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3 min Læsning

Skaber du din målsætning ud fra gæt eller data? Puds AI på dine salgsdata og se ind i fremtiden

Det er helt banalt: Jo bedre, du kan forudsige, hvor mange produkter, der langes over disken om en uge eller en måned, des lettere kan du optimere indkøb, logistik, lager, bemanding, marketing og produktion.Dertil kommer naturligvis det evige behov for at sikre, at der produceres nok, så kunderne aldrig går forgæves.

Vi kobler simpelthen algoritmer på dine salgstal fra i går for bedre at kunne planlægge, hvor meget vi skal producere i morgen.

Så der er gode grunde til at arbejde målrettet med planlægning – og særligt i fødevarebranchen. For skyder vi helt ved siden af, risikerer vi at skulle kassere overskydende færdigproducerede varer eller indkøbte råvarer og ingredienser skal kasseres, fordi de når at løbe på dato.

Hos Columbus har vi i flere år arbejdet med at udnytte de stadig mere imponerende AI-funktionaliteter i Microsofts cloudløsninger. Vi kobler simpelthen algoritmer på dine salgstal fra i går for bedre at kunne planlægge, hvor meget vi skal producere i morgen.

Det giver virkelig gode muligheder for at styrke indsigten i din processer, men også for at anvende eksisterende data fra både egne og eksterne kilder til at spore tendenser af central betydning for planlægning og beslutningstagning.

Planlægning er et af de områder, hvor algoritmerne kommer til deres ret, og derfor har vi bygget en løsning til sales forecasting målrettet fødevarebranchen. Den går i helt korte træk ud på at fodre AI-algoritmer i Microsoft Azure med historiske salgsdata og andre parametre af betydning for salget. Det gør løsningen i stand til at give et ganske kompetent bud på, hvordan det fremtidige salg vil udvikle sig.

Løsningen skal bygges og oplæres, men er i lange stræk selvlærende. Derfor bliver den kun mere præcis, des flere data og tidsperioder, den har at arbejde med. Desuden tager den højde for både sæsonvariationer, uge- og månedsudsving samt variationer som følge af eksempelvis helligdage og ferieperioder.

Fra møjsommeligt gætteri til selvlærende algoritmer

Man kan let koble datasæt på, som erfaringsmæssigt har indflydelse på salget.

Det er store ord, indrømmet. Men AI er et nærmest revolutionerende brud med traditionelle sales forecasting løsninger. For de bygger i vidt omfang på statiske modeller, der løbende skal beriges og korrigeres med input fra erfarne medarbejdere. Hvilket er et møjsommeligt og ganske komplekst arbejde med store indbyggede usikkerheder.

Koblingen mellem AI og sales forecasting ligger derimod lige for, da algoritmerne hurtigt kan analysere og lære af selv meget store mængder information – så længe man blot sørger for, at datasættene er strukturerede, velordnede og valide.

Tilsvarende kan man let koble datasæt på, som erfaringsmæssigt har indflydelse på salget. Producerer man eksempelvis is eller grillpølser, vil udsigten til godt (eller knap så godt!) vejr spille ind som en betydelig parameter.

Kan bruges uden specialkompetencer og data scientists

Vi hjælper gerne med at optimere løsningen, så den passer endnu bedre til den enkelte virksomhed. Men den kan faktisk også anvendes med stort udbytte, selv om din virksomhed ikke har en dedikeret data scientist- eller analytiker ombord.

Det gør den dels særdeles nyttig, hvis man vil kigge kvalificeret ind i fremtiden og blive bedre til at forudse, hvordan salget udvikler sig over de kommende uger og måneder.

Samtidig er det et godt eksempel på den demokratisering af AI, der har fundet sted over de seneste år. Det er trods alt ikke længe siden, at avanceret, selvlærende forecasting var forbeholdt koncerner med udviklings- og driftsbudgetter i den trecifrede millionklasse.

Men i dag er mulighederne reelt tilgængelige for alle virksomheder med en bredbåndsforbindelse og et virkelig godt eksempel på, at diskussionen om datadrevne virksomheder er langt mere end blot luftkasteller og fremtidsmusik. Det er en helt reel mulighed, hvis blot man vil.

 

Emne

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