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# Quality & Productivity Management ISM 3530

University of Central Florida

GPA 3.74

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This 76 page Class Notes was uploaded by Marco Moen on Thursday October 22, 2015. The Class Notes belongs to ISM 3530 at University of Central Florida taught by Stephen Goodman in Fall. Since its upload, it has received 37 views. For similar materials see /class/227444/ism-3530-university-of-central-florida in Information Systems Management at University of Central Florida.

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Date Created: 10/22/15

ISM 3530 Fall 2009 CP5 MRP and CRP Page 1 0f14 HIERARCHICAL PRODUCT STRUCTURES Consider the following structure for some hypothetical product A that is fabricated out of 1 unit of a hypothetical component B which in turn is made out of two units of a hypothetical raw material C In this hierarchical structure A would be considered an independent demand inventory item while B and C would be considered dependent demand inventory items 1 unit of Finished Product A 1 unit of Component B 2 units of Raw Material C Assume that the demand for Finished Product A is a constant uniform 10 units per week which equates to 520 units per year Since there is 1 unit of Component B in each A the annual demand for B is also 520 units Since there are 2 units of Raw Material C in each B the annual demand for C is 1040 units Additional data have been gathered regarding setup costs and holding costs for each of the items and all data are summarized in the following table Average C 1040 If the EOQ formula is used on these data the following results would be obtained Item C 20 Unfortunately these timing and sizing decisions will be very bad for they will not take into consideration the interdependencies that eXist between these hierarchical levels of production The BS need to be available in the correct quantities and at the correct times to support the planned production of the A s and the CS need to be available in the correct quantities and at the correct times to support the planned production of the B s As you will see on the neXt page this is not going to happen with these results ISM 3530 Fall 2009 CP5 MRP and CRP Page 2 0f14 E0 RESULTS ON THE DEPENDENT DEMAND INVENTORY ITEMS The following table shows the progression of orders for A B and C over several successive weeks we are ignoring any lead time issues to get the point across Item Wkl Wk2 Wk3 Wk4 Wk5 Wk6 Wk7 Wk8 Wk9 WklO Wkll Here is the problem We want to make a batch of 60 units of A in week 1 then another in week 7 and so on The product structure information on the previous page indicates that we fabricate an A out of 1 unit of B This means that we would need 60 B s in week 1 to make our batch of 60 A s Unfortunately our schedule above seems to indicate that we will only have 30 B s in week 1 and won t be scheduling another batch of 30 B s until week 4 That same dependency problem can be noted in the relationship between B and C We want to make a batch of 30 B s in week 1 then another in week 4 etc The product structure information indicates that we make a B out of 2 units of C This means that we would need 60 CS in week 1 to make our batch of 30 B s and we just saw above that the 30 B s was a bad decision But our schedule indicates that we will have only 20 CS in week 1 and won t get any more until we make our next batch of 20 in week 2 This is a mess Things are not meshing together to get the finished product completed This little illustration shows the fundamental problem that occurs when we try to apply EOQ logic independently to each of the dependent demand items in a product s structure There is a solution to this problem and it involves the use of a procedure called Material Requirements Planning MRP to generate a set of time phased requirements for each of the items in the product structure and then make the timing and sizing decisions to accommodate those time phased requirements ISM 3530 Fall 2009 CP5 MRP and CRP Page 3 0f14 MRP INPUTS AND OUTPUTS Master Production Schedule MPS 1 Bill of MRP Inventory Materials System Records File BOM IN V Planned Order Releases FOR Master Production Schedule Statement of the gross requirements for the nished product anticipated need schedule for the nished product Bill of Materials Product structure information showing exactly what components are needed to produce a nished product and how they are assembled together the hierarchical product structure information Inventory Records File Information regarding the available on hand inventory and on order inventory for each item in the bill of materials Planned Order Releases These are the answers to the how much and when questions for each inventory item in the product structure They are time phased statements about planned orders ISM 3530 Fall 2009 CP5 MRP and CRP Page 4 0f14 GROSS RE UIREMENTS AND NET RE UIREMENTS Gross Requirements Gross requirements re ect the total need for an inventory item to support demand either demand for a nished product or demand for some higher level item in the product structure Net Requirements Net requirements re ect the production need for an inventory item to support demand Net requirements are generally found by taking the gross requirements total need and subtracting any available inventory either on hand or on order This net requirement is what we actually have to produce ISM 3530 Fall 2009 CP5 MRP and CRP Page 5 0f14 TIME PHASED INVENTORY RECORD The record keeping device used in MRP is a time phased inventory record a table that keeps track of the gross requirements inventory both on hand and on order and the planned order releases for a particular inventory item The format used in the textbook is as follows For demonstration purposes and to aid in the discussion of this device I have added a row Net Requirements that is not displayed in the book examples Normally I would not use this row but it does help in the explanation ISM 3530 Fall 2009 CP5 MRP and CRP Page 6 0f14 DATA FOR SIMPLE MRP ILLUSTRATION The master production schedule for Finished Product A illustrated on page 1 indicates that there is a uniform demand for 10 units of the product each week of the year The bill of materials is the tree diagram shown on page 1 Finally the inventory records le reveals the following We have all the input we need for the MRP process master production schedule bill of materials and inventory records le ISM 3530 Fall 2009 CP5 MRP and CRP Page 7 0f14 MRP EXPLOSION PROCESS gTEXTBOOK CONVENTIO S ISM 3530 Fall 2009 CP5 MRP and CRP Page 8 0f14 MRP EXPLOSION PROCESS ENHANCED CONVENTION FOR EASE OF UNDERSTANDING In this approach the projected available amounts are computed based on the premise that nothing new has been ordered As gross requirements continue to occur you will eventually run out of available inventory at some point When this happens the projected available drops to a negative value Every time another gross requirement occurs the projected inventory level will fall further in the hole ie the projected available will become more negative Net requirements are an indication of when you need something and how much you need to keep from falling in the hole or falling deeper in the hole Then the planned orders provide a response to these net requirements All you need to do is back up the lead time from the rst net requirement to nd the place where the planned order is placed For Finished Product A below our inventory will rst go negative in period 5 so we have a net requirement in period 5 for 10 units We project that we will fall 10 further in the hole in period 6 so we have a net requirement for 10 units in period 6 and so on ISM 3530 Fall 2009 CP5 MRP and CRP Page 9 0f14 MRP LOT SIZING PHILOSOPHIES MRP is very mechanical when it comes to generating the net requirements for each item in a product structure However the generation of the planned order releases the answers to the how much and when questions requires some input as to the lot sizing approach that will be used There are many lot sizing rules in existence A few are displayed below to show the impact of these differing philosophies We will ignore lead times assume they are zero to keep things simple here Assume that MRP has generated the following set of time phased net requirements for a given item I Week I 1234 678 I Net RequirementsI 0 I 0 5 ml 30200502070 50 9 0 Illustration with a xed order quantity of 80 units The 80 unit quantity might have been generated with some economic rationale or it may have been generated by some packaging Illustration with xed period requirements order 4 periods The 4 periods could have been generated using some economic rationale or it might have simply been a convenient order cycle ISM 3530 Fall 2009 CP5 MRP and CRP Page 10 0f14 DATA FOR CAPACITY RE UIREMENTS PLANNING ILLUSTRATION Assume that MRP has generated the following set of