Questions
Case study 4: Belgium Mills Company SAOG (the Company) is engaged in the milling of wheat...

Case study 4: Belgium Mills Company SAOG (the Company) is engaged in the milling of wheat flour, bran and feed and distributing premium quality wheat products to the Oman market as well as export to African and other neighboring countries. The Company is also involved in production and sale of macaroni, pasta and related food products. Furthermore, it is involved in production and sale of propylene bags. The Company's commercial operation commenced on 1 January 1998. The total revenues reached OMR 53.6 Million, showing an increase of 3.1% over the year before because of higher sales volumes. The export revenues represented 53.8% of the total revenues. The net profit made by the company was about OMR 1.6 Million, showing a decrease of 5.1% compared to the previous year because of higher cost of raw materials and declining profit margins as a result of competition. The expansion of production capacity was expected to be completed by the Month of October 2020, which would increase the production capacity by 50%. Based on the Feasibility study and the review carried by Consultant Office, the Board of Directors decide to invest in Joint Venture with giant Ethiopian industrial and trading group by moving one Spaghetti Production Line to Euthopia. For the year 2020 the company had evaluated the following Opportunities and Threats: Threats Despite stiff competition from local Flour Mills and IFFCO – a Flour Mill Company in Sharjah, UAE, Belgium Mills Company is capable of competing by focusing on implementing high quality standards, providing technical assistance and offering competitive prices only by increase in prođuction capacity and implementing improved technology Opportunities: • Belgium Mills Company was established in 1995 and started commercial production in 1998 with a production capacity of 300 MT per day. The production capacity increased over the years to reach 1500 MT per day in 2012. Belgium Mills Company increased wheat storage capacity in June 2015 by adding 12 new silos which can store 120 thousand MT of wheat. Salalah Mills Company owns grain storage capacity of 161,500 Metric Tons, which is the biggest in Oman. • The sales quantity exported to Somalia was increased by 16% compared with 2018. The company wants to expand its capacity in order to cope with the increased demand and is in need for additional funds. The company decided to raise such funds through the issue of right shares. The details of such issue are as under: The issue period will be; Opening Date: 4ª May 2020 Closing Date: 14h May 2020 Rights Entitlement: Every shareholder as on the Record Date is entitled to about 16.5 Offer Shares for every 100 shares held as on the Record Date. • Eligibility for Subscription: Subscription for the Rights Issue is open to the Shareholders whose names appear in the Bank's shareholder register as on the Record Date. Persons who purchase the rights on the MSM within the trading period of the Rights Issue are also eligible to subscribe for the Offer Shares before the Rights Issue closes. The eligibility to subscribe for Offer Shares shall lapse in case the Shareholder neither exercises his/her right of subscription to the Rights Issue nor sells its 'rights' on the MSM đuring the prescribed period Issue Price Baiza 277 per Offer Share, consisting of issue price 275 plus Baiza 2 towards issue expenses, payable in full on submission of Application Form. Allotment and refunds would be within 3 days of the closure of the Rights Issue.
Estimated issue expenses: The issue expenses of the Rights Issue are estimated at RO 86,550. The issue expenses of the Rights Issue will be met from the amounts collected from Applicants at 2 Baiza per Offer Share and the remainder will be borne by the Bank. Any surplus of the collection towards Issue Expenses over the actual expenses incurred will be retained by the Bank and credited to company’s legal reserve or a special reserve to be established pursuant to Article 126 of the CCL The Financial Advisor & Issue Manager are Muscat Capital Markets SAOC; Legal Advisor to the Issue A & D Law Fim and Statutory Auditor Emst & Young LLC The authorized share capital of the Company consists of 778,000,000 shares of RO 0.100 each. The equity details just before the right issue are as follows: RO 45,850,011 Share capital Legal reserve Retained earnings General reserve Dividend Equalization reserve Investment fluctuation reserve 2,250,150 125,600 358,000 112,580 75,800 30% of the shareholders rejected the offer. Post right issue in pursuant with the provisions of Oman commercial law the company board also decided to come up with a bonus issue for its equity shareholders in June 2020. The bonus share of the company can be issued when the articles of the association is authorized to issue the bonus shares. It is essential to know that if the articles of association do not permit to issue bonus shares, the company should pass a special resolution at the general meeting of the company. As part of the procedure, the company has checked the articles of association which allowed issue of bonus shares and the company confimed enough authorized capital is available. It was accorded that a sum of RO 88,000 can be capitalized out of Dividend Equalization reserve and set free for distribution amongst the equity shareholders for bonus. Each shareholder will be eligible for 1 share for every 85 shares held. You are required: a. In your own words highlight upon the various situations presented in the case and how it will affect the company? (3 marks – Min 150 words) b. Pass necessary journal entries for the rights and bonus taking place in the given scenario. Ignore the entry for share issue expenses. c. Prepare necessary abstract to represent such transactions in Statement of Financial Position.

In: Accounting

HOUSE PRICE YRSOLD HSQFT LOTSFT YRBUILT PRICE_PER_SQFT NEB 1 $536,000 2009.00 1,500 4,000 1930 $357 WESTERLEIGH...

