the errors yields a zero, making the MFE equal zero. It is clear, however, that forecast errors do exist, so the MFE is not a good method to find those errors. It does show, however, that in this case the forecasting method was not biased, in that over the full range of the demand history the forecasting method did not over or under project the total demand.
The formula is again given as the name of the term. It literally means the average of the mathematical absolute devia- tions of the forecast errors deviations. The formula is, therefore:. This represents a very important number, as it tells the average forecast error always positive over the time period in question. If we use the same basic data from the table above, we can calculate the true forecast error in Table 2. Tracking Signal. Similar to the concept of control limits for statistical process control charts, the tracking signal provides a somewhat subjective limit for the forecasting method to go "off track" before some action is taken.
In some cases this formula is written using the "running sum of the forecast er- rors," abbreviated as RSFE. The formula then becomes:.
This number is clearly a ratio that has no unit value-it is merely used as a signaL A rule of thumb for use of the tracking signal is that if the value of the tracking signal is larger than 4 or less than -4, the forecasting method may not be effective for tracking demand over the time period in question. It merely calls attention to investigate and adjust the forecasting method as necessary.
The tracking signal emphasizes an important trade-off: By the same token, to allow the method to proceed too long without evaluation could produce serious deterio- ration of the forecasts. The tracking signal, therefore, allows a systematic method to determine when the forecasting method should be evaluated or not.
Some modern packages come with several time series formulas built in with a variety of smoothing factors. Once demand data is input into the package, the system will find the best approach based on the lowest MAD or some other error approach.
The results from these packages can then become direct inputs into other planning and control systems, where they can be a great source to start the planning process. These computer packages allow a fast and inexpensive approach to the process that should be followed with or without the computer package. Once developed, the method should be tested against past data and modified as necessary. Forecast the demand for period 11 using each of the following methods: Compute the MAD for each method to determine which method would be preferable under the cir- cumstances.
Also calculate the bias in the data, if any, for all four methods, and explain the meaning. An Excel spreadsheet was set up to calculate the forecasts for each method using the formulas and approaches outlined in the chapter. The follow- ing chart shows the result from that analysis. Notice that because the overall trend from the data was fairly "flat" and there appeared to be no seasonality or other cyclicality, the coefficient for the period in the regression equation was quite small 0.
The starting exponential smoothing forecast value of was selected as the ac- tual demand from the previous period not shown on the table that was units.
Period Demand 3Mo. MA 3M. WMA Expon. Regression 1 The MADs for each of the forecasting methods were calculated using the formula presented in the chapter. The result was as follows: Method MAD 3-month moving average 7. Given the data and information in the problem, regression should probably be used because of the relatively small MAD compared to the other methods.
As more data is collected this could, of course, change. Calculation of the MFE for each brought an interesting result. The first three methods moving average, weighted moving average, and exponential smoothing each brought a positive number 0.
The interpretation for those is that each of those three methods is biased, specif- ically producing forecasts that are forecasting too low for the demand over the range of data points given. That should not be too surprising, given that the re- gression coefficient is slightly positive-an indication that there is a slight up- ward slope to the data.
Since it was discussed in the chapter how all three of these methods tend to lag behind and trend in the data, it is logical that the fore- casting method is a bit behind biased low. By contrast, the regression method picks up this slight upward trend-so much so that the MFE equals zero, indicating the lack of bias in that method. This chapter presents an overview of the tend to be primarily used for specific some of the major characteristics of product demand, which is again useful forecasting, and categorizes them into for the detailed product planning activi- three major categories: Both qualitative A major characteristic of all fore- and causal methods tend to require a casting methods is that they should be great deal of information about external considered to be incorrect.
The key to markets and environments. Since much future planning methods is the issue of of that information is not readily avail- just how incorrect they really are. For able to the operations manager, the time this reason there should always be an series methods needing only past de- error estimate presented with the fore- mand data are appealing.
Adding to cast. Some of the more common meth- their appeal is the relative ease of calcu- ods for error calculation and use were lation, especially with computers. They also discussed. Fogarty, D. Blackstone, Jr. Production and Inven- for Planners and Managers. Englewood tory Management.
