How to Predict the Future in Uncertain Times Part 2

How to Predict the Future in Uncertain Times Part 2

By Discovery Lean Six Sigma

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Screen Shot 2017-07-06 at 4.20.54 PM

In Part Two of this article on predicting the future with science, we look at the tool from Statistical Process Control that helps us understand behaviour patterns in our processes so we can make more informed decisions.

When is a process predictable?

We say that a process is “predictable”  when it is in a state of  “statistical control”. This means when we measure the process and put the data on a control chart, it oscillates between its natural limits (and complies with some other minor rules).

When a process is in this state, we say it is “predictable” because, if nothing changes, it will continue to oscillate in a predictable manner, between its limits.

IF or WHEN it starts to deviate from the state of statistical stability (its oscillation goes beyond the limits), only at that moment can we say that we detect an “anomaly”, or an extra-ordinary event. Unfortunately, it is only after the fact that we can say that an extra-ordinary event has happened. But at least we have a criterion to sift it out and recognize it as such.

Misleading charts

Below is an example of a “run chart” of a process. It represents the arrival time at the office of an “average” person.

The yellow line is the statistical average of the series of data, and the arrival time oscillates between, more or less, 7.00am and 9.50am. It is a considerably wide range.
If we just look at the series of points, we are led to think that day number 12 and day number 22 are “anomalies” (they are far from the average and they make it look as though “Mr. Average” is late).

This interpretation is a typical mistake we make when we look just at the numbers, without any reference system to give us a meaningful context.

Useful charts

Let’s look at this process in a less naive way. A reference system is provided by Statistical Process Control (SPC) so now we have some context. If we calculate the natural limits of oscillation and we draw them on the chart, this is the result:

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2PZ7oIG8uivNAIaAY2ARkAjcHwjcERsZFeaYy7Yj6cfug0fLFqKSyffASlkw3ef4NeVFjz80SfI/fLfuO2qz3DG8guxf0cGwuM6ISTQEweWvY8F9zyHpAeGw0QFvWH1aiyrnILli2/GJ/NG4YPvN2NC97FNktBYOKwmM+oHNAIaAY2ARkAj4AIComcainHsQtYGH2mRsi0uMWPY5OvwnEcWkr1LWXAlduwvRXrULPSJiETx6Wcj7pmFyDFPwLMPv4fpN16Jk5PS8Nj7XyJq3oXoH1KCcuYKC4uCd9Zb+Hxpd/z6pxcGXRHRIJG5ublYTcVsMplQUVEBrWwbhEl/qRHQCGgENAJuQsDT0xNJSUno2bOnW0pskbINju2Gcfz75quHYK4SG3I5KoqrEWjsoYiyGj0QbPFHhUc8HnnhJirJSnz698dR0u0OvDhoNV7bZIQnn/SoNiE2IxOL3v0EqV5V6OYLyFZunUsA+TkjIwPvvvsuNmzYgIiICIwZMwYGQ/2nVNX6H42ARkAjoBHQCLQKAVnQ+fr6Ii0tDT169HCLvmmRsrW3Qvk4kSjAD15BniiybuH7UbBWlWCrNU895unjg8rkr3D7f99A4Oj9OOu1PThU5oGU0AQMPrgQ2fd+hOXze6Do9/vR77ofcePqXvCvp0f79OmDt956C88//zx+/PFHnHXWWW5pvL0d+lUjoBHQCGgENAJ2BGQxV1ZWhqysLGVJdcfirnXK1lwNC12Rzdy1HdglElFvfoZ1h85B2kcvoXLwHMSiBL+v3o+4hDH44o+NVMKV2P7OC/g6KxiXTB2M9I+iUP7zT8i9KgobfluGgBE3wbOeorU3Xl4tFotSsmJHd0fjHcvW7zUCGgGNgEZAIyAIiH5x536tlNkqZRvWuSfiw0KobIE+08/Hpfv34K/0RLZ6T8Iri+fCw7IHbzy3EudcPwcTRvSV+hCaNwQFWVHoHReK/hfNx8atd+P0056HNWkSnvvfdHippxr+x75Xa39t+Cn9rUZAI6AR0AhoBFqHgLv1TKuU7ai7XqfRuCYZgzDpxkcw6YZHHDZdk7BgYZL9CfUaf9o1uMr+TeRA3PPSV7inoY1a+zP6VSOgEdAIaAQ0Asc5Ai0+Z9tou52Ygd2ap9HC9A8aAY2ARkAjoBFoXwi4X9m2r/ZpajQCGgGNgEZAI9DmCGhl2+ZdoAnQCGgENAIagRMdAa1sT/Qe1u3TCGgENAIagTZHQCvbNu8CTYBGQCOgEdAInOgIaGV7ovewbp9GQCOgEdAItDkCWtm2eRdoAjQCGgGNgEbgREdAK9sTvYd1+zQCGgGNgEagzRHQyrbNu0AToBHQCGgENAInOgJa2Z7oPazbpxHQCGgENAJtjoBWtm3eBZoAjYBGQCOgETjREdDK9kTvYd0+jYBGQCOgEWhzBLSybfMu0ARoBDQCGgGNwImOgFa2J3oP6/ZpBDQCGgGNQJsjoJVtm3eBJkAjoBHQCGgETnQEtLI90XtYt08joBHQCGgE2hyBVl0eX5d6M7L2bcWazcnwj+mB0UN7w9vhbtvqyjxs/mMLCisszOaLPkMHINLfhLTd6/H75jREdBuOYf1i4Xk01L/BAKtHTVP5HmYzDPxzKRmMsHoyr5U33DOvoboasEgbXEuqXiMbxezyj8ovZbmQrJ6efIr0Co6sU+V1IZ96hHWqutuabjPpNhMzV1Nr6DaZYOVfS7CWvrXxSA3eraGbHaba7CqfuJXu5vA22yw8VssjlAmLi3LB/mwNbx/GmwU1VyZbwyOtpPv4lEnpZxn/OhBvs7XtLblJ2VqR+ucSzL/1JlQnnYrsHRsx5so38Pd5gyDqQlLG5u8we9K/0HVEFOU5Dn9/+Un0rlyJG+beicCe3bF9zRO4edGHuGBYlC1DA//6+PjAxIHJz8+P8knGcSHJc+aKClg3/8kxRZSdGZZO0UB8AgxGDsy2kbmRksikhfkwbvpDDSwqb2JXoFMnpf8ayVTzNfOKct25HYb8fMDIz15esHTvCaN/gG2Aa6wAGfirqmDctgWoLOcAyJICAmHt2RsGmTQ0oaxZK5CfB+PObTa6ObGwdOkGQ2Qn1tiUoq+he8c2GAoL1EBo9faGJUno9ndeN+m2kG7T1k2KfgMVliWS/ZmQ6Brd0qe5OTDu2lFDtwXmbkkwRkQ6r5fttQo7HDoI48EDSuFK35p7kObAoCbySicI3ZUwbSHdnBjY6CZWiaRbKW/nmFmF7uxsGHfvVIOagUrW3K07jOERTdQtWAvdKTDyT00UTB6wCN3s76b6WdFdWQHT5o2wUkkqupvD20UFMIpcsI3C2+bELjAwv0DpPNXwCPvJkJcLCqXqL2v3XjAENMHbqmDiXVEO0ybWzf+UXEXH2GSSk1s23En1rLuAMrnxsEyau3SFMYoy7TSfFFlDdx2ZJG8rmXSRt7dtpkxWqsmvRfqX44HrMplLmdx+WCa7JsEQIeOds/Y60F1HJn1g7dUHBsqmUz5RMim8vRnW6qoa3m6mTObU8DbHHuExxdsRLvJ26mGZBGXS0qMXZdJF3q4nk9aoTjBxLDEKvzUxBgpqrUl2/WJ/bU1Z9rxuUrYVWLb0B+wJuQSrX/4HSnd9htNnvYObL07E8rfXYeCpo1CwZx8Cet2L118/GVHxcfCGGZ/+7wMcGv8UfntuEjYtmIKbP1yOOcPORX2i0tLS8MUXX2DZsmVIT0/He++9Z6e/yVcLmS2xpAhTPn8KPuRpI+Uks+/J2HDy6aj08GJ+Z4xuQHzKHvT//FVYydOSd//Js7B54PAm6xWFR9WKoYvfQXjKNg5IlFG/AGyecikORneGyckKwsoBJ7i8BIM/fB7+pVQ+XGwUR3XBmhl/QYkPJxrWplbWbHPyDvRdshBWH9JdBew7ZTa29h3aRHsFDQP7hnQvegthqTuYmXQHhGDTGfNwqFNsk3SHlBVjyIdPw7esCEYuaHN6DceG8VNQ6t003Qa2u+vuLej91duKbgPp3jthLrb1GdxEmw3woIbotWY5uqz+hgqPxgv+bZ16DZLjujqlWTrSykaGlhZiyAdPwKeqXOGV3WckNoybgjIvnybq5hBOupO2/4le370PC/E2sL92n34hdvTo7zSvYO1Juvus+gmJv38HC2mu9jJiC+k+EJvoEt3hxfkY/MFj8OYgKP2c1Xc0/jh5Mio8vJVqaZxRDeh8aB/6f/Yy2OEwVgAHxs7E5sEjnUqEDS+hmzyy5H1EJm+20e3jRd6+Cikx8S7QbUBEYR7xfkzJuklkst84bBg7iTIp0u9cJhMO7Ea/z1+z8TbzJo8/G1v6i0w6y2fjbZtMvk2Z3G6TSf8gbCZvuyKTIUomn4VfKSey7OPc7kOw4ZRpLstkl33bcdIXbx6WyVPPxdaThrhAtxHelPmhi95AWNouJZMVgWFYO/Mq5AaHOsVbxpKQ0iLK5JPwLS+tlck/xk9FmbevU/6UvlYyuXMzen/zjk0mKdN7JpyP7b0HOs1r5+1eq39G1zXfKh4Rmdxy5rXYH9vFKc1Sr5LJkkIM/fBReFeZYSJvH+w1El+OOBXFHp6s23lfSxmtTUZaUHr14gRSJtNuSPX1WguL9EAoVz1hHGCJCXJy0jnjkjmrCSajvFZj14q7kZE5Gm+9uxTLvi3Gfc88yQ6oRu9J/VWdPaech7Rzc1H+CBDQQNs8KIT22YzFVfMcS7YQMGNmBsxeXBlWFqnB0G/bSsStX4lqF/ornAxi4aTXwGdlIA1asQjx3y9qEicpWgZSL5kwM58kY0UxIt9doATVmbVc8vrzAZPkZQ9Z+OeZm4zY5/6BcurZBuBR5Tv+E8Z5hMWvhm4OpsE/foL4bz5xfKTB91K3l51u5pNkLMtH1DtPw8QBxhW6jbK4qaHbZ9daxG1ci3IW3BTd8nuIA90ywQlZ+iHiv/ywybxiowj0Zb3yx/fUBYj48EVYODg4o5mPqucDWIBB8ObD0s++O1YjbsNqVLhIdzBpNdfgLco+5Ot3Ef+58zYLnTJJ8Gd9Fjvd1RZEfvA8ta5rdAcK3YK3ne5tq9B5/SpUukB3WA1vU0QVbkG/LEb8ssUKD8GlsSR0C29z/nSYbq72It97Vk00XME7SDqMdEtSeG9dgbh1K1ySSTvddpkM/vkzxH/3ma0wJ//a6a4jk+WFzZJJo4NMeu9Zj9jN613ibSEr1IG3LeSXoB8+RvzXHzuh2PaT0H2ETJbmIubVRxDggkwKbzvSbZdJV3nbkW4r2xDCSWX8F+87lUmhWXg70JG3KZORH7zAFbZrvO0ok1KvMTcNhtJSWGitEuvRsUh2neOOutymbCfOmoNVa2/EsKFDUV2YgXLvuTAbgzDr4kmKTt9rfsaXV8eia5wvwtdPwdtf/4LJHjQJS49I8vTmyrGqQUGPiYnBZZddhvLycvz888+YM2eO67MNmuW8V62Az59LAVpkhQl8QqKQNGYURwtyQhMzJOPBvTAkr1MkcsGHwLgk9Oots9EmksyGaL722LqKpuhMNeoaiXb0wNGIikvgyOyEWWhyRn4uPHasJM3limYP0tp16GnKnCxmZeeJ88KU3TDs32B7jFUFxfdAr56DnGeTXxXd5fDY8iusRdk1dBsRPWgkomKbptuQlwPTjhVse6Wi2zsoHF1HcrXEVX1TWMvvhgM7+bdR1SsFBHfpjYAeA5znFZrFfL2XZuD03UqSOcdC5EnDEJ6Y5BxraTPxNuRm2ujmLFoMB97BEeg2ajSVkGjBJvBmBuN+0p2yqZbu0K59ENSdE0lneRXdlfDYuxnGLNLNJFbrqH4jERHfpWm6md+Qm2Gjm0pa6PYVuseMcY23U/fBuPd3Va/kDYjthp7C20KXsyS/i/l662806WaoNlPM0GnAKER2TnSN7pw08jf5hMpC0S0yOXY0tQq1kDPMSNcRMtm5O1cgg51RbPtN6G5IJgdRJl3gbeTnkOZfamVS8fYI8raYzpuSSbapViYFXrJUSEIvWvsGNtle1R8cBzy2UiaLKZNMJg8j4kdPgDU03Dnewtt52eQRjiWcEAm0Qne3kaMokzJzaIq3j5TJkC59EEirjdO8grWYgcnbSib5kVZkRPYdjvCEbs5plgbWk0kxBIaGReHM6dNhkS0HV31upKwWJFnNllKxp9GqqrYfpT2tTBQR96QyYxhmXPk/XNO9H4q2vYoZd2xXKwqL7CUZqpG6Kw3mk8cjLFS28Ez4iSaZcs9KbN64BZjRCftXLIXP5