HOUSE PRICE YRSOLD HSQFT LOTSFT YRBUILT PRICE_PER_SQFT NEB
1 $536,000 2009.00 1,500 4,000 1930 $357 WESTERLEIGH
2 $498,000 2009.00 1,563 6,100 1950 $318 WESTERLEIGH
3 $506,500 2009.00 1,536 4,000 1950 $329 WESTERLEIGH
4 $630,000 2009.00 1,152 4,000 1949 $546 WESTERLEIGH
5 $455,000 2009.00 1,214 2,775 1925 $374 WESTERLEIGH
6 $265,000 2009.00 1,627 1,800 1985 $190 WESTERLEIGH
7 $347,500 2009.00 1,100 4,500 1950 $315 WESTERLEIGH
8 $320,000 2009.00 1,104 3,000 1925 $289 WESTERLEIGH
9 $535,000 2009.00 2,400 3,879 2000 $222 WESTERLEIGH
10 $456,300 2009.00 1,650 2,552 2007 $277 WESTERLEIGH
11 $440,000 2009.00 1,124 2,405 1930 $391 WESTERLEIGH
12 $413,000 2009.00 1,410 3,600 1955 $292 WESTERLEIGH
13 $320,000 2009.00 1,740 7,230 1950 $183 WESTERLEIGH
14 $270,000 2009.00 1,080 1,590 1925 $250 WESTERLEIGH
15 $375,000 2009.00 1,158 4,500 1920 $323 WESTERLEIGH
16 $485,000 2009.00 1,685 5,000 1925 $287 WESTERLEIGH
17 $448,000 2009.00 1,776 3,000 1915 $252 WESTERLEIGH
18 $425,000 2009.00 1,148 6,100 1955 $370 WESTERLEIGH
19 $376,500 2009.00 1,237 3,000 1920 $304 WESTERLEIGH
20 $350,000 2009.00 890 3,600 1920 $393 WESTERLEIGH
21 $470,000 2009.00 1,205 5,900 1955 $390 WESTERLEIGH
22 $420,000 2009.00 1,207 3,828 1945 $347 WESTERLEIGH
23 $410,000 2009.00 1,256 3,600 1930 $342 WESTERLEIGH
24 $440,000 2009.00 900 3,600 1960 $488 WESTERLEIGH
25 $395,000 2009.00 1,176 3,920 1930 $335 WESTERLEIGH
26 $355,000 2009.00 1,296 3,000 1940 $304 WESTERLEIGH
27 $415,000 2009.00 1,092 4,000 1960 $380 WESTERLEIGH
28 $495,000 2009.00 1,950 3,600 1920 $253 WESTERLEIGH
29 $355,425 2009.00 1,600 1,744 1993 $222 WESTERLEIGH
30 $410,000 2009.00 1,440 3,742 1965 $284 WESTERLEIGH
31 $447,500 2009.00 1,450 3,000 1970 $308 WESTERLEIGH
32 $420,000 2009.00 1,420 3,758 2006 $296 WESTERLEIGH
33 $455,000 2009.00 1,427 3,800 1920 $318 WESTERLEIGH
34 $380,000 2009.00 1,480 2,100 1970 $256 WESTERLEIGH
35 $400,000 2009.00 1,512 4,000 1960 $264 WESTERLEIGH
36 $310,000 2009.00 1,240 960 1993 $250 WESTERLEIGH
37 $365,000 2009.00 840 5,000 1955 $434 WESTERLEIGH
38 $370,000 2009.00 1,280 3,456 1965 $289 WESTERLEIGH
39 $415,000 2009.00 1,820 4,200 1960 $228 WESTERLEIGH
40 $419,796 2009.00 1,592 7,575 1930 $263 WESTERLEIGH
41 $380,000 2009.00 1,280 3,408 1965 $296 WESTERLEIGH
42 $410,000 2009.00 1,332 2,800 1970 $307 WESTERLEIGH
43 $435,000 2009.00 1,660 2,373 1995 $262 WESTERLEIGH
44 $515,000 2009.00 1,712 5,880 1930 $300 WESTERLEIGH
45 $370,000 2009.00 1,450 4,000 1955 $255 WESTERLEIGH
46 $429,000 2009.00 4,040 4,040 1950 $106 WESTERLEIGH
47 $295,000 2009.00 1,320 2,000 1940 $223 WESTERLEIGH
48 $520,000 2009.00 1,500 5,000 1960 $346 WESTERLEIGH
49 $410,000 2009.00 1,500 3,000 1925 $273 WESTERLEIGH
50 $379,000 2009.00 926 4,000 1955 $409 WESTERLEIGH
51 $487,500 2009.00 2,472 3,420 1970 $197 MARINER
52 $425,000 2009.00 2,400 3,800 1975 $177 MARINER
53 $370,000 2009.00 2,100 5,500 1935 $176 MARINER
54 $300,000 2009.00 1,870 2,500 1920 $160 MARINER
55 $385,000 2009.00 1,340 2,500 1925 $287 MARINER
56 $265,000 2009.00 1,992 3,591 1975 $133 MARINER
57 $300,000 2009.00 2,416 3,325 1980 $124 MARINER
58 $339,000 2009.00 1,820 2,850 1920 $186 MARINER
59 $350,000 2009.00 1,650 2,500 1903 $212 MARINER
60 $460,000 2009.00 1,744 4,419 2008 $263 MARINER
61 $214,200 2009.00 1,270 5,721 1925 $168 MARINER
62 $270,000 2009.00 2,200 1,512 1931 $122 MARINER
63 $220,000 2009.00 1,408 2,560 1901 $156 MARINER
64 $290,000 2009.00 1,540 4,950 1901 $188 MARINER
65 $335,000 2009.00 2,800 2,880 1920 $119 MARINER
66 $400,000 2009.00 2,052 5,900 1920 $194 MARINER
67 $485,000 2009.00 1,884 2,886 1975 $257 MARINER
68 $500,000 2009.00 2,080 4,326 1970 $240 MARINER
69 $414,726 2009.00 2100 3,594 2005 $197 MARINER
70 $415,740 2009.00 1,400 3,594 2005 $296 MARINER
71 $560,000 2009.00 2,568 4,000 1970 $218 MARINER
72 $390,100 2009.00 1,896 3,630 1970 $205 MARINER

You have downloaded the MS_Excel file with data on the prices of homes in two neighborhoods around the City of New York. The data is taken from Staten Island.

Using the MS_Excel, calculate:

a. The Average and the Standard Deviation for Sale Price for houses in the two neighborhoods

and please post the excel chart you come up with thank you

In: Accounting

Kindly summarize this Literature Review Section 3.2 Efficient Techniques and Performance Measurement Recently, developed techniques compare...

