{"id":175298,"date":"2026-02-05T11:17:36","date_gmt":"2026-02-05T10:17:36","guid":{"rendered":"https:\/\/p-hud4k6.project.space\/ai-as-a-service\/"},"modified":"2026-05-07T12:52:44","modified_gmt":"2026-05-07T10:52:44","slug":"ai-as-a-service","status":"publish","type":"page","link":"https:\/\/p-hud4k6.project.space\/en\/ai-as-a-service\/","title":{"rendered":"AI-as-a-Service"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_row unlock_row_content=&#8221;yes&#8221; row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;7&#8243; top_padding=&#8221;7&#8243; bottom_padding=&#8221;7&#8243; back_color=&#8221;color-210407&#8243; back_image=&#8221;173515&#8243; overlay_color=&#8221;color-210407&#8243; overlay_alpha=&#8221;75&#8243; overlay_animated=&#8221;yes&#8221; overlay_animated_size=&#8221;0.7&#8243; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; bottom_divider=&#8221;gradient&#8221; content_parallax=&#8221;3&#8243; uncode_shortcode_id=&#8221;566506&#8243; back_color_type=&#8221;uncode-palette&#8221; overlay_animated_2_color_type=&#8221;uncode-solid&#8221; overlay_animated_2_color_solid=&#8221;#7072e0&#8243; overlay_color_type=&#8221;uncode-palette&#8221;][vc_column column_width_percent=&#8221;100&#8243; position_vertical=&#8221;bottom&#8221; gutter_size=&#8221;4&#8243; style=&#8221;dark&#8221; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/1&#8243; uncode_shortcode_id=&#8221;138994&#8243;][vc_row_inner limit_content=&#8221;&#8221;][vc_column_inner column_width_percent=&#8221;100&#8243; position_horizontal=&#8221;left&#8221; gutter_size=&#8221;3&#8243; style=&#8221;dark&#8221; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;9\/12&#8243; uncode_shortcode_id=&#8221;311432&#8243;][vc_custom_heading heading_semantic=&#8221;h1&#8243; text_font=&#8221;font-161747&#8243; text_size=&#8221;fontsize-155944&#8243; text_weight=&#8221;400&#8243; text_space=&#8221;fontspace-111509&#8243; uncode_shortcode_id=&#8221;171275&#8243;]AI-powered optimisation for production planning \u2013 better decisions in seconds[\/vc_custom_heading][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_visibility=&#8221;yes&#8221; medium_width=&#8221;0&#8243; mobile_visibility=&#8221;yes&#8221; mobile_width=&#8221;0&#8243; width=&#8221;3\/12&#8243; uncode_shortcode_id=&#8221;161238&#8243;][\/vc_column_inner][\/vc_row_inner][vc_separator sep_color=&#8221;&#8221; full_width=&#8221;yes&#8221; uncode_shortcode_id=&#8221;105882&#8243;][vc_row_inner row_inner_height_percent=&#8221;0&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;5&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; limit_content=&#8221;&#8221; uncode_shortcode_id=&#8221;157228&#8243;][vc_column_inner column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_visibility=&#8221;yes&#8221; medium_width=&#8221;0&#8243; mobile_visibility=&#8221;yes&#8221; mobile_width=&#8221;0&#8243; width=&#8221;6\/12&#8243; uncode_shortcode_id=&#8221;177579&#8243;][vc_single_image media=&#8221;175880&#8243; media_width_percent=&#8221;80&#8243; uncode_shortcode_id=&#8221;246575&#8243;][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; style=&#8221;dark&#8221; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;6\/12&#8243; uncode_shortcode_id=&#8221;202754&#8243;][vc_custom_heading heading_semantic=&#8221;h4&#8243; text_font=&#8221;font-161747&#8243; text_size=&#8221;&#8221; text_weight=&#8221;400&#8243; text_height=&#8221;fontheight-357766&#8243; text_space=&#8221;fontspace-111509&#8243; uncode_shortcode_id=&#8221;108644&#8243;]With MCP <strong>AI-as-a-Service<\/strong>, you can seamlessly integrate powerful optimization algorithms into your existing planning environment or software solution.<br data-start=\"539\" data-end=\"542\" \/>Complex planning problems are automatically solved and measurably improved\u2014leading to faster and demonstrably better decisions.[\/vc_custom_heading][vc_button size=&#8221;btn-lg&#8221; radius=&#8221;btn-circle&#8221; hover_fx=&#8221;full-colored&#8221; custom_typo=&#8221;yes&#8221; font_family=&#8221;font-377884&#8243; font_weight=&#8221;500&#8243; border_width=&#8221;0&#8243; scale_mobile=&#8221;no&#8221; link=&#8221;url:https%3A%2F%2Fp-hud4k6.project.space%2Fen%2Fcontact%2F|title:About%20Business&#8221; uncode_shortcode_id=&#8221;159168&#8243; el_class=&#8221;popmake-175542&#8243;]Discover planning potential[\/vc_button][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row unlock_row_content=&#8221;yes&#8221; row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;7&#8243; top_padding=&#8221;5&#8243; bottom_padding=&#8221;5&#8243; overlay_alpha=&#8221;50&#8243; equal_height=&#8221;justify&#8221; gutter_size=&#8221;5&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;669969&#8243;][vc_column column_width_percent=&#8221;100&#8243; position_vertical=&#8221;middle&#8221; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;4&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/2&#8243; uncode_shortcode_id=&#8221;185655&#8243;][vc_row_inner row_inner_height_percent=&#8221;0&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;4&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; limit_content=&#8221;&#8221; uncode_shortcode_id=&#8221;159005&#8243;][vc_column_inner column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/1&#8243; uncode_shortcode_id=&#8221;187117&#8243;][uncode_list icon=&#8221;fa fa-check&#8221; icon_color=&#8221;color-482803&#8243; uncode_shortcode_id=&#8221;130809&#8243; icon_color_type=&#8221;uncode-palette&#8221;]<\/p>\n<ul>\n<li><strong>AI planning made easy:<\/strong> Our solutions can be seamlessly integrated into your existing MES, PPS, or APS software. They deliver optimised and traceable planning results \u2013 without the need for in-house AI development. AI becomes a 24\/7 sparring partner that provides continuous support and relief.AI becomes a 24\/7 partner, providing continuous support and taking pressure off your teams.<\/li>\n<li><strong>Making complexity managable:<\/strong> Many combinatorial problems in production planning are too complex for traditional methods. With our approaches, even highly complex scenarios can be solved efficiently \u2013 automatically and in the shortest possible time.<\/li>\n<li><strong>Numbers instead of gut feeling:<\/strong> Mathematical optimisation delivers reliable decisions: greater adherence to deadlines, shorter set-up times, optimal warehouse utilisation, and more. This not only leads to faster results, but also to demonstrably better ones.