Cincinnati, OH: South- Cliffs, NJ: Prentice-Hall, Western, Think of some of the leading indicators that could be used as a major input to causal forecasts in the economy. Discuss their use. What value does it bring to an operation if a forecasting method is used that only forecasts for families of products? Think of at least three products recently introduced that would probably use the life-cycle analogy.
What products would they "copy"? Why is life-cycle appropri- ate for those products? How should a company include information for their forecast that indicates the economy is headed for a recession? How, if at all, should that information impact time-series forecasting information? Discuss the arguments for using a large smoothing constant for exponential smooth- ing instead of a small one. Under what conditions would each be better? What is the major use of each?
Should they really be used together? Given the following data: Period Demand 1 43 2 37 3 55 4 48 a. Calculate the three-period moving average for period 5. Calculate the exponential smoothed forecast for period 5 using an alpha value of 0.
Assume the forecast for period 4 was the three-period moving average of the first three periods. Which method appeals the most for the data? Given the following demand data: Calculate the four-period weighted moving average for period 9 using weights of 0.
Calculate the forecast for period 9 using a 3-month moving average forecasting method. Which method would you recommend using and why? Given the same data for the previous problem: Use Excel or some other statistical computer package to calculate the regres- sion equation for the data.
Use the regression equation to forecast the demand for period 9. A forecasting method resulted in the following forecasts shown by the data in the following table:. Period Demand Forecast 1 2 3 4 5 6 7 8 9 10 a. Use the data to calculate the MAD. Find a regression equation for the demand data. Use the regression equation to forecast demand for period II.
Is the regression method preferred over the method used? The following demand data was collected over a 3 year period for one product:. Use the data to develop a regression-based forecast. Be sure to note that there is a seasonal factor to the demand. The following information is presented for a product: What is the MAD for the data above? Given the information above, what should the forecast be for the first quarter of if the company switches to exponential smoothing with an alpha value ofO.
What are the seasonal indicies that should be used for each quarter? Consider the forecast results shown below. Does the forecast model under- or over- forecast? A Simple Example 3. I business plans derived from the strategic plans will specify the product and service mix that the company will pursue, and will also indicate planned changes in market penetration, market approaches, and other critical aspects of the business.
This planning activity tends to go by several names, depending on the business and the type of pro- duction in which that business is involved.
Other common names that have been used in the past include aggregate planning, production planning, and, in the case of operations focused more directly in services, staffing planning.
In fact, production. M ITTV!! Instead the primary purpose is to plan for and coordinate resources, in- cluding type, quantity, and timing. Manu- facturing equipment such as specialized machine tools often take well more than a year to design and build, implying firms using such equipment need to have plans that reach that far into the future. The same may be true with cer- tain people with unique skills, either because it may take a long time to iden- tify and recruit those people, or in some cases there may be extensive training programs.
Inventory levels 2. Cash flow 3. Human Resource needs a. Number of people b. Skill levels c. Timing of need d. Training programs 4. Capital needs 5. Production outputs 6. Capacity planning e. Sales and marketing activities a. Sales promotions b. Advertising c. Pricing d. New product introductions e. Support and measure the business plan 2.
Support the customer 3. Ensure that plans are realistic 4. Manage change effectively 5. Control costs 7. Measure performance 8. Build teamwork. The key determi- nant is grouping together products or services that will utilize similar re- sources. This makes sense when you realize that the function of the activity is to plan resources.
For example, a firm may make several different styles of ta- bles, perhaps with different wood and different finishes. From a sales and marketing perspective, they may represent different products for different types of customers, but if they utilize the same resources e.
While a common method of aggregation is product families, some firms use revenues or even labor hours as the unit of analysis. There is at least one other important reason for the aggregation. The pri- mary source of demand estimates that drive the development of the plan is forecasting. Forecasts tend to be more accurate when developed in aggregate as opposed to plans for specific products or services.
These forecasts need to be developed and then coordinated with strategic plans that could provide sig- nificant influence on the actual demand. Examples of plans that could impact demand include: Clearly these plans need to be carefully coordinated with whatever re- sources are needed to accomplish the plans.
