1. periodic review system -target level
a compay orders from supplier once every ten days delivery takes three days, average demand for SKU is 100 units per week(five working days.) they has determined that safety stock is held at one day's supply, inventory on hand 10units, in perioric review system, shich of the following is target level?
safety stock = 20
on hand stock = 10
average demand per week = 100
10일 한번씩 오더
3일 걸림
100*2 + 20 +(20*3) = 220+60 = 280
the quantity equal to the demand during the leadtime plus the demand during the review period plus safety stock is called the target level.
2. inventory turn ratio( the annual cost of goods sold/average inventory)
if the annual revenue is 750,000, the annual cost of goods sold is 500,000 a year and the average inventory is 100,000. what will be the inventory turn ratio?
매출 원가 = 500,000
매출 = 750,000
순이익= 250,000
average inventory = 100,000
100,000/250,000 =10/25 = 2/5 = 4/10 = 0.4?
inventory turn ratio:
ideally, a manufacturer carries no inventory. however, this is not practical since inventory is needed to support manufacturing and often to supply customers. how much inventory is enough? a convenient measure of how effective inventories are beinb used is the inventory turn ratio.
inventory turn = annual cost of goods sold / average inventory in dollar = 500,000/100,000 = 5/
3. CR (order sequencing)
today's data is 20 the order
| order | due date | LT remaining | CR | |
| A | 30 | 10 | (30-20)/10 = 1 | order is on schedule |
| B | 40 | 15 | (40-20)/15 = 20/15 = 4/3 = 1.3*** | order is ahead of schedule |
| C | 35 | 20 | (35-20)/20 =0.6** | order is behind of the schedule |
| D | 45 | 20 | (45-20)/20 = 1.000 | order is ahead of schedule |
CR-
Operation Sequencing
APICS defines operation sequencing as ' a technique for short-term planning of actual job to be run in each work center based on capacity(ie, existing workforce and machine availability) and priorities. priotiry, in this case, is the sequence in which jobs at a work center should be worked on.
the material requirements plan establishes proper need dates and quantities for orders. over time, these dates and quantities change for a variety of reason. customers may require different delivery quantities or dates. delivery of components parts, either from suppliers of internally, may not be met. scrap, shortage, and overages may occuer. in addition, multiple orders may have the same due date, or be scheduled to run on a particular work center the same day,but need to be sequenced. control of priorities is exercised through dispatching.
dispatching
dispatching is the function of selecting and sequencing available jobs to be run at individual work center. the dispatch list is the instrument of priority control. it is a listing by operation of all the jobs available to be run at a work center with the job listed in priority sequence. it normally includes the following information and is updated and published at least daily;
plant, department and work center
part number, shop order number, operation number, and operation description of jobs at the workcenter.
standard hours
priority information
jobs coming to the work center.
dispatching rules : the ranking of jobs for the dispatch list is created through the application of dispatching or priority rules. there are many rules, some attempting to reduce work-in progress inventory, others attempting to minimize the number of late orders or maximize the output of the work center. none is perfect or will satisfy all objective. some commonly used rules are;
first come, first served(FCFS)
Earliest job due date(EDD) : jobs are performed according to their due dates. due dates are considered. but processing time is not.
Earliest operation due date(ODD): job are performed according to their operation due dates. due dates and processing time are taken into account. in addition, the operation due date is easily understood on the shop floor.
Shortest process time(SPT) : this ruls ignores due dates.
Critical Ratio (CR) considers due dates and process time and is an index of the relative priority of an order to other orders at a work center. it is based on the ratio of time remaining to work remaining and is expressed as.
due date - present date / leadtime remaining = actual time remaining / lead time remaining
CR Less than 1 ( actual time less than lead time) - order is behind schedult
CR equal to 1 ( actual time equal to lead time ) - order is on schedule.
CR greater than 1 ( actual time greater than lead time) - order is ahead of schedule.
CR zero or less (today's date greater than due date) - order is already late.
4.
a component has a fixed cost of 1,000 and a variable cost of 5 per unit to produce. what would the average cost per unit be if the company produces 4,000 units?
1,000 + (4000*5) / 4000 = 21,000/4000 = 5.25
5.
manufacturer makes tables consisting of a top, 4 legs, and 4 top trims. demand for the table is 600 per week.
the capacity for the top is 700 per week, the capacity for leg is 2,000 per week, and the capacity for the trim is 2,500 per week. what is the capacity to produce tables? 500 tables!
tables consisting of
a top
4 legs
4 top trims
demand 600 per week.
the capacity
top 700 per week
leg 2,000 per week = 500
trim 2,500 per week. = 625
6. Exponential Smoothing
in using exponential smoothing, the old forecast for june was 100, and the actual demand for june was 150. if a is 0.1. which of the following is the forecast for july?