<\/li>\n<\/ul>\n<p>[\/uncode_list][\/vc_column_inner][\/vc_row_inner][\/vc_column][vc_column column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; style=&#8221;dark&#8221; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_visibility=&#8221;yes&#8221; medium_width=&#8221;0&#8243; mobile_visibility=&#8221;yes&#8221; mobile_width=&#8221;0&#8243; width=&#8221;1\/2&#8243; uncode_shortcode_id=&#8221;803921&#8243;][vc_single_image media=&#8221;174305&#8243; media_width_percent=&#8221;100&#8243; media_ratio=&#8221;four-three&#8221; alignment=&#8221;center&#8221; shape=&#8221;img-round&#8221; radius=&#8221;lg&#8221; advanced=&#8221;yes&#8221; media_overlay_opacity=&#8221;50&#8243; media_overlay_anim=&#8221;no&#8221; media_text_anim=&#8221;no&#8221; media_image_anim=&#8221;scroll&#8221; media_image_scroll=&#8221;zoom&#8221; media_image_scroll_val=&#8221;6&#8243; media_padding=&#8221;2&#8243; uncode_shortcode_id=&#8221;292813&#8243;][\/vc_column][\/vc_row][vc_row unlock_row_content=&#8221;yes&#8221; row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;7&#8243; top_padding=&#8221;5&#8243; bottom_padding=&#8221;5&#8243; back_color=&#8221;color-gyho&#8221; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; top_divider=&#8221;gradient&#8221; css_animation=&#8221;scroll-trigger&#8221; animation_scale_val=&#8221;100&#8243; animation_opacity=&#8221;0&#8243; animation_x=&#8221;0&#8243; animation_y=&#8221;0&#8243; animation_blur=&#8221;0&#8243; animation_rotate=&#8221;0&#8243; animation_perspective=&#8221;0&#8243; animation_offset_top=&#8221;80&#8243; animation_offset_bottom=&#8221;60&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;243773&#8243; back_color_type=&#8221;uncode-palette&#8221;][vc_column column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; style=&#8221;dark&#8221; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/1&#8243; uncode_shortcode_id=&#8221;197315&#8243;][vc_row_inner limit_content=&#8221;&#8221;][vc_column_inner column_width_percent=&#8221;100&#8243; gutter_size=&#8221;2&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;9\/12&#8243; uncode_shortcode_id=&#8221;158719&#8243;][vc_custom_heading text_font=&#8221;font-161747&#8243; text_size=&#8221;&#8221; text_weight=&#8221;400&#8243; text_space=&#8221;fontspace-111509&#8243; uncode_shortcode_id=&#8221;801665&#8243;]Our solutions in action[\/vc_custom_heading][vc_custom_heading heading_semantic=&#8221;p&#8221; text_size=&#8221;&#8221; uncode_shortcode_id=&#8221;126635&#8243;]<\/p>\n<div class=\"wpb_text_column\">\n<div class=\"wpb_wrapper\">\n<p>We solve typical production planning challenges, from detailed planning and resource allocation to production levelling. Our algorithms can not only completely recreate production plans, but also specifically improve existing plans that have been created manually.<\/p>\n<\/div>\n<\/div>\n<p>[\/vc_custom_heading][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; style=&#8221;dark&#8221; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_visibility=&#8221;yes&#8221; medium_width=&#8221;0&#8243; mobile_visibility=&#8221;yes&#8221; mobile_width=&#8221;0&#8243; width=&#8221;3\/12&#8243; uncode_shortcode_id=&#8221;595491&#8243;][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row unlock_row_content=&#8221;yes&#8221; row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;7&#8243; top_padding=&#8221;2&#8243; bottom_padding=&#8221;5&#8243; overlay_alpha=&#8221;50&#8243; equal_height=&#8221;yes&#8221; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; top_divider=&#8221;gradient&#8221; css_animation=&#8221;scroll-trigger&#8221; animation_scale_val=&#8221;100&#8243; animation_opacity=&#8221;0&#8243; animation_x=&#8221;0&#8243; animation_y=&#8221;0&#8243; animation_blur=&#8221;0&#8243; animation_rotate=&#8221;0&#8243; animation_perspective=&#8221;0&#8243; animation_offset_top=&#8221;80&#8243; animation_offset_bottom=&#8221;60&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;133023&#8243;][vc_column width=&#8221;1\/1&#8243;][vc_empty_space][vc_custom_heading heading_semantic=&#8221;h3&#8243; text_size=&#8221;&#8221; uncode_shortcode_id=&#8221;137552&#8243;]Detailed planning[\/vc_custom_heading][vc_tabs vertical=&#8221;yes&#8221; history=&#8221;yes&#8221; target=&#8221;row&#8221; tab_no_fade=&#8221;yes&#8221; typography=&#8221;advanced&#8221; tab_no_border=&#8221;yes&#8221; valign_middle=&#8221;yes&#8221; tab_custom_size=&#8221;yes&#8221; tab_size=&#8221;7&#8243; tab_gap=&#8221;3&#8243; custom_padding=&#8221;yes&#8221; gutter_tab=&#8221;1&#8243; accordion_bp=&#8221;yes&#8221; titles_font=&#8221;font-377884&#8243; titles_size=&#8221;h4&#8243; titles_weight=&#8221;600&#8243; excerpt_text_size=&#8221;yes&#8221; gutter_simple=&#8221;1&#8243; uncode_shortcode_id=&#8221;805288&#8243;][vc_tab icon=&#8221;fa fa-chevron-right&#8221; icon_size=&#8221;sm&#8221; gutter_size=&#8221;2&#8243; column_padding=&#8221;0&#8243; title=&#8221;Setup Time Optimisation&#8221; tab_id=&#8221;1671537737931-0-8167282400389617742602660441775049749812&#8243; excerpt=&#8221;<strong>Optimized Detailed Scheduling for Complex Setup Operations<\/strong> This optimisation scenario addresses detailed planning in discrete manufacturing with multiple parallel production lines. Set-up times occur between orders, which can vary depending on the machine \u2013 as can processing times. The algorithm also takes into account shift calendars, secondary resources and complex dependencies between orders in order to create realistic and efficient production plans.&#8221; slug=&#8221;design&#8221;][vc_single_image media=&#8221;174308&#8243; media_width_percent=&#8221;100&#8243; media_ratio=&#8221;sixteen-nine&#8221; shape=&#8221;img-round&#8221; radius=&#8221;lg&#8221; css_animation=&#8221;zoom-in&#8221; uncode_shortcode_id=&#8221;410570&#8243; el_class=&#8221;max-width-600&#8243;][\/vc_tab][vc_tab icon=&#8221;fa fa-chevron-right&#8221; icon_size=&#8221;sm&#8221; gutter_size=&#8221;2&#8243; column_padding=&#8221;0&#8243; title=&#8221;Food Production Scheduling&#8221; tab_id=&#8221;1671537737963-0-2167282400389617742602660441775049749812&#8243; excerpt=&#8221;<strong>Automated Planning for the Food and Beverage Industry<\/strong> This optimisation algorithm was developed specifically for typical scenarios in food and beverage production. It takes into account both the manufacture of intermediate products and their packaging \u2013 including limited capacities in intermediate storage facilities. Individual restrictions and priorities can be flexibly mapped, such as which products may be stored where or should be given preference.