MID-TERM EXAM/REVISION Business Department SPRING 2016-17 MID-TERM EXAM/REVISION

Section A/Multiple Choice-Example-30% 1) Production/Operations Management is A) responsible for producing goods B) responsible for providing services C) system that create goods or services D) often referred to as the core of scientific management   2) Operations Management does not affect A) The collective success or failure of companies’ POM B) Companies’ financial resources C) Companies’ system D) Companies’ ability to compete

Section A/Multiple Choice-Example 3) Which of the following part indicates the three Basic Functions? A) Finance, marketing and management B) Finance, human resources and management C) Marketing, operations and finance D) Operations, finance and scientific management 4) The steps of the conversion of inputs into outputs are: A) Input, process, transformation B) Labor, control, goods C) capital, land, service D) Input, conversion process, output 5) The operations function involves __________ A) The transformation process of inputs into outputs B) Feedback control C) Value added process Only goods process  

Section A/Multiple Choice-Example 6) Which of the following is not one of the input concepts? A) Labor B) Information C) Data D) Legal constraints 7) In profit organization, value-added of output __________ A) is measured by prices that customers are willing to pay for only those goods B) is measured by prices that customers are willing to pay for those goods and services. C) is their value to society. D) is difference between cost of inputs and price of output 8) The steam engine and advanced the use of mechanical power to increase productivity indicate__________ A)The concepts of standardised parts and interchangeable parts. B) The handicraft era C) The Industrial revolution era D) The advantages of the division of labor  

Section A/Multiple Choice-Example 9) Which of the following indicates the aim of forecasting? A) Short term plan B) Long term operational decision C) Reduce risk and uncertainty D) Predicting past 10) Time series forecast analysis depends on __________ A) Explanatory variables B) Consumer surveys C) methodologies D) The past values 11) Which of the following is not one of example of strategies__________ A) Low cost B) High quality C) Flexible operation D) Technology  

Section A/Multiple Choice-Example 12) Which of the following is not one of the important concepts for Lean and JIT system? A) Quality B) Price C) Skilled labour D) time 13) Hungarian method is associated with__________ A) Matching process B) Colomun reduction C) One to one basis process D) Penalty cost 14) Competitiveness is not based on__________ A) Pricing B) Promotion C) Customers’ needs D) Time 15) Equality and the role of government is the difference between __________ A) Mass production and JIT B) Opportunism and realism C) Production and operations D) capitalism and socialism

Section B/type 1-Example- Productivity-35% Assume 40 hour weeks and an hourly wage of $12. Overhead is 1.5 times weekly labor cost. Material cost is $ 6 per pound. Compute the average multi-factor productivity measure for each of the weeks shown. Calculate the productivity growth rates throughout the weeks. What do the productivity figures suggest? Draw the productivity figures and briefly explain. Week Output (units) Workers Materials (lbs) 1 30,000 6 450 2 33,600 7 470 3 32,200 460 4 35,400 8 480

Section B/type 1-Example- Productivity-35% Week Output (units) Worker cost 12*40 Overhead cost   Materials cost Total cost MFP 1 30,000 2880 4320 2700 9900 3.03 2 33,600 3360 5040 2820 11220 2.99 3 32,200 2760 11160 2.89 4 35,400 3840 5740 12480 2.84

Section B/type 1-Example- Productivity-35% Week 1- 12*40*6 =2880= worker cost Week 1- 12*40*6 =2880* 1.5=4320= overhead cost Week 1- 450*6 = 2700=material cost Week 1- 2880+4320+2700=9900 total cost   Week 1-MFP=output (units)/(labor+materials+overhead) =30000/9900= 3.03 unit per dollar input

Section B/type 1-Example- Productivity-35%

Section B/type 2-Example-Productivity-35% A firm has the following information for the last two years and if the capital cost and the inflation rate are 25% and 15% respectively. Factor Year 1 Year 2 Revenue 220 240 Labor 55 60 Materials 30 35 Overhead costs 20 25 Other costs 15 Stocks 95 85 Buildings 80 90 (a) Calculate TFP for year 1 and 2. Briefly explain (b) Compute total labor productivity (TLP). Briefly explain

Section B/type 2-Example-Productivity-35% (a) TFP for the two year... Year 1 220/55+30+20+15+(0.25)(95+80)=220/163.75=1,34 Year 2 240/1,15*((60+35+25+15+(0,25)(85+90))=240/205.56= 1,16 TFP= (1,16-1,34)/1,34= - 13,4%

Section B/type 2-Example-Productivity-35% (b) TLP for the two year... TLP= Revenue labor+Overhead TLP Year 1: 220/(55+20)=2.93 Year 2: 240/1,15*(60+25)=2.45 (2.45-2.93)/2.93= - 16.38% Decrease in labor productivity may stem from different economic reasons. This answer should be improved…

Section C/type 1-Example-Trend Analysis-35% Use the information in the following table and construct the forecast equation. Use time series regression to forecast the petrol consumption (mn gallons) for the next four year. Compute the correlation coefficient, determination of coefficient and standard deviation. Briefly explain Draw the pattern for data as well as forecasting periods. Briefly explain.  