(0.1*150)+(0.9*100) = 90+15 = 105
moving average are best used for forecasting products with stable demand where there is little trend or seasonality. moving average are also useful to filter out random fluctuations. this has some common sense since periods of high demand are often followed by period of low demand since the total market demand is usually constant and consumers often buy goods ahead of time due to sales or other outside influences, including the weather and holiday events. buying early will lower future sales.
one drawback to using moving averages is the need to retain several periods of history for each item to be forecast. this will require a great deal of computer storage or clerical effort. also the calculations are cumversome. a common forecasting technique, called exponential smoothing, gives the same results as a moving average but without the need to retain as much data and with easier calculation.
Using exponential smoothing, it is not necessary to keep months of history to get a moving average because the previsouly calculated forecast has already allowed for this history. therefore, the forecast can be based on the old calculated forecast ans the new data.
using the date in figure 8.4, suppose an average of the demand of the last six months(80units) is used to forecast january demand. if at the end of january, actual demand is 90 units, july's demand is dropped and january's demand is used to determine the new forecast. however, if an average of the old forecast(80) and the actual demand for january(90) is taken, the new forecast , for feb, is 85 units. this formula puts as much weight on the most recent month as on the old forecast(all previous months). if this does not seem suitable, less weight could be put on the latest actual demand and more weight on the old forecast. perhaps putting only 10% of the weight on the latest month's demand and 90% of the weight on the old forecast would be better.
notice that this forecast did not rise as much as the previous calculation in which the old forecast and the latest actual demand were given the same weight. one advantage to exponential smoothing is that the new data can be given any weight wanted.
the weight given to latest actual demand is called ' smoothing constant' and is represented by the greek letter alpha. it is always expressed a decimal and typically ranges from 0 to 0.3
in general, the formula for calculating the new forecast is as follows;
new forecast = alpha*(latest demand) + (1-alpha)(previous forecast)
the characteristics of exponential smoothing
exponential smoothing provides a routine method for regularly updating item forecasts.
it works quite well when dealing with stable items.
generally it has been found satisfactory for short-range forecasting.it is not satisfactory where the demand is low or intermittent.
exponential smoothing will detect trends, although the forecast will lag actual demand if a definite trend exsits.
notice the forecast with the larger alpha follows actual demand more closely.
if a trend exists, it is possible to use a slightly more complex formula, called double exponential smoothing. the techniques uses the same principles but notes whether each successive value of the forecast is moving up or down on a trend line.
a proble exists in selecting the best alpha factor. if a low factor such as 0.1 is used, the old forecast will be heavily weighted, and changing trends will not be picked up as quickly as might be desired.
if a larger factor such as 0.4 is used, the forecast will react sharply to changes in deamdn and willl be erractic if there is a sizable random fluctuation. using past actual demand, forecasts are made with different alpha factors to see which one best suits the historical demand pattern for particular products.
7. level production.
in based on level production, the required monthly production is total quantity of forecast / how many months durint the forecast.
8.the rated capacity
a work center has 300 available hours, a utilization rate of 80% and an efficienfy rate of 90%. what is the rated capacity of the work center?
the rated capacity is calculated by multiplying the available hours by the utilization percentage by the efficienfy rating of the work center = 300 *0.8*0.9 = 216
9.
a work center has 4 machines operating 6 days for 8 hours per day. what is the available time?
4*6*8 = 48*4 = 192
10. level strategy
a company wants to produce 1,000 units over the next four months using a level strategy. the moths have 20,21,19 and 22 working days in each one. based on this strategy, how much would be produced in the first month?
level = average demand
1,000/(20+21+19+22) * 20
1000/82*20 = around 244
11. level strategy
a company needs to produce 4,000 units over the next two months. there are 21 working says in the first month and 19 working days in the second month. what must the daily rate be with a level strategy?
4000/(21+19) = 4000/40 = 100
12.line-haul cost
for a particulat commodity, the line-haul cost is 2 per mile and the distance is 300 miles and a shipment is 100 cwt. if the shipment is increased to 600 cwt, which of the following is the saving in the line-haul cost?
line-haul cost = 2* 300 = 600
transportation cost elements
there are four basic cost elements in transportation. knowlege of these costs enables a shipper to get a better price by selecting the right shipping mode. the four basic cost are as follows;
- line haul
- when goods are shipped, they are sent in a moving container that has a weight and volume capacity. the carrier, private or for hire, has basic costs to move this container, which exist whether the container is full or not. these are called line haul costs. for a truck, these include such items as the driver's wages and depreciation due to usage. these costs vary with the distance travled, not the weight cariied. the carrier has essentially the same back costs whether the truch moves full or empty. if it is half full, the basic costs must be spread over only those goods in truck.