time phased planned order releases for three items that we manufacture Items L M and N These three items require varying amounts of processing time in three work centers Work Centers X Y and Z as follows This information is what we refer to as a bill of capacity Finally the weekly capacity limitations of each work center are as follows ISM 3530 Fall 2009 CP5 MRP and CRP Page 11 0f14 CAPACITY LOADS GENERATED BY THE GIVEN MRP OUTPUTS Work Center X Source Work Center Y Work Center Z A problem is noted with this set of planned order releases Work Center X does not have suf cient capacity in week 5 to do everything it will be asked This is graphically displayed in the load pro le shown on the next page This information suggests that we should modify our planned production quantities so that we have a feasible production schedule One possible alteration is illustrated on page 13 ISM 3530 Fall 2009 CP5 MRP and CRP Page 12 0f14 CAPACITY REQUIREMENTS PLANNING GRAPHICAL DISPLAY OF LOAD PROFILE FOR WORK CENTER X 80 Capacityljmitf89h wllt M ISM 3530 Fall 2009 CP5 MRP and CRP Page 13 0f14 ALTERED SET OF PLANNED ORDER RELEASES AND THE RESULTING CAPACITY REQUIREMENTS PLAN The MRP generated set of planned order releases has been altered slightly The 900 units of item M in week 5 were going to overload Work Center X To remedy this problem schedulers have decided to take 200 units out of that proposed 900 unit lot size and add those 200 units to the 200 unit lot size for Item M that was scheduled for week 4 The adjusted lot sizes are shown below The resulting capacity requirements on the work centers are as follows Work Centers X and Z are affected since Item M uses capacity in these two work centers Work Center X Work Center Y 24 220 220 Work Center Z ISM 3530 Fall 2009 CP5 MRP and CRP Page 14 0f14 EXPANDED LOOK AT MRP INPUTS AND OUTPUTS Master Production Schedule MPS 1 Bill of MRP Inventory Materials System Records File BOM IN V Planned Order Releases FOR Revise the ORs Capacity Requirements Planning CRP Are the PORs feasible No Release the Orders According to the Schedule ISM 3530 Fall 2009 CP2 Forecasting Page 1 0f41 TYPES OF FORECASTING METHODS Qualitative methods These types of forecasting methods are based on judgments or opinions and are subjective in nature They do not rely on any mathematical computations Quantitative methods These types of forecasting methods are based on quantitative models and are objective in nature They rely heavily on mathematical computations ISM 3530 Fall 2009 CP2 Forecasting Page 2 0f41 QUALITATIVE FORECASTING METHODS Qualitative Methods Executive Opinion Market Research Delphi Method Approach in which a group of managers meet and collectively develop a forecast Approach that uses surveys and interviews to determine customer preferences and assess demand Approach in which a forecast is the product of a consensus among a group of experts ISM 3530 Fall 2009 CP2 Forecasting Page 3 0f41 QUANTITATIVE FORECASTING METHODS Quantitative forecasting methods can be divided into two categories time series models and causal models Quantitative Methods Time Series Models Causal Models Time series models look at past Causal models assume that the patterns of data and attempt to variable being forecasted is predict the future based upon the related to other variables in the underlying patterns contained environment They try to project Within those data based upon those associations ISM 3530 Fall 2009 CP2 Forecasting Page 4 0f41 TIME SERIES MODELS Model Description Na39139ve Uses last period s actual value as a forecast Simple Mean Average Uses an average of all past data as a forecast Simple Moving Average Uses an average of a speci ed number of the most recent observations with each observation receiving the same emphasis weight Weighted Moving Average Uses an average of a speci ed number of the most recent observations with each observation receiving a different emphasis weight Exponential Smoothing A weighted average procedure with weights declining exponentially as data become older Trend Adjusted Exponential Smoothing An exponential smoothing model with a mechanism for making adjustments when strong trend patterns are inherent in the data Seasonal Indexes A mechanism for adjusting the forecast to accommodate any seasonal patterns inherent in the data Linear Trend Line Technique that uses the least squares method to t a straight line to the data ISM 3530 Fall 2009 CP2 Forecasting Page 5 0f41 PATTERNS THAT MAY BE PRESENT IN A TIME SERIES Level or horizontal Data are relatively constant over time with no growth or decline Trend Data exhibit a steady growth or decline over time Seasonality Data exhibit upward and downward swings in a short to intermediate time frame most notably during a year Cycles Data exhibit upward and downward swings in over a very long time frame Random Erratic and unpredictable variation in the data over time ISM 3530 Fall 2009 CP2 Forecasting Page 6 0f41 HYPOTHETICAL PATTERN OF HISTORICAL DEMAND Demand Time ISM 3530 Fall 2009 CP2 Forecasting Page 7 0f41 LEVEL 1 ORIZONTAL COMPONENT IN HISTORICAL DEMAND Demand Time ISM 3530 Fall 2009 CP2 Forecasting Page 8 0f41 TREND COMPONENT IN HISTORICAL DEMAND Demand Time ISM 3530 Fall 2009 CP2 Forecasting Page 9 0f41 SEASONAL COMPONENT IN HISTORICAL DEMAND Demand Year 1 Year 2 Year 3 Time ISM 3530 Fall 2009 CP2 Forecasting Page 10 0f41 CYCLE COMPONENT IN HISTORICAL DEMAND Demand g g Many years or decades Time Time Demand RANDOM COMPONENT IN HISTORICAL DEMAND ISM 3530 Fall 2009 CP2 Forecasting Page 11 0f41 ISM 3530 Fall 2009 CP2 Forecasting Page 12 0f41 DATA SET TO DEMON STRATE FORECASTING METHODS The following data set represents a set of hypothetical demands that have occurred over several consecutive years The data have been collected on a quarterly basis and these quarterly values have been amalgamated into yearly totals For various illustrations that follow we may make slightly different assumptions about starting points to get the process started for different models In most cases we will assume that each year a forecast has been made for the subsequent year Then after a year has transpired we will have observed what the actual demand turned out to be and we will surely see differences between what we had forecasted and what actually occurred for after all the forecasts are merely educated guesses Finally to keep the numbers at a manageable size several zeros have been dropped off the numbers ie these numbers represent demands in thousands of units Year ISM 3530 Fall 2009 CP2 Forecasting Page 13 0f41 ILLUSTRATION OF THE NATVE METHOD Nai39ve method The forecast for next period period tl will be equal to this period s actual demand A In this illustration we assume that each year beginning with year 2 we made a forecast then waited to see what demand unfolded during the year We then made a forecast for the subsequent year and so on right through to the forecast for year 7 Actual Demand Forecast Year AL FL Notes 1 100 There was no prior demand data on which to base a forecast for period 1 2 300 100 From th1s p01nt forward these forecasts were made on a yearbyyear bas1s 3 200 300 4 500 200 5 600 500 6 700 600 7 700 ISM 3530 Fall 2009 CP2 Forecasting Page 14 0f41 MEAN SIMPLE AVERAGE METHOD Mean simple average method The forecast for next period period tl will be equal to the average of all past historical demands In this illustration we assume that each year beginning with year 2 we made a forecast then waited to see what demand unfolded during the year We then made a forecast for the subsequent year and so on right through to the forecast for year 7 Actual Demand Forecast Year AL FL Notes 1 100 There was no prior demand data on which to base a forecast for period 1 2 300 100 From th1s pomt forward these forecasts were made on a yearbyyear bas1s 3 200 200 4 500 200 5 600 275 6 700 340 7 400 ISM 3530 Fall 2009 CP2 Forecasting Page 15 0f41 SIMPLE MOVING AVERAGE METHOD Simple moving average method The forecast for next period period tl will be equal to the average of a speci ed number of the most recent observations with each observation receiving the same emphasis weight In this illustration we assume that a 2year simple moving average is being used We will also assume that in the absence of data at startup we made a guess for the year 1 forecast 200 Then after year 1 elapsed we made a forecast for year 2 using a naive method 100 Beyond that point we had sufficient data to let our 2year simple moving average forecasts unfold throughout the years Actual Demand Forecast Year AL FL Notes 1 100 200 Thls forecast was a guess at the beg1nn1ng 2 300 100 Th1s forecast was made us1ng a naive approach 3 200 200 From th1s p01nt forward these forecasts were made on a yearbyyear bas1s 4 500 250 5 600 3 50 6 700 5 50 7 650 ISM 3530 Fall 2009 CP2 Forecasting Page 16 0f41 ANOTHER SIMPLE MOVING AVERAGE ILLUSTRATION In this illustration we assume that a 3year simple moving average