Kindly summarize this Literature Review Section 3.2 Efficient Techniques and Performance Measurement Recently, developed techniques compare the efficiency of similar service organizations by explicitly considering their use of multiple inputs to produce multiple outputs. These new efficiency techniques are often divided into two categories. One broad category consists of the linear programming procedures used in this paper (DEA). The second category is a set of regression-based techniques that derive inefficiency estimates from two-part error terms, and has been called the econometric or stochastic frontier approach. Both techniques use sample firms to construct an efficient production frontier. The frontier is efficient in the sense that a firm operating on the frontier could not increase output without increasing its input utilization, or it could not reduce its input utilization without decreasing output. Deviations from the frontier represent inefficiencies, and are termed X-inefficiencies in the finance and economics literature. Efficient frontier techniques avoid the need to develop a standard cost for each service provided and are more comprehensive and reliable that using a set of operating ratios and profit measures. These techniques permit managers and researchers to service organizations and identify units that are relatively inefficient, determine the magnitude of the inefficiency, suggest alternative strategies to reduce the inefficiencies, all in a composite measure. Moreover, these techniques provide an estimate of the overall efficiency level of the market that is under consideration. We know of only two studies that use efficient frontier techniques in the hotel industry. The first is that of Morey and Ditman (1995) who measure the relative performance of hotel general managers using DEA. The authors gathered input-output data for 54 hotels from a geographically dispersed area. They found that managers were operating 89 percent efficiency. In other words, given their output, managers on average could reduce their inputs by 11 percent. The study reported that the least efficient hotel was 64 percent efficient. These results are relatively high compared to those found in other industry studies that utilize DEA. Large efficiency scores are indicators of High performance and competition (Leibenstein 1966). Thus in an economic context, the market for lodging services appears to be operating efficiently. Anderson et al. (1998) argue for the benefits of using a stochastic frontier methodology in addition to DEA in order to accurately assess performance. Using a classical stochastic frontier model, they also find the hotel industry to be performing relatively efficiently, with efficiency measures above 90 percent. While both of these studies are informative, neither provides any information on the source of the inefficiencies. The source of the inefficiencies, whether technical or allocative in nature, is important information that managers need in order to take proactive positions to increase performance. We re-examine hotel efficiency using a method of DEA that provides significantly more detailed results and we further analyze the inefficiency sources. The following section describes our procedure.

SECTION 4 EFFICIENCY DETERMINATION

Section 4.1 The DEA Technique

Within the DEA framework, performance of an individual firm is evaluated with respect to an efficient frontier, which is constructed by taking linear combinations of existing firms. While there are several DEA approaches, wee use an unput-base approach, assuming that inputs are contracted proportionally with exogenous outputs. The procedure relies on sophisticated mathematics; however, the following simplified graphical example deomstates how th eefficiency measures are computed.

Figure 1 displays tha overall (OE) and (TE), and allocativ (AE) efficiency measures. In this example, we assume two inputs (X1 and X2), one output (Y), and constant returns to scale. Additionally, we assume that technology is fixed and that input prices are represented as PP. Firm A is X-efficient since it produces along output isoquant Y by utilizing the least inputs. Suppose thee is a firm operating at point C and producing an output equivalent of that produced along Y. C is uses more inputs than A to produce the output Y and is classified as inefficient with an overall efficiency score of 0D/0C )or equivalenly and inefficiency score of DC/0C).

Overall inefficiency can be decomposed into its techhnical and allocattive components. Without being able to alter input allocations, the bestt that firmC could have done was to operate at point B. The "extra" input usage that was incurred by firm C as a percentage of total input usage is the technical inefficiency measure and can be dpressed as BC/0C The technical efficiency of firm C is ecpresses as 0B/0C. Allocative inefficiency representts managerial failurd to use the optimal input mix. Here, allocative inefficiencies for firm C can be represented by DB/0B, and allocatvie effficiency is expressed as 0D/0B.

Technical efficiency can be further decomposed into technical (PTE) and scale (SE) efficiency measures. Pure technical inefficiency simply refers to deviations from the efficient frontier that result rom failure to utilize the employed resoures efficiently. Hence, this measure assumes that firms are operating at constant return to scale. Scale ineficiencies, on the other hand are losses due tofailure to operate at constant returns to scale. Figure 2 illustrates these two efficiency measures. In this figure, the Y-axis represents output and the X-axis represents input conbinations that contain an equal amount of both input 1 an dinput 2. The graph shows three observations denoted A, B, and C, respectively. Two frontiers are illustrated, a fronier assuming constant returns to scale instead of decreasing or increasing returns toscale.

After completing this analysis, we examine the SE measure to determine if it equals one. If the SE measure equals one, firms are operating at constant returns to scale. If SE does not equal one, we then determine whether the firms are oeprating at increasing or decreasing returns to scale (see Appendix A for a mathematical treatment of DEA).

In: Economics

Case Study 2: Forecasting Box Office Returns For years, people in the motion picture industry –...

Case Study 2: Forecasting Box Office Returns

For years, people in the motion picture industry – critics, film historians, and others – have eagerly awaited the second issue in January of Variety. Long considered the show business bible, Variety is a weekly trade newspaper that reports on all aspects of the entertainment industry; movies, television, recordings, concert tours, and so on. The second issue in January, called the Anniversary Edition, summarizes how the entertainment industry fared in the previous year, both artistically and commercially.

In this issue, Variety publishes its list of All Time Film Rental Champs. This list indicates, in descending order, motion pictures and the amount of money they returned to the studio. Because a movie theater rents a film from a studio for a limited time, the money paid for admission by ticket buyers is split between the studio and theater owner. For example, if a ticket buyer pays $8 to see a particular movie, the theater owner keeps about $4 and the studio receives the other $4. The longer a movie plays in a theater, the greater the percentage of the admission price returned to the studio. A film playing for an entire summer could eventually return as much as 90% of the $8 to the studio. The theater owner also benefits from such a success because although the owner’s percentage of the admission price is small, the sales of concessions (candy, soda and so on) provide greater profits. Thus, both the studio and the theater owner win when a film continues to draw audiences for a long time. Variety lists the rental figures (the actual dollar amounts returned to the studios) that the films have accrued in their domestic releases (United States and Canada).

In addition, Variety provides a monthly Box-Office Barometer of the film industry, which is a profile of the month’s domestic box-office returns. This profile is not measure in dollars, but scaled according to some standard. By the late 1980’s, for example, the scale was based on numbers around 100, with 100 representing the average box-office return of 1980. The figures from 1987 and 1996 are given in the table below and in the file BoxOffice.xlsx in blackboard.

All the figures are scaled around the 1980’s box-office returns, but instead of dollars, artificial numbers are used. Film executives can get a relative indication of the box-office figures compared to the arbitrary 1980 scale. For example, in January 1987 the box-office returns to the film industry were 95% of the average that year, whereas in January 1988 the returns were 104% of the average of 1980 (or, they were 4% above the average of 1980’s figure).

