[{"data":1,"prerenderedAt":72},["ShallowReactive",2],{"legacy:insights\u002Fwms-wave-picking-accuracy:en":3},{"id":4,"title":5,"description":6,"keywords":7,"author":8,"robots":9,"bodyClass":10,"image":11,"jsonld":12,"html":71},"insights\u002Fwms-wave-picking-accuracy","Night-shift errors cluster—when wave, bin, and check are misaligned, training cannot patch structure - JEDP Digital Platform","If the picking accuracy cannot improve, it is mostly due to overly large waves, similar items being adjacent, or the review only involving signing. First, classify according to error types, then adjust wave segmentation, location isolation, and dynamic review, and use the same inventory account for FIFO\u002Fbatch verification to avoid disguising progress with inflated figures.","Warehouse Picking Accuracy, WMS Wave Picking, Picking Errors, Warehouse Outbound Verification","Shenzhen Langbowan Information Technology Co., Ltd.","index, follow","page-insights","https:\u002F\u002Fwww.jedp.cn\u002Fres\u002Fimages\u002Fproduct\u002Fwms-slide6.webp",[13,31,45],{"@context":14,"@type":15,"@id":16,"headline":17,"description":6,"inLanguage":18,"datePublished":19,"dateModified":20,"articleSection":21,"keywords":22,"image":11,"author":23,"publisher":25,"isPartOf":26,"mainEntityOfPage":28},"https:\u002F\u002Fschema.org","Article","https:\u002F\u002Fwww.jedp.cn\u002Finsights\u002Fwms-wave-picking-accuracy#article","Night-shift errors cluster—when wave, bin, and check are misaligned, training cannot patch structure","zh-CN","2026-05-28","2026-10-17","WMS · Warehouse","warehouse picking accuracy, WMS wave picking, picking errors, warehouse outbound verification",{"@id":24},"https:\u002F\u002Fwww.jedp.cn\u002F#organization",{"@id":24},{"@id":27},"https:\u002F\u002Fwww.jedp.cn\u002Finsights#collection",{"@type":29,"@id":30},"WebPage","https:\u002F\u002Fwww.jedp.cn\u002Finsights\u002Fwms-wave-picking-accuracy",{"@context":14,"@type":32,"itemListElement":33},"BreadcrumbList",[34,39,43],{"@type":35,"position":36,"name":37,"item":38},"ListItem",1,"Home","https:\u002F\u002Fwww.jedp.cn\u002F",{"@type":35,"position":40,"name":41,"item":42},2,"Insights","https:\u002F\u002Fwww.jedp.cn\u002Finsights",{"@type":35,"position":44,"name":17,"item":30},3,{"@context":14,"@type":46,"@id":47,"mainEntity":48},"FAQPage","https:\u002F\u002Fwww.jedp.cn\u002Finsights\u002Fwms-wave-picking-accuracy#faq",[49,55,59,63,67],{"@type":50,"name":51,"acceptedAnswer":52},"Question","Does every order need to be reviewed?",{"@type":53,"text":54},"Answer","High-risk clients, expiration dates, high value, and frequent historical errors are recommended for full inspection or dual review; low-risk items can be spot-checked, but the proportion and sampling method must be auditable and not predictable on site.",{"@type":50,"name":56,"acceptedAnswer":57},"How to choose between seeding and picking methods?",{"@type":53,"text":58},"Stable order mix and concentrated SKUs favor put-to-wall (batch\u002Fsow); high-mix low-volume often favors pick-to-order (or a hybrid). Decide from pilot error and labor metrics, not slogans.",{"@type":50,"name":60,"acceptedAnswer":61},"Can paper-based picking achieve high accuracy?",{"@type":53,"text":62},"It can reach a certain level, but it is difficult to trace back to the person and the time, and it is also hard to intercept immediately in case of misclassification\u002Fmistakes. The value of the system lies in interception and record-keeping, not just printing tasks.",{"@type":50,"name":64,"acceptedAnswer":65},"How to set the accuracy target?",{"@type":53,"text":66},"First stabilize the statistical criteria (row level, including in-warehouse findings and customer rejections), then backtrack the target based on claims and line stoppage costs; announcing the target first and then changing the criteria is equivalent to inflating the numbers.",{"@type":50,"name":68,"acceptedAnswer":69},"Will accuracy and FIFO conflict with each other?",{"@type":53,"text":70},"No. Using the wrong batch is itself a type of error; use verification to ensure