{"id":2102,"date":"2024-04-09T08:27:46","date_gmt":"2024-04-09T07:27:46","guid":{"rendered":"https:\/\/www.flumiq.ch\/?p=2102"},"modified":"2024-04-16T15:10:11","modified_gmt":"2024-04-16T14:10:11","slug":"productivity-why-is-average-not-good-enough","status":"publish","type":"post","link":"https:\/\/www.flumiq.ch\/en\/2024\/04\/09\/productivity-why-is-average-not-good-enough\/","title":{"rendered":"Productivity: Why is average not good enough?"},"content":{"rendered":"

[et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” custom_margin=”0px||0px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][et_pb_row custom_padding_last_edited=”on|tablet” _builder_version=”4.21.0″ _module_preset=”default” custom_padding_tablet=”” custom_padding_phone=”0px||||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.20.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.0″ _module_preset=”default” link_font=”|300|||||||” link_text_color=”#6667ac” link_font_size=”18px” link_line_height=”25px” global_colors_info=”{}”]back to overview<\/a>[\/et_pb_text][et_pb_text content_last_edited=”off|desktop” admin_label=”Text” _builder_version=”4.24.2″ _module_preset=”default” text_font=”|300|||||||” text_text_color=”#000000″ text_font_size=”18px” text_line_height=”25px” header_font=”||||||||” header_text_color=”#6667ac” header_font_size=”35px” header_line_height=”32px” header_2_font=”||||||||” header_2_text_color=”#6667ac” width=”80%” width_tablet=”80%” width_phone=”100%” width_last_edited=”on|phone” custom_margin_tablet=”” custom_margin_phone=”” custom_margin_last_edited=”on|desktop” custom_padding=”||0px|||” custom_padding_tablet=”” custom_padding_phone=”|0px||0px|false|false” custom_padding_last_edited=”on|phone” header_font_size_tablet=”35px” header_font_size_phone=”26px” header_font_size_last_edited=”on|phone” header_line_height_tablet=”32px” header_line_height_phone=”30px” header_line_height_last_edited=”on|phone” module_alignment_tablet=”” module_alignment_phone=”” module_alignment_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\n

Productivity management<\/strong> for a warehouse operation is a very sensitive<\/strong> topic. It can lead into frustration<\/strong> of the workforce rather than the expected improvements<\/strong>. In this post we\u2019ll explore the hurdles<\/strong> through the example of a picking process using the typical metric \u201cpicks per hour\u201d. A different approach will show, how this can be turned into a continuous improvement process<\/strong> with potential for even more benefits.<\/p>\n

[\/et_pb_text][et_pb_image src=”https:\/\/www.flumiq.ch\/wp-content\/uploads\/2024\/04\/AdobeStock_475490286_1920.jpg” alt=”Treasure Hunt in the Warehouse” title_text=”Average Color Mix” _builder_version=”4.24.2″ _module_preset=”default” width=”80%” global_colors_info=”{}”][\/et_pb_image][et_pb_text content_last_edited=”off|desktop” admin_label=”Text” _builder_version=”4.24.2″ _module_preset=”default” text_font=”|300|||||||” text_text_color=”#000000″ text_font_size=”18px” text_line_height=”25px” header_font=”||||||||” header_text_color=”#6667ac” header_font_size=”35px” header_line_height=”32px” header_2_font=”||||||||” header_2_text_color=”#6667ac” width=”80%” width_tablet=”80%” width_phone=”100%” width_last_edited=”on|phone” custom_margin_tablet=”” custom_margin_phone=”” custom_margin_last_edited=”on|desktop” custom_padding=”||0px|||” custom_padding_tablet=”” custom_padding_phone=”|0px||0px|false|false” custom_padding_last_edited=”on|phone” header_font_size_tablet=”35px” header_font_size_phone=”26px” header_font_size_last_edited=”on|phone” header_line_height_tablet=”32px” header_line_height_phone=”30px” header_line_height_last_edited=”on|phone” module_alignment_tablet=”” module_alignment_phone=”” module_alignment_last_edited=”on|phone” global_colors_info=”{}”]<\/p>\n

An average \u201cPicks per hour\u201d fails as productivity metric<\/h2>\n

When we compare the “picks per hour<\/strong>” metric across different hours or between different pickers, we often ignore crucial details. This comparison comes with the assumption<\/strong> that each picker’s tasks, from walking distances to quantities and types of items picked, are identical<\/strong>. We are calculating a flat average<\/strong>. Variables like weight, volume, and physical manageability of articles can vary significantly, making a direct comparison misleading. Such a flattened view doesn’t cope with the dynamic nature of logistics<\/strong>, particularly over short time frames.<\/p>\n