Month

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

Jan

95

104

101

88

132

125

111

127

119

147

Feb

94

100

96

110

109

118

123

129

147

146

Mar

98

99

82

129

101

121

121

132

164

133

Apr

96

88

84

113

111

140

139

108

135

148

May

95

89

85

114

140

141

119

115

124

141

Jun

115

108

124

169

179

201

156

149

168

191

Jul

107

109

134

131

145

152

154

155

159

178

Aug

104

101

109

139

140

138

136

129

137

156

Sep

96

106

121

120

120

137

105

117

149

119

Oct

112

102

111

115

129

138

132

166

159

138

Nov

98

78

101

116

118

144

123

152

175

175

Dec

102

111

112

128

139

148

164

173

195

188

From the time series given in the above table, you will make a forecast for the 12 months of the next year, 1997.

Managerial Report is due on … Thursday, 19 Sept (40 pts)

  1. Produce a time series plot of the data. From this graph, do you see a pattern? Can you see any seasonality in the data?
  2. Use exponential smoothing to fit the data. Select an appropriate constant a based on the variation you see in the data. Comment on the appropriateness of exponential smoothing on this data set. Plot the predictions from this model on the graph with the original data. How well does this technique fit the data? Make forecasts for 1997.
  3. Use regression to build a linear trend model. Comment on the goodness-of-fit of this model to the data (or, how well does R2 explain the variance in the data?). Plot the predictions from this model on the graph with the original data.
  4. Develop multiplicative seasonal indices for the linear trend model developed in question 3. Use these indices to adjust predictions from the linear trend model from question 3 above for seasonal effects. Plot the predictions from this model on the graph with the original data. How well does this technique fit the data? Make forecasts for the next 12 months of 1997 using this technique.
  5. Which forecasting method of those that you tried do you have the most confidence for making accurate forecasts for 1997? Use MAPE (mean absolute percent error) as your criterion to justify your decision.

Enrichment (5 pts): Use Optimization (and Solver in Excel) to find the optimal smoothing constant in problem 2 above (by minimizing the Mean Squared Error or MSE).

In: Statistics and Probability

It is rare that you will find a gas station these days that only sells gas....

It is rare that you will find a gas station these days that only sells gas. It has become more common to find a convenient store that also sells gas. The data named “Convenient Shopping data” the sales over time at a franchise outlet of the major US oil company. Each row summarize sales for one day. This particular station sells gas and has a convenient store and car awash. The column labeled Sales gives the dollar sales of the convenient store and the column Volume gives the number of gallons of gas sold.

Sales (Dollars) Volume (Gallons)
1756 2933
2203 3329
1848 3043
2016 3043
2346 3450
2410 3478
2050 3347
2097 3708
2311 3467
2419 4114
2523 3721
2061 3448
2247 3230
3479 3557
2135 3060
2102 3619
2536 3256
1227 1757
1966 2891
2219 3381
2226 2970
1969 3301
2044 3178
2360 3426
1907 3118
2156 3037
1816 3537
1897 3808
2051 3145
2079 3766
2328 2916
1841 3957
2104 3980
1973 3675
2089 3516
2266 4149
2327 3733
2032 3738
2137 4012
2186 4114
2369 3795
2087 3543
2273 3681
2113 3618
2181 4452
2776 4346
2652 4073
2250 4260
2548 4113
2678 3829
2878 4137
2220 4269
2303 3989
2718 4238
2317 3658
2338 4005
2143 3996
2402 4077
2401 3610
2051 3701
2468 3844
2398 3904
2106 3879
2461 3266
2466 3513
2745 4052
1994 4052
2020 2874
2241 3526
2648 3487
2022 3499
2524 3236
1919 2422
2164 2876
2074 2883
2310 2771
2062 2362
1807 2564
1976 2708
2171 2519
1745 2638
2108 3448
2057 1993
1679 2560
2014 2777
2109 3097
2274 2750
2640 3260
1664 2050
1913 2921
2331 2970
1920 2624
2074 3496
2272 3729
1651 2302
1996 2672
2093 3150
1995 2948
2337 3520
2433 3195
1731 2232
2183 2979
1795 3178
1689 2618
2040 3117
2076 2847
1483 2150
930 1528
1674 2309
1934 2805
2011 2721
2172 2812
1612 2173
1780 2767
2116 2544
1937 2805
1866 2131
2099 3292
2082 2221
1788 2816
2004 2686
1868 3207
2038 2925
2596 3603
1700 2165
1815 3338
1917 3107
2143 2906
2420 3448
2486 3433
1812 2104
2463 3283
2222 3750
2324 3494
2219 3154
2505 3465
2047 2216
2231 3236
2067 3425
2293 3667
2152 3618
1366 2257
2210 3606
2029 3460
2742 2336
2161 3113
2223 3058
2186 2429
2306 3501
1933 3183
2485 3337
2817 3566
2491 3398
1896 2519
2382 3716
2552 3856
2094 3488
2447 3457
2440 3831
2041 2280
2261 2411
2114 3208
2866 3539
2752 3719
2502 4150
1786 2927
2157 3044
2025 3390
2327 3840
2502 3697
2552 4104
2017 3749
2019 3511
2302 3972
2419 3413
2921 3882
2273 3950
2183 3292
2428 3979
2489 4668
2037 3832
2324 3930
2591 3853
2362 4014
3001 4759
1801 2661
1744 4165
2428 4139
2409 3664
2819 3851
1897 2522
1536 1208
2475 3844
2484 3766
2117 3535
2488 3900
2553 3900
2251 3814
2435 3387
2446 4009
2063 1951
2582 3779
1663 2368
2302 3379
2248 3549
2712 3807
2307 4009
2576 3759
1978 2378
2116 4090
2292 3241
2373 3874
2444 4142
2578 3645
1953 2419
2151 3289
2901 3872
2514 4136
2078 3626
2492 4240
1897 2415
2072 3028
2538 3731
2422 3851
2415 3818
2969 4268
1775 2514
2082 3708
2121 3367
2471 3685
2467 3415
2671 4226
1876 2061
1976 3805
2156 3427
2339 3670
2258 3939
2776 3798
2084 2668
2346 3945
2320 3787
2539 3854
2393 3598
2629 3717
2044 2536
2018 401
2350 2361
2452 4005
2041 2391
2038 3129
2181 3874
2516 4072
2181 3603
2427 4173
2111 3993
2182 3153
2794 3812

1) Draw a scatter plot for Sales on Volume where Sales is dependent on Volume of gas sold. Does there appear to be a linear pattern that relates to these two sequences?

