In: Accounting
| 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 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 – 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)
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 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 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 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 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 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 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