is being used We will also assume that in the absence of data at startup we made a guess for the year 1 forecast 200 Then after year 1 elapsed we used a naive method to make a forecast for year 2 100 and year 3 300 Beyond that point we had sufficient data to let our 3year simple moving average forecasts unfold throughout the years Actual Demand Forecast Year A Ft Notes 1 100 200 Th1s forecast was a guess at the beg1nn1ng 2 300 100 Th1s forecast was made us1ng a naive approach 3 200 300 Th1s forecast was made us1ng a na1ve approach 4 500 200 From th1s pomt forward these forecasts were made on a yearbyyear bas1s 5 600 333333 6 700 433333 7 600 ISM 3530 Fall 2009 CP2 Forecasting Page 1 7 0f41 WEIGHTED MOVING AVERAGE METHOD Weighted moving average method The forecast for next period period tl will be equal to a weighted average of a speci ed number of the most recent observations In this illustration we assume that a 3year weighted moving average is being used We will also assume that in the absence of data at startup we made a guess for the year 1 forecast 200 Then after year 1 elapsed we used a naive method to make a forecast for year 2 100 and year 3 300 Beyond that point we had sufficient data to let our 3year weighted moving average forecasts unfold throughout the years The weights that were to be used are as follows Most recent year 5 year prior to that 3 year prior to that 2 Actual Demand Forecast Year AL FL Notes 1 100 200 Th1s forecast was a guess at the beg1nn1ng 2 300 100 Th1s forecast was made us1ng a naive approach 3 200 300 Th1s forecast was made us1ng a na1ve approach 4 500 210 From th1s p01nt forward these forecasts were made on a yearbyyear bas1s 5 600 370 6 700 490 7 630 ISM 3530 Fall 2009 CP2 Forecasting Page 18 of 41 EXPON EN TIAL SMOOTHIN G METHOD Exponential smoothing method The forecast for next period period tl will be calculated as follows this box contains all you need to know to apply exponential smoothing Fm xAt lxFt equation 1 Where 0c is a smoothing coef cient whose value is between 0 and l this box is to convince the skeptics that exponential smoothing does incorporate all past data Although the exponential smoothing method only requires that you dig up two pieces of data to apply it the most recent actual demand and the most recent forecast forecasts made with this model will include a portion of every piece of historical demand Furthermore there will be different weights placed on these historical demand values with older data receiving lower weights This can be observed by expanding the above formula as follows When we made the forecast for last period Ft it was made in the following fashion Ft xAH lxFt1 equation 2 If we substitute equation 2 into equation 1 we get the following Fm xAt 1xxA11 1xF11 Which can be cleaned up to the following Fm xAt 0cl0cAt1 lx2F11 equation 3 We could continue to play that game by recognizing that FH xAH lxFtz equation 4 If we substitute equation 4 into equation 3 we get the following Fm xAt 0cl0cAt1 lx2xA12 lxFtz Which can be cleaned up to the following Fm OLAt xlxAt1 x10czAtz lx3Ftz If you keep playing that game you should recognize that FM OLAt xlxAt1 oc1x2At2 0cl0c3At3 0c lx4At4 0c lx5At5 As you raise those decimal weights to higher and higher powers the values get smaller and smaller ISM 3530 Fall 2009 CP2 Forecasting Page 19 0f41 EXPON EN TIAL SMO OTHING ILLUSTRATION In this illustration we assume that in the absence of data at startup we made a guess for the year 1 forecast 200 Then for each subsequent year beginning with year 2 we made a forecast using the exponential smoothing model After the forecast was made we waited to see what demand unfolded during the year We then made a forecast for the subsequent year and so on right through to the forecast for year 7 This set of forecasts was m ule using an on value of 1 Actual Demand Forecast Year AL FL Notes 1 100 200 Th1s was a guess s1nce there was no prlor demand data 2 300 190 From th1s pomt forward these forecasts were made on a yearbyyear bas1s 3 200 201 4 500 2009 5 600 230 81 6 700 267729 7 3 10956 1 ISM 3530 Fall 2009 CP2 Forecasting Page 20 0f41 A SECOND EXPONENTIAL SMOOTHING ILLUSTRATION In this illustration we assume that in the absence of data at startup we made a guess for the year 1 forecast 200 Then for each subsequent year beginning with year 2 we made a forecast using the exponential smoothing model After the forecast was made we waited to see what demand unfolded during the year We then made a forecast for the subsequent year and so on right through to the forecast for year 7 This set of forecasts was m ule using an a value of 2 Actual Demand Forecast Year AL FL Notes 1 100 200 Th1s was a guess s1nce there was no prlor demand data 2 300 180 From th1s pomt forward these forecasts were made on a yearbyyear bas1s 3 200 204 4 500 2032 5 600 26256 6 700 330048 7 4040384 ISM 3530 Fall 2009 CP2 Forecasting Page 21 0f41 A THIRD EXPON EN TIAL SMOOTHIN G ILLUSTRATION In this illustration we assume that in the absence of data at startup we made a guess for the year 1 forecast 200 Then for each subsequent year beginning with year 2 we made a forecast using the exponential smoothing model After the forecast was made we waited to see what demand unfolded during the year We then made a forecast for the subsequent year and so on right through to the forecast for year 7 This set of f0 recasts was m ule using an a value of 4 Actual Demand Forecast Year AL FL Notes 1 100 200 Th1s was a guess s1nce there was no prlor demand data 2 300 160 From th1s pomt forward these forecasts were made on a yearbyyear bas1s 3 200 216 4 500 2096 5 600 32576 6 700 435456 7 5412736 ISM 3530 Fall 2009 CP2 Forecasting Page 22 0f41 LINEAR TREND LINE Linear trend line method This method is a version of the linear regression technique It attempts to draw a straight line through the historical data points in a fashion that comes as close to the points as possible Technically the approach attempts to reduce the vertical deviations of the points from the trend line and does this by minimizing the squared values of the deviations of the points from the line Ultimately the statistical formulas compute a slope for the trend line b and the point where the line crosses the yaxis a This results in the straight line equation YabX Where X represents the values on the horizontal axis time and Y represents the values on the vertical axis demand For the demonstration data computations for b and a reveal the following b 120 a20 Y 20 120X This equation can be used to forecast for any year into the future For example Year 7 Forecast 20 1207 820 Year 8 Forecast 20 1208 940 Year 10 Forecast 20 12010 1180 ISM 3530 Fall 2009 CP2 Forecasting Page 23 0f41 STABILITY VS RESPONSIVENESS IN FORECASTING All demand forecasting methods vary in the degree to which they emphasize recent demand changes when making a forecast Forecasting methods that react very strongly or quickly to demand changes are said to be responsive Forecasting methods that do not react quickly to demand changes are said to be stable One of the critical issues in selecting the appropriate forecasting method hinges on the question of stability versus responsiveness How much stability or how much responsiveness one should employ is a function of how the historical demand has been uctuating If demand has been showing a steady pattern of increase or decrease then more responsiveness is desirable for we would like to react quickly to those demand increases or decreases when we make our next forecast On the other hand if demand has been uctuating upward and downward then more stability is desirable for we do not want to over react to those up and down uctuations in demand For some of the simple forecasting methods we have examined the following can be noted Moving Average Approach Using more periods in your moving average forecasts will result in more stability in the forecasts Using fewer periods in your moving average forecasts will result in more responsiveness in the forecasts Weighted Moving Average Approach Using more periods in your weighted moving average forecasts will result in more stability in the forecasts Using fewer periods in your weighted moving average forecasts will result in more responsiveness in the forecasts Furthermore placing lower weights on the more recent demand will result in more stability in the forecasts Placing higher weights on the more recent demand will result in more responsiveness in the forecasts Simple Exponential Smoothing Approach Using a lower alpha at value will result in more stability in the forecasts Using a higher alpha at value will result in more responsiveness in the forecasts ISM 3530 Fall 2009 CP2 Forecasting Page 24 0f41 CALCULATING SEASONAL INDEX VALUES Begin by dividing the total annual demand by 4 which is the number of periods in the year to see what the quarterly demand would have been if the annual demand had been distributed evenly throughout