{"id":44425,"date":"2023-07-10T15:43:46","date_gmt":"2023-07-10T15:43:46","guid":{"rendered":"https:\/\/www.sisqualwfm.com\/sisqualwfm-innovates-with-new-forecasting-model\/"},"modified":"2025-03-10T18:06:22","modified_gmt":"2025-03-10T18:06:22","slug":"sisqualwfm-innovates-with-new-forecasting-model","status":"publish","type":"post","link":"https:\/\/www.sisqualwfm.com\/es-mx\/sisqualwfm-innovates-with-new-forecasting-model\/","title":{"rendered":"SISQUAL\u00aeWFM innovates with new Forecasting model"},"content":{"rendered":"

On 30\/06, the results of the Project ‘RH 4.0 FeD – Forecast and Automatic Sizing for Retail Teams<\/a>‘, developed in co-promotion with the University of Aveiro (UA) were presented to the public.<\/p>\n

Following the release of the upgrade made to the existing SISQUAL\u00ae Forecast<\/a> module, the project was based on the creation of new forecasting models to generate the sizing of teams according to different external variables (such as events, weather conditions, among others) and its application in order to reduce possible errors.<\/p>\n

“The goal is to use data science in an automatic way to forecast and dimension teams without human intervention”, says Jorge Costa, SISQUAL\u00ae WFM<\/a>‘s Chief Product Officer.<\/p>\n

New Technologies make processes easier<\/strong><\/p>\n

The ‘RH 4.0 FeD<\/a>‘ project was based on the implementation of emerging technologies (Machine Learning) in order to perform tasks automatically using historical data. This means that “the algorithm used works from scattered data, transforming them so that they all focus on the same dimension” explained Martim Sousa, Information Systems’ Technician from University of Aveiro, who was one of the team members involved in the project.<\/p>\n

In its first phase, Machine Learning models were used to predict time series<\/a>, which could determine customer flows. After the exploratory phase, and after testing several prediction models, the UA team identified a model with superior performance for customer forecasting\/team sizing, allowing the integration of machine learning models with SISQUAL\u00aeWFM<\/a>.<\/p>\n

“We now have a completely different Forecast<\/a> \/ sizing module in terms of automation, and we’re able to sell the model as a service, project, or even as consultancy,” says Jorge Costa, CPO of SISQUAL\u00ae WFM.<\/p>\n

 <\/p>\n

Model is adjustable and universal<\/strong><\/p>\n

The new SISQUAL\u00ae Forecast<\/a> ML (Machine Learning) has the advantage of being able to adjust to new markets, serving as a universal model for different geographies.<\/p>\n

Another benefit of the solution is the upgrade to perform as a “short-term version” of SISQUAL\u00ae Forecast<\/a>, allowing the use of short-term execution plans for each business area i.e., this automation allows managers to redistribute work to the team at least one day prior.<\/p>\n

More than 75,000 managers worldwide use SISQUAL\u00ae WFM, making it a global solution, placing the software at the forefront of mechanisms that boost workforce management and increase companies’ productivity.<\/p>\n

 <\/p>\n

See the event’s gallery below:<\/p>\n\n

 <\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

On 30\/06, the results of the Project ‘RH 4.0 FeD – Forecast and Automatic Sizing for Retail Teams‘, developed in co-promotion with the University of Aveiro (UA) were presented to the public. Following the release of the upgrade made to the existing SISQUAL\u00ae Forecast module, the project was based on the creation of new forecasting […]<\/p>\n","protected":false},"author":8,"featured_media":44426,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1608],"tags":[],"class_list":["post-44425","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-events"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.sisqualwfm.com\/es-mx\/wp-json\/wp\/v2\/posts\/44425","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sisqualwfm.com\/es-mx\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sisqualwfm.com\/es-mx\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sisqualwfm.com\/es-mx\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sisqualwfm.com\/es-mx\/wp-json\/wp\/v2\/comments?post=44425"}],"version-history":[{"count":2,"href":"https:\/\/www.sisqualwfm.com\/es-mx\/wp-json\/wp\/v2\/posts\/44425\/revisions"}],"predecessor-version":[{"id":55815,"href":"https:\/\/www.sisqualwfm.com\/es-mx\/wp-json\/wp\/v2\/posts\/44425\/revisions\/55815"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.sisqualwfm.com\/es-mx\/wp-json\/wp\/v2\/media\/44426"}],"wp:attachment":[{"href":"https:\/\/www.sisqualwfm.com\/es-mx\/wp-json\/wp\/v2\/media?parent=44425"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sisqualwfm.com\/es-mx\/wp-json\/wp\/v2\/categories?post=44425"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sisqualwfm.com\/es-mx\/wp-json\/wp\/v2\/tags?post=44425"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}