There are other issues in the design that need to be addressed, including the aggregation of time. For example, it is preferable to examine "buckets" of time that represent a week, a month, or some other unit of time. Again, the an- swer lies in a basic trade-off between the level of detail that is useful for plan- ning and the amount of effort necessary to obtain information.
The general rule is to aggregate as much as possible to the point where useful resource plans can be made. Aggregation of time and production units allow for ease of plan development and tend to be more accurate in the aggregate, but aggrega- tion should not be done to the point where useful information is lost. The cor- rect amount of aggregation is highly dependent on the type of product or service, the nature of the customers being served, and the processes being used to deliver the product or service.
In order to accomplish this process, it is important for Sales, Marketing, Operations, Finance, and Product Development to all work to- gether, guided by the strategic plan and vision for the future of the firm. Once the strategic planning process is completed in a firm, it is generally used to make a business plan, which is usually expressed in financial terms. There are other rea- sons top management involvement is important-their involvement provides a clear "message" to all in the firm that the process and outcome of the process are important activities, and therefore the resulting plans should be followed.
There are several issues that should be noted on this sample. First, note that the sales history for the last 3 months shows that overall there were 11, more units sold than was called for in the plan, and the production was 4, less than called for in the plan.
Production planning and control
This meant that over the 3 months the in- ventory would have dropped by 15, units, since they would have used fin- ished goods inventory to satisfy customer requirements. You can easily see the month-to-month calculations as well. Standard Polybobs Unit of measure: Month 14 -8 5 Cumulative 6 Month 3 -5 -2 Cumulative -2 That is what brought the planned inventory down from , units to , units.
You can also see how they plan to make up for the shortfall in their target level of 15 days 15, units in inventory. In November they plan to produce 5, more than expected sales, and then produce 10, more than sales for December. By the end of December they should then be back on target. Of course, these plans need to be reviewed and revised at the end of each month, since neither sales nor production are likely to exactly equal projec- tions.
In addition, conditions, policies, and other business plans may change. As each month passes, in fact, most companies will add an additional month to the end of the plan to maintain the same time horizon. The orders are taken and then production starts to that order.
The orders that exist awaiting production are typically called a backlog. Deluxe Polybobs Unit of measure: Each Target Order Backlog: Month 2 -1 1 Cumulative 1 2.
Month 0 1 1 Cumulative 1 2. The rest of the chapter focuses on some of the approaches and trade-offs that can be used to more specifically look at production planning, keeping in mind that the major focus should be on planning resources.
Some Techniques Mathematically, there are several approaches used to develop plans. In the past some companies would often try to put the major information into math- ematical algorithms to search for an optimal combination of products to maxi- mize an objective function, often defined in terms of profitability.
While that approach is still taken in some environments where capacity and outputs are well defined and not too complex such as in some process industries such as chemicals , many companies opt for different approaches for several reasons: When simplifying assumptions are made to make the mathematical model manageable, the simpler model often does not adequately reflect the envi- ronment itself.
Many managers have not been adequately trained in modeling techniques to the point where they can understand how to manage the process. In any case, the mathematical optimization techniques are beyond the in- tended scope of this book.
If the reader is interested in learning more about them, they are urged to consult one of the several good books on management science or operations research. A second approach is to simulate the production environment in a com- puter simulation, allowing rapid and effective solutions to scenarios that can be input into the program.
This approach is gaining in popularity as fast and effi- cient computers become plentiful and inexpensive, and as the simulation pro- gramming packages become more powerful and easier to use.
While it is often difficult to initially build the simulation model, once built the approach can be quite effective in developing "what if" approaches to the planning process. The third popular approach is really a subset of the second. It involves simulating the demand on an production resource environment in the form of a computer spreadsheet. As with a simulation of the entire production envi- ronment, once the spreadsheet format has been established it becomes rela- tively easy to investigate various approaches in a "what if" format.
A major difference in both the computer model simulation and the spreadsheet ap- proach is that they usually do not give an optimal solution - merely a rapid and fairly simple approach to search for a satisfactory solution for the various combinations of conditions being input.
This third approach, while not gener- ally yielding an optimal solution, is heavily used because of the ease of use and the widespread knowledge and acceptance of spreadsheet software. It is there- fore this approach that will be used in the remainder of the chapter. Often "best" means an attempt to maximize firm profits, but other conditions can also be established to define "best" in the context of the firm's strategic plan.