compliance, use waves and storage locations to improve efficiency, and do not turn off verification to speed things up.","\u003Carticle class=\"insight-article\">\n    \u003Cdiv class=\"insight-container\">\n      \u003Cnav class=\"breadcrumb\" aria-label=\"Breadcrumb\">\n        \u003Ca href=\"\u002F\">Home\u003C\u002Fa>\n        \u003Cspan class=\"breadcrumb-sep\">\u002F\u003C\u002Fspan>\n        \u003Ca href=\"\u002Finsights\">Insights\u003C\u002Fa>\n        \u003Cspan class=\"breadcrumb-sep\">\u002F\u003C\u002Fspan>\n        \u003Cspan>WMS · Warehouse\u003C\u002Fspan>\n      \u003C\u002Fnav>\n\n      \u003Cheader class=\"insight-hero\">\n        \u003Cdiv class=\"insight-tags\">\u003Cspan class=\"r-tag\">Wave Picking\u003C\u002Fspan>\u003Cspan class=\"r-tag\">Outbound Accuracy\u003C\u002Fspan>\u003Cspan class=\"r-tag\">Review\u003C\u002Fspan>\u003C\u002Fdiv>\n        \u003Ch1>Night-shift errors cluster—when wave, bin, and check are misaligned, training cannot patch structure\u003C\u002Fh1>\n        \u003Cp class=\"insight-lede\">The 'high accuracy' on the report is comforting, but when looking at absolute error lines, customer complaints and production line stoppages often cost more than a scanner. What needs to change is not 'being a little more careful,' but reducing the structures that are prone to errors.\u003C\u002Fp>\n        \u003Cdiv class=\"insight-meta\">\n          \u003Cspan>Updated 2026-05-28\u003C\u002Fspan>\n          \u003Cspan class=\"dot\" aria-hidden=\"true\">\u003C\u002Fspan>\n          \u003Cspan>~9 min read\u003C\u002Fspan>\n          \u003Cspan class=\"dot\" aria-hidden=\"true\">\u003C\u002Fspan>\n          \u003Cspan>Manufacturing digitalization · WMS · Warehouse\u003C\u002Fspan>\n        \u003C\u002Fdiv>\n      \u003C\u002Fheader>\n      \u003Csection class=\"insight-takeaways\">\n        \u003Ch2>What you'll take away\u003C\u002Fh2>\n        \u003Cul>\u003Cli>First, categorize the errors: taking the wrong item, picking too many or too few, mixing up orders, batch errors, and inventory location mistakes. The countermeasures for each are different.\u003C\u002Fli>\u003Cli>Wave size is constrained by the number of rows, path duration, and item similarity; bigger doesn't necessarily mean faster\u003C\u002Fli>\u003Cli>Similar SKUs being adjacent is a hotbed; it must be countered with isolation, reinforced labeling, and forced scanning.\u003C\u002Fli>\u003Cli>The review is conducted by sampling or full inspection based on customers, goods value, and historical errors, with a fixed annual proportion likely turning into a signing ceremony.\u003C\u002Fli>\u003Cli>The denominator for accuracy should be based on outbound lines or pieces, and should be considered together with the FIFO enforcement; it is prohibited to check only the quantity without checking the batch.\u003C\u002Fli>\u003C\u002Ful>\n      \u003C\u002Fsection>\n\n      \u003Cdiv class=\"insight-layout\">\n        \u003Cdiv class=\"insight-body\">\n\u003Ch2 id=\"复核台上一叠-看起来对-的周转箱\">A stack of 'looks correct' turnover boxes on the review counter\u003C\u002Fh2>\n\u003Cp>Forty minutes before the night shift ended, the reviewer opened a box: the item number was correct, the quantity was correct, but the batch was not the one assigned by the system. The picker said, 'It's easier to take from there'; the team leader said, 'The customer is waiting, send it out first'; the next day a customer complaint came in, but the report still counted this order as 'accurate'—because only the quantity was checked at the time.\u003C\u002Fp>\n\u003Cp>If accuracy is a structural design issue, emphasizing responsibility will only result in a seasonal curve of 'dropping a bit when busy.' Large wave sizes, similar items placed next to each other, containers not bound, and checks that are only for signing—if these four structural gaps are not fixed, training is just a placebo.\u003C\u002Fp>\n\u003Ch2 id=\"先给差错起名字\">First, give the error a name\u003C\u002Fh2>\n\u003Cp>No classification, so the countermeasure can only rely on guesswork. It is recommended to unify the reason codes at the four points of discovery: picking, checking, customer rejection, and production line feeding.