Section C/type 1-Example-Trend Analysis-35% YEAR 2004 2005 2006 2007 2008 2009 2010 2011 PETROLSALE(Y) 1 3 4 2 5

Section C/type 1-Example-Trend Analysis-35% Year Trend (t) PETROLSALES (Y) (t) SQ Y*t (y) SQ (Σt) SQ (ΣY) SQ 2004 1   2005 2 3 4 6 9 2006 12 16 2007 8 2008 5 25 2009 36 18 2010 7 49 35 2011 64 24 (sum) Σ 22 204 109 74 1296 484 a 1.678 b 0.238

Section C/type 1-Example-Trend Analysis-35% Y9=1.678+0.238(9) = 3.83 in 2012 Y10=1.678+0.238(10) = 4.06 in 2013 Y11=1.678+0.238(11) = 4.30 in 2014 Y12=1.678+0.238(12) = 4.53 in 2015 Note: Petrol sales are expected to increase by 0.238 mn gallons per year. This answer should be improved.

Section C/type 1-Example-Trend Analysis-35% Sxy = 1.36   Sxy is a measure of how historical data points have been dispersed about the trend line. If it is large (reference point in mean of the data) , the historical data points have been spread widely about the trend line and if otherway around, the data points have been grouped tightly about the trend.

Section C/type 1-Example-Trend Analysis-35% r lies between -1 and 1, -1 is strong negative whereas 1 is strong positive. 0 means that there is no relationship between the two variables (x and y). In this case, there is a strong positive relationship between the two variables and if an increase in independent variable, it will be a rise in dependent variable.   R2=0.18. It varies between 0 and 1.0 means that there is no relationship between the two variables whereas 1 indicates that there is a perfect relationship. 18.0% variation in dependent variable can be explained by the variation happened in the independent variable. It is worth to emphasize that 82% shows unexplained part of the relationship.

Section C/Type2-Example-Forecast-35% A company records indicate that monthly sales for a seven-month period are as follows:.  

Section C/Type2-Example-Forecast-35% a) Use a simple two-month moving average and single exponential smoothing technique to find the next period employing smoothing constant and 7. period forecast value are 21.3 and 0.4 respectively.   Use a simple two-month moving average and single exponential smoothing technique to find all periods. First month forecast value is 15. c) Use RMSE error model and decide which technique is better explain the data (MA and ES). d) Plot the monthly data, two-month moving average estimates as well as exponential smoothing estimates. Briefly explain the patterns.

Section C/Type2-Example-Forecast-35%

Section C/Type2-Example-Forecast-35% MA02: F3= 15+23/2=19 F8=21+24/2=22.5   ***ES(α=0.4) Ft = F (t-1)+α (A(t-1)-F(t-1)) F1= 15 (it is the average of series if it is not given) F2= 15+0.4(15-15)=15 F8=21.3+0.4(21-21.3)=21.2

Section C/Type2-Example-Forecast-35%

Section C/Type2-Example-Forecast-35% (A - F )2 RMSE n = Σ RMSE(MA02) = 3,11 RMSE(ES04) = 2,37   ES 04 is better explain the pattern of the data than MA 02 because ES 04 gives less error compared to MA 02. This answer should be improved.

Section C/Type2-Example-Forecast-35%

Section D/Bonus 1-Example-VOGEL/NCW NWC To (Cost) From 1 2 3 supply A $ 6 7 4 100 B $ 5 3 6 175 C $ 8 5 7 200 Demand 90 195 190 Use Vogel method and calculate the optimal solution. Use NCW method and calculate the optimal solution. Discuss the answers calculated in section a and b.

Section D/Bonus 1-Example-NCW NWC To (Cost) From 1 2 3 supply A $ 6*90 7*10 4 100 B $ 5 3*175 6 175 C $ 8 5 *10 7 *190 200 Demand 90 195 190 A → 1 =90*6 =540 B → 1 =7*10 =70 B → 2 =3*175 =525 C → 2=5*10 =50 C → 3 =7*190=1330 NWC Total= 2,515 VOGEL total cost is 2465

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