{"id":24624,"date":"2021-04-23T17:19:01","date_gmt":"2021-04-23T15:19:01","guid":{"rendered":"https:\/\/sixphere.com\/project\/prediction-of-manufacturing-times-using-time-series\/"},"modified":"2025-04-09T17:38:27","modified_gmt":"2025-04-09T15:38:27","slug":"prediction-of-manufacturing-times-using-time-series","status":"publish","type":"project","link":"https:\/\/sixphere.com\/en\/project\/prediction-of-manufacturing-times-using-time-series\/","title":{"rendered":"Prediction of manufacturing times using time series"},"content":{"rendered":"\n\n\n\n\n\n\n\n\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; 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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;right&#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;10px||0px||false|false&#8221; custom_padding=&#8221;||0px||false|false&#8221; header_4_text_align_tablet=&#8221;left&#8221; header_4_text_align_phone=&#8221;left&#8221; header_4_text_align_last_edited=&#8221;on|phone&#8221;]<h4>starting point<\/h4>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.7.7&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;Oswald|300|||||||&#8221; text_font_size=&#8221;15px&#8221; text_letter_spacing=&#8221;2px&#8221; text_orientation=&#8221;right&#8221; custom_margin=&#8221;-10px||||false|false&#8221; text_orientation_tablet=&#8221;left&#8221; text_orientation_phone=&#8221;left&#8221; text_orientation_last_edited=&#8221;on|tablet&#8221;]Do you feel identified?[\/et_pb_text][\/et_pb_column][et_pb_column type=&#8221;2_3&#8243; _builder_version=&#8221;4.7.7&#8243; _module_preset=&#8221;default&#8221; background_enable_color=&#8221;off&#8221; custom_padding=&#8221;20px|10px|20px|20px|false|false&#8221; 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|phone&#8221; border_width_left=&#8221;4px&#8221; border_color_left=&#8221;rgba(51,51,51,0.31)&#8221; border_width_left_tablet=&#8221;0px&#8221; border_width_left_phone=&#8221;0px&#8221; border_width_left_last_edited=&#8221;on|phone&#8221;][et_pb_text _builder_version=&#8221;4.9.4&#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>Anomaly detection, trends, and forecasting of future values. Prediction of manufacturing times. <\/h2>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.9.4&#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; hover_enabled=&#8221;0&#8243; sticky_enabled=&#8221;0&#8243;]<p>When designing a <a href=\"https:\/\/sixphere.com\/en\/comprehensive-digitalization\/\"><span style=\"text-decoration: underline;\">digitization program<\/span><\/a> to address the different processes in a production environment, one of the first aspects that often arises is Production Control, especially <strong>the control of manufacturing times and work in progress<\/strong>. This is because the execution time of a task and the ways to optimize it are, in principle, the variables that will offer the greatest benefits when improvements are implemented. <\/p>\n<p>In these cases, it is common to immediately apply changes to the manufacturing routes, the work station structure, the tools used, the available space, logistics, and many other options that are likely to yield positive results. However, it is surprising to find that <strong>rarely is a detailed study carried out on the usage and behavior of the resources <\/strong>and agents involved in the process, such as trend and anomaly analysis, or, in general, an effective exploitation of the data that is likely already available about the process. <\/p>\n<p>Just by having historical data of the manufacturing times of an item, it is possible to conduct <strong>trend studies, identify anomalies, and even make predictions<\/strong> that are very useful in determining the most appropriate and optimal improvement actions for a production flow.<\/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; 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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;4.9.4&#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>Is it possible to obtain useful information with just manufacturing times? <\/h2>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.9.4&#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; hover_enabled=&#8221;0&#8243; text_orientation_tablet=&#8221;&#8221; text_orientation_phone=&#8221;left&#8221; text_orientation_last_edited=&#8221;on|phone&#8221; sticky_enabled=&#8221;0&#8243;]<p>We start with the need to <strong>study and analyze the behavior of the manufacturing process <\/strong>of specific items, using only the manufacturing times of each one as data.