\u003C\u002Fp>\n\u003Cdiv class=\"table-scroll\">\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Type\u003C\u002Fth>\u003Cth>Common root cause\u003C\u002Fth>\u003Cth>Priority action\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Picked the wrong SKU\u003C\u002Ftd>\u003Ctd>Similar products are adjacent, labels are weak, codes are close\u003C\u002Ftd>\u003Ctd>Location Isolation  Check Code\u002FScan Code Enforcement\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Pick more \u002F Pick less\u003C\u002Ftd>\u003Ctd>Unclear packaging specifications and confusing units\u003C\u002Ftd>\u003Ctd>Packaging Master Data   Count\u002FWeight Recheck\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>linked orders\u003C\u002Ftd>\u003Ctd>Excessive wave size, mixed use of containers, incorrect sowing grid\u003C\u002Ftd>\u003Ctd>Container bound to orders\u002Fwaves; grid check\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Batch \u002F SN Error\u003C\u002Ftd>\u003Ctd>FIFO can be bypassed, mixed batches\u003C\u002Ftd>\u003Ctd>Batch\u002FSN Mandatory Verification\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Wrong inventory location entry\u003C\u002Ftd>\u003Ctd>Placed on the wrong shelf, warehouse transfer not posted\u003C\u002Ftd>\u003Ctd>Shelf Verification   Cycle Counting\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Wrong document\u003C\u002Ftd>\u003Ctd>The wave includes canceled lines and versions that have not been updated\u003C\u002Ftd>\u003Ctd>Locked Order Effective Version Before Generation\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cp>If the Top type and corresponding storage area cannot be sorted out recently, it means it is still in the 'retraining' stage and has not entered governance. When errors pile up during the night shift, first look at the wave row numbers and lighting labels, then discuss whether people are tired.\u003C\u002Fp>\n\u003Ch2 id=\"波次是设计-不是越大越好\">Wave is a design; bigger is not necessarily better.\u003C\u002Fh2>\n\u003Cp>Excessively large waves lengthen the path, increase container mixing and interruption recovery costs, and accuracy and labor efficiency may both drop. Segmentation is at least constrained by these factors:\u003C\u002Fp>\n\u003Cdiv class=\"table-scroll\">\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Constraint\u003C\u002Fth>\u003Cth>Approach\u003C\u002Fth>\u003Cth>Remarks\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Row \u002F Item Limit\u003C\u002Ftd>\u003Ctd>Frozen after actual measurement by warehouse type and tool\u003C\u002Ftd>\u003Ctd>Breaking the wave beyond the limit, without relying on people to tough it out\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Path Duration\u003C\u002Ftd>\u003Ctd>Control within continuous operation section\u003C\u002Ftd>\u003Ctd>Overtime waves should be able to interrupt continuous picking and have a clear status\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Item similarity\u003C\u002Ftd>\u003Ctd>High-confusion SKUs and the same task require strict verification\u003C\u002Ftd>\u003Ctd>Avoid putting it into the same task when there is no verification\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Order Priority\u003C\u002Ftd>\u003Ctd>Isolation between urgent orders channel and regular waves\u003C\u002Ftd>\u003Ctd>Prevent last-minute orders from disrupting the entire batch\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Batch strategy\u003C\u002Ftd>\u003Ctd>Allocation has been locked according to FIFO\u002FFEFO\u003C\u002Ftd>\u003Ctd>Wave optimization cannot reverse open batch allocation\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cp>Sowing or picking: Sowing is suitable for structurally stable and concentrated items; for multiple varieties in small batches, picking or mixing is more common. A two-week pilot to compare errors and route durations is more effective than slogans. When receiving congestion causes simultaneous receiving, shelving, and picking, drastic changes in storage locations can distort wave paths—accuracy projects need to share the same management calendar as scheduled receiving.