1 00:00:00,973 --> 00:00:04,107 2014 is a very special year for me: 2 00:00:04,107 --> 00:00:06,034 20 years as a consultant, 3 00:00:06,034 --> 00:00:07,706 20 years of marriage, 4 00:00:07,706 --> 00:00:10,887 and I'm turning 50 in one month. 5 00:00:10,887 --> 00:00:14,208 That means I was born in 1964 6 00:00:14,208 --> 00:00:16,437 in a small town in Germany. 7 00:00:16,437 --> 00:00:18,155 It was a grey November day, 8 00:00:18,155 --> 00:00:20,035 and I was overdue. 9 00:00:20,035 --> 00:00:23,518 The hospital's maternity ward was really stressed out 10 00:00:23,518 --> 00:00:25,840 because a lot of babies were born 11 00:00:25,840 --> 00:00:28,023 on this grey November day. 12 00:00:28,023 --> 00:00:30,995 As a matter of fact, 1964 13 00:00:30,995 --> 00:00:34,571 was the year with the highest birth rate ever in Germany: 14 00:00:34,571 --> 00:00:36,591 more than 1.3 million. 15 00:00:36,591 --> 00:00:39,725 Last year, we just hit over 600,000, 16 00:00:39,725 --> 00:00:41,490 so half of my number. 17 00:00:41,490 --> 00:00:43,324 What you can see here 18 00:00:43,324 --> 00:00:45,321 is the German age pyramid, 19 00:00:45,321 --> 00:00:48,130 and there, the small black point at the top, that's me. 20 00:00:48,130 --> 00:00:52,124 (Laughter) (Applause) 21 00:00:54,307 --> 00:00:58,440 In red, you can see the potential working age population, 22 00:00:58,440 --> 00:01:02,689 so people over 15 and under 65, 23 00:01:02,689 --> 00:01:04,361 and I'm actually only interested 24 00:01:04,361 --> 00:01:06,404 in this red area. 25 00:01:06,404 --> 00:01:08,447 Now, let's do a simple simulation 26 00:01:08,447 --> 00:01:10,189 of how this age structure will develop 27 00:01:10,189 --> 00:01:12,975 over the next couple of years. 28 00:01:12,975 --> 00:01:14,414 As you can see, 29 00:01:14,414 --> 00:01:16,713 the peak is moving to the right, 30 00:01:16,713 --> 00:01:19,941 and I, with many other baby boomers, 31 00:01:19,941 --> 00:01:23,238 will retire in 2030. 32 00:01:23,238 --> 00:01:25,513 By the way, I don't need any forecasts 33 00:01:25,513 --> 00:01:28,648 of birth rates for predicting this red area. 34 00:01:28,648 --> 00:01:31,132 The red area, so the potential working age 35 00:01:31,132 --> 00:01:33,291 population in 2030 36 00:01:33,291 --> 00:01:36,589 is already set in stone today, 37 00:01:36,589 --> 00:01:40,141 except for much higher migration rates. 38 00:01:40,141 --> 00:01:43,160 And if you compare this red area in 2030 39 00:01:43,160 --> 00:01:46,271 with red area in 2040, 40 00:01:46,271 --> 00:01:49,382 it is much, much smaller. 41 00:01:49,382 --> 00:01:51,820 So before I show you the rest of the world, 42 00:01:51,820 --> 00:01:55,117 what does this mean for Germany? 43 00:01:55,117 --> 00:01:59,204 So what we know from this picture is that labor supply, 44 00:01:59,204 --> 00:02:01,131 so people who provide labor, 45 00:02:01,131 --> 00:02:04,800 will go down in Germany, and will go down significantly. 46 00:02:04,800 --> 00:02:07,400 Now, what about labor demand? 47 00:02:07,400 --> 00:02:10,372 That's where it gets tricky. 48 00:02:10,372 --> 00:02:12,253 As you might know, the consultant's 49 00:02:12,253 --> 00:02:14,970 favorite answer to any question is, 50 00:02:14,970 --> 00:02:16,711 "It depends." 51 00:02:16,711 --> 00:02:19,079 So I would say it depends. 52 00:02:19,079 --> 00:02:21,448 We didn't want to forecast the future. 53 00:02:21,448 --> 00:02:22,957 Highly speculative. 