- therefore, the total line haul cost varies directly with the cost per mile and the distance shipped, not on the weight shipped.
- even thought weight is not a factor in line haul costs, it can be used to designate the cost to the shipper.
- pickup and delivery
- terminal-handling
- billing and collection
| full load shipment | |||
| shipper | terminal | terminal | consignee |
| local | full load long distance | local | |
13.
a work center includes four machines working 16 hours per day, five days per week. what is the weekly available time?
16*5 = 80 *4 = 320
14. Break-even Point
a manufacturing company produces a particular component. this component requires an overhead cost 1000 and a variavle unit cost of 6 per unit, so the break-even point occurs when 300 units are sold. if the firm is willing to decrease the break-even point at 250 units, which of the following should be the selling price per unit?
1000+6*300 =2800/300 =9.3* 300
2800/250=selling price for 250 unit in order to make bp point.
11.2
15. MAD
the forecast for a part is 200 units per week. the actual shipements for the last 5 weeks have been 250,260,210,240 and 220.
if the MAD is 20, what would be the tracking signal equal?
50 60 10 40 20
180/20 = 9
Tracking the Forecast
forecastst are usually wrong. there are several reasons for this, some of which are related to human involvement and others to the behavior of the economy and customers. if there were a method of determining how good a forecast is, forecasting methods could be improved and better estimates could be made accounting for the srror. there is no point in continuing with a plan based on poor forecast data, so the forecast must be tracked. tracking forecast is the process of comparing actual demand with the forecast.
forecast error
forecast error is the difference between actual demand and forecast demand. error can occur in two ways; bias and random variation.
bias
cumulative actual demand may not be the same as forecast. bias exist when the cumulative actual deman varies from the cumulative forecast. this means the forecast average demand has been wrong.
bias is a systematic error in which the actual demand is consistently above or below the forecast demand. when bias exists, the forecast should be evaluated and possibly changed to improve its accuracy.
the purpose of tracking the forecast is to be able to react to forecast error by planning acound it or by reducing it. when an unacceptably large error or bias is observed, it should be investigated to determine its cause.
random variation
in a given period, actual demand will vary about the average demand. the variability will depend upon the demand pattern of the product. some products will have a stable deamnd, and the variation will not be large. other will be unstable and will have a large variation.
mean absoulte deviation
forecast error must be measured before it can be used to revise the forecast or to help in planning. there are several ways to measure error. one method commonly used due to its ease of calculation is mean absolute deviation(MAD).
consider the data on variability. although the total error(variation) is zero, there is still considerable variation each month.
total error would be useless to measure the variation. one way to measure the variability is to calculate the total error ignoring the plus and minust signs and take the average. this is mean absolute deviation;
mean implies average
absolute means without reference to plus and minus
deviation refers to error.
MAD = sum of absolute deviations/ number of observation.
use of mean absoulte deviation
1) tracking signal
bias exists when cumulative actual demand vaires from forecast. the problem is in guessing whether the variance is due to random variation or bias. if the variation is due to random variation, the error will correc it self, and nothing should be done to adjust the forecast. however, if the error is due to bias, the forecast should be corrected. using the mean absoulte deviation, judgement can be made about the reasonableness of the error.
under normal circumstances, the actual period demand will be within +- 3 MAD of the average 98% of the time.
if actual period demand varies from the forecast by more than 3MAD, there is about 98% probability that the forecast is in error.
a tracking signal can be used to monitor the quality of the forecast. there are several precedures used, but one of the simpler ones is based on a comparisons of the cumulative sum of the forecast error to the mean absoulte deviation.
tracking signal = algebraic sum of forecast errors/ MAD
2) contigency planning
3)safety stock
16. cost of ordering
given the following annual costs, calculate the cost of creating one order.
250,000/5,000 = 50
17.
a companry wants to store 15,000 cartons with 20 cartons on each pallet. the warehouse is set up to store pallets 5 high. how many pallet positions are needed?
15,000/20 = 750
750/5 = 150 positing.