2) Estimate the linear regression model using excel analysis tool I showed you in class. Write the linear model and interpret the slope (b1).

3) Interpret the R2 and tell if your linear model is a good fit or not.

4) Estimate the difference in sales at the convenient store (on average) between a day with 3,500 gallons sold and a day with 4,000 gallons sold.

5) With regard to inference statistics, formulate a hypothesis test for the slope (b1) and decide if it is statistically significant or not.

6) Construct a 95% confidence interval for the slope.

In: Statistics and Probability

Vanguard Method as opposed to the traditional managerial thinking typically found in many organisations ( Jaaron...

Vanguard Method as opposed to the traditional managerial thinking typically found in
many organisations ( Jaaron and Backhouse, 2012).
The Vanguard Method embraces the principle that employees need to think, analyse,
judge, and make decisions on the work on hands. Therefore, team members training is
not the focus in the preparation process for this kind of job, it is actually educating them
on “why” a failure happen and then finding ways to eliminate it from the system. To
accommodate for the requirements of the Vanguard Method, managers’ role shifts from
command-and-control to supporters. This keeps managers very close to their employees
to interact with their work when necessary. Bhat et al. (2012) provide a constructive view
about the interactive leadership style and organisational learning. According to them, the
capacity of an organisation to learn how to learn, to change old ways of doing things, and
to produce original knowledge is positively related to interactive leadership styles. Due to
this type of relationship and due to the whole service processes being owned by team
members, the structure of the organisation changes. The organisation becomes
organically structured ( Jaaron and Backhouse, 2014).
The Vanguard Method in practice
The above philosophy usually follows three main practical steps of “check-plan-do” for
implementation. These steps are summarised in Table II.
Check. This stage aims at understanding the system and why it behaves in such a
way that failure demand is achieved. A specially formed team, called the check team,
from the workplace collates information about what customers expect and want from
the organisation and what matters to them most, they need to be able to use views of
different people involved in the problematic system to build the “real situation”
(Checkland, 1995). Once the team understands the type of demand received and how
capable the system is to respond to it, it can start to map the flow of processes in the
system. For this purpose, a visual representation of each operation carried out in the
workplace is developed as a flow chart. Identification of waste (actions not adding any
value from the customer’s point of view) present in the service operations flow is then

carried out (Seddon, 2008). All processes classified as waste are marked in red on the
process flow chart. While processes that add value from a customer’s point of view are
marked in green.
Plan. This stage starts with redesigning the processes flow charts taking into
account what has been learned by considering the customer “wants” and then mapping
out the new service system design. Typically, this stage is focussed on minimising
non-value adding activities from a customer point of view. The final step in the “plan”
process is to build performance measures and the future system success criterion. This
is usually how good employees are in creating a value demand and the percentage of
value demand out of the total demand received ( Jaaron and Backhouse, 2012).
Do. At this stage the new design is used in an experimental environment with the
check team using the new model after it has been discussed with the people doing
the work. The new processes are induced gradually with careful observation of both
employees’ reaction to it and customers feedback. The processes are tested,
re-designed, and re-tested again to make sure that customers get the best possible
service before going fully live. This is much slower process than the check phase as the
slogan at this stage is to “do it right rather than do it quick” ( Jackson et al., 2008).
The Vanguard Method cycle starts with the “check” stage in order to show business
managers the failings of their current system, and to provide them with a solid evidence
for the need to change the way they think and manage things ( Jackson et al., 2008).
To ensure continuous improvement of the new system, the check-plan-do cycle is a
continuous cycle (Seddon, 2008; Jackson et al., 2008). It is, therefore, a learning system
by itself: the process of acquiring knowledge and taking action to improve the situation
is continuous ( Jackson et al., 2008). In addition to continuously altering business
processes to improve the service offered, the Vanguard Method cycle involves the
identification of new demands coming in to the service department. This is followed by
designing new processes to ensure dealing with new demands as value demands
(Seddon, 2008).
4. Research methodology
A case study approach is adopted in this research inquiry in order to build an
understanding of the nature of the research phenomena (Voss et al., 2002). Case studies
have the advantage of being able to answer questions like “what”, “how”, and “why”
(Yin, 2009). This accommodates the type of question presented at the beginning of this
paper. Two case studies were chosen with the help of “extreme case sampling”
technique (Patton, 2002; Creswell, 2004) that displayed evidence of full employment of
the Vanguard Method in their logistics service operations. An earlier research work
conducted with the help of the Vanguard Method consultant of these two case studies
helped researchers in confirming that the Vanguard Method is fully employed in their
logistics operations, and also ensured easy access to both case studies.
According to Aastrup and Halldórsson (2008), the use of case studies in logistics
management research is an enabler for the causal depth required for understanding the
real domain of logistics operations and its performance. Case study research design
typically has the unique strength in providing a full range of evidence through the use
of multi-sources of data, which can achieve data triangulation (Voss et al., 2002). For
this purpose, the mixed methods design (Tashakkori and Teddlie, 1998) is used as the
technique for conducting the research process. Three different sources of data
collection methods are used in the two case studies; these are semi-structured

What is the particularity of vanguard method? (1point)

Why we need this method in logistics? (1 point)

In: Operations Management

The Farr-Kroger Classic is a women’s professional golf tournament played each year in Ohio. Listed below...

The Farr-Kroger Classic is a women’s professional golf tournament played each year in Ohio. Listed below are the total purse winnings (the amount of money that is distributed to the top golfers) and the prize for the winner for the 15 years from 1991 through 2005. The operators of this golf tournament believe that there is a relationship between the purse winnings and the prize and the prize is related to the purse winnings. In addition to the data provided, some of the possible linear regression relationships are provided.   These might be of help in your analysis.