FnwddImMwGWhlfTScnlpT1nQAZ2TnWn7mTMOJpRKlE+eCyK5l7MWTmn5s6YjQD7/rkewa+X0CzJ/eLqChSfPgvFZ57VNGgc7Q1k7tCXn4Hndtlf8oI1KAQF512Oyv5UHlWc3jWWmNcjJwehz5P2nEzO4qphpjk07+pbYA4Na5rRhO7f1yD4zQUw+/jSBFOFoqnnoGTydOftFXqEbg5IoS89CU+aj8QJxhoSbqO7b7+m6c7KQuiztG/QTGg0V6Gy7zDmnQdLCDveBQHxXf0rgt99AWYum4zcPy2ePgclE6dyMKM2aCyxvTLTDfhqEfy++wQWL5qsaSIrnHspyofSvOgMaymTbfbMSEOI0F1cqOiu6D8Cheddwll0oPO6a2jy/2U5Aj94iXgHwGg1o2j6BSidwImmkzYbTUaUFlWg5PUP4Hfwc/IYN1dMfigacyESzhnHfU3aSJ0loTs1FaEL6JNQISbCSlRQcRTOJd6yv94EZj6bNiDk1eIa3i5HyYSZpPts1uhECIUe4RHyVOgrC+C5dR3N9t6w+gei8PzLUTGAEzpX8ObeeuiCMq5yqmn+Lkf5kJNJ98W0AjUxuRHe/uN3BC8UusnbIpOTz0HxlBnOkLL9JnSLTL70NDx3bLDJZLDI5BWo7Efl4Yxu5vXg3mXoc1ROnJgZKZOVJw1VPGIOdYG3he61vyH4rWfJI+Rt7p8WT52NkknTXJbJsBeeVBMzq4EyGRaJvMtuQHV856bpzsy0yWQR97opkxX9hpNu8gjHI1cc4vx++wVB771ok0m2u2jaXJROnNIkf4kTauCXi+D7/ac2maTPTcFc8siQYc5pruExT24ZhjxLGSgttvF2j37cZuEmF3nPKW83zQlNPiH6RfSNOxNN34dnNk899ZS1rKwMd911V7Pr2Lj0Rdz50LuYfu1V2PTZf1A26X0svDoKLz69BmPOHIGNj8/Ff3LH464Zcfjoxfdx7sMvY7hpJa75v+fxl6v/iuX/exVjHn0d107q0WjdCxYswIoVKzB//nzXla2UJoMxO0icSGRGKQ4/Slk7tL3RSplXOUTI9JvJyhWmy/sFklc6jIKllLpM/+kk5RKj1Axo4rCjloiylyWeo84GUMdG1KPblVV8bXbJS4cZUDBV3bLcag7dMoO2Y03hMMhK3RWs7QRwRlz7vAy+rrRZaJY6uOJSiXtVEOcRV/JKBuavwyNsr4F9UEuHrVQn/7L+KtZtb6cLVhOTyYCc/Go8/fJefPVdKjxo5fH28cDVl3bHJbNjUF7hgrDXp9ubdEvb7XQ4oVja7BbeFiahAgDrbjHex1Ima3mbdLdCJsXhUTnQucpj0hct4W3JJ31VO5bws8ikJ/GuGZfkkUZTfR5pNm+zZEe6OZF1tV7FD5JXksiTWC5cxcsddNtqbva/oh9EF2ZyojJr1iySTtpdTFu2bMEtt9yCpUuXstMOJ7etbAecfike9YvBVyu3Y/AF7+LS2WJmyEVc51j4+4Xgosc/Qvirb2NDSgGupWKdMoKeeOiGlx4Lx6KftmPSoy9grhNFe5jkFrzjwGNz2aeyYlLK0pXBSB6W50TR1CSXFa08L3lFKOTPnlxlND6njlYoh5GazK7mlcfr0e3S4GunUfLSxK/+7N+5WrfQrY4stQBre10ikPbkar1CsyRRcvbkal55nvlbR7fwiQPddnrstDT2yudkGmc2eNKiyuNalAr51+XUGrqFxrbgbWlcW9LdGt4WeayRSTUWNIfHpN2OPNKcvNJX9ccSVxRta7GW/JIc6W5GvWqS0GYyaSO9vfzrNmVLux3605tR/mqTIQzTZ9PsqVIUptxwG2h8cEgm9Bo5E3fy76gnYdaWpuMxr7RV0928Hm8NXi3AW6rz9aHneQAnJtwrFi88b3pahocddgZ0qQGtobut8rYArzpYtBXdram3lW0WA5F9qSSjWVPbxG7Dq5V0t9k4VAeAtv/gPmXb9m3RFGgEjisELBwtg+hOPGZ4KD7+LgOVxdUIDfREn27+tOK3YnJ4XKGgiW0KASNNmlV0qPzbf3dhw9YiGpwM6BTpjYdu746YTt70YdG80hSG7eF31w3R7YFaTYNG4ARDQAbSgkI62xRVY/SIMOQWVmHdpiJuz9vXMCdYg3Vzmo2AePHm5lXhwMEypKWWIuVQKbbvKUZqOj3CHXaoml2wznBMEdDK9pjCrSvTCBxGQMyCJWVm7E4uhU+AB/5yTizKK81Y80cBnURl91YnjYANAbFec15m+4eMw8Vtc3b2NYztAAGtbNtBJ2gSOiYCRg6aWdlV+HNrIUYPDkHPJH+MHhSKTTuLsO8Az1UelUDhHRPr47nVomj9fE20dnC4lo1aTsS8GWnM15eudCpO6fHcuo5Du1a2HaevdUvbGQKibHPzq5Ry7d87EMGBHjjjlEgcTC7B7v0ljL6m17btrMvahBwzlWtYqCciI+hpbzHAjw51/n4eCCG/WJvlJdUm5OtKaxDQylazgkagjRAwc6BMSStHVW4lenX35+rFiH69AhAc4U1TciEKingZhbIdthGButp2gYCcx87MqsTB1DL07h2AeefEIZnv9x4oU2ez2wWRmogmEdDKtkmI9AMaAfcjIDq0rNyMjVsKERLri8hwLxVHJILHfiaODcdPq7KQSROzOMfo1LERkNvx9lCxbttdhDNOi8TkUyIYZMGg9vaLShiBS4/ixwWD6G46LrpJE3kiIlDOu53/3FaEBCrbCJoJxVwYSBPhyCEhyOSe7Q56nMrxIJ06LgIyKavgrRKbtxUyqpsFIwaGICbKE9OpdJeuzKaHMuOvM+ynTu0fAd1L7b+PNIUnIAISDi6foRq3by1A1wRezsGrVUTZSvjU3kl+iO/hjyXfZzJkHL2S9dbtCcgBrjVJ9u3TMivx9U/ZOG18FGKjvRHgb8JUKtuMQ2X45fd8KmPGUNc84hqgbfiUVrZtCL6uuuMiIEp1y65iGHxMSErwqz0vKcEs4nkzVp/ugfh1XR4yc5q4jKDjQtghWi52jX00Ie/ZUoDxI8MQGuKpthu6JfqhT58gfPF9Bs9p81J4rW3bPT9oZdvuu0gTeCIiIMc51m8qYCQgH+UcZb9gRL6XgBbDBgbDi4r451W52knqRGQAF9tUwa2GH2guln39XrR4iE6ViFHRkV4YOiAYezlh27qrRFlEXCxSP9ZGCGhl20bA62o7NgKyF7uBzlERNB/36OZXJzxjVZUVo4cGI45eyT/8ksOrj/W+bUfkFlGshXSA+mFVDnp3D0BiZ9/a0IzCEuNGhiKK3y36JrNV4Yc7IrZt0WatbNsCdV1nh0ZAjvMcOFSO1OxKdEv0RRQ9kR0doWTvNrGzH4b0C8Leg6UMelFMM7PelOtoTCOm4VXcky1llLERtHRIHG07n1RVWTB8UDAGnxSIXzbkMQpZmfJQ7mgYHU/t1cr2eOotTesJgYAozo30QvbyMKJvz8AGr/c0M/D8qWNsN2Yt+zUHnnxWp46FgEyvvv05G7G8dGDM0BCIxcMxCR9NGBOuLib4elm2PnPrCE47fO92CbY2547GGkDss7V2iI8mSSPgdgTkXOSfvL3Fk4Nl/14N3/Aj+3JD+gchItgTf24uVI5ScrZSp46BgPT1flo/5MKBbgn+6N0joNaEbEdAVrcn05QcQ8vIL7/nIJvOdJpH7Oi0v1f3XbFnKcfO9T9i+eodqArqgVnnnoEY38PFV5Ycwjeff4fMUjP3F4Jx+uzpSPQrx6qvvsDGA+nwjumNaWeegU6Bhy9qb39waYo0Aq1DQPbhyhnMYve+EgQFmNC9i1+tabB+yRL/dsLYCHz8VRpWry/AjMkRzFt3dVM/j/58YiDgxTjIP3K/Xo71nMbVa0O3UogznS+d6KacEoWXP0rBsl9zcf5Z0ZpH2ikLHNaGrSQwb/OPePTWW+AxZgZyN72DtXuj8drfh9aWms7fb7rmFYyd2YU3mkRjwKTTkL/0CVz6lxU4/6aTsO29/8OvhSvw8lUnN8RXqhwfHx/uXZng7+9/DF3dLbxew+H4hYEXfUOH9antWP2mWQh40By8YXMuQzFWY8TgKISEBNZxjnIsTBTzmafH4Y2PDynP5Tkz4mkqJDtqfesI0wn3Xvq9srIKa/4sQKC/N04/JRbe3p7wamAdIraOmVPi8frHafiN+7sXzkpEQIBcUHDCwXJMG2Q/SmV/dUfl7lO2Kbvg4Z2Eux/5L4oXXYdz3j5I+sJxz3VfYNrVF8C0bTN8Ey/ANdcMROKAsUgMMWLnoaF44tvbGA0lHAfeWYqT1+6BGSejPlFbt27Fgw8+iJ07d9JMYsQtt9zijra7UAYDCnh0RrXPZI5w5VTw3jBVrYG1YqsLefUjGoEjETAazMgo7Ius/O7YtP4T3HBDhpOr0qxUxB4wWfvjp18rce31H8CIfBbq9t2fIwnV37QZAgbySFl1EjbtPQkm807888GPGrV+CJGiWI2VSfhtYz9cd/PD8Dal8ju9IGhNB8o5eD8/P5x11lluW9jV12stpq/zkGHo1OlxTB08BObiTMxdcD8HEX9MnzsWXeK8seLNJ1BtHIP1G7bi7hvvw+2vf4Rzxp6Dnqyx4sBy3PBiNS6+bVCDa8aYmBhcd911+PDDD7Fnzx5Mnz7dbQA01eClKyvx+TcZ5GhZ4RoxZHA8rpjTw8kA2VSJ+veOjICXpxWvfFCFylIrLrv+FN7cUuF0FSJ7cIm9fPDGm3noOXUcBp3keYSjTEfG81i3vZL7pCL8snAUByUP/rk7efGc9cJPy1CSZ8WdN45AQkxfpzwidAwb44uHnyxAyIjBmDxuJEkyOM3jbppPxPIs9D/y9fV1W9PcpGzNWLn0O6zJGIcvfliAtB+ewfwHnseNZzyEMacOVsSePn87TrkvBuFBRiTtGIsPvvsVM4fMQmnySlx8xQ0InfYY7p0xsEETcmhoKE455RRs3rwZOTk5GD169DFTtntSU2Hy28drJE3qOisv33D0G9DbFsmFsUq1ucZtvHjCFySKs7TMAvNHWxHfuQzjTx7Iq9LIV05MfnKHaUhEId7/ehPKzXEYNCheBb1wlueEB7INGih9l5VTgVv/vh3pfC3jXuqkMRG45apEhAR5Ol15NodcCc+Yzy2G0qpt6NylApNP64NQxs121t+SJ4k0Lf1lJ/ZnWNGjV29ERdjCfzanbv3sYQTEfFxWVob09HRiLyFTWz+pcpOyreAVULwqzH8ouoUFI+bkEQj+20JUkPaKCl6C7WHB+s8/QuGU2zE7zIQylMLAGX5u8m+444brMegvj+