54 00:02:22,957 --> 00:02:24,513 We did something else. 55 00:02:24,513 --> 00:02:27,903 We looked at the GDP and productivity growth of Germany 56 00:02:27,903 --> 00:02:29,598 over the last 20 years 57 00:02:29,598 --> 00:02:32,082 and calculated the following scenario: 58 00:02:32,082 --> 00:02:36,563 if Germany wants to continue this GDP and productivity growth, 59 00:02:36,563 --> 00:02:38,676 we could directly calculate 60 00:02:38,676 --> 00:02:40,580 how many people Germany would need 61 00:02:40,580 --> 00:02:42,647 to support this growth. 62 00:02:42,647 --> 00:02:45,619 And this is the green line: labor demand. 63 00:02:45,619 --> 00:02:48,614 So Germany will run into a major 64 00:02:48,614 --> 00:02:51,331 talent shortage very quickly. 65 00:02:51,331 --> 00:02:53,165 Eight million people are missing, 66 00:02:53,165 --> 00:02:55,928 which is more than 20 percent of our current work force, 67 00:02:55,928 --> 00:02:58,598 so big numbers, really big numbers. 68 00:02:58,598 --> 00:03:00,711 And we calculated several scenarios, 69 00:03:00,711 --> 00:03:04,565 and the picture always looked like this. 70 00:03:04,565 --> 00:03:06,446 Now, to close the gap, 71 00:03:06,446 --> 00:03:08,722 Germany has to significantly 72 00:03:08,722 --> 00:03:10,509 increase migration, 73 00:03:10,509 --> 00:03:12,715 get many more women in the work force, 74 00:03:12,715 --> 00:03:14,480 increase retirement age. 75 00:03:14,480 --> 00:03:16,895 — by the way, we just lowered it this year — 76 00:03:16,895 --> 00:03:19,658 and all these measures at once. 77 00:03:19,658 --> 00:03:21,817 If Germany fails here, 78 00:03:21,817 --> 00:03:23,721 Germany will stagnate. 79 00:03:23,721 --> 00:03:26,043 We won't grow anymore. Why? 80 00:03:26,043 --> 00:03:29,456 Because the workers are not there who can generate this growth. 81 00:03:29,456 --> 00:03:31,616 And companies will look 82 00:03:31,616 --> 00:03:34,123 for talents somewhere else. 83 00:03:34,123 --> 00:03:36,143 But where? 84 00:03:36,143 --> 00:03:39,556 Now, we simulated labor supply 85 00:03:39,556 --> 00:03:40,996 and labor demand 86 00:03:40,996 --> 00:03:43,945 for the largest 15 economies in the world, 87 00:03:43,945 --> 00:03:46,104 representing more than 70 percent 88 00:03:46,104 --> 00:03:47,637 of world GDP, 89 00:03:47,637 --> 00:03:49,935 and the overall picture looks like this 90 00:03:49,935 --> 00:03:51,584 by 2020. 91 00:03:51,584 --> 00:03:54,277 Blue indicates a labor surplus, 92 00:03:54,277 --> 00:03:56,762 red indicates a labor shortfall, 93 00:03:56,762 --> 00:04:00,454 and grey are those countries which are borderline. 94 00:04:00,454 --> 00:04:04,540 So by 2020, we still see a labor surplus 95 00:04:04,540 --> 00:04:06,560 in some countries, 96 00:04:06,560 --> 00:04:08,743 like Italy, France, the U.S., 97 00:04:08,743 --> 00:04:10,252 but this picture will change 98 00:04:10,252 --> 00:04:13,340 dramatically by 2030. 99 00:04:13,340 --> 00:04:17,961 By 2030, we will face a global work force crisis 100 00:04:17,961 --> 00:04:20,956 in most of our largest economies, 101 00:04:20,956 --> 00:04:23,371 including three out of the four BRIC countries. 102 00:04:23,371 --> 00:04:26,529 China, with its former one-child policy, will be it, 103 00:04:26,529 --> 00:04:30,453 as well as Brazil and Russia. 104 00:04:30,453 --> 00:04:34,168 Now, to tell the truth, 105 00:04:34,168 --> 00:04:36,815 in reality, the situation 106 00:04:36,815 --> 00:04:38,974 will be even more challenging. 107 00:04:38,974 --> 00:04:42,317 What you can see here are average numbers. 108 00:04:42,317 --> 00:04:44,013 We de-averaged them 109 00:04:44,013 --> 00:04:45,777 and broke them down 110 00:04:45,777 --> 00:04:47,240 to different skill levels, 111 00:04:47,240 --> 00:04:48,633 and what we found 112 00:04:48,633 --> 00:04:50,305 were even higher shortfalls 113 00:04:50,305 --> 00:04:51,884 for high-skilled people 114 00:04:51,884 --> 00:04:53,764 and a partial surplus 115 00:04:53,764 --> 00:04:56,063 for low-skilled workers. 