Year Purse Winnings Prize Ind Var Year SUMMARY OUTPUT
1991 $225,000 $33,750 Dep var Purse Winnings
1992 $275,000 $41,250 Regression Statistics
1993 $325,000 $41,250 Multiple R 0.969387633
1994 $325,000 $48,750 R Square 0.939712382
1995 $350,000 $52,500 Adjusted R Square 0.935074873
1996 $400,000 $60,000 Standard Error 65072.5152
1997 $450,000 $67,500 Observations 15
1998 $500,000 $75,000
1999 $500,000 $75,000 ANOVA
2000 $575,000 $86,250 df SS MS F Significance F
2001 $700,000 $105,000 Regression 1 8.58036E+11 8.58036E+11 202.6330017 2.62887E-09
2002 $800,000 $120,000 Residual 13 55047619048 4234432234
2003 $800,000 $120,000 Total 14 9.13083E+11
2004 $1,000,000 $150,000
2005 $1,000,000 $150,000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -110055238.1 7769893.698 -14.16431709 2.79418E-09 -126841072.9 -93269403.32 -126841072.9 -93269403.32
Regression Relationship Independent Variable Dependent Variable Value of b Value of a Coefficent of Determination, r2 Year 55357.14286 3888.826592 14.23492191 2.62887E-09 46955.84379 63758.44192 46955.84379 63758.44192
Regression 1 Year Purse Winnings 55,357.14 -110,055,238.10 0.94
Regression 2 Purse Winnings Prize 0.15 -1,505.89 1.00
Regression 3 Prize Purse Winnings 6.57 11,179.24 1.00
Regression 4 Prize Year 0.00 1,988.85 0.94 Ind Var Purse Winnings SUMMARY OUTPUT
Regression 5 Year Prize 8,437.50 -16,776,375.00 0.94 Dep var Prize
Regression Statistics
a)      x = $996,430 Multiple R 0.998828015
y = -1505.89 + 0.15x = $149,786.51 R Square 0.997657404
Adjusted R Square 0.997477205
b)     x = 2006 Standard Error 1949.897566
y = -110055238.10 + 55357.14x = $991,190.48 Observations 15
ANOVA
df SS MS F Significance F
Regression 1 21049947693 21049947693 5536.399574 1.7382E-18
Residual 13 49427306.74 3802100.519
Total 14 21099375000
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -1505.886648 1226.975151 -1.227316337 0.241465598 -4156.605301 1144.832005 -4156.605301 1144.832005
Purse Winnings 0.151834444 0.002040594 74.40698606 1.7382E-18 0.147426009 0.156242879 0.147426009 0.156242879
Ind Var Prize SUMMARY OUTPUT
Dep var Purse Winnings
Regression Statistics
Multiple R 0.998828015
R Square 0.997657404
Adjusted R Square 0.997477205
Standard Error 12827.21014
Observations 15
ANOVA
df SS MS F Significance F
Regression 1 9.10944E+11 9.10944E+11 5536.399574 1.7382E-18
Residual 13 2138985160 164537320
Total 14 9.13083E+11
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 11179.24109 7942.610888 1.407502048 0.182737687 -5979.726488 28338.20867 -5979.726488 28338.20867
Prize 6.57069226 0.088307464 74.40698606 1.7382E-18 6.379915582 6.761468937 6.379915582 6.761468937
Ind Var Prize SUMMARY OUTPUT
Dep var Year
Regression Statistics
Multiple R 0.971981516
R Square 0.944748067
Adjusted R Square 0.940497919
Standard Error 1.090890292
Observations 15
ANOVA
df SS MS F Significance F
Regression 1 264.5294588 264.5294588 222.2858866 1.48781E-09
Residual 13 15.47054119 1.19004163
Total 14 280
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 1988.846441 0.675479471 2944.347723 3.02269E-39 1987.387156 1990.305726 1987.387156 1990.305726
Prize 0.00011197 7.51011E-06 14.90925507 1.48781E-09 9.57455E-05 0.000128195 9.57455E-05 0.000128195
Ind Var Year SUMMARY OUTPUT
Dep var Prize
Regression Statistics
Multiple R 0.971981516
R Square 0.944748067
Adjusted R Square 0.940497919
Standard Error 9469.713869
Observations 15
ANOVA
df SS MS F Significance F
Regression 1 19933593750 19933593750 222.2858866 1.48781E-09
Residual 13 1165781250 89675480.77
Total 14 21099375000
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -16776375 1130718.09 -14.83692102 1.57972E-09 -19219142.92 -14333607.08 -19219142.92 -14333607.08
Year 8437.5 565.9236469 14.90925507 1.48781E-09 7214.896294 9660.103706 7214.896294 9660.103706

Using linear regression relationships, answer the questions a) through c) below and on the following page.

a) Develop a projection for the amount of the prize for the winner for the year 2008 if the purse winnings for that year are projected to be $996,430. As part of your answer, include the independent and dependent variables and the accompanying linear regression relationship.

b) Now let’s suppose that we believe the prize for the winner is a function of time (dependent on time). Given this belief, develop a projection for the amount of the prize for the winner for the year 2008 and discuss your results compared to what you found in part a)

c) Would you recommend using the forecasts you found in parts a) and b) based on the strengths of the relationship? Why?

In: Operations Management

Jack, Jills and the Buffalo Bills Before the 2014 season, Cailin Ferrari had conflicting thoughts about...

Jack, Jills and the Buffalo Bills

Before the 2014 season, Cailin Ferrari had conflicting thoughts about continuing her dream of being a member of the Buffalo Jills, (the Buffalo Bill’s cheerleading team) or to seek employment elsewhere. For the past 48 years, the Jills were an important part of the Bills organization, entertaining fans both on and off the playing field. However, after some careful research, the Jills found themselves wondering if they should continue to entertain fans under tense circumstances.

Buffalo Jills

Established in 1967, the Jills began as a permanent replacement for the cheerleaders from Buffalo State College who previously cheered from the Buffalo Bills sidelines. The Jills cheerleaders recognized for their high spirit, dedication, and humanitarian nature, had become a favorite for the city of Buffalo. After 42 seasons of entertaining Bills fans, the Jills established the Buffalo Jills Alumni Association.


Buffalo Bills

The Buffalo Bills, located in Buffalo NY, is currently owned by Terrence and Kim Pegula. In 2016, Forbes reported the team value at one billion, five-hundred million dollars (see exhibit 1). New Era Field, formally Rich Stadium and later Ralph Stadium, has been the home for the Buffalo Bills since 1973. The stadium has a capacity seating for 71,870 Bills fans. NEF is currently within the top 15 in capacity in the National Football League.

Exhibit 1: Bills Value Breakdown

Financial Data

Sport

$1,118M

Market

$179M

Stadium

$139M

Brand

$63M

Legal Issues

In April 2014, five former Bills cheerleaders sued the team over a pay system that had them working hundreds of hours for free at games and at mandatory public appearances. Soon after, management suspended the dance team.