Examples of other conditions may be: Attempting to meet all expected customer demand e Attempting to minimize inventory investment.. Attempting to minimize the adverse impact on people, often experienced with volatility in the workforce caused by frequent layoffs It is frequently impossible to establish perfect conditions, so these trade- off criteria are important to understand as the plan is being developed.
In such a case the firm has to make a conscious decision as to whether it is better to allow customer service to fall or to accept the negative financial implications of attempting to meet customer demand. If the decision criteria used to make the final decision are established before the development of the plan, it often leads to a much smoother plan development with smaller proba- bility for functional "battles" based on functionally focused criteria.
There are three general categories of approaches used. They are: As the name implies, a level planning approach establishes a level set of resources and implies the demand will fluctuate around those available re- sources, or, in some cases, attempts to alter the demand patterns themselves to more effectively match the resource levels established.
This approach tends to be more common and certainly more appealing in environments where re- sources are difficult or expensive to alter. This also tends to be the approach used in many "lean production" environments. Examples include: Professional services tend to use appointments to alter and smooth demand patterns around the availability of the relatively expensive and difficult-to-alter resource represented by the doctors and dentists themselves.
In both cases the resources rooms and seats, respec-. Appointments under a different name reservations are again used to alter demand pat- terns. In addition, pricing strategies weekend rates and super-saver tick- ets, for example are used to once again alter demand patterns to smooth out the demand closer to the resource availability.
Many restaurants and automobile repair facilities also fit into this same category. Some manufacturing processes have similar characteristics. Some chemi- cal processes cannot be turned off without expensive and time-consuming startup activities, and they additionally cannot be sped up or slowed down. An example is making certain glass products in large volume. The glass furnace may need to be continually run, as shutting it down implies clean- ing out the entire furnace and starting it over.
The one "luxury" that man- ufacturing processes have over the two previous examples is the ability to inventory the output as an alternative to altering the demand. The only alternative is to build an inventory in low de- mand times and use it when demand is high. This approach represents the other extreme, in that demand is not al- tered, but resources are.
In fact in a "pure" chase environment the resources are continually being raised or lowered to meet the demand as it fluctuates under normal market conditions. As the approach is the opposite of level, so too are the typical characteristics of the environments where chase strategies may be appealing or, in some cases, the only alternative. Examples of such environments include: The demand for such products are often resulting from customers two or more levels down the supply chain, making it difficult if not impossible to alter demand.
For ex- ample, a supplier of light bulbs for automobiles is reacting to demand from the automobile manufacturers, which in turn are reacting to the con- sumers of the automobiles. The light bulb manufacturer has little ability to influence the demand for automobiles themselves.
Some examples include: Grocery stores and banks, where demand is often not recognized until the customer actually walks in and declares what they want. This approach is by far the most common approach. As the name implies, companies using this approach will "mix and match," altering de- mand and resources in such a way to maximize performance to their established criteria, including profit, inventory investment, and the impact on people.
Graphically, the differences in the three approaches can be illustrated in Figures 3. In Figure 3. It is at that point that inventory is being built at the maximum rate. The combination line also shows a building of inventory, but less than the level ap- proach. By the time that point B is reached, demand exceeds the level produc- tion rate, and far exceeds the production rate for the combination approach.
It is at that point that the inventory built in the low demand period will be used fairly rapidly. In the combination approach, far less inventory would have been built than with the level method. Combination Production. Level ". P'Od"cUoe Demand. At point C, therefore, note that the combi- nation approach production rate exceeds that of the level, and is therefore using the relatively smaller amount of inventory less rapidly.
Also note the combination method illustrated in the diagram is but one of what could be an infinite combination of production rates and timing changes for those rates. In general, they fall into two categories. One focuses on the supply side Operations to attempt to change the production supply. The strategies are summarized below.
Internal strategies focusing on operations-the supply side:. Hire and fire- As the name implies, this strategy focuses on altering the number of workers.