116 00:04:56,063 --> 00:04:59,221 So on top of an overall labor shortage, 117 00:04:59,221 --> 00:05:02,652 we will face a big skill mismatch in the future, 118 00:05:02,652 --> 00:05:05,156 and this means huge challenges 119 00:05:05,076 --> 00:05:07,289 in terms of education, qualification, 120 00:05:07,289 --> 00:05:12,013 upskilling for governments and companies. 121 00:05:12,013 --> 00:05:14,543 Now, the next thing we looked into 122 00:05:15,286 --> 00:05:18,375 was robots, automation, technology. 123 00:05:18,375 --> 00:05:21,393 Will technology change this picture 124 00:05:23,347 --> 00:05:23,808 and boost productivity? 125 00:05:25,808 --> 00:05:26,548 Now, the short answer would be 126 00:05:26,548 --> 00:05:26,798 that our numbers already include 127 00:05:26,798 --> 00:05:30,359 a significant growth in productivity 128 00:05:30,359 --> 00:05:33,308 driven by technology. 129 00:05:33,308 --> 00:05:36,628 A long answer would go like this. 130 00:05:36,628 --> 00:05:39,113 Let's take Germany. 131 00:05:39,113 --> 00:05:41,667 The Germans have a certain reputation in the world 132 00:05:41,667 --> 00:05:44,244 when it comes to productivity. 133 00:05:44,244 --> 00:05:47,077 In the '90s, I worked in our Boston office 134 00:05:47,077 --> 00:05:48,841 for almost two years, 135 00:05:48,841 --> 00:05:51,488 and when I left, an old senior partner 136 00:05:51,488 --> 00:05:52,998 told me literally, 137 00:05:52,998 --> 00:05:54,600 "Send me more of these Germans, 138 00:05:54,600 --> 00:05:56,480 they work like machines." 139 00:05:56,480 --> 00:06:01,287 (Laughter) 140 00:06:01,287 --> 00:06:04,259 That was 1998. 141 00:06:04,259 --> 00:06:07,742 Sixteen years later, you'd probably say the opposite. 142 00:06:07,742 --> 00:06:09,553 "Send me more of these machines. 143 00:06:09,553 --> 00:06:11,410 They work like Germans." 144 00:06:11,410 --> 00:06:15,566 (Laughter) (Applause) 145 00:06:18,353 --> 00:06:20,768 Technology will replace 146 00:06:20,768 --> 00:06:22,811 a lot of jobs, regular jobs. 147 00:06:22,811 --> 00:06:24,808 Not only in the production industry, 148 00:06:24,808 --> 00:06:26,619 but even office workers are in jeopardy 149 00:06:26,619 --> 00:06:29,521 and might be replaced by robots, 150 00:06:29,521 --> 00:06:33,120 artificial intelligence, big data, or automation. 151 00:06:33,120 --> 00:06:34,954 So the key question is not 152 00:06:34,954 --> 00:06:38,042 if technology replaces some of these jobs, 153 00:06:38,042 --> 00:06:41,688 but when, how fast, and to what extent, 154 00:06:41,688 --> 00:06:43,383 or in other words, 155 00:06:43,383 --> 00:06:45,194 will technology help us 156 00:06:45,194 --> 00:06:48,839 to solve this global work force crisis? 157 00:06:48,839 --> 00:06:51,254 Yes and no. 158 00:06:51,254 --> 00:06:55,317 This is a more sophisticated version of "it depends." 159 00:06:55,317 --> 00:06:58,684 Let's take the automotive industry 160 00:06:58,684 --> 00:07:00,310 as an example, 161 00:07:00,310 --> 00:07:02,213 because there, more than 40 percent 162 00:07:02,213 --> 00:07:04,814 of industrial robots are already working 163 00:07:04,814 --> 00:07:08,761 and automation has already taken place. 164 00:07:08,761 --> 00:07:12,360 In 1980, less than 10 percent 165 00:07:12,360 --> 00:07:14,659 of the production cost of a car 166 00:07:14,659 --> 00:07:17,329 was caused by electronic parts. 167 00:07:17,329 --> 00:07:20,580 Today, this number is more than 30 percent 168 00:07:20,580 --> 00:07:23,621 and it will grow to more than 50 percent 169 00:07:23,621 --> 00:07:25,618 by 2030. 