&#8221; slug=&#8221;look&#8221;][vc_single_image media=&#8221;174310&#8243; media_width_percent=&#8221;100&#8243; media_ratio=&#8221;sixteen-nine&#8221; shape=&#8221;img-round&#8221; radius=&#8221;lg&#8221; css_animation=&#8221;zoom-in&#8221; uncode_shortcode_id=&#8221;173493&#8243; el_class=&#8221;max-width-600&#8243;][\/vc_tab][vc_tab icon=&#8221;fa fa-chevron-right&#8221; icon_size=&#8221;sm&#8221; gutter_size=&#8221;2&#8243; column_padding=&#8221;0&#8243; title=&#8221;Process Industry Scheduling&#8221; tab_id=&#8221;1671537738002-0-10167282400389617742602660441775049749812&#8243; excerpt=&#8221;<strong>Intelligent Production Planning for the Process Industry<\/strong> This algorithm automatically creates optimised production plans for the process industry \u2013 for example, in mixed feed production. It takes into account factors such as contamination risks, time intervals between orders, incompatible sequences and the availability of equipment and intermediate storage facilities. The aim is to minimise delays, set-up costs and cleaning, ensure even utilisation of resources and avoid downtime.&#8221; slug=&#8221;trends&#8221;][vc_single_image media=&#8221;174447&#8243; media_width_percent=&#8221;100&#8243; media_ratio=&#8221;sixteen-nine&#8221; shape=&#8221;img-round&#8221; radius=&#8221;lg&#8221; css_animation=&#8221;zoom-in&#8221; uncode_shortcode_id=&#8221;903894&#8243; el_class=&#8221;max-width-600&#8243;][\/vc_tab][vc_tab icon=&#8221;fa fa-chevron-right&#8221; icon_size=&#8221;sm&#8221; gutter_size=&#8221;2&#8243; column_padding=&#8221;0&#8243; title=&#8221;Paint Shop Scheduling&#8221; tab_id=&#8221;1671537738002-0-101672824003896177426026604417750497498129&#8243; excerpt=&#8221;<strong>Efficient Planning for Paint Shops<\/strong><br \/>\nPlanning paint shops is complex \u2013 numerous parts have to be efficiently controlled through the plant every day. Goods carriers move via conveyor systems, and planning is done in rounds. Factors such as colour sequences, block sizes, cleaning processes, technical restrictions and parallel booths increase the complexity. The aim is to create a plan with as few colour changes and low costs as possible \u2013 taking into account deadlines and technical specifications. Our solution is suitable for painting systems in the automotive industry, the electronics industry and metal processing, among others.&#8221;][vc_single_image media=&#8221;174312&#8243; media_width_percent=&#8221;100&#8243; media_ratio=&#8221;sixteen-nine&#8221; shape=&#8221;img-round&#8221; radius=&#8221;lg&#8221; css_animation=&#8221;zoom-in&#8221; uncode_shortcode_id=&#8221;172320&#8243; el_class=&#8221;max-width-600&#8243;][\/vc_tab][vc_tab icon=&#8221;fa fa-chevron-right&#8221; icon_size=&#8221;sm&#8221; gutter_size=&#8221;2&#8243; column_padding=&#8221;0&#8243; title=&#8221;Artificial Teeth Scheduling&#8221; tab_id=&#8221;1671537738002-0-1016728240038961774260266044177504974981299&#8243; excerpt=&#8221;<strong>Optimized Rotary Line Scheduling for Dental Production<\/strong><br \/>\nIn circular systems for tooth production, the base material is injected into metal moulds and processed into raw teeth in a multi-stage cycle. The challenge lies in efficiently loading the system, taking into account delivery dates and the limited availability of the moulds as production aids. Similar planning requirements can also be found in rotary systems in the food, cosmetics, pharmaceutical, chemical and packaging industries.&#8221;][vc_single_image media=&#8221;174314&#8243; media_width_percent=&#8221;100&#8243; media_ratio=&#8221;sixteen-nine&#8221; shape=&#8221;img-round&#8221; radius=&#8221;lg&#8221; css_animation=&#8221;zoom-in&#8221; uncode_shortcode_id=&#8221;446291&#8243; el_class=&#8221;max-width-600&#8243;][\/vc_tab][\/vc_tabs][\/vc_column][\/vc_row][vc_row unlock_row_content=&#8221;yes&#8221; row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;7&#8243; top_padding=&#8221;5&#8243; bottom_padding=&#8221;5&#8243; back_color=&#8221;color-gyho&#8221; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; top_divider=&#8221;gradient&#8221; inverted_device_order=&#8221;yes&#8221; css_animation=&#8221;scroll-trigger&#8221; animation_scale_val=&#8221;100&#8243; animation_opacity=&#8221;0&#8243; animation_x=&#8221;0&#8243; animation_y=&#8221;0&#8243; animation_blur=&#8221;0&#8243; animation_rotate=&#8221;0&#8243; animation_perspective=&#8221;0&#8243; animation_offset_top=&#8221;80&#8243; animation_offset_bottom=&#8221;60&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;203246&#8243; back_color_type=&#8221;uncode-palette&#8221;][vc_column column_width_percent=&#8221;100&#8243; position_vertical=&#8221;middle&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/1&#8243; uncode_shortcode_id=&#8221;960013&#8243; el_class=&#8221;image-center&#8221;][vc_custom_heading heading_semantic=&#8221;h3&#8243; text_size=&#8221;&#8221; uncode_shortcode_id=&#8221;131647&#8243;]Resource allocation[\/vc_custom_heading][vc_tabs vertical=&#8221;yes&#8221; history=&#8221;yes&#8221; target=&#8221;row&#8221; tab_no_fade=&#8221;yes&#8221; typography=&#8221;advanced&#8221; tab_no_border=&#8221;yes&#8221; valign_middle=&#8221;yes&#8221; tab_custom_size=&#8221;yes&#8221; tab_size=&#8221;7&#8243; tab_gap=&#8221;3&#8243; custom_padding=&#8221;yes&#8221; gutter_tab=&#8221;1&#8243; accordion_bp=&#8221;yes&#8221; titles_font=&#8221;font-377884&#8243; titles_size=&#8221;h4&#8243; titles_weight=&#8221;600&#8243; excerpt_text_size=&#8221;yes&#8221; gutter_simple=&#8221;1&#8243; uncode_shortcode_id=&#8221;178844&#8243;][vc_tab icon=&#8221;fa fa-chevron-right&#8221; icon_size=&#8221;sm&#8221; gutter_size=&#8221;2&#8243; column_padding=&#8221;0&#8243; title=&#8221;Core Resource Assignment&#8221; tab_id=&#8221;1671537737931-0-81672824003896177426026604417750497498121775050100063&#8243; excerpt=&#8221;<strong>Automatic Allocation of Limited Resources Such as Tools<\/strong> A production plan can only be implemented if all the necessary resources are available \u2013 such as production aids or interim storage capacities. The allocation of these secondary resources, such as boilers, tanks or tools, is highly complex and almost impossible to manage manually. Mathematical optimisation can be used to find solutions in seconds that are significantly better than any manual planning \u2013 fast, precise and resource-efficient.&#8221;][vc_single_image media=&#8221;174316&#8243; media_width_percent=&#8221;100&#8243; media_ratio=&#8221;sixteen-nine&#8221; shape=&#8221;img-round&#8221; radius=&#8221;lg&#8221; css_animation=&#8221;zoom-in&#8221; uncode_shortcode_id=&#8221;665334&#8243; el_class=&#8221;max-width-600&#8243;][\/vc_tab][vc_tab icon=&#8221;fa fa-chevron-right&#8221; icon_size=&#8221;sm&#8221; gutter_size=&#8221;2&#8243; column_padding=&#8221;0&#8243; title=&#8221;Employee Resource Assignment&#8221; tab_id=&#8221;1671537737963-0-21672824003896177426026604417750497498121775050100063&#8243; excerpt=&#8221;<strong>Intelligent Staff Assignment<\/strong><\/p>\n<p>This algorithm assigns available employees to the appropriate workstations. Various objectives, such as employee or workstation priorities, qualifications or frequency of deployment, can be flexibly weighted and combined. The algorithm is called up from MCP Workforce Management, among other places, and ensures that planned orders are processed in the best possible way within a given time period.