the year this is column 7 below Q1 Annual 4 For each quarter in each year divide the actual quarterly demand by the colunm 7 average This gives a measure of how each quarter s demand compared to a uniform split each computation is a seasonal indeX for the quarter and year in question Then compute an average value for the seasonal indexes for each quarter average for the numbers in columns 2 3 4 and 5 Annual4 25 75 50 125 150 175 Q1 Q2 Q3 Q4 800 1120 1360 720 773 1147 1387 693 800 1080 1440 680 Avg 832 1120 1392 656 773 1133 1400 693 7776 11316 14066 6866 793 1122 1397 688 Note The average value of all the seasonal indexes should 10 subject to slight rounding errors Check these 793 1122 1397 6884 1000 ISM 3530 Fall 2009 CP2 Forecasting Page 25 0f41 AN ALTERNATE WAY TO CALCULATE SEASONAL INDEX VALUES Begin by calculating the average demand in each of the four quarters of the year Col 1 Col 2 Col 3 Col 4 Col 5 Col 6 Annual Year Q1 Q2 Q3 Q4 Demand 1 20 28 34 18 100 2 58 86 104 52 300 3 40 54 72 34 200 4 104 140 174 82 500 5 116 170 210 104 600 6 136 198 246 120 700 2058 2086 34104 1852 Avg 40 54 72 3482 Deman 104 116 140 170 174 210 104 Per Qtr 1366 1986 2466 1206 79 11267 140 6833 Next note that the total demand over these six years of history was 2400 ie 100 300 200 500 600 700 If this total demand of 2400 had been evenly spread over each of the 24 quarters in this six year period the average quarterly demand would have been 100 units But the numbers above indicate that the demand wasn t evenly distributed over each quarter In Quarter 1 the average demand was considerably below 100 it averaged 79 in Quarter 1 In Quarters 2 and 3 the average demand was considerably above 100 with averages of 11267 and 140 respectively Finally in Quarter 4 the average demand was below 100 it averaged 6833 in Quarter 4 We can calculate a seasonal index for each quarter by dividing the average quarterly demand by the 100 that would have occurred if all the demand had been evenly distributed across the quarters This would result in the following alternate seasonal index values Year Q1 Q2 Q3 Q4 Seasonal 79100 11267100 140100 6833100 Index 7900 11267 14000 6833 A quick check of these alternate seasonal index values reveals that they average out to 10 as they should 7900 11267 1400 68334 1000 These seasonal index values are slightly different from the ones obtained using the method demonstrated in the textbook We cannot claim that one of these is right and one of these is wrong It is simply two different ways of analyzing the same set of data ISM 3530 Fall 2009 CP2 Forecasting Page 26 0f41 USING SEASONAL INDEX VALUES The following forecasts were made for the next 4 years using the linear trend line approach the trend line formula developed was Y 20 120X where Y is the forecast and X is the year number 10 1180 If these annual forecasts were evenly distributed over each year the quarterly forecasts would look like the following Q1 Annual 4 31 793 1122 1397 688 However seasonality in the past demand suggests that these forecasts should not be evenly distributed over each quarter We must take these even splits and multiply them by the seasonal index 81 values to get a more reasonable set of quarterly forecasts The results of these calculations are shown below You will note that I used the seasonal index values calculated using the method illustrated on page 24 I could have used the seasonal index values calculated using the method illustrated on page 25 and would have gotten slightly different forecasts Q1 Q2 Q3 Q4 Annual If you check these nal splits you will see that the sum of the quarterly forecasts for a particular year will equal the total annual forecast for that year sometimes there might be a slight rounding discrepancy ISM 3530 Fall 2009 CP2 Forecasting Page 27 0f41 OTHER METHODS FOR MAKING SEASONAL FORECASTS Let s go back and reexamine the historical data we have for this problem I have put a little separation between the columns of each quarter to let you better Visualize the fact that we could look at any one of those vertical strips of data and treat it as a time series For example the Q1 column displays the progression of quarter 1 demands over the past six years One could simply peel off that strip of data and use it along with any of the forecasting methods we have examined to forecast the Q1 demand in year 7 We could do the same thing for each of the other three quarterly data strips To illustrate I have used the linear trend line method on the quarter 1 strip of data which would result in the following trend line Y 28000 233714X For year 7 X 7 so the resulting Q1 forecast for year 7 would be 160800 We could do the same thing with the Q2 Q3 and Q4 strips of data For each strip we would compute the trend line equation and use it to project that quarter s year 7 demand Those results are summarized here Q2 trend line Y 6133 339429X Year 7 Q2 forecast would be 231467 Q3 trend line Y 8000 422857X Year 7 Q3 forecast would be 288000 Q4 trend line Y 3067 204000X Year 7 Q4 forecast would be 139733 These forecasts are in the same ballpark as those made with the seasonal index values on the prior page They differ a bit but we cannot say one is correct and one is incorrect They are just slightly different predictions of what is going to happen in the future ISM 3530 Fall 2009 CP2 Forecasting Page 28 0f41 CAUSAL MODELS METHOD Causal models assume that the variable being forecasted the dependent variable is related to other variables independent variables in the environment This approach tries to project demand based upon those associations In its simplest form linear regression is used to t a line to the data That line is then used to forecast the dependent variable for some selected value of the independent variable The textbook illustrates an example in which the sales for a product seem to be related to the amount of money spent on advertising The following table shows varying amounts that have been spent on advertising on past occasions and the sales that corresponded to these expenditures The independent variable X is the advertising expenditure The dependent variable Y is the sales dollars Application of regression formulas yields the following forecasting model Y 9283 115X Ifthe company plans to spend 53000 on advertising in a particular year ie X 53 the forecast for sales will be Y 9283 11553 15387 which means sales projections are 153870 ISM 3530 Fall 2009 CP2 Forecasting Page 29 0f41 MEASURING FORECAST ACCURACY Mean Forecast Error MFE Forecast error is a measure of how accurate our forecast was in a given time period It is calculated as the actual demand minus the forecast or EAF Forecast error in one time period does not convey much information so we need to look at the accumulation of errors over time We can calculate the average value of these forecast errors over time ie a Mean Forecast Error or MFEUnfortunately the accumulation of the E values is not always very revealing for some of them will be positive errors and some will be negative These positive and negative errors cancel one another and looking at them alone or looking at the MFE over time might give a false sense of security To illustrate consider our original data and the accompanying pair of hypothetical forecasts made with two different forecasting methods Hypothetical Hypothetical Forecasts Forecast Forecasts Forecast Actual Made With Error With Made With Error With Demand Method 1 Method 1 Method 2 Method 2 Year AL F A F1 F A Ft 1 100 105 5 160 60 2 300 310 10 390 90 3 200 195 5 1 10 90 4 500 490 10 620 120 5 600 585 15 540 60 6 700 715 15 5 80 120 Accumulated Forecast Errors Mean Forecast Error MFE 06 0 06 0 Based on the accumulated forecast errors over time the two methods look equally good But clearly Method 1 is generating better forecasts than Method 2 ISM 3530 Fall 2009 CP2 Forecasting Page 30 0f41 MEASURING FORECAST ACCURACY Mean Absolute Deviation MAD To eliminate the problem of positive errors canceling negative errors a simple measure is one that looks at the absolute value of the error size of the deviation regardless of sign When we disregard the sign and only consider the size of the error we refer to this deviation as the absolute deviation If we accumulate these absolute deviations over time and nd the average value of these absolute deviations we refer to this measure as the mean absolute deviation MAD For our hypothetical two forecasting methods the absolute deviations can be calculated for each year and an average can be obtained for these yearly absolute deviations as follows Hypothetical Forecasting Method 1 Hypothetical Forecasting Method 2 Actual Forecast Absolute Forecast Absolute Demand Forecast Error Deviation Forecast Error Deviation Year AL F A Ft At F11 F A Ft At F11 1 100 10 5 5 5 160 60 60 2 300 310 10 10 390 90 90 3 200 19 5 5 5 1 10 90 90 4 500 490 10 10 620 120 120 5 600 5 8 5 15 15 5 40 60 60 6 700 715 15 15 580 120 120 Total Absolute