\u003C\u002Fp>\n\u003Ch2 id=\"易混货架比-再仔细-便宜\">Easily confused shelves are cheaper than 'even more careful'\u003C\u002Fh2>\n\u003Cp>Errors with similar SKUs often concentrate on a few shelves. First make a 'commonly confused pairs' list, rather than relabeling the entire warehouse:\u003C\u002Fp>\n\u003Cul>\u003Cli>\u003Cstrong>Physical isolation\u003C\u002Fstrong>: If they look very similar and only differ in specifications, do not consider them separated just because there is a single column between them.\u003C\u002Fli>\u003Cli>\u003Cstrong>Label reinforcement\u003C\u002Fstrong>: Amplify key differences such as specifications, polarity, left and right parts, etc., and align with the system display fields.\u003C\u002Fli>\u003Cli>\u003Cstrong>Scan code mandatory\u003C\u002Fstrong>: Tracing and high-mixed materials are prohibited from “confirming no sweep”.\u003C\u002Fli>\u003Cli>\u003Cstrong>Packaging Master Data\u003C\u002Fstrong>Clearly indicate the number of inner packages and the hierarchical units, specially for handling picking too much or too little.\u003C\u002Fli>\u003Cli>\u003Cstrong>Lighting \u002F Electronic Tags\u003C\u002Fstrong>(High-density area): Still needs to be linked with containers and orders; otherwise, orders will still get mixed up.\u003C\u002Fli>\u003C\u002Ful>\n\u003Cp>When the equipment is not easy to use, first address usability: a high scan failure rate will force on-site skipping; once skipping becomes a habit, accuracy projects are basically meaningless.\u003C\u002Fp>\n\u003Ch2 id=\"复核是风险阀门-不是二次劳动\">Review is a risk valve, not a second labor.\u003C\u002Fh2>\n\u003Cp>The fixed annual sampling ratio, once the pattern is known on-site, causes re-inspections to degrade into a signing formality. Strategies should change according to customer level, value of goods, expiration dates, and historical errors: for example, full inspections for new customers initially, full inspections for high-value items, increased sampling for areas or personnel with high error rates, and gradually reducing sampling in stable low-risk areas. Strategies should be written into the system to avoid team leaders verbally saying, 'Let's be stricter with sampling today.'\u003C\u002Fp>\n\u003Cp>Errors found during outbound review need to be written back to the wave and picking tasks, forming a drill-down of \"who made a mistake in which storage area and under which wave structure,\" rather than just recording an overall accuracy rate. Without this layer, next month we will still rely on guesswork for cutting-edge technology.\u003C\u002Fp>\n\u003Ch2 id=\"口径先稳住-再谈目标\">First stabilize the definition, then discuss the target.\u003C\u002Fh2>\n\u003Cdiv class=\"table-scroll\">\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>definition item\u003C\u002Fth>\u003Cth>Suggestion\u003C\u002Fth>\u003Cth>Watered-down writing\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>denominator\u003C\u002Ftd>\u003Ctd>Outbound line or item\u003C\u002Ftd>\u003Ctd>Only use the outbound order header, multiple lines are diluted\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Molecule\u003C\u002Ftd>\u003Ctd>Including re-inspection findings, customer returns, and production line material errors\u003C\u002Ftd>\u003Ctd>Only count customer complaints\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Due Date\u003C\u002Ftd>\u003Ctd>By delivery date\u003C\u002Ftd>\u003Ctd>Postponed until next month to 'disappear'\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Wrong batch\u003C\u002Ftd>\u003Ctd>Accounted as an error\u003C\u002Ftd>\u003Ctd>Ignore quality and storage\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Goal\u003C\u002Ftd>\u003Ctd>Decide after the definition stabilizes\u003C\u002Ftd>\u003Ctd>First set extremely high goals, then adjust the standards.