&#8221;][vc_single_image media=&#8221;174318&#8243; media_width_percent=&#8221;100&#8243; media_ratio=&#8221;sixteen-nine&#8221; shape=&#8221;img-round&#8221; radius=&#8221;lg&#8221; css_animation=&#8221;zoom-in&#8221; uncode_shortcode_id=&#8221;431545&#8243; el_class=&#8221;max-width-600&#8243;][\/vc_tab][\/vc_tabs][\/vc_column][\/vc_row][vc_row unlock_row_content=&#8221;yes&#8221; row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;7&#8243; top_padding=&#8221;5&#8243; bottom_padding=&#8221;5&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; top_divider=&#8221;gradient&#8221; css_animation=&#8221;scroll-trigger&#8221; animation_scale_val=&#8221;100&#8243; animation_opacity=&#8221;0&#8243; animation_x=&#8221;0&#8243; animation_y=&#8221;0&#8243; animation_blur=&#8221;0&#8243; animation_rotate=&#8221;0&#8243; animation_perspective=&#8221;0&#8243; animation_offset_top=&#8221;80&#8243; animation_offset_bottom=&#8221;60&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;285205&#8243;][vc_column width=&#8221;1\/1&#8243;][vc_custom_heading heading_semantic=&#8221;h3&#8243; text_size=&#8221;&#8221; uncode_shortcode_id=&#8221;142449&#8243;]From rough-cut planning to detailed scheduling[\/vc_custom_heading][vc_tabs vertical=&#8221;yes&#8221; history=&#8221;yes&#8221; target=&#8221;row&#8221; tab_no_fade=&#8221;yes&#8221; typography=&#8221;advanced&#8221; tab_no_border=&#8221;yes&#8221; valign_middle=&#8221;yes&#8221; tab_custom_size=&#8221;yes&#8221; tab_size=&#8221;7&#8243; tab_gap=&#8221;3&#8243; custom_padding=&#8221;yes&#8221; gutter_tab=&#8221;1&#8243; accordion_bp=&#8221;yes&#8221; titles_font=&#8221;font-377884&#8243; titles_size=&#8221;h4&#8243; titles_weight=&#8221;600&#8243; excerpt_text_size=&#8221;yes&#8221; gutter_simple=&#8221;1&#8243; uncode_shortcode_id=&#8221;169218&#8243;][vc_tab icon=&#8221;fa fa-chevron-right&#8221; icon_size=&#8221;sm&#8221; gutter_size=&#8221;2&#8243; column_padding=&#8221;0&#8243; title=&#8221;Production Leveling&#8221; tab_id=&#8221;1671537737931-0-816728240038961774260266044177504974981217750501000631775050170312&#8243; excerpt=&#8221;<strong>Smoothing Production Volumes According to the Heijunka Principle<\/strong><br \/>\nProduction levelling involves distributing production volume (total and per product) evenly across individual periods. A period can be, for example, a shift, a day, a week or a month. The aim is to achieve an even utilisation of production capacity and a high degree of flexibility in response to fluctuations in demand. Production levelling is an important part of implementing the Heijunka principle. However, levelling production also often plays a decisive role in long-term capacity\/production planning.&#8221;][vc_single_image media=&#8221;174320&#8243; media_width_percent=&#8221;100&#8243; media_ratio=&#8221;sixteen-nine&#8221; shape=&#8221;img-round&#8221; radius=&#8221;lg&#8221; css_animation=&#8221;zoom-in&#8221; uncode_shortcode_id=&#8221;706634&#8243; el_class=&#8221;max-width-600&#8243;][\/vc_tab][vc_tab icon=&#8221;fa fa-chevron-right&#8221; icon_size=&#8221;sm&#8221; gutter_size=&#8221;2&#8243; column_padding=&#8221;0&#8243; title=&#8221;Batch Optimisation&#8221; tab_id=&#8221;1671537737963-0-216728240038961774260266044177504974981217750501000631775050170312&#8243; excerpt=&#8221;<strong>Forming Compatible Batches for Complex Requirements<\/strong><br \/>\nWhen planning orders in batches \u2013 for example, for heat treatment in furnaces \u2013 compatible orders can be processed together. Restrictions such as availability, release dates, set-up times and capacities must be taken into account. The aim is to create optimal batches to minimise running and throughput times \u2013 while meeting the required deadlines. Typical areas of application are heat treatments in electronics manufacturing and in the chemical, metal and packaging industries.&#8221;][vc_single_image media=&#8221;174322&#8243; media_width_percent=&#8221;100&#8243; media_ratio=&#8221;sixteen-nine&#8221; shape=&#8221;img-round&#8221; radius=&#8221;lg&#8221; css_animation=&#8221;zoom-in&#8221; uncode_shortcode_id=&#8221;279003&#8243; el_class=&#8221;max-width-600&#8243;][\/vc_tab][\/vc_tabs][\/vc_column][\/vc_row][vc_row row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;2&#8243; top_padding=&#8221;7&#8243; bottom_padding=&#8221;7&#8243; back_image=&#8221;175397&#8243; overlay_color=&#8221;color-105898&#8243; overlay_alpha=&#8221;70&#8243; gutter_size=&#8221;4&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;164049&#8243; overlay_color_type=&#8221;uncode-palette&#8221;][vc_column column_width_use_pixel=&#8221;yes&#8221; position_vertical=&#8221;middle&#8221; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; style=&#8221;dark&#8221; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/1&#8243; column_width_pixel=&#8221;1000&#8243; uncode_shortcode_id=&#8221;278539&#8243;][vc_custom_heading text_size=&#8221;h3&#8243; uncode_shortcode_id=&#8221;139590&#8243;]Interested in one of our optimization solutions, or dealing with a different planning challenge?[\/vc_custom_heading][vc_column_text uncode_shortcode_id=&#8221;449170&#8243;]Let\u2019s talk and explore together what approach best fits your needs.[\/vc_column_text][vc_button size=&#8221;btn-lg&#8221; radius=&#8221;btn-circle&#8221; border_width=&#8221;0&#8243; uncode_shortcode_id=&#8221;945221&#8243; link=&#8221;url:https%3A%2F%2Fp-hud4k6.project.space%2Fen%2Fcontact%2F|title:Contact&#8221; el_class=&#8221;popmake-175542&#8243;]Let\u2019s talk about it[\/vc_button][\/vc_column][\/vc_row][vc_section overlay_alpha=&#8221;50&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;730912&#8243;][vc_row row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;2&#8243; top_padding=&#8221;5&#8243; bottom_padding=&#8221;2&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;148436&#8243;][vc_column column_width_percent=&#8221;100&#8243; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/1&#8243; uncode_shortcode_id=&#8221;344880&#8243;][vc_custom_heading text_color=&#8221;color-jevc&#8221; text_size=&#8221;&#8221; uncode_shortcode_id=&#8221;210737&#8243; text_color_type=&#8221;uncode-palette&#8221;]Easy Integration[\/vc_custom_heading][\/vc_column][\/vc_row][vc_row unlock_row_content=&#8221;yes&#8221; row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;7&#8243; top_padding=&#8221;2&#8243; bottom_padding=&#8221;5&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; top_divider=&#8221;gradient&#8221; css_animation=&#8221;scroll-trigger&#8221; animation_scale_val=&#8221;100&#8243; animation_opacity=&#8221;0&#8243; animation_x=&#8221;0&#8243; animation_y=&#8221;0&#8243; animation_blur=&#8221;0&#8243; animation_rotate=&#8221;0&#8243; animation_perspective=&#8221;0&#8243; animation_offset_top=&#8221;80&#8243; animation_offset_bottom=&#8221;60&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;945111&#8243;][vc_column column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/2&#8243; uncode_shortcode_id=&#8221;639263&#8243;][vc_custom_heading text_color=&#8221;color-jevc&#8221; heading_semantic=&#8221;h3&#8243; text_size=&#8221;&#8221; uncode_shortcode_id=&#8221;146118&#8243; text_color_type=&#8221;uncode-palette&#8221;]AI \u2026[\/vc_custom_heading][vc_custom_heading heading_semantic=&#8221;div&#8221; text_size=&#8221;&#8221; text_weight=&#8221;400&#8243; uncode_shortcode_id=&#8221;141999&#8243;]Production planning is full of complex optimisation problems. It requires solutions that are intelligent, efficient and robust.