KhS6ccbmkj76TR8iczDnc0vpFq6nwt0Xzk3KNc2AwK2G+rsvGPJ3fhvJkx9BYNVEHA1Wy3Ti79QSNwJAISMD4tvQwH00sxigHlTSYreYsrJSdJjnvEdvLGxJHhWLo8C3NnRKETg11oD34noB2Fn0zcvkpOKeNeewUn/BzZOBxs21WAHF6PGBp0+Pxra6uWPf0DPFu9cVsB5s3uTP8UoxrvnClb+U0uspg0LhwPP74TO+nBHBEWonmkFZ0h+kX0jDuTmzZ//DBh2gTEmj/CmefOxVmzH0Ofq65GJ+sB3Hn9QqzYUAzfqn14YOoEzJ07E68cGIWrpw/D4v9egTe/3IlVn7yIaVPPwJx7l8DBFcmd7WxRWTKbHchD49xIxqihYbj9pu645vKuWMcwe1fduYlKdw82b+d5Sa4+3DDxaRGNOtPxgwB9+5CaUY4MxrodwZt9hG+aSqJU5ZKCsTzicZAXym/azpuAnI28TRWof28ZAlzYyES/SibdNd0m44M75V7KkvCMazYUqF2r8exzH2/nlg9pjLCDycMW4jMqzg8ff5mhrm90J20tA03nckTATStbIKr36fjnExH4be0OeIZ0wYTJo+FlKMaFl01EQpdQRA98FAu7fofthbwoe+REDO0eicSrX8YPZ5eitLSUAwjg3WlAg3u2jgQfy/ciXNmcuco0dvrpnTB7WiRKSujCNTwUHyxOw6Kv07FuI49kTIrG3Jmd6DnooWeTx7KDjqO6ZOCr5FVpyQfLGPzYgK7xvhDzn1w80FQShdujqx86MTTfDyuyGZA+VEeUago0N/9uNFrpEV6IrLwqQBY8HBtEMdoME2JibLofmyJJVlMFHB9/4pGf0UNDERftoxR8U/nk92ruJQtPDegdhO9/y0ZaRgK6xPu4klU/c4wQcJuyleleQo8h6u8w7QEYMbZ7zUdPDJkxG0MO/4iEAWOQ4PC5vb0VZfvdz1mIjPJGz26+NscUylW/PgFI6toNY0eE4pX3UvDiewfwPYOFXz43DqeODoOXt5h+xOTd3lqk6WkrBNRAyr04WZme1CsQIcEeLvNHFX0DZCDt0cUfy9fajg2FN7GP11btPBHrFQvEmj+4b74kDZ2jfHDndUlYsSYX7396EHuTS3hW2n1ONClp5di8MR933NoDUbxsQAJXuJJkseLjDZzMMWn5+lx8yyv5/npJvG0LzJUC9DNHHYGm7VhHnYT2WYHMVQuL6PjFs2vd4v3VnaNVjOojClSi+4gATj4lHK8+1h9Xn5+AopIq3PWfHXiQpuVtvIlDnhPnFp00AoKArGwLi6q4F1eI/rwaLTjAdWUrvOTtZWIs3BCunwwMHJOnV7bHiK1kD3X7nhL897k9PHYIPHB7d14AEIILZsUijvGrX3wnBemZFW4xJ1to5fhhZR78uCd/Us8ANNfRmUdzMX50CLrxhqCly7NRUlrdoMPpMYJOV1MPAa0N6gFi/yju9Ks5m63gzHJw3yD4+NhWq/bfZQCU/fMARgG65i+d8cTf++CcqdH4dkUWrrl7CxZ+nIoUXu7s72dUlz2L16mfr1GHU7MD2AFfs7glkbG/FH0Yei+AWw6curmMgqxux40IUUr6J8ZK1unoIyDKNTO7Ao+/nIwdXMHefX0Sj14FoZJOa2KinTmpE/ZxH/3jLzLdItdlFWZGgcpGD95V250XVFRyUt+cJNa0yDBPjBocisz8Sqxck++SX0Bz6tDPthwBN5qRW05Ee8wpzg/f/JQFf18TJo8PV/ttDdEpSldMPX14Bda9Nydh6oRIfERz04IX9uKrHzLRO8mf3ti2iC5BXM1cMCsGMdH0JnVhr66h+vR3xycCwiN/bCqmP4Mnb/TxhoH8JbzjahKPeLlkXvbk/uBF4lt2lKCvxFVu5oDsan0d/TmR/6Liavzz6b2cdOfjZjpGirevXHco/VZdDVx4TjS27CzG24sP8f7hQLXPWt2Ed3ljuMr+vThGZXJCNu20SHW0sKzcNROyY5niF3DOtCh8/E0qj2FmYeLJ4WoiIFtiOrUtAlrZNoC/MP7e/WXYw1lr90R/dEnwbXLvQ47/iKlwxOBgFe/2tLHhePndFHxFxQvGuBWnCk8Gle9D81ACHV0q7LeFN1C//urEQkD4QlamG7YWMkABr9SjmdDMz81NZg6YE08Ow89rcpQpeVDfI4PTN7dM/fyRCEh/iTw/+8YB7s3m4MKZcbjw7GjlxGnXWbKKDPT3xKXnxWI9Jz+vfXAI/XoH0JLVMidJsaQtXZ6L0EAPdU+tbFm1JMnerRwVGz0oFBt4s9S6jYUYMzyk0cVCS+rQeVqGgDYjN4Cbl5eBt2jkMaReJQPBMwi4i3wvgljJs7hBFJipnJ3++86ejP4TANAtHzQhe/Loh3gw8rilTh0MgYoKK9ZxUI6N8eY5WQYckFGxmUlWtyMHh8CPWxp/bC5QKy9ZgenkPgTEkc1IxffyO4fw0VfpmHRyJK67lDGpKbSiYB2TWCvErDxnagzWby3Ah4vTeRVi8/tD+vAgHaO27CxEfIwPlTbjZbdQ2drpO2tKJ2TxmNny1bl639YOShu/6mG/XgfYZrU2N39ZfIwdxsPh9ulsvWcb+2jzROb+Ca++CgzgxQU8LgRGDirNr8K2nUUUJFG4zRfKxurT37d/BPbybK3c9tOVTjV+3JpoJkvVNlD2/SfyJqBdvDVo34HyFg3utYXpN3UQENn3oLL8fnkOXv8khRezB2H+tV3o5XukorVnFFmXVW8Cj+l89m0G9qWwT5rpGCnOlrK/ms3xQSxislfc2tSnhz+6c2trHY8rpdGB62iElWwtjR0tvxu69cSCTJgyJbUCu+kQMXpACMJaeMRCtm78/U00+0Vg6owYTJ8WjS5JAfj0+wx88hWDz3O0lVm0Tic+AtLPG3l/bTAnXr27+6n9vpa2WsyNp40ORz4H0B17S1w1urS0ug6VT6JEbdhSiKde3odErjD/77qunDA7D5Uok6ZQ7sNfc1E8DtEh8ivuk4p1y1XRluckitha3vAjATPGjQjj59bDHsixZ8bEKOxk0J3dyWXKR6D1peoSWoOA3rOth55E+dnBcGcHeHznuksS6M3XPEcWe3GiTEVxX8azt/aUzDBsT72yHw89sZueyhU8B9dZK1w7OCfwqxgxVm/IRyj37Pv3kSv1mr9fa4dHFHfPJD/0ZGSzH3l/qZyrDONgX9/EaX9ev7qGgJhyd+4txr2P7EQ5Tf6P3NdLeY27cs5VFOapY8Nw3lmxeOXtZPZxAJ2leEzLBfOFOE/uTS7Fll2FOGVYGPdbvVzK56xVUq0X/URGc6/2vc+98d1PmRhAvvPl9oMrNDkrW//WcgT0ytYBOxG4Ap6tXUuvwFA6svTu7t8ql35henGMkcFV/rrQhHj3jd0wgucl3/g4BU+/ekCZlLVF2aETTrC3MhDnFVRjHyNHRXFbISHWp0lnO2cQyGApge9lL3E5nXcOca9PVrs6tRwBwS+DR3z+s2AfcgqqcPOVXdCPvhay3eNKkiOAvj4eOOfMaISFe+OFtw7wTDXPuLrQLbKdtJNbAmncZjjj1HC39aXs+cbTP2DS+CgsYYALOcLkCj2utFc/0zIEtLJ1wE2UXmZ2JZb/loMJY8IY3NubM0GHB1r4VsqwK96IMC/84/964NRR4Xh70SE89kKyOk8ne0U6nXgIyGC6dVeJOh7Wj5GjWjviCR/JHuLg/kH0cLfyuAjPgtPpTqeWISDWpzLupT/2bDL+3FHIADXxDL8apSbJzZF92/E/XinKsK6b6ZfxKbeKmvLLkMl9fkEljxYVIJyKUQJZyHfuSEK7eEuPHBKsbiv7kWezm+t74g46dBmHEdDK9jAWyhS3e1+pCjwwgmagQAascLd5TjxKI3jw/D6eyT2dji6ffpGG5xemMOZytdtmtQ5N0m/bGAExE8plFRIDeQDNi+oGqVbSJGUlxPngpB6BKpxoaZm5tTq8lRQdn9lFsQmWb3+Shh9/ycK5U6Jx8exYZYVqSYukrPPPikY/9sv7PHu7m+ZhZ97JciohPYuTeypCGQts2wEtqbnhPGJVkwAqvbsGYMn3mcpBr+En9bfHAgGtbGtQFhNLGVcIchA8jl58Pbv6H7X9DTE7hXD/7kGGfpt3Xjze/DAF9z+2BxJhyB2eiMeCcdpjHZ5UbOKtK3tT8ueuVUJr2irbB5t2FKkiBtC71R2TN1HYMVGeGNwvGLvpX7CN4QR1ch0Bb+5nSmQ38c9444NUTnaTMZtOjLdcldAqnpHjXJ0ivXH9ZYnI5QkEOadbXGJpsExZv8pZWjkHm5NVoRyj5CITd+6pivLvxK2LMTxRcZAOdb+szdcTetfZxO1PdiAHqfrmGdpZ6qV87tes/D0Xp44MQ3ydvTXHvEfmq1dMvY+OeeUnW34ZdOUIyF8Z6rGwsBqLvk1HNbXw/TclIZwrX1HItDnKPw6pNXW3Jq+Q0Jz8x5ZumSjxsjMsXppFT+90+PJaMrma7FpiK85Erq8m3Uu37AUeoIeqxM7tyUsExFLSuGnSse6msbZaDYyVHIyv6PwiR1VGDAxuIZ80r16HSvjWMa/80jTd7svvWLdr9QqfCP7/fWE/L4QooocuGGikCH17B5NXEiiPHtynbaosx3qPbLOYk4cNCMJZp0VhEU8eLFmaydVyNJ2u7C0/nF8sEj/whp9EnlLowosmmp6IHc5rK60pWnnun6vbSeMj8Pl3mVj8XTbjuUfWENJ0XjvFtlfHuo9dXuFze7LtOTe3bnvutn/tIMpWPIMpXIK3gRd2WxjBx+xXB33x8vyZF8NbzCbeYRvGwO+eNXdCVtMUVEgpZW7mrTb7Uyi86uR19sHDVMKZLa/pI49YeYGg5LdaOaVmqmLINy8GmJ//1+48JuSFdz5PxZ3/3oN7qHAl9qqHUWjmOQDWa7F6ciDwZ67DzKcKaeAfxY7WKnh7yt2nzMEsFZV+pIPXgriYPEzFfJ6RzRug23kRVuIldHO2UIu13IrSNN1SrtFQxbOOxQ54BxBvnlV2kqTv8umQsmVHDo9tpRM3kzrruG5TICN2Bbnm6EJaPRWPtJBuYiWY1fJJNQPJE+/N2yuQklGFU0ZH1fZ7/aaYjOLkxKv3yGNW3p7lyCP1n7V/riBLDR0YToerVHrRppGfhWfJLQ3wtj1P/VeDwcy+KjhMc4t523W6a2kwWIh3oY0rFJ+I45hrt+cYDC2TSbF0ZOVUYu+BLOw/lMf+MiEqjEepxsYjOtJPyXstfQ2+qcvbFmJdrWiuy9tG8uO8OQmM9FWA9z/PZASnCHTmnqzVIrxtG0sMvLIvY78Ra38rwFTGVI+N9m9yn7i+TFaZGTDH6tw4abEYEBTgRWepap6ASMelt2TRQTMG583shsBAypWVx5SIi32rWI0djm1X8l8Jb4+S2rGknGOJ8LYrqS5vM8ylObBROXAsT/oqgyv+R5/7Q5m/yyurMWpINC46pwtD6DJKV+OzVsdi2tX7DqBsDfD2zkCv+Nt5i0YBFUg68oquQEbuFHa6DOI29pJZbn5BCqaeUo7TTy7kQECTLq+D9Pfdi4ROt3HAjyZTpiMj7y56l450oRNFAC3oHP0WAn0+UwxWZR5Ic86VKK+M5yBDTVuTfKi7776OdwIzWNXyVcn4+odc3HBZBPp0fQaexk3UUyU0cU/ioH05B1Nf5j1CJOxFqVdRsNVV29Av6R6UlYXDxzcHv2+6E/5Bo/m787w2pWhBfPQb8PdZzMdNnB0PwaGsy0k3jyo50F2nUvVBou+UomvMf4nfDir5EuQXX4z03OnEz6dJuqXuAL9tiI+6uxbv1OwHUVg80CndMlgU89alay9aifeeeJ4OQ4EorYjCNysuZZ/TguDDPc0jiXX4xsDLt4vQJeYRBvtKJ49kI6/4EvLImRyLZFBxjpncxBMcsAlxkfdzsmbjk0OZ/0KleQCzpqF/92xMHM1Qfr6cONUpSnqSwU9Cf0Jk8FPkEX/2b2ekZN7I/k5kqYd5xIHY2rfBXClfdm4e/jLrGQT6UXl45JK3LyfdU+vwdm2GOm8M8PXdj8RON7ONNpoz8+5EbsGoOk81/EHQtCKu09sI8v1E0V1t7ka6ryWPJDRJt/Szp2c++eQ+9azIZG7RVaR7Mos9LJON1e3vtxsJUfNreSQ99x7kFw5v+HGHb2UA9/GqwBuPLUaX2C+p3EHv40H4ffPF5FfyLvmk8VTD29GPc4KyTclkYQl5O2dagzLZIxG4/wYTFr53EAdTjQz7GoIA3x3k7TsV3UZTOpKTb8MY7tWeeVoxnZn2o7JRRzfbWJIQ/Tp5eYniocrqkTiQcR0VdJhTvI3cGK6qysCyd16iwtzPsbAAP60+FylpkxFeHsTtFuEDI3HhAMhJgpIT/mN/FQub0bAR/ZMeQCnHEm/vPOJ1D/yDhxGqOsxcDzo7by8nbz9BHmNcaWsox7+bKJtdndIsBdnoTsPil57m5DmdF8HkYenKiynbl1GOQpqoux4p7eSjwXGP4KmnnrKWlZXhrrvuaiZ51djx+1d46/O17Bgqt4AAevJ2x9yrzkFoTUnl+bux8MV3cYiOQFZrBM6//gr0DirDD++/jJX7OKtn7/aYdDn+Mq5Lo3UvWLAAK1aswPz588kXih0afdb+g5Uzv9DADYzw8ihnsjvsXytmqmUVvjFSxs1yLzSZy5PvbWbc2scPv5FMrlV9OI+L74yc+lSWyyoUNGmBs0flcKpyq+bW1K3ey4TW/tcQPbKYlrGDv8kkUM1c7XdJs42yYJbFZ6Pt5M8tTo4YOb5vQYHSNCmiocQFLDvL9ouFZjoJDu9ZM32UNlexPz342WRfWPN3LvgbLa9OHcyv6m4I2zoPNvxBZeM/8ioWDA/S2lg76pcgeVx6lg8JBlyc1D6v6nU1f/2KpVJ7AfV/c/NnOz/b2+lymxuiwwW6lS+EGKTI92bKhnxu0eLIsS7H9/XokvZJm+TaO3njRR5UvGdvsO1rKmobLa1qf726639UZbNeWVCoxPdFNMJwsVj7WWiU32WcEGzUn7znnydx41xEtUONJXwrQ0hLkhTjAEGjRSjZlj6qwU9o27pzIvJL76A1UEzhLaWg0Srr/CD6RXRhZmYmZs2aRTxIgItpy5YtuOWWW7B06VJpbm2qGZpqP7fwjQF+QVHo2aMHDFwRLrn+euy94DlcxNJkH0JmlPvXLcNjz+ThH4+OoAkgAIE0n2btWoGP//cKBt33T4Rw1I8Or2vabSExR2STzjXU2P5FCCoqRzLu8Qw1I5WHJXDF9yvyafIr4a09YbxIgCatmr4M9t+EEL83aq01peVnIrtwgpQoWRtPVPImmpCjgpeQOVYrBrNaw2juPBeFpSdxsnDkLFpKlCNAB9N4yfi23ZgxYTkdLrarOkpLk5BfdjFKS4PVmcDMzCqkZlQgnUeVCovNCmcZdGVVK+x8xrgDzP8p+8MmMKv/mIQ3F/VTfRHFqDg9uvmiVzcf5dAhJhnpJ6V4Fd3FpPsLRbc002LphLzCc1BU1qtBuhWBqlpxTDqE8IBFMHrslokyB5shyCqYQUXI2ShNh86SzIWDA9Yj2O9dZY2VZ4vLZiGncFydbIKThMTbsbuEwdvzSZsZiXHApefupDnwS9UOoyEQh9LPxhufxNHxrIwzdxMviZCbWWje5chh719VMNvs55Oi6DZ47K2hexjpnkm65bhOE3Tz95CA37nK+6CW7pLyOdiTMoJHQLJ48YAnzjg

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Discovery Lean Six Sigma

Dummy user for scooping articles

I'm a dummy user created for scooping  great articles in the network for the community.