<\/p>\n<p>Is it possible to obtain useful information just from manufacturing times? <strong>Of course<\/strong>. Manufacturing time is a clear indicator of many situations, but fundamentally, it is a variable that is heavily influenced by any <strong>anomalies that occur in the process<\/strong> in any area. <\/p>\n<p>Whether an operator faces a problem executing an operation due to inadequate training, or if there is a logistical issue in dispatching the necessary materials to carry out the task, the manufacturing time will be directly affected.<\/p>\n<p>However, the manufacturing time <strong>tends to be as close as possible to the estimated<\/strong> manufacturing time for each task. This makes the distribution of manufacturing times, by its very nature, stationary, which makes it <strong>ideal for detecting anomalies<\/strong>, trends, and fluctuations, as well as<strong> predicting future values<\/strong>. <\/p>\n<p>The <strong>objective <\/strong>is to analyze and attempt to explain anomalies and trends in the manufacturing process of certain items, using only the manufacturing time as the main variable. At the same time, this analysis aims to be used for making forecasts. <\/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; _builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;0px||0px|||&#8221;][et_pb_column_inner saved_specialty_column_type=&#8221;1_2&#8243; _builder_version=&#8221;4.7.7&#8243; _module_preset=&#8221;default&#8221;][et_pb_image src=&#8221;https:\/\/sixphere.com\/wp-content\/uploads\/2019\/06\/contact-us-person-bw.png&#8221; title_text=&#8221;contact-us-person-bw&#8221; align=&#8221;right&#8221; _builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221; module_alignment=&#8221;right&#8221; height=&#8221;150px&#8221; custom_padding=&#8221;0px|||||&#8221;][\/et_pb_image][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner module_class=&#8221;popup-content-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|20px||0px|false|false&#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; 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;right&#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;20px||0px||false|false&#8221;]<h4>Would you prefer to schedule a meeting?<\/h4>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;Roboto Condensed||||on||||&#8221; text_text_color=&#8221;#ffa327&#8243; text_font_size=&#8221;22px&#8221; text_orientation=&#8221;right&#8221; link_option_url=&#8221;https:\/\/meetings.hubspot.com\/jose-antonio-mora&#8221; link_option_url_new_window=&#8221;on&#8221;]Meet with us[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=&#8221;1_2&#8243; 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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;4.9.4&#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; hover_enabled=&#8221;0&#8243; sticky_enabled=&#8221;0&#8243;]<h3>Analysis of the distribution of manufacturing operation times<\/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; 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button_border_color=&#8221;#543855&#8243; button_border_radius=&#8221;50px&#8221; button_font=&#8221;|300|||||||&#8221; button_icon=&#8221;%%3%%&#8221; button_icon_color=&#8221;#ffa327&#8243; custom_margin=&#8221;20px||20px||false|false&#8221; custom_padding=&#8221;5px||5px||false|false&#8221; box_shadow_style=&#8221;preset2&#8243; box_shadow_horizontal=&#8221;15px&#8221; box_shadow_vertical=&#8221;15px&#8221; box_shadow_blur=&#8221;15px&#8221;][\/et_pb_button][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=&#8221;4.9.4&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;rgba(104,168,222,0.05)&#8221; custom_padding=&#8221;20px|20px|0px|20px|false|false&#8221; hover_enabled=&#8221;0&#8243; sticky_enabled=&#8221;0&#8243;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.7.7&#8243; _module_preset=&#8221;default&#8221;][et_pb_text _builder_version=&#8221;4.9.4&#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;20px||||false|false&#8221; hover_enabled=&#8221;0&#8243; text_text_align=&#8221;center&#8221; sticky_enabled=&#8221;0&#8243;]<h4>Determining analogous work units<\/h4>\n<p>The first problem to solve is determining which units of work are analogous to each other, so that they can be compared. In manufacturing industry, the concept of a <strong>production order<\/strong> executed on a specific item is commonly used. <\/p>\n<p>However, a production order is usually made up of a <strong>set of operations<\/strong>, the ones that need to be carried out on the material. <strong>In this case, it is the operations<\/strong> that determine the unit of work, not the production orders. It is the execution time of the same operation over time that we can study as a time series. <\/p>\n<p>A time series is a set of values of a variable arranged chronologically, not necessarily in equal intervals. In this type of variable, studies can be conducted on the dependency of the value of the variable at a given moment on its previous moments. This is known as <strong>Autoregressive Analysis<\/strong>, which essentially involves a model where the dependent variable is explained through a certain number of past observations.  <\/p>\n<h4>Autoregressive analysis and stochastic methods<\/h4>\n<p>There are many methods for autoregressive analysis (AR), but focusing on stochastic methods, there are a series of <strong>AR-based models<\/strong> that are very useful, easy to implement, and typically yield good results for this type of study.<\/p>\n<p>These are the <strong>Autoregressive Moving Average (ARMA) models<\/strong>, which add a moving average effect to the basic autoregressive model; the Autoregressive Integrated Moving Average (ARIMA) models, which include a component for correcting non-stationary series; or the Seasonal Autoregressive Integrated Moving Average (SARIMA) models, which allow for the study of series that fluctuate across different seasons.<\/p>\n[\/et_pb_text][et_pb_image src=&#8221;https:\/\/sixphere.com\/wp-content\/uploads\/2021\/04\/newplot-4.png&#8221; alt=&#8221;Big Data, anomalies, and prediction&#8221; title_text=&#8221;Big Data, anomalies, and prediction&#8221; _builder_version=&#8221;4.9.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;80%&#8221; width_tablet=&#8221;100%&#8221; width_phone=&#8221;&#8221; width_last_edited=&#8221;on|phone&#8221; module_alignment=&#8221;center&#8221; box_shadow_style=&#8221;preset1&#8243; locked=&#8221;off&#8221;][\/et_pb_image][et_pb_text _builder_version=&#8221;4.9.4&#8243; text_font=&#8221;|300|||||||&#8221; header_4_font=&#8221;|300|||||||&#8221; header_4_line_height=&#8221;1.3em&#8221; custom_margin=&#8221;||13px||false|false&#8221; custom_padding=&#8221;20px||||false|false&#8221; hover_enabled=&#8221;0&#8243; text_text_align=&#8221;center&#8221; sticky_enabled=&#8221;0&#8243;]<h4>A solution: Box-Jenkins methodology<\/h4>\n\n<p>The solution to our problem lies in applying the Box-Jenkins methodology, which is based on identifying candidate models and estimating their parameters, and analyzing the goodness of fit of the model through the study of the residuals, known as white noise.<\/p>\n\n<p>Once the best model to explain the behavior of a series has been determined, as well as the estimation of its parameters, predictions are used not only to anticipate the manufacturing time in this case, but also to determine that if a real observation deviates too much from the forecast, it is very likely that it is an anomaly in the series, and therefore, an action may be required.<\/p>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=&#8221;4.9.4&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;rgba(224,43,32,0.61)&#8221; background_enable_image=&#8221;off&#8221; background_size=&#8221;contain&#8221; background_position=&#8221;bottom_left&#8221; custom_margin=&#8221;30px||30px||false|false&#8221; custom_padding=&#8221;20px|20px|20px|20px|false|false&#8221; hover_enabled=&#8221;0&#8243; background_last_edited=&#8221;on|desktop&#8221; background_enable_image_tablet=&#8221;off&#8221; locked=&#8221;off&#8221; sticky_enabled=&#8221;0&#8243;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.9.4&#8243; _module_preset=&#8221;default&#8221;][et_pb_text _builder_version=&#8221;4.9.4&#8243; _module_preset=&#8221;default&#8221; header_font=&#8221;Oswald|300|||||||&#8221; header_text_align=&#8221;center&#8221; header_text_color=&#8221;#FFFFFF&#8221; header_2_font=&#8221;Oswald|300|||||||&#8221; header_2_text_align=&#8221;center&#8221; header_2_text_color=&#8221;#FFFFFF&#8221;]<h2 style=\"font-size: 30px\">Consult a complete example of the analysis performed on a specific manufacturing operation<\/h2>[\/et_pb_text][et_pb_button button_url=&#8221;https:\/\/6phere.github.io\/arima_example\/example1.html&#8221; 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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;right&#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;10px||0px||false|false&#8221; custom_padding=&#8221;||0px||false|false&#8221; header_4_text_align_tablet=&#8221;left&#8221; header_4_text_align_phone=&#8221;&#8221; header_4_text_align_last_edited=&#8221;on|tablet&#8221;]<h4>Added value<\/h4>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.7.7&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;Oswald|300|||||||&#8221; text_font_size=&#8221;15px&#8221; text_letter_spacing=&#8221;2px&#8221; text_orientation=&#8221;right&#8221; custom_margin=&#8221;-10px||||false|false&#8221; text_orientation_tablet=&#8221;left&#8221; text_orientation_phone=&#8221;&#8221; text_orientation_last_edited=&#8221;on|phone&#8221;]Do you need results?