[\/vc_custom_heading][uncode_list icon=&#8221;fa fa-check&#8221; icon_color=&#8221;color-482803&#8243; uncode_shortcode_id=&#8221;154823&#8243; icon_color_type=&#8221;uncode-palette&#8221;]<\/p>\n<ul>\n<li><strong>Mathematical Optimization<\/strong>: Methods such as constraint programming and integer programming deliver exact, traceable solutions \u2013 ideal for structured planning problems with clear constraints.<\/li>\n<li><strong>Metaheuristics<\/strong>: Methods such as simulated annealing efficiently traverse large search spaces and offer a flexible alternative when classical methods reach their limits.<\/li>\n<li><strong>Machine Learning<\/strong>: Algorithms that learn from data, adapt and dynamically make better decisions. Complements mathematical optimisation methods with intelligent, data-driven support.<\/li>\n<\/ul>\n<p>[\/uncode_list][\/vc_column][vc_column width=&#8221;1\/2&#8243;][vc_custom_heading text_color=&#8221;color-jevc&#8221; heading_semantic=&#8221;h3&#8243; text_size=&#8221;&#8221; uncode_shortcode_id=&#8221;191452&#8243; text_color_type=&#8221;uncode-palette&#8221;]\u2026 as-a-Service[\/vc_custom_heading][vc_custom_heading heading_semantic=&#8221;p&#8221; text_size=&#8221;&#8221; text_weight=&#8221;400&#8243; uncode_shortcode_id=&#8221;103447&#8243;]Our solutions can be easily integrated into your planning module \u2013 all you need to do is set up the interface. No complex project, no extensive adjustments.[\/vc_custom_heading][uncode_list icon=&#8221;fa fa-check&#8221; icon_color=&#8221;color-482803&#8243; uncode_shortcode_id=&#8221;166297&#8243; icon_color_type=&#8221;uncode-palette&#8221;]<\/p>\n<ul>\n<li><strong>Input<\/strong>: Your planning data \u2013 e.g. orders, resources, set-up times \u2013 is transferred to our AI via a REST API. The call can be integrated directly into your existing software.<\/li>\n<li><strong>Optimization<\/strong>: The AI processes the data and calculates an optimised solution \u2013 quickly, reliably and transparently. Complex interdependencies are automatically taken into account.<\/li>\n<li><strong>Output<\/strong>: The result is an optimised production plan that is returned via the API \u2013 ready for display, further processing or transfer to downstream systems.<\/li>\n<\/ul>\n<p>[\/uncode_list][\/vc_column][\/vc_row][\/vc_section][vc_section back_color=&#8221;color-gyho&#8221; overlay_alpha=&#8221;50&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;125040&#8243; back_color_type=&#8221;uncode-palette&#8221;][vc_row row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;2&#8243; top_padding=&#8221;5&#8243; bottom_padding=&#8221;2&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;651519&#8243;][vc_column column_width_percent=&#8221;100&#8243; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/1&#8243; uncode_shortcode_id=&#8221;138307&#8243;][vc_custom_heading text_color=&#8221;color-jevc&#8221; text_size=&#8221;&#8221; uncode_shortcode_id=&#8221;121829&#8243; text_color_type=&#8221;uncode-palette&#8221;]Extensive expertise[\/vc_custom_heading][\/vc_column][\/vc_row][vc_row unlock_row_content=&#8221;yes&#8221; row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;3&#8243; top_padding=&#8221;2&#8243; bottom_padding=&#8221;2&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; top_divider=&#8221;gradient&#8221; css_animation=&#8221;scroll-trigger&#8221; animation_scale_val=&#8221;100&#8243; animation_opacity=&#8221;0&#8243; animation_x=&#8221;0&#8243; animation_y=&#8221;0&#8243; animation_blur=&#8221;0&#8243; animation_rotate=&#8221;0&#8243; animation_perspective=&#8221;0&#8243; animation_offset_top=&#8221;80&#8243; animation_offset_bottom=&#8221;60&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;189268&#8243; css=&#8221;.vc_custom_1776171631956{padding-bottom: 200px !important;}&#8221;][vc_column column_width_percent=&#8221;100&#8243; position_vertical=&#8221;middle&#8221; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; style=&#8221;light&#8221; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/1&#8243; uncode_shortcode_id=&#8221;122692&#8243;][vc_custom_heading text_color=&#8221;color-jevc&#8221; heading_semantic=&#8221;h3&#8243; text_size=&#8221;&#8221; uncode_shortcode_id=&#8221;209581&#8243; text_color_type=&#8221;uncode-palette&#8221;]Scientifically based[\/vc_custom_heading][vc_custom_heading heading_semantic=&#8221;p&#8221; text_size=&#8221;&#8221; text_weight=&#8221;400&#8243; uncode_shortcode_id=&#8221;156400&#8243;]<\/p>\n<div class=\"wpb_text_column\">\n<div class=\"wpb_wrapper\">\n<p>Through regular exchanges with scientific partners, the latest research findings are incorporated directly into our development work \u2013 resulting in solutions that are technologically advanced and scientifically sound.<\/p>\n<\/div>\n<\/div>\n<p>[\/vc_custom_heading][vc_tabs typography=&#8221;advanced&#8221; border_100=&#8221;yes&#8221; titles_size=&#8221;h5&#8243; uncode_shortcode_id=&#8221;171868&#8243; el_class=&#8221;wissenshaft-tabs&#8221;][vc_tab gutter_size=&#8221;2&#8243; column_padding=&#8221;2&#8243; title=&#8221;TU Vienna&#8221; tab_id=&#8221;1774264618-1-69&#8243;][vc_row_inner][vc_column_inner width=&#8221;1\/1&#8243;][vc_empty_space empty_h=&#8221;2&#8243;][\/vc_column_inner][\/vc_row_inner][vc_row_inner row_inner_height_percent=&#8221;0&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; inverted_device_order=&#8221;yes&#8221; uncode_shortcode_id=&#8221;126888&#8243;][vc_column_inner column_width_percent=&#8221;100&#8243; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/4&#8243; uncode_shortcode_id=&#8221;133060&#8243;][vc_single_image media=&#8221;173517&#8243; media_width_use_pixel=&#8221;yes&#8221; alignment=&#8221;right&#8221; uncode_shortcode_id=&#8221;179805&#8243; media_width_pixel=&#8221;700&#8243;][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; position_vertical=&#8221;middle&#8221; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;3\/4&#8243; uncode_shortcode_id=&#8221;101216&#8243;][vc_custom_heading heading_semantic=&#8221;h5&#8243; text_size=&#8221;h5&#8243; text_weight=&#8221;400&#8243; text_height=&#8221;fontheight-357766&#8243; uncode_shortcode_id=&#8221;150539&#8243;]From 2017 to early 2025, our Christian Doppler Laboratory was based at the Institute for Logic and Computation at TU Vienna. Together with Bosch and Ximes, we conducted basic research there on new algorithms and the use of AI in production planning.