{"id":24625,"date":"2021-04-06T17:22:52","date_gmt":"2021-04-06T15:22:52","guid":{"rendered":"https:\/\/sixphere.com\/project\/anomaly-detection-in-production-flows-through-data\/"},"modified":"2025-04-09T18:33:12","modified_gmt":"2025-04-09T16:33:12","slug":"anomaly-detection-in-production-flows-through-data","status":"publish","type":"project","link":"https:\/\/sixphere.com\/en\/project\/anomaly-detection-in-production-flows-through-data\/","title":{"rendered":"Anomaly detection in production flows through data"},"content":{"rendered":"\n\n[et_pb_section fb_built=&#8221;1&#8243; custom_padding_last_edited=&#8221;on|tablet&#8221; _builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;rgba(147,149,151,0.1)&#8221; custom_padding=&#8221;||150px||false|false&#8221; custom_padding_tablet=&#8221;0px|0px|100px|0px|false|false&#8221; custom_padding_phone=&#8221;|0px|100px|0px|false|false&#8221;][et_pb_row column_structure=&#8221;3_5,2_5&#8243; module_class=&#8221;print-only&#8221; 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custom_margin=&#8221;0px||20px||false|false&#8221; custom_padding=&#8221;0px||||false|false&#8221;]<h2>Anomaly detection in production flows, or how to use data to uncover hidden secrets in our processes<\/h2>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.9.2&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;|300|||||||&#8221; header_font=&#8221;|300|||||||&#8221; custom_padding=&#8221;0px|20px|20px|20px|false|false&#8221; custom_padding_tablet=&#8221;|0px||0px|false|false&#8221; custom_padding_phone=&#8221;0px|0px|20px|0px|false|false&#8221; custom_padding_last_edited=&#8221;on|phone&#8221;]<p>Most companies, to a greater or lesser extent, are already aware of the <strong>advantages of digitalization<\/strong>. The implementation of information systems has streamlined processes, facilitated the recording and sharing of information, and has been crucial in improving overall daily operations. <\/p>\n<p>However, not all companies know (yet) that the digitized data generated by information systems are not just historical leftovers to store &#8220;just in case.&#8221; <strong>These data are pure gold<\/strong>.<a href=\"https:\/\/sixphere.com\/blog\/explotar-datos-proceso-digitalizacion\/\"><span style=\"text-decoration: underline;\"> In this article, we explain some ways to make the most of them<\/span><\/a>. Here, we will share a specific case about anomaly detection. <\/p>\n<p>Although the case presented pertains to a specific functional scenario, the science behind it is applicable to any other relevant scenario.<\/p>[\/et_pb_text][et_pb_text disabled_on=&#8221;on|on|off&#8221; module_class=&#8221;popup-infography&#8221; _builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;Oswald||||on||||&#8221; text_font_size=&#8221;20px&#8221; background_color=&#8221;#FFFFFF&#8221; width=&#8221;300px&#8221; max_width=&#8221;300px&#8221; custom_margin=&#8221;||10px|-50px|false|false&#8221; custom_padding=&#8221;10px||10px||false|false&#8221; custom_css_main_element=&#8221;cursor:pointer;&#8221;]How is a project with Sixphere?[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; disabled_on=&#8221;on|on|off&#8221; module_class=&#8221;popup-overlay-infography&#8221; _builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221; 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custom_padding_tablet=&#8221;0px|0px|0px|0px|false|false&#8221; custom_padding_phone=&#8221;0px|0px|0px|0px|false|false&#8221; custom_padding_last_edited=&#8221;on|tablet&#8221; border_width_left=&#8221;4px&#8221; border_color_left=&#8221;rgba(255,163,39,0.5)&#8221; border_width_all_tablet=&#8221;&#8221; border_width_all_phone=&#8221;0px&#8221; border_width_all_last_edited=&#8221;on|tablet&#8221; border_width_left_tablet=&#8221;0px&#8221; border_width_left_phone=&#8221;&#8221; border_width_left_last_edited=&#8221;on|phone&#8221;][et_pb_text _builder_version=&#8221;4.9.2&#8243; _module_preset=&#8221;default&#8221; header_2_font=&#8221;Oswald|300|||||||&#8221; header_2_text_align=&#8221;left&#8221; header_2_font_size=&#8221;20px&#8221; header_2_line_height=&#8221;1.2em&#8221; custom_margin=&#8221;0px||20px||false|false&#8221; custom_padding=&#8221;0px||||false|false&#8221; hover_enabled=&#8221;0&#8243; sticky_enabled=&#8221;0&#8243;]<h2>What is the starting point, what data do we want to use, and for what purpose?