Temporary workers-In some industries, this alternative is becoming in- creasingly popular. Recently, companies in some environ- ments are recognizing that it occasionally may be a wise move to continue to keep and pay workers, but to not expect them to produce product un- less there is demand for that product.
Subcontracting-Also called "outsourcing" in some environments, this op- tion has also become popular in some companies in recent years. Basically, it means that the company will contract with a supplier or other contrac- tor to produce some or all of a required output. Inventory-This is a very common option in manufacturing companies.
Basically, the implication is that inventory will be produced during times of low demand and used to meet demand during times of high demand.
Backlog-As the name implies, this means the company will take the cus- tomer order even if it does not have inventory or capacity to meet the im- mediate demand, but will promise delivery when capacity is available. Do not meet demand - Again, as the name implies, this options means the company will simply decline to take a customer order if they do not have the inventory or capacity to meet the order requirements.
Change production rates- This option is rarely used, since it has a poten- tially negative impact on the workers. It implies the capability to slow down or speed up the rate of production. It can potentially have a nega- tive impact on both morale and output quality. External strategies focusing on the customer to alter demand rates in- clude the following:. Pricing-As the name implies, this involves changing the price of the product or service. Generally, lowering the price will increase demand while raising prices will decrease demand.
Promotions - Offering special incentives "rebates," for example are oc- casionally used to increase demand. Advertising- A very common strategy used to increase customer aware- ness and increase demand.
Reservations-Often used when capacity is scarce or very expensive such as in some restaurants, in doctor's offices, dentist's offices, etc. It can be said that in general the internal strategies are more commonly used in "chase" approaches, while the external strategies are more commonly used in "level" approaches.
An exception to that may be the use of inventory. The Waldorf Sport Boat Company has a demand forecast for all its alu- minum fishing boats under 15 feet for the next 6 months. The forecast is: Month Demand January February March April May June There are currently 10 workers assigned to the production line, each capable of producing approximately 15 boats per month December is typically the slowest month for sales. For this simple example we will assume each month has the same number of production days.
They recognize this lost profit selling price less stan- dard cost as a stockout cost. They currently have no boats in inventory. Using this simple data, the following tables will illustrate planning ap- proaches using chase, level, and a combination strategy. Chase In this example, we will use a minimum number of workers to meet all de- mand. No inventory will be allowed, and overtime can be used if necessary rather than adding another worker who could potentially add inventory.
Over- time production will be limited to 15 boats per month, for at that level it is bet- ter to hire another worker. OT Month Demand Workers produc. The number of workers necessary is calculated by dividing the demand by 15 the regular production per worker per month. For example, in January, di- viding the demand of by 15 yields That implies 16 workers are needed, giving a regular production of and leaving the additional 10 to be produced in overtime.
If we divide the total 6 months' demand units by 6, we see the average de- mand is about boats. Establishing the level production at 30 work- ers , we ensure that we will approximately meet the average demand, although it is obvious that inventory or shortages will occur since every month is not av- erage.
We will allow the inventory or shortage conditions, but will always have a constant, level production rate. On the negative side, however, there are 20 customers in June that did not get the boat they wanted. Note that to save space only columns that had relevant activity are included. Combination As mentioned before, there are numerous approaches that can be taken under the "combination" category.
We will illustrate but one. In this alternative, we start with 25 workers-plenty of workers to meet early-year demand and build some inventory. As demand grows, we will use the inventory and start to authorize overtime. We want to meet all demand, so eventually we will have to add workers. With a three-boat limit per worker per month on overtime, the 25 workers will only be authorized to produce 75 extra boats on overtime, and this may not be enough for some months. It will be a policy to add the mini- mum number of workers, however.
OT Reg. HIP Inv. OT Mnth Dem.
When we reached May we did not have enough capacity to meet demand even with overtime. The inventory was used up in April, and with 25 workers we could only produce units each worker can produce in regular time and 3 in overtime. The decision was to hire nine workers. Recall they want to minimize the total number of workers.
To come to the number of 34 workers, we divided the May demand of by 18 to obtain That implies that 34 workers could meet demand using almost all the authorized overtime.