170 00:07:25,618 --> 00:07:27,731 And these new electronic parts 171 00:07:27,731 --> 00:07:31,005 and applications require new skills 172 00:07:31,005 --> 00:07:33,652 and have created a lot of new jobs, 173 00:07:33,652 --> 00:07:35,720 like the cognitive systems engineer 174 00:07:35,720 --> 00:07:38,365 who optimizes the interaction 175 00:07:38,365 --> 00:07:42,081 between driver and electronic system. 176 00:07:42,081 --> 00:07:46,446 In 1980, no one had the slightest clue 177 00:07:46,446 --> 00:07:49,534 that such a job would ever exist. 178 00:07:49,534 --> 00:07:53,063 As a matter of fact, the overall number of people 179 00:07:53,063 --> 00:07:55,246 involved in the production of a car 180 00:07:55,246 --> 00:07:57,103 has only changed slightly 181 00:07:57,103 --> 00:07:58,659 in the last decades, 182 00:07:58,659 --> 00:08:01,677 in spite of robots and automation. 183 00:08:01,677 --> 00:08:03,443 So what does this mean? 184 00:08:03,443 --> 00:08:06,135 Yes, technology will replace a lot of jobs, 185 00:08:06,135 --> 00:08:08,806 but we will also see a lot of new jobs 186 00:08:08,806 --> 00:08:11,731 and new skills on the horizon, 187 00:08:11,731 --> 00:08:14,935 and that means technology will worsen 188 00:08:14,935 --> 00:08:17,768 our overall skill mismatch. 189 00:08:17,768 --> 00:08:19,510 And this kind of de-averaging 190 00:08:19,510 --> 00:08:21,274 reveals the crucial challenge 191 00:08:21,274 --> 00:08:24,478 for governments and businesses. 192 00:08:24,478 --> 00:08:29,006 So people, high-skilled people, 193 00:08:29,006 --> 00:08:31,096 talents, will be the big thing 194 00:08:31,096 --> 00:08:33,046 in the next decade. 195 00:08:33,046 --> 00:08:35,670 If they are the scarce resource, 196 00:08:35,670 --> 00:08:38,688 we have to understand them much better. 197 00:08:38,688 --> 00:08:41,637 Are they actually willing to work abroad? 198 00:08:41,637 --> 00:08:44,261 What are their job preferences? 199 00:08:44,261 --> 00:08:47,651 To find out, this year we conducted 200 00:08:47,651 --> 00:08:49,973 a global survey 201 00:08:49,973 --> 00:08:52,564 among more than 200,000 job seekers 202 00:08:52,564 --> 00:08:55,722 from 189 countries. 203 00:08:55,722 --> 00:09:00,157 Migration is certainly one key measure 204 00:09:00,157 --> 00:09:03,013 to close a gap, at least in the short term, 205 00:09:03,013 --> 00:09:05,613 so we asked about mobility. 206 00:09:05,613 --> 00:09:07,680 More than 60 percent 207 00:09:07,680 --> 00:09:10,234 of these 200,000 job seekers 208 00:09:10,234 --> 00:09:12,834 are willing to work abroad, 209 00:09:12,834 --> 00:09:14,994 for me a surprisingly high number. 210 00:09:14,994 --> 00:09:18,407 If you look at the employees aged 21 to 30 211 00:09:18,407 --> 00:09:20,706 this number is even higher. 212 00:09:20,706 --> 00:09:24,026 If you split this number up by country, 213 00:09:24,026 --> 00:09:26,185 yes, the world is mobile, 214 00:09:26,185 --> 00:09:29,227 but only partly. 215 00:09:29,227 --> 00:09:30,713 The least mobile countries 216 00:09:30,713 --> 00:09:34,358 are Russia, Germany, and the U.S. 217 00:09:34,358 --> 00:09:37,632 Now, where would these people like to move to? 218 00:09:37,632 --> 00:09:39,490 Number seven is Australia, 219 00:09:39,490 --> 00:09:42,462 where 28 percent could imagine moving to. 220 00:09:42,462 --> 00:09:45,387 Then France, Switzerland, Germany, 221 00:09:45,387 --> 00:09:49,126 Canada, U.K., and top choice worldwide 222 00:09:49,126 --> 00:09:50,937 is the U.S. 223 00:09:50,937 --> 00:09:54,605 Now, what are the job preferences of these 200,000 people? 