18. elapsed operation time = set up time + run time per piece divided by the number of pieces per machine
a part made on a work center has a setup time of 50 minutes and run time of two minutes per peice. an order for 600 parts needs to be processed on two machines at the same time. the machines can be set up at the same time the elapse operation time will be;
set up time = 50 minutes
600 parts on two machine
the machines can be set up at the same time. the
50+ (2*300) = 650
19. Projected Available
on hand 100
| 1 | 2 | 3 | 4 | |
| forecast | 200 | 200 | 200 | 200 |
| projected avail | 100 | 0 | 100 | 200 |
| MPS receipt | 300 | 300 | 300 |
20. operation time
a work center is to process a batch of 100 units of a specific item. the setup time is 1 hour/batch, queue time is 2 hours/ batch, move time is 0.1 hour/batch, run time is 0.2 hour/unit, wait time is 0.1 hour/ batch. which of the following is rhe operation time?
set up = 1 hour
queue = 2 hour
move = 0.1 hour
run = 0.2*100 =20 hour
wait - 0.1 hour
total 22 hour
a batch 이니까 queue나 wait time은 없
21. moving average
(210+190+170)/3 = 190
some important internal techniques
the average of last year's demand can be used as an estimate for january demand. such a simple average would not be responsive to trends or changes in level demand. a better method would be to used a moving average.
average demand
this raises the question of what to forecast. as discussed previously, deamand can fluctuate because of random variation. it is best to forecast the average demand rather than second guess what the effect of randon fluctuation will be. the second principle of forecasting discussed previously said that a forecast should include an estimate of error. this range can be estimated, so a forecast of average demand should be made, and the estimate of error applied to it.
moving average
one simple way to forecast is to take the average demand for , say, the last three or six periods and use that figur as rhe forecast for the next period. at the end of the next period, the first period demand is dropped and the latest period demand added to determine a new average to be used as a forecast. this forecast would always be based on the average of the actual demand over the specified period.
the characteristics of moving average
moving average are best used for forecasting products with stable demand where there is little trend or seasonality. moving average are also useful to filter out random fluctuations.
a common forecasting technique, called exponential smoothing, gives the same results as a moving average but without the need to retain as much data and with easier calculations.
22. order point
lead time = 6 weeks
average demand = 150 units per week
safety stock = 300
the order quantity = 2,000
150* 6 + 300 = 900 + 300 = 1200
23. order point 2 -> 이거 뭔지 모르겠다
| 1 | 2 | 3 | 4 | 5 | |
| Customer order | 60 | 30 | 20 | 40 | |
| MPS | 110 | 110 | |||
| ATP | 20 | 70 | 50 |
24. level production = average of demand
begin = 500
end = 200
400+ 300+400+400+ 300 /5 =(1800-300*)/5 = 300
*300은 ending inventory - beginning inventory = used on hand inventory
25. seasonal index
the annual demand for a period is 1200. but during july, the product sells 150. what is the seasonal index for the product in july?
1200/12= 100
150/100 = 1.5
seasonality
many products have a seasonal or periodic demand pattern; skis, lawnmowers, bathing suits, and christmas tree light anr examples. less obvious are products whose demand varies by the time of day, week or month. examples of these might be electric power usage duting the day or grocery shopping during the week.
seasonal index
a useful indication of the degree of seasonal variation for a product is the seasonal index.
this index is an estimate of how much the demand during the season will be above or below the average demand for the product.
the formula for seasonal index is as follows;
seasonal index = period average demand/average demand for all periods
the period can be daily, weekly, monthly, or quarterly depending on the basis for the seasonality of demand.
the average demand for all periods is a value that averages out seasonality. this is called the deseasonalized demand.
deseasonalized demand = period average demand/ seasonal index
26. level rate -> 이거 뭔지 모르겠다..
27. order point -> 이것도 뭔지 잘 모르겠다..
for the particular item, the annual demand is 5,200 units, it is ordered in quantities of 325, and the MAD during the the leadtime is 50 units. LT is 4 weeks. which of the following is nearest order point for this item? (safety factor = 1.95)
5200/12 * 4 + (50*1.95) = 1733 + 97.5 = 1831
order point = demand during the leadtime + safety stock during the leadtime(MAD*safety stock)
28. safety stock
to ensure coverage of product 99.99999% of the time, it would require 5 MAD for safety stock. there for 5*100 = 500
29. MAD
2+2+5+6 =15/5 = 3
30. operation time
2h + 10*100 minute = 120 +1000= 1120minutss
31. MAD
10+5+10+10+20 = 55/5 =11
32. Inventory turn
40/10 = 4
40/ * = 20
40/2 = 20
33. inventory turn
(100+50+250+300) = 700
*/700 = 4
* = 2800
the annual cost of goods / the average inventory = inventory turn.
34. operation time
0.5 hours for set up + 500*3 minute for running = 30 minutes+ 1500 minutes = 30 minutes + 1500 minutes = 1,530 minutes/60 = 25.5
35. order point
36. moving average
98+101+103 = 204+98 = 301 / 3 = 101
38. EOQ 이거 모르겠다
42. rated capacity
44. utilization
4m * 8hours per daty * 5days = 32*5 = 160 hours per week.
120%
efficiency = standard hours of work production / hours actually worked *
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