Deviation 60 540 Mean Absolute Deviation 60610 540 690 Clearly Method 1 has provided more accurate forecasts over this six year horizon as evidenced by its considerably smaller MAD ISM 3530 Fall 2009 CP2 Forecasting Page 31 0f41 MEASURING FORECAST ACCURACY Mean Squared Error MSE Another way to eliminate the problem of positive errors canceling negative errors is to square the forecast error Regardless of whether the forecast error has a positive or negative sign the squared error will always have a positive sign If we accumulate these squared errors over time and nd the average value of these squared errors we refer to this measure as the mean squared error MSE For our hypothetical two forecasting methods the squared errors can be calculated for each year and an average can be obtained for these yearly squared errors as follows Hypothetical Forecasting Method 1 Hypothetical Forecasting Method 2 Actual Forecast Squared Forecast Squared Demand Forecast Error Error Forecast Error Error Year A F A F A Fol F A F A Fol 1 100 105 5 25 160 60 3600 2 300 310 10 100 390 90 8100 3 200 195 5 25 110 90 8100 4 500 490 10 100 620 120 14400 5 600 585 15 225 540 60 3600 6 700 715 15 225 580 120 14400 Total Squared Error 700 52200 7006 522006 Mean Squared Error 11667 8700 Clearly Method 1 has provided more accurate forecasts over this six year horizon as evidenced by its considerably smaller MSE The Question often arises as to why one would use the more cumbersome MSE when the MAD calculations are a bit simpler you don t have to square the deviations MAD does have the advantage of simpler calculations However there is an advantage to the MSE method Since this method squares the error term large errors tend to be magni ed Consequently MSE places a higher penalty on large errors This can be useful in situations where small forecast errors don t cause much of a problem but large errors can be devastating ISM 3530 Fall 2009 CP2 Forecasting Page 32 0f41 ILLUSTRATION OF THE THREE FORECAST ACCURACY MEASURES Here is a further illustration of the three measures of forecast accuracy this time using hypothetical forecasts that were generated using some different methods than the previous illustrations called forecasting methods A and B actually these forecasts were made up for purposes of illustration Demand Forecast Error Deviation Deviation Forecast Error Deviation Deviation T otals 06 1206 24006 06 1206 72006 You can observe that for each of these forecasting methods the same MFE resulted and the same MAD resulted With these two measures we would have no basis for claiming that one of these forecasting methods was more accurate than the other However even an untrained observer could look at the forecasts with method A and note that they were pretty consistent in that they were always missing by a modest amount in this case missing by 20 units each year However forecasting method B was very good in some years and extremely bad in some years missing by 60 units in years 5 and 6 That observation would probably lead many to prefer the accuracy and consistency of forecasting method A This causal observation is formalized in the calculation of the MSE Forecasting method A has a considerably lower MSE than forecasting method B The squaring magni ed those big misses that were observed with forecasting method B ISM 3530 Fall 2009 CP2 Forecasting Page 33 0f41 MONITORING FORECAST ACCURACY OVER TIME Tracking Signal A tracking signal TS is a tool used to continually monitor the quality of our forecasting method as we progress through time A tracking signal value is calculated each period and a determination is made as to whether it falls into an acceptable range An upper limit and a lower limit will have been established for the tracking signal and these values de ne the acceptable range If the tracking signal drifts outside of the acceptable range that is an indication that the forecasting method being used is no longer providing accurate forecasts Tracking signals also help to indicate whether there is bias creeping into the forecasting process Bias is a tendency for the forecast to be persistently under or persistently over the actual value of the data Tracking signal is calculated as follows algebraic sum of forecast errors ASFEQ MAD Tracking signal Illustration of the computation of tracking signals to accompany the progression of forecasts made over time with hypothetical forecasting Method 1 rst displayed on page 29 above 6 700 715 15 Keep in mind that each line in the above table would have been calculated in successive years At the end of each year we can look back at the most recent year and compare the forecast we made with the actual demand that occurred The next several pages show how these calculations would have unfolded through the years and how they would have been plotted on a graph to determine whether our forecasting method still appeared to be working well ISM 3530 Fall 2009 CP2 Forecasting Page 34 0f41 We now begin illustrating the computation and plotting of tracking signals to accompany the progression of forecasts made over time with hypothetical forecasting Method 1 In this illustration we will assume that the upper limit has been set at a value of 3 and the lower limit has been set at a value of 3 In practice these limits may be higher or lower than these values and they do not necessarily need to have the same numerical value The values for these limits are largely a function of how costly or disruptive inaccurate forecasts are As we run through time assume that the forecast made for year 1 was 105 and the subsequent demand that occurred in year 1 was 100 The tracking signal calculated and plotted after year 1 would be as follows Total lAt lAt 39 Year At Ft At Ft ASFE Ftl Ftl MAD TS l 100 105 75 75 5 5 51 5 755 7100 ISM 3530 Fall 2009 CP2 Forecasting Page 35 0f41 Continuing with our movement through time assume that the forecast made for year 2 was 310 and the subsequent demand that occurred in year 2 was 300 The tracking signal calculated and plotted after year 2 would be as follows Total lAt 39 lAt 39 Year AL FL At Ft ASFE Ftl Ftl MAD TS l 100 105 75 7 5 5 51 5 755 7100 510 510 2 300 310 710 7 5 10 15 152 75 71575 7200 ISM 3530 Fall 2009 CP2 Forecasting Page 36 0f41 Continuing with our movement through time assume that the forecast made for year 3 was 195 and the subsequent demand that occurred in year 3 was 200 The tracking signal calculated and plotted after year 3 would be as follows Total lAt 39 lAt 39 152 75 71575 7200 203 667 710667 7150 ISM 3530 Fall 2009 CP2 Forecasting Page 3 7 0f41 Continuing with our movement through time assume that the forecast made for year 4 was 490 and the subsequent demand that occurred in year 4 was 500 The tracking signal calculated and plotted after year 4 would be as follows Total lAt 39 lAt 39 152 75 71575 7200 203 667 710667 7150 304 75 075 0 ISM 3530 Fall 2009 CP2 Forecasting Page 38 0f41 Continuing with our movement through time assume that the forecast made for year 5 was 585 and the subsequent demand that occurred in year 5 was 600 The tracking signal calculated and plotted after year 5 would be as follows Total lAt 39 1A1 152 75 203 667 304 75 455 9 71575 7200 710667 7150 075 0 159 167 ISM 3530 Fall 2009 CP2 Forecasting Page 39 0f41 Continuing with our movement through time assume that the forecast made for year 6 was 715 and the subsequent demand that occurred in year 6 was 700 The tracking signal calculated and plotted after year 6 would be as follows 152 75 71575 7200 203 667 710667 7150 20 304 75 075 0 4559 159l67 606 10 ISM 3530 Fall 2009 CP2 Forecasting Page 40 0f41 SOME NOTES ABOUT TRACKING SIGNALS Tracking signal review Recall that the tracking signal is calculated as follows Tracking Signal TS algebra1c sum of forechast errors ASFE The ASFE can be either positive or negative since each forecast error A Ft can be either positive or negative If you forecast too low in any period ie the forecast is below the demand the forecast error will be positive If you forecast too high in any period ie the forecast is above the demand the forecast error will be negative The MAD will always be positive Consequently the tracking signal could end up being either a positive number or a negative number What to watch for If the tracking signal plots outside the acceptable range ie above the upper limit or below the lower limit that is an indication that things are no longer going well in our forecasting and we should reexamine our method However even when the tracking signal plots between the upper limit and lower limit this is not always an indication that things are going well in our forecasting Consider the following When we forecast we expect to miss ie make forecast errors If we are unbiased in our forecasting approach we should expect our forecast to be too high on some occasions and too low on other occasions Ultimately if we are making reasonably accurate forecasts the ASFE should uctuate