<\/h2>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.9.2&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;|300|||||||&#8221; header_font=&#8221;|300|||||||&#8221; custom_padding=&#8221;0px|20px|20px|20px|false|false&#8221; text_orientation_tablet=&#8221;&#8221; text_orientation_phone=&#8221;left&#8221; text_orientation_last_edited=&#8221;on|phone&#8221;]<p>As part of the <a href=\"https:\/\/sixphere.com\/project\/eficiencia-y-reduccion-de-costes-paperless\/\"><span style=\"text-decoration: underline;\">digitization project<\/span><\/a> carried out for one of our main clients, one of the areas addressed was proximity-based presence control and IoT, along with <strong>the real-time registration of the entry and exit<\/strong> of tools, parts, and equipment across different virtual regions of the production plant.<\/p>\n<p>These data, once processed, allow us to approximately locate each element in real-time and, furthermore, accurately trace the paths they follow within the production flow.<\/p>\n<p>Ultimately, the information provided by the collected data offers a real-time snapshot of the production status from a physical perspective. However, a greater challenge was posed: <strong>Can we use this data to determine if something is going wrong<\/strong>? The answer is yes. <\/p>\n<p>Objective: Implement an <strong>AI model<\/strong> that, continuously fed with data related to positioning and entries and exits, <strong>alerts in real-time when something may be going wrong<\/strong>. Most analysis systems are limited to comparing deviations from a pre-established plan. However, we want a solution capable of <strong>discovering hidden patterns<\/strong>.   <\/p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; specialty=&#8221;on&#8221; padding_top_bottom_link_1=&#8221;false&#8221; padding_top_bottom_link_2=&#8221;false&#8221; padding_left_right_link_1=&#8221;false&#8221; padding_left_right_link_2=&#8221;false&#8221; padding_bottom_1=&#8221;0px&#8221; padding_bottom_2=&#8221;0px&#8221; disabled_on=&#8221;on|on|off&#8221; module_class=&#8221;popup-overlay-contact&#8221; _builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||0px||false|false&#8221; custom_padding=&#8221;||0px||false|false&#8221;][et_pb_column type=&#8221;1_2&#8243; specialty_columns=&#8221;2&#8243; _builder_version=&#8221;3.25&#8243; custom_padding=&#8221;|||&#8221; custom_padding__hover=&#8221;|||&#8221;][et_pb_row_inner module_class=&#8221;popup-content-contact&#8221; _builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221; width=&#8221;100%&#8221; max_width=&#8221;100%&#8221; custom_margin=&#8221;20px|auto|0px|auto|false|false&#8221; custom_padding=&#8221;0px||0px|||&#8221;][et_pb_column_inner saved_specialty_column_type=&#8221;1_2&#8243; _builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221;][et_pb_text _builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221; header_text_align=&#8221;right&#8221; header_3_font=&#8221;Oswald|300|||||||&#8221; header_3_text_align=&#8221;right&#8221; header_3_font_size=&#8221;30px&#8221; header_4_font=&#8221;Oswald|300|||||||&#8221; header_4_text_align=&#8221;right&#8221; header_4_font_size=&#8221;20px&#8221; header_4_line_height=&#8221;1.3em&#8221; custom_margin=&#8221;||0px||false|false&#8221; custom_padding=&#8221;||0px||false|false&#8221;]<h3><strong><span style=\"color: #ffa327;\">Efficient <\/span>digitalization to drive your business forward.