The class action lawsuit claimed the Jills cheerleaders were paid below minimum wage and were required to attend unpaid events. The former cheerleaders also alleged that the Jills were wrongly classified as independent contractors and were subjected to policies that violate the state's $8 per hour minimum wage law and other workplace rules (Rodak, 2014). The Jills were not paid for games or practices and had to make 20 to 35 community and charity events each season.

The Jills stated that at some of these sponsored events, they were made to feel uncomfortable by male attendees. They were forced to adhere to strict dress codes and behavioral guidelines set by the team. According to the Jills, the Buffalo Bills controlled everything from their physical appearance to music selection (Garcia, 2016). The Bills organization claimed the Jills were not traditional employees but independent contractors.

In a 1995 ruling by the National Labor Relations Board, the Jills were classified as non-exempt employees. A former employee of Cumulus Broadcasting Co. (formally Citadel Broadcasting Co), named Stephanie E. Mateczun, managed the Jills. The contracts gave Citadel/Cumulus the exclusive rights to run the Jills, and required each member of the cheerleading squad to sign independent contractor agreements that the Jills would not be paid for working Bills games (Davis, 2017).

National Football Association

Currently, only six teams in the National Football Association (NFL) do not have a cheerleading team, either by personal choice or in the Jills case, suspension: Buffalo Bills, Cleveland Browns, New York Giant, Pittsburgh Steelers, Green Bay Packers, and Chicago Bears.

The NFL has remained quiet with this issue. Rodger Goodell, the commissioner of the NFL stated, he had no knowledge of the Jills’ selection, training, compensation and/or pay practices. According to the NFLPA (National Football League Players Association), the NFL protects its players but has no mention of its cheerleader teams. As reported by the NFLPA website, the National Football League Players Association:

Represents all players in matters about wages, hours and working conditions.

Protects their rights as professional football players

Assures that all the terms of the Collective Bargaining Agreement are met.

Decision

New York State Supreme Court Justice Mark A. Montour decided the cheerleaders' 2005 agreement they signed were unenforceable, and that the plaintiffs were non-exempt employees and they were misclassified as independent contractors.

In response to the lawsuit, the Cheerleaders' Fair Pay Act would force team owners to treat the Jills as employees rather than independent contractors. The change would mean teams like the Buffalo Bills would have to comply with much stricter New York labor laws when it comes to cheerleaders' wages and workplace protections. Was the contract negotiable between both parties? Was the contract by the Jills signed under duress? What employment laws did the Buffalo Bills violate? Should the NFL create a regulated pay scale for all NFL cheerleaders?

Questions to Answer.

1. What employment laws (if any) did the Buffalo bills violate? Please explain your answer thoroughly in either scenario?

2. Do you think the ruling was fair? Was there any ethical concerns in the case? Discuss your view point.

3. Discuss the social responsbility (if any) for the NFL and the Buffalo Bills.

4. Should the NFL creat a regulated pay scale for all NFL Cheerleaders? Or a union for the cheerleading team? Why or why not?

5. Was the contract negotiable between both parties?

In: Operations Management

Tesco Exits South Korea Tesco was founded in 1919 by Jack Cohen (Cohen), who invested his...

Tesco Exits South Korea

Tesco was founded in 1919 by Jack Cohen (Cohen), who invested his serviceman’s gratuity of £30 in a grocery stall. The first private label product introduced by Cohen was Tesco Tea. The name Tesco was a combination of the initials of the tea supplier TE Stockwell, and the first two letters of Cohen’s name. Tesco opened its first store in 1929 in Edgware, London. In 1947, Tesco Stores (Holdings) Limited was floated on the Stock Exchange with a share price of 25 pence and the first supermarket was opened in 1956 in Maldon, Essex, England. The first superstore was opened in 1968 in Crawley, West Sussex. In the 1960s, Tesco went on an expansion spree and acquired several store chains. The Retail Price Maintenance (RPM) Act in Britain prohibited large retailers from pricing goods below a price agreed upon by the suppliers. To overcome this obstacle to price reduction, Tesco introduced trading stamps. These were given to customers when they purchased products and could be traded for cash or other gifts. RPM was abolished in 1964, and from then on, Tesco was able to offer competitively priced products to its customers in a more direct manner. The first Tesco superstore, with an area of 90,000 square feet, was opened in 1967.

TESCO’S GLOBAL EXPANSION
Tesco’s global expansion began in 1979, when it entered Ireland by acquiring a 51% equity stake in ‘3 Guys stores’. In 1986, Tesco divested itself of the stores after it found that it could not sustain its operations in the country as customers were rejecting the British products that it sold. During the late 1980s and the early 1990s, Tesco examined the options available in the US and European countries after the British government introduced new regulations on ‘out-of-town’ stores. In December 1992, Tesco entered France by acquiring an 85% equity holding in Catteau supermarkets, which operated under the Cedico brand with 72 superstores, 7 hypermarkets, and 24 small stores. However, Tesco failed to sustain itself in the market due to competition from French retailers like Carrefour and Promodès. In 1995, a law was passed in France which prohibited the opening of new large retail stores. Moreover, the company failed to adapt its products to suit local tastes and lost market share. In 1996, in spite of investing an additional £ 300 million in France, sales in the country grew by a mere 1%. In the year 1997, Tesco sold its operations in France to Prom odes.

TESCO IN SOUTH KOREA
In the early 1990s, there was a growing demand from consumers in South Korea for a modern shopping experience owing to rapid economic growth and increasing disposable incomes. The government had adopted protectionist policies and the retail sector was not open for foreign direct investment (FDI). Tesco

entered South Korea in 1999 through a joint venture with Homeplus, a unit of the country’s biggest business group Samsung Corporation (Samsung) . In the next few years, Tesco became the most successful international retailer in the country. Its success was attributed to its ability to localize its products and stores to appeal to the South Korean consumers; its operating through local management; and its strong presence through different store formats. South Korea went on to become Tesco’s most successful international business in terms of revenue. As of 2014, it operated d 140 hypermarkets, 609 supermarkets, and 326 convenience stores.

TESCO’S STRATEGIES IN SOUTH KOREA
Immediately after entering into the joint venture, Tesco went about upgrading the store layouts. The stores were modified to resemble department stores, which were spacious and clean. Tesco’s stores in Korea did not resemble its stores in the UK or in other European locations like Hungary, Poland, the Czech Republic, and Ireland.