between positive and negative values always hovering around zero Suppose the TS is consistently plotting in the positive range This would be an indication that we are consistently incurring a lot of positive forecast errors ie forecasting too low This would be an indication that bias has crept into our forecasting approach ie a bias toward forecasting too low and we should reexamine our forecasting approach Suppose the TS is consistently plotting in the negative range This would be an indication that we are consistently incurring a lot of negative forecast errors ie forecasting too high This would be an indication that bias has crept into our forecasting approach ie a bias toward forecasting too high and we should reexamine our forecasting approach ISM 3530 Fall 2009 CP2 Forecasting Page 41 0f41 What are reasonable tracking si al limits How tight or how loose the tracking signal limits are set is a function of the consequences of forecast errors The upper limit and the lower limit do not have to be the same distance from the zero mark Suppose that it is not a big deal if we forecast too high negative forecast error and make too much of a product We can hold the excess in inventory and sell it at a later date However if we forecast too low positive forecast error we will not have enough product to satisfy customer demand and we are likely to lose customers to our competitors In such a case we would probably have a tight range on the positive side of our tracking signal graph and a relatively loose range on the negative side of our tracking signal graph Alternatively suppose that if we forecast too high negative forecast error and make too much of a product the cost consequences are severe for this product has a short shelf life or becomes obsolete quickly and excess inventory will quickly become worthless However if we forecast too low positive forecast error and do not have enough product to satisfy customer demand it is no big deal for customers are willing to wait for later deliveries In such a case we would probably have a tight range on the negative side of our tracking signal graph and a relatively loose range on the positive side of our tracking signal graph THREE PRACTICE EOQ ILLUSTRATIONS Illustration 1 Daily demand d 2000 units per day Company operates 365 days per year hence annual demand D 2000 units per day X 365 days per year 730000 units per year Annual demand D 730000 units per year Ordering cost S 50 per order Holding cost H 73 per unit per year Item purchase price P 50 per unit Lead time 2 days Results of computations EOQ 2DSH 27300005073 10000 units per order Number of orders placed per year DQ 730000 unitsyr10000 unitsorder 73 ordersyr Average inventory level Q2 10000 units 2 5000 units Annual ordering cost DQS 7300001000050 3650 Annual holding cost Q2H 10000273 3650 Time between placement of orders Qd 10000 unitsorder2000 units day 5 days order or TBO daysyear ordersyr 365 daysyear73 ordersyear 5 daysorder Total annual holding ordering inventory cost 3650 3650 7300 Total annual purchasing cost PD 50730000 365000 Total annual inventory cost holding ordering purchase 372300 Illustration 2 Given the following data for an inventory scenario whose characteristics t the assumptions of the classic EOQ situation D 8000 units per year S 50 per order H 20 per unit per year P Item purchase price 200 per unit LT Replenishment lead time 5 days Assume we have 250 operating days per year Find the following 90809 9 Average daily demand EOQ Number of orders placed per year Total annual ordering cost Total annual holding cost Total annual inventory cost including item purchase cost Time between orders Reorder point in units Answers 1 wsgweww Average daily demand 32 units per day EOQ 2000 units Number of orders placed per year 4 orders per year Total annual ordering cost 200 per year Total annual holding cost 200 per year Total annual inventory cost including item purchase cost 16400 per year Time between orders 625 days Reorder point in units 160 units Illustration 3 Given the following data for an inventory scenario whose characteristics t the assumptions of the classic EOQ situation D 15000 units per year S 3 per order H 1 per unit per year P Item purchase price 50 per unit LT Replenishment lead time 1 day Assume we have 300 operating days per year Find the following 908994P N Average daily demand EOQ Number of orders placed per year Total annual ordering cost Total annual holding cost Total annual inventory cost including item purchase cost Time between orders Reorder point in units Answers 1 90899 9 Average daily demand 50 units per day EOQ 300 units Number of orders placed per year 50 orders per year Total annual ordering cost 150 per year Total annual holding cost 150 per year Total annual inventory cost including item purchase cost 7800 per year Time between orders 6 days Reorder point in units 50 units ILLUSTRATION OF FAULTY LOGIC AND SOUND LOGIC IN THE COMPUTATION OF GROSS AND NET RE UIREMENTS REMINDER We saw the following de nitions in CPS page 4 Gross Requirements Gross requirements re ect the total need for an inventory item to support demand either demand for a nished product or demand for some higher level item in the product structure Net Requirements Net requirements re ect the production need for an inventory item to support demand Net requirements are generally found by taking the gross requirements total need and subtracting any available inventory either on hand or on order This net requirement is what we actually have to produce ILLUSTRATION Assume we have the following product structure 1 unit of Finished Product A 1 unit of Component B 2 units of Raw Material C Assume that the demand for Finished Product A will be 520 units over the upcoming year Assume that we have the following inventory information for our three inventory items A B and C FAULTY LOGIC Gross requirements Since we need a total of 520 A we will need a total of 520 B since there is l B in each A and we will need a total of 1040 C since there are 2 C in each B Net requirements We can subtract beginning inventories and scheduled receipts from these gross requirements to determine the net requirements as in the following table Results Of Our Faulty Logic But this logic will overstate our true production or ordering need for B and C ie our net requirements and will result in us producing or ordering too many of these items You can see below why this logic overstated our net requirements for B and C SOUND LOGIC We must proceed level by level in the determination of the gross requirements and then subsequently the net requirements The net requirements at one level dictate the gross requirements at the next lower level GR 520 1 unit of leshed Product A NR 520 20 20 480 GR 480 1 unit of Component B NR 480 15 0 465 GR465X2930 2 units ofRaw Materlal C NR 930 10 150 770 As you can see in this sound logic upon determining that our production need net requirement for A was 480 units we nd that we only need a total gross requirement of 480 units of B to support this planned production But we do not have to produce the entire 480 units of B since we have some B 15 units in inventory Our net requirement is only 465 units of B Continuing this logic since we are only going to make 465 units of B our total need gross requirement for C is 2 X 465 or 930 units of C But we do not have to order the entire 930 units of C Since we have some C 10 units in inventory and some C 150 units scheduled to be received we will only have to order 770 units of C ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 1 0f16 AGGREGATE PLANNING Aggregate plan Also called the production plan it details the aggregate production rate decisions work force decisions and inventory scheduling decisions over an intermediate planning horizon The planning horizon is usually stated as 6 to 18 months however if there is seasonality in the demand for the output of the system the plan should normally cover at least 12 months so that it can react to all the seasonal swings in the demand Aggregate plan units At this level of planning there is not a lot of detail Individual end product identity is typically not present Instead planning is performed for a composite or average unit of product in a particular family of similar products For example we may plan for units of hair dryers without regard for whether they are 1500 watt dryers 1600 watt dryers 1875 watt dryers travel dryers etc Decisions are typically aggregated into monthly time periods Weekly or daily detail is not needed at this level of decision making Those ner time breakdowns will be made at lower levels of decision making still to come ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 2 0f16 NATURE OF THE AGGREGATE PLANNING PROBLEM Units of Aggregate Product Demand Forecast Regular or Normal Capacity Limit I Time 1 year ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 3 0f16 AGGREGATE PLANNING STRATEGIES Strategies