<\/strong><\/h3>\n<h4>Contact us, <span style=\"color: #ffa327;\">we will be happy to assist you<\/span><\/h4>[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner module_class=&#8221;popup-content-contact&#8221; 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custom_padding=&#8221;20px||0px||false|false&#8221;][et_pb_row use_custom_gutter=&#8221;on&#8221; gutter_width=&#8221;1&#8243; make_equal=&#8221;on&#8221; _builder_version=&#8221;4.7.7&#8243; custom_margin=&#8221;0px||||false|false&#8221; custom_padding=&#8221;0px||0px&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.7.7&#8243; background_color=&#8221;rgba(104,168,222,0.1)&#8221; custom_padding=&#8221;20px|10px|10px|10px|false|false&#8221; custom_padding__hover=&#8221;|||&#8221;][et_pb_text _builder_version=&#8221;4.7.7&#8243; header_font=&#8221;||||||||&#8221; header_3_font=&#8221;|600|||||||&#8221; header_3_font_size=&#8221;40px&#8221; header_3_line_height=&#8221;1.1em&#8221; header_4_font=&#8221;|700||on|||||&#8221; header_4_text_align=&#8221;left&#8221; header_4_text_color=&#8221;#255d83&#8243; header_4_font_size=&#8221;13px&#8221; header_4_letter_spacing=&#8221;2px&#8221; header_4_line_height=&#8221;1.8em&#8221; custom_margin=&#8221;||0px&#8221;]<h4>strategy &amp; applied solution<\/h4>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.9.2&#8243; header_font=&#8221;||||||||&#8221; header_3_font=&#8221;|600|||||||&#8221; header_3_text_align=&#8221;left&#8221; header_3_font_size=&#8221;40px&#8221; header_3_line_height=&#8221;1.1em&#8221; header_4_font=&#8221;|700||on|||||&#8221; header_4_text_color=&#8221;#255d83&#8243; header_4_font_size=&#8221;13px&#8221; header_4_letter_spacing=&#8221;2px&#8221; header_4_line_height=&#8221;1.8em&#8221; custom_margin=&#8221;||0px&#8221; custom_padding=&#8221;||0px&#8221;]<h3>Pattern detection and machine learning, the recipe to catch potential anomalies <\/h3>[\/et_pb_text][et_pb_button url_new_window=&#8221;on&#8221; button_text=&#8221;I want to start my project with Sixphere&#8221; button_alignment=&#8221;left&#8221; disabled_on=&#8221;on|on|off&#8221; module_class=&#8221;popup-contact&#8221; _builder_version=&#8221;4.8.1&#8243; custom_button=&#8221;on&#8221; button_text_size=&#8221;18px&#8221; button_text_color=&#8221;#ffffff&#8221; 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text_text_align=&#8221;center&#8221;]<h4>What data do I want: patterns from time series<\/h4>\n<p>The first question we ask, obviously, is <strong>which specific data we should use<\/strong>. This choice determines the quality of the model, as it is built around this data. <\/p>\n<p>Every time an entity enters or exits a specific region, the system stores a real-time record, thus generating time series of events that can be exploited for extracting <strong>behavior models or patterns<\/strong>. The solution, therefore, involves implementing a model capable of detecting anomalies in these patterns. <\/p>\n<p>However, the<strong> first problem<\/strong> arises from not having pre-labeled or categorized records at the start, meaning there is no prior information or knowledge about what constitutes an anomaly and what does not within the analysis of the defined series.<\/p>\n<h4>We overcome the obstacle with the use of an unsupervised system<\/h4>\n<p>Until the system receives the labeled information, only an unsupervised method can be used, essentially consisting of a <strong>process of classifying records into several groups<\/strong>, each with its own characteristics and calculated centroids.