In June, the demand of divided by the 18 gives Other alternatives may prove to be significantly cheaper. Even once the plan is made and appears to be satisfactory, great care must be taken in using these plans. They must be considered as rough planning esti- mates only. Not only are some of the input numbers generalized and therefore lack precision, but there are several qualitative issues that may confound the plan as well. Some of those issues are described in the next section.
There are several other issues that must be examined as well. The discussion below summarizes some of those qualitative issues: The "human" factor. Some of these approaches imply the "manipulation" of humans in the operation. I Layoffs will often impact the morale of the people.
Clearly that will. Others will have friends or relatives who were laid off. This condition can have negative impacts on efficiency and effectiveness. Also, once those on layoff are called back, many will have not only lost some of the "edge" on their skill, but will often have less of a feeling of dedication toward the company. This, too, may affect their approach to the job in a negative fashion.
I Hiring, too, has implications. The learning curve is a well-documented. These new people may also adversely impact the efficiency of the existing personnel as the new people at- tempt to learn by asking existing personnel, "Can you show me how to do something?
As an extreme example, one company with very sea- sonal demand patterns built two identical production processes. In peri- ods of high demand, both processes were run, each staffed with a combination of regular, highly skilled workers and low-skilled tempo- rary workers.
Clearly the processes had to be carefully designed to allow for rapid "insertion" of the temporary workers with little skill. When the low demand periods came, the company merely shut down one of the processes and staffed the remaining one with just the highly skilled reg- ular workers. The implication is that the positions normally taken by a low-skilled temporary worker was now being filled by a highly skilled and highly paid regular worker.
Even with a stable workforce, the assumption of a fixed output per worker built in to the sample model clearly may not hold due to the impact of learning and experience. The customer factor. The model almost assumes that customer-based ac- tions price changes, promotions, etc. Such actions can have longer-range impressions on cus- tomers or potential customers, however, and as such may not be wisely used without discretion.
Even some approaches typically viewed as opera- tional backordering and planned stock outs can impact customer impres- sions and buying habits. The forecast factor.
One of the most important characteristics of forecast- ing, described earlier in Chapter 2, is that a forecast should always be con- sidered to be incorrect. It is for that reason that a good forecasting model should give both the forecast and the error estimate. Notice that the model development earlier in this chapter does nothing about the ex- pected error. What this implies is that the company needs to make de- pending on the flexibility inherent in the process some specific contingency plans to deal with incorrect forecasts.
Generally, this contin- gency planning will use buffer inventory, buffer capacity, or both. The size of the buffer is generally based on the size of the forecast error. External environmental factors. Some of the more com- mon of these include: Labor contracts or union activities that can constrain the ability to ob- tain the right number of people with the right skills. Also, the cost of these resources will potentially be impacted.
Government regulation, especially concerning environmental, health, and safety issues, can impact both costs and resources. Competitive forces in the market can always impact demand. The important overriding message in the uncertainty of the model based on both quantitative and qualitative factors points again to the key point in developing these higher-level plans for the operation.
The type of resources, the quantity needed, and the timing of the need are the major focus of this planning activity, but management must be prepared to be flexible in this planning to adjust to the reality of the un- known factors in the process.
To illustrate the potential application in a more service-oriented setting, consider Example 3. They are relatively small, with only 15 full-time accountants. The accountants are highly trained, and any layoffs are out of the question for the firm.
They also believe that for now the staff of 15 is as many as they can afford to keep on a full-time basis. During the tax season January through April the demand on the accountant's time is very heavy. They are paid on the basis of a hour work week, but during tax season they can be expected to work a maximum of 60 hours per week. The partners believe that any more hours than that will hurt productivity and concentration to the point where major mistakes and inefficiencies are probable. When they use the clerical help, they merely adjust the workload so the accountants can do the technical work while the clerks can concentrate on more structured tasks.
The company will save the overtime hours for the accountants in an "inven- tory" of hours. The accountants are then expected to use those hours to take time off during the period oflight demand usually in the summer. The partners have developed a forecast of demand in hours for the next 8 months based on past experience of client tax needs. The partners need an estimate of the financial impact to determine if their decision to keep the staff at 15 is a good one.