<\/p>\n<p>Since 2024, we have been funding a doctoral position at the Doctoral College iCAIML \u2013 thus remaining closely connected to research in areas such as hyperheuristics and automated algorithm selection.[\/vc_custom_heading][\/vc_column_inner][\/vc_row_inner][\/vc_tab][vc_tab gutter_size=&#8221;2&#8243; column_padding=&#8221;2&#8243; title=&#8221;KIT&#8221; tab_id=&#8221;1774264618-1-697&#8243;][vc_row_inner][vc_column_inner width=&#8221;1\/1&#8243;][vc_empty_space empty_h=&#8221;2&#8243;][\/vc_column_inner][\/vc_row_inner][vc_row_inner row_inner_height_percent=&#8221;0&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; inverted_device_order=&#8221;yes&#8221; uncode_shortcode_id=&#8221;146998&#8243;][vc_column_inner column_width_percent=&#8221;100&#8243; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/4&#8243; uncode_shortcode_id=&#8221;190581&#8243;][vc_single_image media=&#8221;173519&#8243; media_width_use_pixel=&#8221;yes&#8221; alignment=&#8221;center&#8221; uncode_shortcode_id=&#8221;150003&#8243; media_width_pixel=&#8221;300&#8243;][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; position_vertical=&#8221;middle&#8221; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;3\/4&#8243; uncode_shortcode_id=&#8221;124766&#8243;][vc_custom_heading heading_semantic=&#8221;h5&#8243; text_size=&#8221;h5&#8243; text_weight=&#8221;400&#8243; text_height=&#8221;fontheight-357766&#8243; uncode_shortcode_id=&#8221;114202&#8243;]Together with the Karlsruhe Institute of Technology (KIT), we are researching methods for making industrial processes more energy-flexible. The aim is to develop AI-supported methods that make energy-intensive production more grid-friendly and sustainable. This collaboration combines our many years of expertise in APS with KIT\u2019s cutting-edge research in the field of energy system design. As an implementation partner, we translate research results into industrial practice \u2013 for sustainable production.[\/vc_custom_heading][\/vc_column_inner][\/vc_row_inner][\/vc_tab][vc_tab gutter_size=&#8221;2&#8243; column_padding=&#8221;2&#8243; title=&#8221;University of Duisburg-Essen&#8221; tab_id=&#8221;1774267976200-2-5&#8243;][vc_row_inner row_inner_height_percent=&#8221;0&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; inverted_device_order=&#8221;yes&#8221; uncode_shortcode_id=&#8221;359041&#8243;][vc_column_inner column_width_percent=&#8221;100&#8243; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/2&#8243; uncode_shortcode_id=&#8221;324775&#8243;][vc_single_image media=&#8221;173523&#8243; media_width_use_pixel=&#8221;yes&#8221; alignment=&#8221;right&#8221; uncode_shortcode_id=&#8221;211083&#8243; media_width_pixel=&#8221;500&#8243;][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; position_vertical=&#8221;middle&#8221; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/2&#8243; uncode_shortcode_id=&#8221;755041&#8243;][vc_custom_heading heading_semantic=&#8221;h5&#8243; text_size=&#8221;h5&#8243; text_weight=&#8221;400&#8243; text_height=&#8221;fontheight-357766&#8243; uncode_shortcode_id=&#8221;210280&#8243;]<\/p>\n<p style=\"text-align: left;\">Selected Research Areas<\/p>\n<ul>\n<li style=\"text-align: left;\">Stochastic modeling<\/li>\n<li style=\"text-align: left;\">Capacity Planning<\/li>\n<\/ul>\n<p>[\/vc_custom_heading][\/vc_column_inner][\/vc_row_inner][\/vc_tab][vc_tab gutter_size=&#8221;2&#8243; column_padding=&#8221;2&#8243; title=&#8221;University of Siegen&#8221; tab_id=&#8221;1774268022091-3-8&#8243;][vc_row_inner row_inner_height_percent=&#8221;0&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; inverted_device_order=&#8221;yes&#8221; uncode_shortcode_id=&#8221;181543&#8243;][vc_column_inner column_width_percent=&#8221;100&#8243; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/2&#8243; uncode_shortcode_id=&#8221;141720&#8243;][vc_single_image media=&#8221;173521&#8243; media_width_use_pixel=&#8221;yes&#8221; alignment=&#8221;right&#8221; uncode_shortcode_id=&#8221;174919&#8243; media_width_pixel=&#8221;500&#8243;][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; position_vertical=&#8221;middle&#8221; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/2&#8243; uncode_shortcode_id=&#8221;824140&#8243;][vc_custom_heading heading_semantic=&#8221;h5&#8243; text_size=&#8221;h5&#8243; text_weight=&#8221;400&#8243; text_height=&#8221;fontheight-357766&#8243; uncode_shortcode_id=&#8221;832112&#8243;]<\/p>\n<div style=\"text-align: left;\">Selected Research Areas:<\/div>\n<ul>\n<li style=\"text-align: left;\">Human Factors<\/li>\n<li style=\"text-align: left;\">Industry 4.0<\/li>\n<li style=\"text-align: left;\">Decision Analysis<\/li>\n<li style=\"text-align: left;\">Project Management<\/li>\n<\/ul>\n<p>[\/vc_custom_heading][\/vc_column_inner][\/vc_row_inner][\/vc_tab][\/vc_tabs][\/vc_column][\/vc_row][vc_row unlock_row_content=&#8221;yes&#8221; row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;5&#8243; top_padding=&#8221;3&#8243; bottom_padding=&#8221;5&#8243; back_color=&#8221;color-482803&#8243; overlay_color=&#8221;color-482803&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;738489&#8243; overlay_color_type=&#8221;uncode-palette&#8221; back_color_type=&#8221;uncode-palette&#8221;][vc_column width=&#8221;1\/1&#8243;][vc_empty_space empty_h=&#8221;2&#8243;][vc_custom_heading text_color=&#8221;color-jevc&#8221; heading_semantic=&#8221;h3&#8243; text_size=&#8221;&#8221; uncode_shortcode_id=&#8221;190388&#8243; text_color_type=&#8221;uncode-palette&#8221;]Tried and tested[\/vc_custom_heading][vc_custom_heading heading_semantic=&#8221;p&#8221; text_size=&#8221;&#8221; text_weight=&#8221;400&#8243; uncode_shortcode_id=&#8221;159446&#8243;]Thanks to our many years of experience from numerous industrial projects, we know exactly what matters in production planning.<\/p>\n<p>We are familiar with the specific requirements and typical challenges of many industries, from mixed feed production to the food industry, from cosmetics and pharmaceuticals to electronics manufacturing.<\/p>\n<p>Our solutions are proven, robust and flexible enough to deliver real added value in complex production environments as well as in software products for these industries.