CHANGES IN THE OPERATING ENVIRONMENT
In October 2012, when Tesco posted its first fall in profits in 20 years, the company also announced that its profits in South Korea would take a £ 100 million hit due to the "retail market development bill” that had been passed by the government in November 2010. However, changes in the operating environment in South Korea due to new laws that were enforced beginning 2010 to protect small retailers and merchants started to impact Tesco and other large retailers. These laws placed restrictions on the locations where supermarkets could be opened. The Distribution Industry Development Act passed in 2012 imposed restrictions on the time for which the stores could remain open and also specified that on two weekends every month the large retail stores should be closed. As most Koreans shopped during the weekends, these restrictions started to impact Tesco, which made losses in 2015. Under the impact of the global recession, the private spending in South Korea fell. Another factor that impacted Tesco in South Korea was its UK business, which was not doing well.

TESCO’S EXIT FROM SOUTH KOREA
On September 07, 2015, Tesco PLC (Tesco), a British multinational grocery and general merchandise retailer, announced that it had sold its South Korean business, operated under the name Homeplus, for £4.2 billion to a consortium of companies led by MBK Partners, a South Korean buyout firm. The consortium included Canada Pension Plan Investment Board, Public Sector Pension Investment Board, and Temasek Holdings (Private) Limited

Question - Case study

Use the case study above to answer the question

What do you think did not work well for Tesco?

Using the Tesco Case discuss the need for companies to consider push and pull factors for international expansion.

In: Economics

Tesco Exits South Korea Tesco was founded in 1919 by Jack Cohen (Cohen), who invested his...

Tesco Exits South Korea

Tesco was founded in 1919 by Jack Cohen (Cohen), who invested his serviceman’s gratuity of £30 in a grocery stall. The first private label product introduced by Cohen was Tesco Tea. The name Tesco was a combination of the initials of the tea supplier TE Stockwell, and the first two letters of Cohen’s name. Tesco opened its first store in 1929 in Edgware, London. In 1947, Tesco Stores (Holdings) Limited was floated on the Stock Exchange with a share price of 25 pence and the first supermarket was opened in 1956 in Maldon, Essex, England. The first superstore was opened in 1968 in Crawley, West Sussex. In the 1960s, Tesco went on an expansion spree and acquired several store chains. The Retail Price Maintenance (RPM) Act in Britain prohibited large retailers from pricing goods below a price agreed upon by the suppliers. To overcome this obstacle to price reduction, Tesco introduced trading stamps. These were given to customers when they purchased products and could be traded for cash or other gifts. RPM was abolished in 1964, and from then on, Tesco was able to offer competitively priced products to its customers in a more direct manner. The first Tesco superstore, with an area of 90,000 square feet, was opened in 1967.

TESCO’S GLOBAL EXPANSION
Tesco’s global expansion began in 1979, when it entered Ireland by acquiring a 51% equity stake in ‘3 Guys stores’. In 1986, Tesco divested itself of the stores after it found that it could not sustain its operations in the country as customers were rejecting the British products that it sold. During the late 1980s and the early 1990s, Tesco examined the options available in the US and European countries after the British government introduced new regulations on ‘out-of-town’ stores. In December 1992, Tesco entered France by acquiring an 85% equity holding in Catteau supermarkets, which operated under the Cedico brand with 72 superstores, 7 hypermarkets, and 24 small stores. However, Tesco failed to sustain itself in the market due to competition from French retailers like Carrefour and Promodès. In 1995, a law was passed in France which prohibited the opening of new large retail stores. Moreover, the company failed to adapt its products to suit local tastes and lost market share. In 1996, in spite of investing an additional £ 300 million in France, sales in the country grew by a mere 1%. In the year 1997, Tesco sold its operations in France to Prom odes.

TESCO IN SOUTH KOREA
In the early 1990s, there was a growing demand from consumers in South Korea for a modern shopping experience owing to rapid economic growth and increasing disposable incomes. The government had adopted protectionist policies and the retail sector was not open for foreign direct investment (FDI). Tesco

entered South Korea in 1999 through a joint venture with Homeplus, a unit of the country’s biggest business group Samsung Corporation (Samsung). In the next few years, Tesco became the most successful international retailer in the country. Its success was attributed to its ability to localize its products and stores to appeal to the South Korean consumers; its operating through local management; and its strong presence through different store formats. South Korea went on to become Tesco’s most successful international business in terms of revenue. As of 2014, it operated d 140 hypermarkets, 609 supermarkets, and 326 convenience stores.

TESCO’S STRATEGIES IN SOUTH KOREA
Immediately after entering into the joint venture, Tesco went about upgrading the store layouts. The stores were modified to resemble department stores, which were spacious and clean. Tesco’s stores in Korea did not resemble its stores in the UK or in other European locations like Hungary, Poland, the Czech Republic, and Ireland.

CHANGES IN THE OPERATING ENVIRONMENT
In October 2012, when Tesco posted its first fall in profits in 20 years, the company also announced that its profits in South Korea would take a £ 100 million hit due to the "retail market development bill” that had been passed by the government in November 2010. However, changes in the operating environment in South Korea due to new laws that were enforced beginning 2010 to protect small retailers and merchants started to impact Tesco and other large retailers. These laws placed restrictions on the locations where supermarkets could be opened. The Distribution Industry Development Act passed in 2012 imposed restrictions on the time for which the stores could remain open and also specified that on two weekends every month the large retail stores should be closed. As most Koreans shopped during the weekends, these restrictions started to impact Tesco, which made losses in 2015. Under the impact of the global recession, the private spending in South Korea fell. Another factor that impacted Tesco in South Korea was its UK business, which was not doing well.

TESCO’S EXIT FROM SOUTH KOREA
After several months of speculation, Tesco sold its South Korean stores to Asian private equity firm MBK Partners for £4.2 billion on September 07, 2015. On September 07, 2015, Tesco PLC (Tesco), a British multinational grocery and general merchandise retailer, announced that it had sold its South Korean business, operated under the name Homeplus, for £4.2 billion to a consortium of companies led by MBK Partners, a South Korean buyout firm. The consortium included Canada Pension Plan Investment Board, Public Sector Pension Investment Board, and Temasek Holdings (Private) Limited.

Case study question
The extract above mentions changes in operating environment in which Tesco functions.

Discuss in this context, the nuances of a Task environment.

In: Economics