<\/p>\n<p>For the construction of the model, the <a href=\"https:\/\/en.wikipedia.org\/wiki\/K-means_clustering\"><span style=\"text-decoration: underline;\">K-means algorithm <\/span><\/a>was used, implemented through the <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.cluster.KMeans.html\"><span style=\"text-decoration: underline;\">scikit-learn library<\/span><\/a> integrated into the project. The resulting model is a cluster trained with three centroids, so that all points (entry or exit events in the practical scenario) classified by the model within a cluster containing less than 20% of the total points (events) <strong>could be considered an anomaly<\/strong>. <\/p>\n<p>Regarding the <strong>model&#8217;s training<\/strong>, entry and exit events of entities on virtual regions are continuously calculated and stored by the system&#8217;s core. Specifically, in the initial tests, 77,000 records were used for this. In the graph, you can differentiate, by colors, the group or cluster in which each entry or exit event is classified. It is evident from the results that the model considers as anomalies those cases that show high values.   <\/p>[\/et_pb_text][et_pb_image src=&#8221;https:\/\/sixphere.com\/wp-content\/uploads\/2021\/04\/deteccion-de-anomalias.png&#8221; alt=&#8221;Anomaly detection, patterns, and classification&#8221; title_text=&#8221;Anomaly detection, patterns, and classification&#8221; _builder_version=&#8221;4.9.2&#8243; _module_preset=&#8221;default&#8221; max_width=&#8221;75%&#8221; max_width_tablet=&#8221;100%&#8221; max_width_phone=&#8221;&#8221; max_width_last_edited=&#8221;on|desktop&#8221; module_alignment=&#8221;center&#8221;][\/et_pb_image][et_pb_text _builder_version=&#8221;4.9.2&#8243; text_font=&#8221;|300|||||||&#8221; header_4_font=&#8221;|300|||||||&#8221; header_4_line_height=&#8221;1.3em&#8221; custom_margin=&#8221;||35px||false|false&#8221; custom_padding=&#8221;10px||||false|false&#8221; text_text_align=&#8221;center&#8221;]<h4>A couple of improvements: integration and check<\/h4>\n<p>Very well, but <strong>how is the model used<\/strong>? 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border_width_left_last_edited=&#8221;on|tablet&#8221;][et_pb_column_inner saved_specialty_column_type=&#8221;2_3&#8243; _builder_version=&#8221;4.7.7&#8243; _module_preset=&#8221;default&#8221;][et_pb_text _builder_version=&#8221;4.9.2&#8243; _module_preset=&#8221;default&#8221; header_2_font=&#8221;Oswald|300|||||||&#8221; header_2_text_align=&#8221;left&#8221; header_2_font_size=&#8221;20px&#8221; header_2_line_height=&#8221;1.2em&#8221; custom_margin=&#8221;||2px||false|false&#8221; custom_margin_tablet=&#8221;15px||2px||false|false&#8221; custom_margin_phone=&#8221;15px||||false|false&#8221; custom_margin_last_edited=&#8221;on|phone&#8221; custom_padding=&#8221;0px|20px|0px|20px|false|false&#8221; custom_padding_tablet=&#8221;0px|0px|0px|0px|false|false&#8221; custom_padding_phone=&#8221;&#8221; custom_padding_last_edited=&#8221;on|phone&#8221;]<h2>A step further in process improvement through the use of data<\/h2>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.9.2&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;|300|||||||&#8221; header_font=&#8221;|300|||||||&#8221; custom_margin=&#8221;||2px|||&#8221; custom_margin_tablet=&#8221;5px||2px||false|false&#8221; custom_margin_phone=&#8221;5px||||false|false&#8221; custom_margin_last_edited=&#8221;on|desktop&#8221; custom_padding=&#8221;0px|20px|0px|20px|false|false&#8221; custom_padding_tablet=&#8221;|0px||0px|false|false&#8221; custom_padding_phone=&#8221;&#8221; custom_padding_last_edited=&#8221;on|tablet&#8221;]<p>The solution provided, beyond being a functional element that contributes to process improvement, introduces novel and differentiating mechanisms that open a new avenue for progress: the use of data.<\/p>[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner column_structure=&#8221;1_3,1_3,1_3&#8243; use_custom_gutter=&#8221;on&#8221; gutter_width=&#8221;2&#8243; custom_padding_last_edited=&#8221;on|phone&#8221; _builder_version=&#8221;4.9.2&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;40px|20px|20px|20px|false|false&#8221; 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