They also need to determine if it may be feasible to take on additional work, should a new client request their serVIces. The following table shows the financial impact of their aggregated de- mand. Total time Temp. Demand time needed hours OT Temp. Month hours avail. They include the level of detail the level of aggregation and the length of the planning horizon.
To provide a brief synopsis: The issue here is first to determine whether the time aggre- gation should be in weeks, months, quarters, or some other unit of time. The general rule to dictate the logical time aggregation is to link it to the volatility in the market the company is serving. Keeping in mind that the primary purpose of this level of planning is to plan resources, one must first determine how quickly change is likely to occur in the market, and how much such a change is likely to impact the resources that are being planned.
Also at issue is whether the operation can or chooses to react to changes quickly. A highly capital-intensive operation producing a high- volume standard product, for example, is more likely to look at quarterly figures instead of monthly, especially if their strategy is largely based on level planning.
The level of aggregation of products or services must also be addressed. The general rule of thumb here is to aggregate whatever products or ser- vices that utilize the same basic category of resources being planned as long as it is possible to generate a projection of total demand on those re- sources. Time horizon. The next question is how far into the future should the plan go?
The general answer is that the plan should have a horizon at least as long as the time it takes to make the change in the resource base being planned. For example, if the resources are workers, we must know how long it will take to hire and train if necessary the workers necessary. The same is true for equipment-how long will it take to obtain and imple- ment any equipment changes necessary? When making these determina- tions, one must have some ideas as to the present conditions, including: Flexibility of the existing workforce-can they be moved from one re- source base to another with relative ease?
Flexibility of the existing equipment-can it be used for producing mul- tiple categories of aggregated outputs? Ease of obtaining capital and the amount of time it takes to obtain the capital- most resource changes especially resource additions will re- quire financing, and the company must know how that will be done and how long it will take to obtain.
This chapter focuses on the approach to be applied, and each with certain envi- developing intermediate-term strate- ronments with which they fit more gies for the best use of resources to appropriately. The key issue is to recog- meet the expected customer demand. Hoffmann, Production and Inventory and Operations Planning.
South- International Conference Proceedings. Why- Wallace, T. Irwin McGraw-Hill, Discuss the approach to Sales and Operations Planning that might be the most appropriate for the following companies. Explain your reasoning: A bank.. A fast-food restaurant.. An automobile service center attached to a dealership.. A hotel.. An attorney's office.. A retail clothing store 2. A company has traditionally used a level strategy for planning for several years.
Discuss the potential changes in their environment that could make them con- sider using more chase tactics. What changes, if any, will the perishability of the inventory have on the capability of a company in using a level strategy. Consider, for example, a meat market and a fast-food restaurant. Give several examples. For example, what are the trade-offs when deciding to aggregate the data into quarters instead of by month? What are the trade-offs when deciding how many product definitions to include in a "product family"?
The ABC Company has recognized the following demand for the next four quar- ters: Quarter Demand 1 3, units 2 4, units 3 4, units 4 3, units ABC has traditionally used the hiring and firing of workers to accommodate the changes in demand for their products, but is considering maintaining a stable workforce and subcontracting production when demand exceeds the capability of the workforce.
They currently have 30 workers, each capable of producing units per quarter. Given this information, should they continue their current prac- tice or move to subcontracting the production over what their current workforce can produce? Use the data from exercise 1 to consider another possibility: They could hire a set of workers at the beginning of the year and build inventory.
They could then use the inventory and some subcontracting, if necessary to deal with the quar- ters where demand exceeds production. Will this option be more or less attractive than the alternatives considered in exer- cise 1? The Icanride Bicycle Company has the following projected sales demand for the next 6 months assume each month has the same number of production days: They currently have 30 workers, each capable of producing bikes per month.
Compute the cost of using overtime and inventory production without short- ages. The need for modu la r construction, ease of access for maintenance and ergonomic considerations were also included. A percentage of the purchase cost was withheld until delivery of all maintenance manuals and initial spares was completed. Initial design work included the col I ecti on of historic infor marion o n pl ant pe rfo rmance, listing o f maintenance characteristics, layout and flow studies, Maintenance records for previous plant were examined in detail in order to estimate maintenance man p o w e r and freq uencies for preventive maintenance schedules.