for coping with uctuating demand are categorized as either operations oriented strategies or marketing oriented strategies Operations oriented strategies can be further classi ed as either a leveling strategy or a chase strategy Marketing tactics for dealing with demand seasonality include inducing demand shifts or offer counter seasonal products Aggregate Planning Strategies Operations Oriented Marketing Oriented Leveling strategy Shifting demand Chase strategy Counter seasonal products ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 4 0f16 EXAMPLES OF OPERATIONS STRATEGIES Leveling strategy Maintain output at a constant level rate throughout the planning horizon Accommodate seasonal variations in demand through the accumulation and depletion of inventories Chase strategy Vary the output rate to match the seasonal variations in demand Output rate can be varied by l hiring and ring workers 2 utilizing overtime and idle time or 3 subcontracting Hybrid strategy A cost effective aggregate plan may require the use of a combination of the pure strategies listed above Such a plan is referred to as a hybrid plan ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 5 0f16 SIMPLE DATA SET TO DEMON STRATE DIFFERENT STRATEGIES Demand forecasts have been made for our composite product and they appear in the following table I have scaled the numbers down to a manageable size Demands were originally in thousands of units but I am knocking off three zeros to make it more compact IMonth IJANIFEB IMARIAPRIMAYI JUNIJUL IAUGI SEPIOCTINOVIDEC IDemandForecast 100 150 250 400 250 50 90 160 260 390 240 60 The total annual demand from the above set of forecasts is 2400 units Additional Data Hiring cost 200 per worker hired Firing cost 100 per worker red Regular pay 5000 per worker per month Overtime pay 15 times regular pay Inventory carrying cost 10 per unit based on end of month balances Backorder cost 20 per unit based on end of month shortage Productivity 10 units per worker per month this has also been scaled down from what was originally expressed in thousands of units per month Beginning workforce size 20 workers available at the beginning of January Beginning inventory 0 units no inventory available at the beginning of January ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 6 0f16 LEVEL STRATEGY WITH INVENTORY AND BACKORDERS This strategy will produce at a constant rate and simply accumulate and deplete inventory throughout the year A rst step is to determine what the monthly production needs to be in order to produce enough to satisfy the demand for the year and determine whether our initial work force size is suf cient to produce at this rate Total annual demand 2400 units Average monthly production needed 2400 units 12 months 200 units per month Current work force size is 20 workers This is just enough because productivity 10 units per worker per month Month JAN FEB MAR APR MAY IUN JUL AUG SEP OCT NOV DEC Demand Forecast 100 150 250 400 250 50 90 160 260 390 240 60 Production 200 200 200 200 200 200 200 200 200 200 200 200 Beginning 1W 0 100 150 100 100 150 0 110 150 90 100 140 Ending Inv 100 150 100 100 150 0 110 150 90 100 140 0 Cmyi g C051 1000 1500 1000 1100 1500 900 Backorder Cost 2000 3000 2000 2800 Regular Labor Cost 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 Overtime Cost Total Inventory Carrying Cost 7000 Total Backorder Cost 9800 Total Hiring Cost 0 Total Firing Cost 0 Total Regular Payroll Cost 1200000 Total Overtime Payroll Cost 0 Total Annual Cost 1216800 ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 7 0f16 CHASE STRATEGY WITH HIRING AND FIRING This strategy will produce exactly what is demanded each month It will adjust capacity by hiring and ring workers at the beginning of each month so that the work force is the proper size to produce just what is demanded each month Month JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Demand Forecast 100 150 250 400 250 50 90 160 260 390 240 60 Workers Needed 10 15 25 40 25 5 9 16 26 39 24 6 Number Hired 5 10 15 4 7 10 13 Number Fired 10 15 20 15 18 Hiring Cost 1000 2000 3000 800 1400 2000 2600 Firing Cost 1000 1500 2000 1500 1800 Labor Cost 50000 75000 125000 200000 125000 25000 45000 80000 130000 195000 120000 30000 Overtime Cost Total Inventory Carrying Cost 0 Total Backorder Cost 0 Total Hiring Cost 12800 Total Firing Cost 7800 Total Regular Payroll Cost 1200000 Total Overtime Payroll Cost 0 Total Annual Cost 1220600 ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 8 0f16 CHASE STRATEGY WITH OVERTIME AND IDLE TIME This strategy will produce exactly what is demanded each month by maintaining its current workforce size of 20 workers at a constant value It will adjust capacity by using overtime when demand exceeds the regular time capacity and allow workers to sit idle when demand is less than the regular time capacity Month JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Demand Foreca 100 150 250 400 250 50 90 160 260 390 240 60 Capacity 20 20 20 20 20 20 20 20 20 20 20 20 Capacity Needed 10 15 25 40 25 5 9 16 26 39 24 6 Overtime 5 20 5 6 l9 4 Idle Time 10 5 15 ll 4 l4 Regular Labor Cost 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 Overtime Cost 37500 150000 37500 45000 142500 30000 These items are expressed in worker months of output For example 20 workers on the payroll we have the equivalent of 10 worker months of idle time during January In January we need 100 units of output The productivity of one worker is 10 units per month so we would need 10 fully active workers worth of output during January to get 100 units of output Since we actually have In March we have our rst overtime which is the equivalent of 5 worker months of overtime One worker month of labor on a regular time basis costs 5000 Since the overtime rate is given as time and a half a worker month of overtime would cost 7500 Total Inventory Carrying Cost Total Backorder Cost Total Hiring Cost Total Firing Cost Total Regular Payroll Cost Total Overtime Payroll Cost 0000 1200000 442500 Total Annual Cost 1642500 ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 9 0f16 COMMENTS ABOUT THE PURE STRATEGIES DEVELOPED Although the least costly of the three strategies developed above is the level strategy and the most costly is the chase strategy using overtime don t try to draw any general conclusions from these results The costs generated are purely a function of the relative costs of hiring ring overtime inventory carrying and backorders If these costs were to have had different values the results could have been quite different Also no claims are being made about the practicality of these pure strategies There could be plenty of reasons why one or more would be impractical For example if the skilled workers are in great demand and hard to nd one would probably think twice about ring workers whenever they were not immediately needed It might be quite dif cult to replace them when the need arose Also close examination of the overtime strategy would reveal that the month of April is a killer for the workforce and October isn39t much kinder In April we need the equivalent of 40 worker months of output Since we have a workforce size of 20 that is equivalent to needing 20 worker months of overtime To put this in perspective if our workers normally work 40 hours per week then each will have to work 80 hours per week in the month of April In reality a company39s aggregate plan will probably be a hybrid plan that may have used a combination of several of the pure strategies mentioned For example to ease the burden on the work force during April we probably would not have allowed our workers to sit idle during January and February but instead had them using their full time capacity to produce in excess of January and February demand Those excess units would have been held in inventory to lessen the need for overtime in the later months Even with that overtime needs might have been excessive so it may have been necessary to hire some temporary workers during those peak months to help get by Or we might have subcontracted out some of the work or we might have accepted some backorders with the unsatis ed demand being satis ed in some of the later months when demand tails off In short the pure strategies might provide a starting point in the development of an aggregate plan but those will almost certainly have to be tweaked to get a plan that is practical ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 10 0f16 CHECKING THE FEASIBILITY OF THE AGGREGATE PLAN Once an aggregate plan has been developed it is a good idea to perform a check to make sure the plan will not overtaX some critical or limited resource for the organization This is very similar to what we saw in MRP when we used capacity requirements planning to check the feasibility of the materials plan If the planned order releases were going to overburden a department work center or machine we found it necessary