[\/vc_custom_heading][vc_empty_space empty_h=&#8221;2&#8243;][vc_gallery el_id=&#8221;gallery-23456&#8243; 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position_vertical=&#8221;middle&#8221; gutter_size=&#8221;1&#8243; style=&#8221;dark&#8221; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;3\/4&#8243; uncode_shortcode_id=&#8221;124827&#8243; css=&#8221;.vc_custom_1775818714994{padding-right: 80px !important;}&#8221;][vc_custom_heading text_color=&#8221;color-jevc&#8221; heading_semantic=&#8221;h5&#8243; text_size=&#8221;h5&#8243; text_weight=&#8221;400&#8243; text_height=&#8221;fontheight-524109&#8243; uncode_shortcode_id=&#8221;665846&#8243; text_color_type=&#8221;uncode-palette&#8221;]<strong>Optwisier A.I. Solutions\u2019 <\/strong>modern supply chain planning software relies on optimisation algorithms from MCP.<br \/>\nOur solution enables automated detailed planning specifically for the food industry, including multi-stage processes that take setup times and intermediate storage capacities into account.[\/vc_custom_heading][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; position_vertical=&#8221;middle&#8221; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/4&#8243; uncode_shortcode_id=&#8221;178986&#8243;][vc_single_image media=&#8221;174455&#8243; media_width_use_pixel=&#8221;yes&#8221; alignment=&#8221;center&#8221; uncode_shortcode_id=&#8221;809382&#8243; media_width_pixel=&#8221;500&#8243;][\/vc_column_inner][\/vc_row_inner][vc_row_inner row_inner_height_percent=&#8221;0&#8243; 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text_color_type=&#8221;uncode-palette&#8221;]Our optimization technology integrates seamlessly with Siemens <strong>Opcenter APS.<\/strong><br \/>\nWhether for detailed planning or resource allocation, complex planning problems are solved directly in the existing system\u2014efficiently and flexibly, with minimal effort for the user.[\/vc_custom_heading][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; position_vertical=&#8221;middle&#8221; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/4&#8243; uncode_shortcode_id=&#8221;168668&#8243;][vc_single_image media=&#8221;173532&#8243; media_width_use_pixel=&#8221;yes&#8221; uncode_shortcode_id=&#8221;425373&#8243; media_width_pixel=&#8221;500&#8243;][\/vc_column_inner][\/vc_row_inner][vc_row_inner row_inner_height_percent=&#8221;0&#8243; 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text_color_type=&#8221;uncode-palette&#8221;]The Employee Resource Assignment algorithm is used in<strong> MCP Workforce Management.<\/strong><br \/>\nThis ensures that the right people are in the right place at the right time\u2014which not only increases efficiency but also improves adherence to deadlines and utilisation.[\/vc_custom_heading][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; position_vertical=&#8221;middle&#8221; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/4&#8243; uncode_shortcode_id=&#8221;623286&#8243; css=&#8221;.vc_custom_1778140207422{padding-top: 30px !important;padding-right: 30px !important;padding-bottom: 30px !important;padding-left: 30px !important;}&#8221;][vc_single_image media=&#8221;175873&#8243; media_width_use_pixel=&#8221;yes&#8221; uncode_shortcode_id=&#8221;178324&#8243; media_width_pixel=&#8221;500&#8243;][\/vc_column_inner][\/vc_row_inner][vc_row_inner row_inner_height_percent=&#8221;0&#8243; overlay_alpha=&#8221;50&#8243; equal_height=&#8221;yes&#8221; gutter_size=&#8221;1&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; uncode_shortcode_id=&#8221;609436&#8243; el_class=&#8221;no-padding&#8221; css=&#8221;.vc_custom_1775820014902{padding-right: 60px !important;padding-left: 60px !important;}&#8221;][vc_column_inner column_width_percent=&#8221;100&#8243; position_vertical=&#8221;middle&#8221; gutter_size=&#8221;3&#8243; style=&#8221;dark&#8221; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;3\/4&#8243; uncode_shortcode_id=&#8221;756602&#8243; css=&#8221;.vc_custom_1775818765465{padding-right: 80px !important;}&#8221;][vc_custom_heading text_color=&#8221;color-jevc&#8221; heading_semantic=&#8221;h5&#8243; text_size=&#8221;h5&#8243; text_weight=&#8221;400&#8243; text_height=&#8221;fontheight-524109&#8243; uncode_shortcode_id=&#8221;229133&#8243; text_color_type=&#8221;uncode-palette&#8221;]<\/p>\n<p style=\"text-align: left;\">GR\u00dcN GQM uses MCP&#8217;s AI-as-a-Service in its established MES software for the food and beverage industry.<\/p>\n<p>Automatic line sequence optimization enables planners to improve existing schedules at the click of a button\u2014resulting in shorter setup times and improved on-time delivery.<br \/>\nFor end customers, this means increased capacity in both production and planning.[\/vc_custom_heading][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; position_vertical=&#8221;middle&#8221; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/4&#8243; uncode_shortcode_id=&#8221;152949&#8243;][vc_single_image media=&#8221;173528&#8243; media_width_use_pixel=&#8221;yes&#8221; uncode_shortcode_id=&#8221;213754&#8243; media_width_pixel=&#8221;500&#8243;][\/vc_column_inner][\/vc_row_inner][\/uncode_slider][vc_empty_space empty_h=&#8221;5&#8243;][\/vc_column][\/vc_row][vc_section back_color=&#8221;color-xsdn&#8221; overlay_alpha=&#8221;50&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;873098&#8243; back_color_type=&#8221;uncode-palette&#8221;][vc_row unlock_row_content=&#8221;yes&#8221; row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;5&#8243; top_padding=&#8221;2&#8243; bottom_padding=&#8221;7&#8243; overlay_alpha=&#8221;50&#8243; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;480862&#8243;][vc_column column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/1&#8243; uncode_shortcode_id=&#8221;230828&#8243;][vc_empty_space empty_h=&#8221;4&#8243;][vc_row_inner][vc_column_inner column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; style=&#8221;dark&#8221; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;2\/3&#8243; uncode_shortcode_id=&#8221;804382&#8243;][vc_custom_heading text_color=&#8221;color-jevc&#8221; text_size=&#8221;&#8221; uncode_shortcode_id=&#8221;144079&#8243; text_color_type=&#8221;uncode-palette&#8221;]Frequently Asked Questions about AI\u2011as\u2011a\u2011Service[\/vc_custom_heading][\/vc_column_inner][vc_column_inner column_width_percent=&#8221;100&#8243; gutter_size=&#8221;3&#8243; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/3&#8243; uncode_shortcode_id=&#8221;156221&#8243;][vc_button size=&#8221;btn-lg&#8221; radius=&#8221;btn-circle&#8221; border_width=&#8221;0&#8243; uncode_shortcode_id=&#8221;186826&#8243; link=&#8221;url:https%3A%2F%2Fp-hud4k6.project.space%2Fsunstice%2Ffaq-ai-as-a-service%2F|title:FAQ%20AI-as-a-Service&#8221;]View all FAQs and technical details[\/vc_button][\/vc_column_inner][\/vc_row_inner][vc_empty_space empty_h=&#8221;2&#8243; medium_visibility=&#8221;yes&#8221; mobile_visibility=&#8221;yes&#8221;][vc_accordion typography=&#8221;advanced&#8221; gutter_simple=&#8221;1&#8243; active_txt_color=&#8221;color-jevc&#8221; heading_semantic=&#8221;h4&#8243; uncode_shortcode_id=&#8221;861520&#8243; active_txt_color_type=&#8221;uncode-palette&#8221;][vc_accordion_tab gutter_size=&#8221;2&#8243; column_padding=&#8221;2&#8243; title=&#8221;When does MCP AI\u2011as\u2011a\u2011Service pay off economically?