[\/et_pb_text][\/et_pb_column][et_pb_column type=&#8221;2_3&#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 use_custom_gutter=&#8221;on&#8221; gutter_width=&#8221;1&#8243; make_equal=&#8221;on&#8221; custom_padding_last_edited=&#8221;on|phone&#8221; _builder_version=&#8221;4.7.7&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;45px|10px|0px|20px|false|false&#8221; custom_padding_tablet=&#8221;0px|0px||0px|false|false&#8221; custom_padding_phone=&#8221;0px|0px|0px|0px|false|false&#8221; border_width_left=&#8221;4px&#8221; border_color_left=&#8221;rgba(104,168,222,0.24)&#8221; border_width_left_tablet=&#8221;0px&#8221; border_width_left_phone=&#8221;0px&#8221; 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;4.9.4&#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; hover_enabled=&#8221;0&#8243; sticky_enabled=&#8221;0&#8243;]<h2>Manufacturing prediction, anomaly detection&#8230; and decision-making<\/h2>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.9.4&#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; hover_enabled=&#8221;0&#8243; sticky_enabled=&#8221;0&#8243;]<p>The study of time series is not only useful for obtaining forecasting models but also for anomaly detection.<\/p>\n<p>In our case study, a method was developed in which, for each of the operations under analysis, the <strong>estimated manufacturing time<\/strong> for the next piece is obtained.<\/p>[\/et_pb_text][et_pb_image src=&#8221;https:\/\/sixphere.com\/wp-content\/uploads\/2021\/04\/newplot-4.png&#8221; alt=&#8221;Big Data, anomalies, and prediction&#8221; title_text=&#8221;Big Data, anomalies, and prediction&#8221; _builder_version=&#8221;4.9.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;100%&#8221; width_tablet=&#8221;100%&#8221; width_phone=&#8221;&#8221; width_last_edited=&#8221;on|desktop&#8221; module_alignment=&#8221;center&#8221; hover_enabled=&#8221;0&#8243; box_shadow_style=&#8221;preset1&#8243; locked=&#8221;off&#8221; sticky_enabled=&#8221;0&#8243; custom_margin=&#8221;30px||30px||false|false&#8221;][\/et_pb_image][et_pb_text _builder_version=&#8221;4.9.4&#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; hover_enabled=&#8221;0&#8243; sticky_enabled=&#8221;0&#8243;]<p>However, at the same time, for each observed value, and depending on the difference between that value and the one predicted by the model, a <strong>very versatile mechanism for anomaly detection can be established<\/strong>.<\/p>\n<p>In our case, this method was used to implement a mechanism where, once the actual manufacturing time of a part is recorded and compared with the estimated time, it is considered an anomaly or not depending on whether the difference is greater than the standard.<\/p>[\/et_pb_text][et_pb_image src=&#8221;https:\/\/sixphere.com\/wp-content\/uploads\/2021\/04\/newplot-5.png&#8221; alt=&#8221;Big Data, anomalies, and prediction&#8221; title_text=&#8221;Big Data, anomalies, and prediction&#8221; _builder_version=&#8221;4.9.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;100%&#8221; width_tablet=&#8221;100%&#8221; width_phone=&#8221;&#8221; width_last_edited=&#8221;on|desktop&#8221; module_alignment=&#8221;center&#8221; hover_enabled=&#8221;0&#8243; box_shadow_style=&#8221;preset1&#8243; locked=&#8221;off&#8221; sticky_enabled=&#8221;0&#8243; custom_margin=&#8221;30px||30px||false|false&#8221;][\/et_pb_image][et_pb_text _builder_version=&#8221;4.9.4&#8243; _module_preset=&#8221;default&#8221; text_font=&#8221;|300|||||||&#8221; header_font=&#8221;|300|||||||&#8221; custom_margin=&#8221;||30px||false|false&#8221; custom_margin_tablet=&#8221;5px||30px||false|false&#8221; custom_margin_phone=&#8221;5px||||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|false|false&#8221; custom_padding_phone=&#8221;&#8221; custom_padding_last_edited=&#8221;on|tablet&#8221; hover_enabled=&#8221;0&#8243; sticky_enabled=&#8221;0&#8243;]<p>But perhaps the most important value obtained in this case was achieving methods that, using very common data, such as the recording of production times for a piece, provide highly valuable <strong>information for decision-making<\/strong>.