Over the long term, one might argue that the variations in tasks balance out. However, this perspective doesn’t consider external factors that significantly influence productivity metrics. Seasonal demand fluctuations<\/strong>, evolving order structures<\/strong>, and changes in the article portfolio<\/strong> can all impact the relevance and accuracy of using “picks per hour” as a productivity measure.<\/p>\n

Even in a best-case scenario, where the variations are as small, that the average works out, the accuracy doesn\u2019t allow to dive deeper for a root cause analysis<\/strong> in case anomalies would be identified.<\/p>\n

Through the eyes of the warehouse associates<\/strong> the mentioned variations are very evident. Employees are more likely to remember and react to situations that make their work harder<\/strong>, such as extraordinary walking distances or dealing with cumbersome articles. These challenging instances stand out more than days when the pick lists are rather business-as-usual. A metric like \u201cpicks per hour\u201d, which doesn\u2019t consider those elements, is seen as unfair<\/strong> and will raise negative emotions.<\/p>\n

Moreover, events perceived as disruptions<\/strong> or inconveniences<\/strong>, like needing to change the battery on equipment, can be interpreted as personal setbacks<\/strong>. As a result, rather than fostering a constructive dialogue about productivity and workplace improvements, the “picks per hour” metric may incite resistance<\/strong> and negative emotions<\/strong>.<\/p>\n

An efficiency based approach overcomes the issues<\/h2>\n

nstead of the flat average metric \u201cpicks per hour\u201d efficiency evaluates how actual performance<\/strong> compares to what was expected<\/strong> under given conditions. The result is a percentage, above 100% is better than expected, below is worse. It looks similar, but the beauty is that the calculation of efficiency can be simplified to a comparison of expected effort to actual effort<\/strong>. If we consider walking distance, picked quantity, number of picks, weight, volume, physical manageability etc. for the expected effort, we are overcoming the issues created by the simplifying metric. We take care about the dynamics<\/strong> and worker’s impressions<\/strong> mentioned earlier on.<\/p>\n

Implementing such a solution might be easier<\/strong> than expected. he flumiq 3P model is a strategic approach to calculate expected effort per warehouse task with high granularity. It leverages historical data<\/strong> from the WMS or other sources. This data-driven approach ensures that the expectations are grounded in reality<\/strong>, reflecting the actual performance levels achieved in the past by this specific operation. Such realistic benchmarks are building trust<\/strong> and transparency<\/strong> rather than negative emotions.<\/p>\n

The event impact analysis<\/strong> takes care about the disruptions and inconveniences, no matter if the are reported directly by the warehouse associates through a shopfloor application or if they are identified based on the low efficiency at a particular point in time. Visibility<\/strong>, classification<\/strong>, quantification<\/strong> of the impact will lead almost instantly to improvements<\/strong>. Involving the workforce in this process, especially in understanding and addressing the disruptions, can lead to more effective solutions and foster a more engaged and collaborative workplace culture<\/strong>.<\/p>\n

Aim for improvements and further benefits<\/h2>\n

With this approach productivity management<\/strong> is NOT about performance management, it\u2019s about continuous improvement<\/strong> and fosters a collaborative culture. The risk of negative emotions turns into a motivation booster<\/strong>.<\/p>\n

Utilizing expected task times as a basis for productivity management in the warehouse is indeed a strategic approach that can offer additional benefits. Capacity management<\/strong> is a typical use case with the objective \u201cjust enough\u201d. The data can also be instrumental upstream in the supply chain<\/strong>, particularly for pricing and portfolio decisions.<\/p>\n

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Productivity management for a warehouse operation is a very sensitive topic. It can lead into frustration of the workforce rather than the expected improvements. In this post we\u2019ll explore the hurdles and show a solution approach, which leads to a continuous improvement process and a motivation booster.<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[38,41,37],"tags":[],"class_list":["post-2102","post","type-post","status-publish","format-standard","hentry","category-logistics","category-productivity","category-warehousing"],"yoast_head":"\nProductivity: Why is average not good enough? - flumiq<\/title>\n<meta name=\"description\" content=\"Productivity management for a warehouse operation is a very sensitive topic. 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