224 00:09:54,605 --> 00:09:57,043 So what are they looking for? 225 00:09:57,043 --> 00:09:59,644 Out of a list of 26 topics, 226 00:09:59,644 --> 00:10:03,475 salary is only number eight. 227 00:10:03,475 --> 00:10:08,003 The top four topics are all around culture: 228 00:10:08,003 --> 00:10:09,558 number four, 229 00:10:09,558 --> 00:10:12,554 having a great relationship with the boss; 230 00:10:12,554 --> 00:10:16,571 three, enjoying a great work-life balance; 231 00:10:16,571 --> 00:10:20,332 two, having a great relationship with colleagues; 232 00:10:20,332 --> 00:10:22,329 and top priority worldwide 233 00:10:22,329 --> 00:10:28,528 is being appreciated for your work. 234 00:10:28,528 --> 00:10:31,152 So do I get a thank you? 235 00:10:31,152 --> 00:10:32,801 Not only once a year 236 00:10:32,801 --> 00:10:34,496 with the annual bonus payment, 237 00:10:34,496 --> 00:10:36,701 but every day. 238 00:10:36,701 --> 00:10:39,720 And now, our global work force crisis 239 00:10:39,720 --> 00:10:41,926 becomes very personal. 240 00:10:41,926 --> 00:10:44,967 People are looking for recognition. 241 00:10:44,967 --> 00:10:47,429 Aren't we all looking for recognition 242 00:10:47,429 --> 00:10:49,959 in our jobs? 243 00:10:49,959 --> 00:10:54,882 Now, let me connect the dots. 244 00:10:54,882 --> 00:10:57,250 We will face a global work force crisis 245 00:10:57,250 --> 00:11:00,222 which consists of an overall labor shortage 246 00:11:00,222 --> 00:11:02,173 plus a huge skill mismatch 247 00:11:02,173 --> 00:11:05,307 plus a big cultural challenge. 248 00:11:05,307 --> 00:11:06,886 And this global work force crisis 249 00:11:06,886 --> 00:11:09,231 is approaching very fast. 250 00:11:09,231 --> 00:11:11,971 Right now, we are just at the turning point. 251 00:11:11,971 --> 00:11:13,689 So what can we, what can governments, 252 00:11:13,689 --> 00:11:16,359 what can companies do? 253 00:11:16,359 --> 00:11:17,962 Every company, 254 00:11:17,962 --> 00:11:19,749 but also every country, 255 00:11:19,749 --> 00:11:21,677 needs a people strategy, 256 00:11:21,677 --> 00:11:24,649 and act on it immediately, 257 00:11:24,649 --> 00:11:26,762 and such a people strategy consists 258 00:11:26,762 --> 00:11:28,596 out of four parts. 259 00:11:28,596 --> 00:11:30,407 Number one, a plan 260 00:11:30,407 --> 00:11:33,565 for how to forecast supply and demand 261 00:11:33,565 --> 00:11:36,537 for different jobs and different skills. 262 00:11:36,537 --> 00:11:38,232 Work force planning 263 00:11:38,232 --> 00:11:42,109 will become more important than financial planning. 264 00:11:42,109 --> 00:11:45,732 Two, a plan for how to attract great people: 265 00:11:45,732 --> 00:11:49,865 generation Y, women, but also retirees. 266 00:11:49,865 --> 00:11:52,837 Three, a plan for how to educate 267 00:11:52,837 --> 00:11:54,207 and upskill them. 268 00:11:54,207 --> 00:11:55,925 There's a huge upskilling challenge 269 00:11:55,925 --> 00:11:57,666 ahead of us. 270 00:11:57,666 --> 00:11:59,500 And four, 271 00:11:59,500 --> 00:12:02,333 for how to retain the best people, 272 00:12:02,333 --> 00:12:03,726 or in other words, 273 00:12:03,726 --> 00:12:06,698 how to realize an appreciation 274 00:12:06,698 --> 00:12:11,215 and relationship culture. 275 00:12:11,215 --> 00:12:15,382 However, one crucial underlying factor 276 00:12:15,382 --> 00:12:18,424 is to change our attitudes. 277 00:12:18,424 --> 00:12:22,627 Employees are resources, are assets, 278 00:12:22,627 --> 00:12:25,250 not costs, not head counts, 279 00:12:25,250 --> 00:12:28,176 not machines, not even the Germans. 280 00:12:28,176 --> 00:12:30,544 Thank you. 281 00:12:30,544 --> 00:12:34,431 (Applause)