<\/p>[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; module_class=&#8221;study-case-more&#8221; _builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;rgba(104,168,222,0.1)&#8221; custom_margin=&#8221;0px||||false|false&#8221; custom_padding=&#8221;100px||100px|||&#8221;][et_pb_row _builder_version=&#8221;4.7.7&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;0px||0px|||&#8221; global_module=&#8221;10278&#8243;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.7.7&#8243; _module_preset=&#8221;default&#8221;][et_pb_text _builder_version=&#8221;4.8.1&#8243; header_font=&#8221;||||||||&#8221; header_text_align=&#8221;left&#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||0px||false|false&#8221; custom_padding=&#8221;0px||0px||false|false&#8221; header_3_text_align_tablet=&#8221;&#8221; header_3_text_align_phone=&#8221;right&#8221; header_3_text_align_last_edited=&#8221;on|phone&#8221;]<h3>Explore more<span style=\"color: #ffa327;\"> case studies<\/span><\/h3>[\/et_pb_text][et_pb_button button_text=&#8221;I want more information&#8221; button_alignment=&#8221;left&#8221; button_alignment_tablet=&#8221;&#8221; button_alignment_phone=&#8221;left&#8221; 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custom_padding_last_edited=&#8221;on|phone&#8221; link_option_url=&#8221;https:\/\/sixphere.com\/en\/our-success-stories\/&#8221; text_orientation_tablet=&#8221;&#8221; text_orientation_phone=&#8221;right&#8221; text_orientation_last_edited=&#8221;on|phone&#8221; module_alignment_tablet=&#8221;&#8221; module_alignment_phone=&#8221;right&#8221; module_alignment_last_edited=&#8221;on|desktop&#8221; custom_css_main_element=&#8221;cursor: pointer;&#8221;]See all cases[\/et_pb_text][et_pb_portfolio fullwidth=&#8221;off&#8221; posts_number=&#8221;4&#8243; include_categories=&#8221;all&#8221; show_pagination=&#8221;off&#8221; zoom_icon_color=&#8221;#ffffff&#8221; hover_overlay_color=&#8221;rgba(104,168,222,0.5)&#8221; hover_icon=&#8221;%%51%%&#8221; disabled_on=&#8221;on|on|off&#8221; module_class=&#8221;study-cases-grid&#8221; _builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221; title_font=&#8221;Oswald|300|||||||&#8221; title_text_align=&#8221;left&#8221; title_font_size=&#8221;24px&#8221; 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_builder_version=&#8221;4.8.1&#8243; _module_preset=&#8221;default&#8221; title_font=&#8221;Oswald|300|||||||&#8221; title_text_align=&#8221;left&#8221; title_font_size=&#8221;24px&#8221; title_line_height=&#8221;1.2em&#8221; caption_font=&#8221;Oswald|500|||||||&#8221; caption_text_align=&#8221;left&#8221; caption_text_color=&#8221;#666666&#8243; caption_font_size=&#8221;17px&#8221; caption_letter_spacing=&#8221;1px&#8221; caption_line_height=&#8221;1.2em&#8221; custom_margin=&#8221;||0px||false|false&#8221; custom_padding=&#8221;||0px||false|false&#8221; animation_style=&#8221;fade&#8221; custom_css_portfolio_image=&#8221;display: none;&#8221; custom_css_portfolio_post_meta=&#8221;margin-top: 60px;||margin-left: 20px;||pointer-events: none;||cursor: default;||text-decoration: none;||color: black;&#8221;][\/et_pb_portfolio][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; custom_padding_last_edited=&#8221;on|tablet&#8221; disabled_on=&#8221;off|off|on&#8221; 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This makes it the ideal candidate to serve as the basis for intelligent prediction. Let&#8217;s see how.  <\/p>\n","protected":false},"author":2,"featured_media":23822,"template":"","meta":{"_acf_changed":false,"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","content-type":"","footnotes":""},"project_category":[219],"project_tag":[224,222,223],"class_list":["post-24624","project","type-project","status-publish","has-post-thumbnail","hentry","project_category-data-ai","project_tag-ai","project_tag-big-data-en","project_tag-machine-learning-en"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Prediction of manufacturing times and anomalies with Big Data - Sixphere - Innovaci\u00f3n que conecta tu empresa<\/title>\n<meta name=\"description\" content=\"Optimization of production flows through the prediction of manufacturing times using time series analysis based on data.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sixphere.com\/en\/project\/prediction-of-manufacturing-times-using-time-series\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Prediction of manufacturing times and anomalies with Big Data - 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