How to Unlock the Power of Manufacturing Data with Machine Learning in Alteryx  

In the dyna­mic land­scape of manu­fac­tu­ring, enter­pri­ses are facing mul­ti­ple chal­lenges that can impact their bot­tom line and com­pe­ti­ti­ve­ness. From sup­p­ly chain dis­rup­ti­ons to qua­li­ty con­trol issues and sus­taina­bi­li­ty con­cerns, the com­ple­xi­ties are vast. Howe­ver, amidst the­se chal­lenges lies a bea­con of hope—data ana­ly­tics and machi­ne lear­ning. Lever­aging the­se powerful tools, par­ti­cu­lar­ly through plat­forms like Alte­ryx AutoML and Machi­ne Lear­ning in Alte­ryx Desi­gner, can engen­der an evo­lu­ti­on of manu­fac­tu­ring pro­ces­ses and crea­te oppor­tu­ni­ties and savings.  
While cen­tra­li­zed data sci­ence teams—if existing—are usual­ly the go-to resour­ces for initia­ti­ves that aim to opti­mi­ze pro­ces­ses based on data, they may not always be available.  
On the other hand, sub­ject mat­ter experts, peo­p­le working in and/or over­see­ing the pro­duc­tion pro­ces­ses, are usual­ly very edu­ca­ted and expe­ri­en­ced to read and inter­pret data that is being coll­ec­ted. And they usual­ly also have ide­as on what could be impro­ved. 

Why SME bene­fit from ‘self-ser­vice machi­ne lear­ning’  
Rapid pro­to­ty­p­ing: Models are built, eva­lua­ted and com­pared quick­ly – and with no coding effort. This allows to deter­mi­ne easi­ly and effi­ci­ent­ly  

a) if the data at hand can be used for buil­ding a model, i.e. for ans­we­ring the ques­ti­on at hand,
which model might be most sui­ta­ble, 
c) what the most important fea­tures are and  
c) what measures/ actions might be sui­ta­ble for tack­ling the issue or ques­ti­on.  

Some key chal­lenges in manu­fac­tu­ring, like sup­p­ly chain plan­ning and opti­miza­ti­on, pre­dic­ti­ve main­ten­an­ce and down­ti­me ana­ly­sis, and pro­cess opti­miza­ti­on, can and must be sol­ved with ana­ly­ti­cal approa­ches. And com­pa­nies are incre­asing­ly tur­ning to empowe­ring SME to address the­se chal­lenges heads-on.  

Use Case: Opti­mi­zing Ener­gy Con­sump­ti­on  
One of the most pres­sing chal­lenges for manu­fac­tu­r­ers is opti­mi­zing effi­ci­en­cy while redu­cing ener­gy con­sump­ti­on and envi­ron­men­tal impact. Alte­ryx AutoML and Machi­ne Lear­ning in Alte­ryx Desi­gner offer a potent solu­ti­on in this regard. 
Ima­gi­ne a manu­fac­tu­ring plant see­king to mini­mi­ze ener­gy con­sump­ti­on while main­tai­ning ope­ra­tio­nal effi­ci­en­cy. By lever­aging his­to­ri­cal ener­gy con­sump­ti­on data, pro­duc­tion metrics, and exter­nal fac­tors like wea­ther pat­terns, Alteryx’s pre­dic­ti­ve mode­ling capa­bi­li­ties can deve­lop opti­mi­zed ener­gy usa­ge models. 
By ana­ly­zing pro­duc­tion sche­du­les and ener­gy con­sump­ti­on pat­terns, the solu­ti­on can iden­ti­fy oppor­tu­ni­ties for load shif­ting and demand respon­se, enab­ling the plant to levera­ge off-peak ener­gy rates and redu­ce cos­ts. 
Fur­ther­mo­re, Alteryx’s simu­la­ti­on capa­bi­li­ties can be used to iden­ti­fy pro­cess opti­miza­ti­ons and equip­ment upgrades to enhan­ce ener­gy effi­ci­en­cy. By con­ti­nuous­ly moni­to­ring and ana­ly­zing data, manu­fac­tu­r­ers can achie­ve signi­fi­cant reduc­tions in ener­gy con­sump­ti­on while main­tai­ning or even impro­ving pro­duc­ti­vi­ty levels, ther­eby dri­ving sus­taina­bi­li­ty initia­ti­ves and redu­cing ope­ra­tio­nal cos­ts. 

The mar­ria­ge of data ana­ly­tics, machi­ne lear­ning, and plat­forms like Alte­ryx AutoML and Machi­ne Lear­ning in Alte­ryx Desi­gner holds immense poten­ti­al for revo­lu­tio­ni­zing manu­fac­tu­ring ope­ra­ti­ons. By harnes­sing the power of data-dri­ven insights, manu­fac­tu­r­ers can over­co­me chal­lenges, dri­ve inno­va­ti­on, and chart a cour­se towards sus­tainable growth and com­pe­ti­ti­ve­ness in an ever-evol­ving indus­try land­scape. 

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Sasha Shiran­gi (Head of Sales)