Plant availability estimates were based on recorded mechanical a n d electrical brea kd own. All equipment drawings were examined for spares requirements, one of the aims of the rationalization program being the reduction of the varieW of spares.
For example, all pipework was designed in seven basic sizes and only three types of hydraulic :pump were used. Extensive rationalization was also achieved in the required electrical spares. In order to carry out much of the above it was essential that an experienced maintenance engineer was recruited as a senior member of the management team. He was involved in all stages of the project, including the design: A notable consequence of this was that the building exhibited some unique features that were designed specifically to facilitate maintenance organization.
For example, the mill bay had two floor levels, an elevated rolling level and a lower sewices and maintenance level. The advantages of this underground services floor were considerable and included routing of distribution and services pipework which was an improvement from the point of view of both installation and maintenance, it gave ease of preventive maintenance with underground test points , lubrication points and readily accessible drive equipment, without disrupting production flow on the upper level.
In addition, scrap collection was facilitated by a 'drive-in' arrangement and road vehicles. Maintenance of rolls was facilitated by passing the roll assemblies through the floor of the production bay directly into the roll and guide shop.
After preparation the new roll assemblies were simply craned up to the production floor and refitted.
Installation was supervised by a team of installation engineers who formed part of the project management team. Normal recording of plant installation problems was carried out.
The installation engineers compiled lists of checks required for each plant, this work demanding considerable study of drawings and design information prior to installation, and ensured that they gained considerable familiarity with the plant design. Control of the issue of the commissioning check cards was related to a computerized 19 20 Strategic Maintenance Planning installation network program and cards were issued to appropriate staff when predetermined stages were achieved in the program.
A computer terminal was available for regular updating of the network, and for reviewing checks required, on a day-to-day:basis while maintaining an overall picture of the installation and commissioning phase.
The company's own experience, supplemented by visits to similar plants in other countries, suggested that lengthy plant commissioning times had been due to insufficient attention to training. It was therefore decided that all management, operatives, artisans, engineers and supporting personnel should be adequately trained in the theory and practice needed to meet both the desired reduction in commissioning time and the required operational performance of the new mill.
Initial instruction and training was given in 2 to 4 weeks of formal lectures and discussion groups. Multi-skill training was given where considered desirable. Simulated control panels and layouts were built and used extensively. Each artisan's dossier of experience was matched against a skill and know! Electricians and fitters were recruited 3 months before mill startup and were given formal lectures, site work and project worki Regular tests were given and the training programs were also reviewed in the light of the participants' comments on their effectiveness.
For example, the recruitment of the installation engineers took account of their potential for subsequent transfer to maintenance department when the mill became;opera tional.
As a result, nine installation engineers were transferred to permanent maintenance engineering positions. Team training was also applied and the management team were involved in a series of courses designed to improve personal :and team effectiveness. Weekend sessions, for fostering teamwork, were undertakenby t h e management, production operatives, artisans and engineers of each shift.
As a result of the considerable prior effort described, the operational and maintenance practice that will now be outlined was made much easier. Performance standards were derived, for output, yield, defectives, accidents, fuel consumption, labor, maintenance, etc. Preventive maintenance routines and tasks were designed to be carried out, wherever possible, while the plant was running, the remainder being done at weekends or when the plant was standing for product changes, etc. As equipment was installed, plant history cards were opened, maintenance routines analyzed, preventive maintenance frequencies determined and a computer-controlled preventive maintenance system adopted.
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A readily assessed and continually updated inventory of routines and repetitive jobs was established in the computer data bank, which also contained more detailed information for the execution of specific jobs. Work planning was based on computerized job cards and used 'work measured' job Plant acquisition policy and maintenance life-cycle costs times for repetitive work.Once demand data is input into the package, the system will find the best approach based on the lowest MAD or some other error approach.
We wish to express our appreciation to all supporters for their contributions. Sales and marketing activities a. They also believe that for now the staff of 15 is as many as they can afford to keep on a full-time basis. The formula is, therefore:. Even with a stable workforce, the assumption of a fixed output per worker built in to the sample model clearly may not hold due to the impact of learning and experience.
Wysocki, 5-Phase York: A forecasting method resulted in the following forecasts shown by the data in the following table:.
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