&#8221; tab_id=&#8221;1773739771-1-801777360410644a448-3b05&#8243;][vc_column_text text_color=&#8221;color-jevc&#8221; uncode_shortcode_id=&#8221;187490&#8243; text_color_type=&#8221;uncode-palette&#8221;]<\/p>\n<p data-local-id=\"522594ba7432\" data-prosemirror-content-type=\"node\" data-prosemirror-node-name=\"paragraph\" data-prosemirror-node-block=\"true\" data-pm-slice=\"1 1 []\">\n<style>\na {<br \/>    text-decoration: none;<br \/>    color: #464feb;<br \/>}<br \/>tr th, tr td {<br \/>    border: 1px solid #e6e6e6;<br \/>}<br \/>tr th {<br \/>    background-color: #f5f5f5;<br \/>}<br \/><\/style>\n<\/p>\n<div>\n<p>AI\u2011as\u2011a\u2011Service is particularly worthwhile when existing planning systems no longer deliver stable or economically optimal results. This is typically the case when:<\/p>\n<ul>\n<li>production plans require frequent manual adjustments<\/li>\n<li>bottlenecks and conflicting objectives cannot be resolved systematically<\/li>\n<li>existing APS solutions or rule\u2011based approaches reach their limits under high complexity<\/li>\n<\/ul>\n<p>&gt; In these situations, AI\u2011based optimization enables significant improvements in on\u2011time delivery, resource utilization, and planning stability.<\/p>\n<p>If these challenges currently exist, a structured potential assessment is the most sensible next step.<\/p>\n<\/div>\n<p>[\/vc_column_text][\/vc_accordion_tab][vc_accordion_tab gutter_size=&#8221;2&#8243; column_padding=&#8221;2&#8243; title=&#8221;Which planning problems can be solved with MCP AI\u2011as\u2011a\u2011Service?&#8221; tab_id=&#8221;1773740711161-2-61777360410644a448-3b05&#8243;][vc_column_text uncode_shortcode_id=&#8221;146779&#8243;]<\/p>\n<p data-local-id=\"287cf5660c20\" data-prosemirror-content-type=\"node\" data-prosemirror-node-name=\"paragraph\" data-prosemirror-node-block=\"true\">AI\u2011as\u2011a\u2011Service is well suited for complex planning problems with many dependencies, constraints, and conflicting objectives. Typical use cases include:<\/p>\n<div>\n<ul>\n<li>Production scheduling \/ detailed production planning<\/li>\n<li>Setup time optimization<\/li>\n<li>Resource allocation (machines, tools, personnel)<\/li>\n<li>Production leveling and batch optimization<\/li>\n<\/ul>\n<p>Wherever classical planning logic reaches its limits, AI\u2011based optimization opens up entirely new solution spaces.<\/p>\n<\/div>\n<p>[\/vc_column_text][\/vc_accordion_tab][vc_accordion_tab gutter_size=&#8221;2&#8243; column_padding=&#8221;2&#8243; title=&#8221;What distinguishes AI\u2011based optimization from classical APS rules?&#8221; tab_id=&#8221;1777361162242-9-4a448-3b05&#8243;][vc_column_text uncode_shortcode_id=&#8221;181410&#8243;]<\/p>\n<p data-local-id=\"0177734256f4\" data-prosemirror-content-type=\"node\" data-prosemirror-node-name=\"paragraph\" data-prosemirror-node-block=\"true\" data-pm-slice=\"1 3 []\">\n<style>\na {<br \/>    text-decoration: none;<br \/>    color: #464feb;<br \/>}<br \/>tr th, tr td {<br \/>    border: 1px solid #e6e6e6;<br \/>}<br \/>tr th {<br \/>    background-color: #f5f5f5;<br \/>}<br \/><\/style>\n<\/p>\n<div>\n<p>Classical heuristic rules make planning decisions step by step and on a local basis.<br \/>\nAI\u2011based optimization, by contrast, evaluates the entire planning scenario at once and systematically accounts for interactions between resources, schedules, and constraints. The result:<\/p>\n<ul>\n<li>more stable production plans<\/li>\n<li>better resource utilization<\/li>\n<li>reduced conflicts between objectives<\/li>\n<li>higher overall economic quality of planning<\/li>\n<\/ul>\n<\/div>\n<p>[\/vc_column_text][\/vc_accordion_tab][\/vc_accordion][vc_empty_space empty_h=&#8221;3&#8243;][\/vc_column][\/vc_row][\/vc_section][vc_row row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;2&#8243; top_padding=&#8221;7&#8243; bottom_padding=&#8221;7&#8243; back_image=&#8221;172458&#8243; overlay_color=&#8221;color-105898&#8243; overlay_alpha=&#8221;85&#8243; gutter_size=&#8221;4&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; content_parallax=&#8221;0&#8243; uncode_shortcode_id=&#8221;151481&#8243; overlay_color_type=&#8221;uncode-palette&#8221;][vc_column column_width_use_pixel=&#8221;yes&#8221; position_vertical=&#8221;middle&#8221; align_horizontal=&#8221;align_center&#8221; gutter_size=&#8221;3&#8243; style=&#8221;dark&#8221; overlay_alpha=&#8221;50&#8243; shift_x=&#8221;0&#8243; shift_y=&#8221;0&#8243; shift_y_down=&#8221;0&#8243; z_index=&#8221;0&#8243; medium_width=&#8221;0&#8243; mobile_width=&#8221;0&#8243; width=&#8221;1\/1&#8243; column_width_pixel=&#8221;800&#8243; uncode_shortcode_id=&#8221;125930&#8243;][vc_custom_heading text_size=&#8221;&#8221; uncode_shortcode_id=&#8221;666494&#8243;]Your Next Step Toward Optimized Production Planning with AI[\/vc_custom_heading][vc_column_text uncode_shortcode_id=&#8221;460674&#8243;]In a structured discussion, we\u2019ll analyze your planning challenges and show you how optimization algorithms can specifically enhance your existing systems and deliver measurable improvements. [\/vc_column_text][vc_button size=&#8221;btn-lg&#8221; radius=&#8221;btn-circle&#8221; border_width=&#8221;0&#8243; uncode_shortcode_id=&#8221;179587&#8243; el_class=&#8221;popmake-175542&#8243; link=&#8221;url:https%3A%2F%2Fp-hud4k6.project.space%2Fen%2Fcontact%2F|title:Contact&#8221;]Schedule a Potential Analysis[\/vc_button][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>[vc_row unlock_row_content=&#8221;yes&#8221; row_height_percent=&#8221;0&#8243; override_padding=&#8221;yes&#8221; h_padding=&#8221;7&#8243; top_padding=&#8221;7&#8243; bottom_padding=&#8221;7&#8243; back_color=&#8221;color-210407&#8243; back_image=&#8221;173515&#8243; overlay_color=&#8221;color-210407&#8243; overlay_alpha=&#8221;75&#8243; overlay_animated=&#8221;yes&#8221; overlay_animated_size=&#8221;0.7&#8243; gutter_size=&#8221;3&#8243; column_width_percent=&#8221;100&#8243; shift_y=&#8221;0&#8243; z_index=&#8221;0&#8243; bottom_divider=&#8221;gradient&#8221; 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