to adjust the planned order releases In a similar fashion we use a process called resource requirements planning to see if we will have suf cient resources to carry out our aggregate plan These resources can be many and varied For example there may be limitations on our storage space which impacts inventory limitations on our personnel department capabilities which impacts hiring and ring limitations on machine capacity which impacts production scheduled limitations on energy availability which impacts production scheduled etc etc etc ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 11 0f16 BRIEF ILLUSTRATION OF RESOURCE REQUIREMENTS PLANNING To get a avor for the concept of resource requirements planning consider the following small portion of an aggregate plan that was developed for some company the company had a beginning workforce of 120 workers at the start of January and zero beginning inventory These rst two columns These last ve columns represent the results of our aggregate re ect the demand planning process We put together a plan that had production forecasts We developed decisions in each month some inventory accumulation and an aggregate plan that depletion and some workforce size adjustments hiring and ring would accommodate Before we implement this plan it will be necessary to check to see if these f recasts we have enough resources to make it work Ending Workforce Number Number Month Demand Production Inventory Size Hired Fired January 800 1000 200 100 20 February 1200 2000 1000 200 100 March 1800 1500 700 150 50 April 2000 1800 500 180 30 Etc Etc Etc Etc Etc Etc Etc Suppose that the warehouse has a storage limitation of 800 units This plan will overtaX that resource in February We will either have to scale down the production quota for February or nd some alternative place for inventory storage Suppose that the personnel department staff is only capable of recruiting interviewing and training at most 50 workers in a given month This plan will overtaX the personnel department in February with its requirement to hire 100 workers Suppose that the machines that make the product have a design capacity of only 1800 units per month This plan will overtaX the equipment in February with its production quota of 2000 units As a result of all these observations we should be doing a little tweaking of this plan to get it in a form that is doable Otherwise if we simply try to implement it as it stands we are in for some trouble down the road ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 12 0f16 FACTS ABOUT THE MASTER PRODUCTION SCHEDULE The master production schedule MPS is an anticipated build schedule for end products While it is a plan to satisfy customer demand it is a statement of production and not a statement of demand The master production schedule is a disaggregation break down of the aggregate plan It contains more product detail than the aggregate plan distinct end items rather than composite or average units of product and it uses ner time intervals weeks rather than months The master production schedule is the driVing force behind the generation of the material requirements plan ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 13 0f16 BREAKING DOWN THE AGGREGATE PLAN In this illustration we manufacture two different models Model A and Model B of some product The aggregate plan generated the following schedule of planned production for an average unit of our product I am displaying only the rst few months of the aggregate plan which would have spanned at least a full year In addition I have scaled down the numbers by dropping several zeros A e ate Plan Month I Jan I Feb I Mar I Apr I Flamed Production 200 100 300 400 For the purpose of projecting the needs for individual models we have looked at historical records and nd that Model A accounts for 60 of the demand for product while Model B accounts for 40 of the demand We will use those percentages and our knowledge that demand typically occurs uniformly throughout a month to project the week by week production needs for our two models A e ate Plan Month I Jan I Feb I Mar I Apr I Flamed Production 200 100 300 400 l These numbers can be viewed as a projection of our master schedule needs we can refer to them as a quotforecastquot of MP8 needs In actual practice the process of developing a master production schedule is quite a bit more complex than what is pictured above That was just a rst pass Additional factors such as available inventories of nished products lot sizing issues with production batches etc result in further tweaking of the numbers to get to a nal master production schedule ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 14 0f16 TESTING THE FEASIBILITY OF THE MPS ROUGHCUT CAPACITY PLANNING Roughcut capacity planning RCCP can be performed in a variety of ways Some are simple and straightforward but perhaps less precise while others are more cumbersome and complex but perhaps more precise The simplest of these approaches is illustrated here It uses historical averages to project capacity loads in the system Historical Record 1 Overall labor hours per unit and machine hours per unit for each model I Labor hours per unit I Machine hours per unit I Model A 2 1 Model B 3 2 Historical Record 2 Breakdown of loads imposed on work centers by our two models Work Center 1 30 I Work Center 2 I 70 I the implication of this is that our two models require work in only these two work centers and that Work Center 1 is used for 30 of the work on these models while Work Center 2 is used for 70 of the work These historical data can be used in conjunction with the master production schedules generated for Model A and Model B to project labor and machine time loads ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 15 0f16 CONTINUATION OF ROUGHCUT CAPACITY PLANNING EXAMPLE These projected labor hour and machine hour projected needs can be divided among the two work centers involved using the 30 and 70 historical allocation factors noted earlier WC 2 105 35 70 35 These gures can be used to develop graphic load pro les standard bar charts that provide a quick visual display of loads and available capacities The following illustrates such a load pro le for machine hours required in Work Center 1 Machine Hours W ellt1zcapacityLimit ELWBrE 96962 1 l 2 3 4 5 6 7 8 The horizontal line indicates a hypothetical weekly limit of 50 hours per week in this work center If any of the bars protruded above this line the implication would be that the MP8 is not feasible and needs to be revised Additional load pro les such as this could be developed for both machine hours and labor hours in Work Centers 1 and 2 ISM 3530 Fall 2009 CP6 Aggregate Planning and Master Scheduling Page 16 0f16 TIME FENCES IN THE MASTER PRODUCTION SCHEDULE Things change over time orders get cancelled new orders are placed items in inventory get damaged quality problems cause us to scrap a batch of items etc etc Circumstances like these impact our projections for MPS quantities Sometimes these circumstances suggest that we must change the MPS to re ect these new conditions Not all portions of the MPS are readily changeable what changes we can make depend upon how far out into the future they are In that context the MPS is often thought of as having the following three portions Liquid portion of the MPS This is the portion of the MPS that is beyond the total cumulative lead time for manufacturing the item We haven t yet even started to make or order the lowest level items in the bill of materials for those MPS quantities Therefore we can easily change those MPS values with little or no impact on our planning system Slushy portion of the MPS This is the portion of the MPS that is inside the total cumulative lead time for manufacturing the item but beyond the lead time for the nal assembly of components into the finished product We can make limited changes at this stage for we have already begun building the pieces and parts that will make up our nal product We can make changes like product mix changes changing models or versions of the product by assembling slightly different configurations of the already manufactured components Frozen portion of the MPS This is the portion of the MPS that is inside the nal assembly lead time for the item At this point we have already begun assembling the MPS batches of the items in question and it is difficult to undo this or make model switches at this point A time fence is simply a point in time that de nes the dividing line between two adjacent portions of the MPS The demand time fence is the point in time on the time horizon that separates the frozen portion of the MPS from the slushy portion of the MPS Further out on the time horizon you will nd the planning time fence which is the point in time on the time horizon that separates the slushy portion of the MPS from the liquid portion of the MPS

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