\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cp>Do not use a single KPI to pit efficiency against accuracy. A more stable approach is a dual-track system: for efficiency, look at average lines per person and path duration, with the safeguard being that the scan skip rate does not increase; for accuracy, look at line-level accuracy and type improvement, with the safeguard being that the average lines per batch do not go out of control; for compliance, look at FIFO\u002Fbatch compliance rate, with the safeguard being a limit on exceptions. When a safeguard is breached, first reduce batch size and increase checks, rather than increasing penalties—penalties in the face of structural deficiencies will only lead to more skips and subsequent corrections.\u003C\u002Fp>\n\u003Ch2 id=\"系统抓手-任务结构-扫描拦截-复核策略\">System Handles: Task Structure   Scan Interception   Review Strategy\u003C\u002Fh2>\n\u003Cp>JEDP-WMS inbound and outbound operations cover wave picking and outbound verification, and support SN\u002Fbatch\u002FSKU\u002Fcontainer code tracking. The key factors affecting accuracy are how tasks are divided, whether incorrect codes are blocked, and whether verification is risk-based—not having an extra paper checklist. Wave generation should follow rules for segmentation, isolate urgent orders, and exclude canceled lines from waves; picking execution should scan location\u002Fitems\u002Fcontainer and block incorrect codes; batch management should be consistent with FIFO enforcement, and batches should not be changed arbitrarily by default; containers should be linked to orders\u002Fwaves to reduce order mix-ups.\u003C\u002Fp>\n\u003Cp>It is not necessary to implement everything in twelve weeks at once: first, freeze reason codes and row-level definitions → then prioritize Top1 type changes for storage location\u002Ftag\u002Fpackaging → next, tighten wave limits and container binding → finally, implement dynamic review and coordinate with batch verification. When storage locations and master data are still disorganized, first enforce shelving standards and cycle counting; when outbound volume is very low or scanning devices are unavailable, first supplement hardware and perform simple checks, without forcibly implementing a complex wave engine. Pure bulk loose material filling is not suitable for a row-level picking model and should switch to measurement verification and tank\u002Fbatch control. When the effective version of orders is frequently voided, first stabilize order collaboration, then stabilize picking.\u003C\u002Fp>\n\u003Ch2 id=\"中断-插单与-万能跳过\">Interruptions, Queue Jumping, and 'Universal Skip'\u003C\u002Fh2>\n\u003Cp>Wave picking design is often ruined by three common on-site habits. First, when a task is interrupted to handle an urgent issue and returns to find that containers and documents don't match, the risk of order mixing rises sharply — interrupted picking must retain the task status and already picked lines, and resuming cannot rely on memory. Second, urgent orders are directly inserted into an ongoing wave, and after the path is disrupted, pickers start 'picking what is convenient first' — urgent orders should go through isolated lanes or independent short waves, not contaminate the entire wave. Third, skip codes prepared for scan failures are set too generously: a single network glitch can become a permanent workaround, rendering accuracy metrics effectively meaningless.\u003C\u002Fp>\n\u003Cp>If the rules for the line-side warehouse, consignment warehouse, and finished goods warehouse are different, the wave strategy and verification intensity need to be configured separately. Using a single \"warehouse-wide wave limit\" table indiscriminately will either slow down the slower warehouses or make high-mix areas too loose. The cost of errors is also different for production material picking versus sales shipment: picking the wrong materials can stop the production line, while shipping the wrong goods can lead to customer complaints and return logistics—the verification strategy should be tiered according to the type of outbound activity, rather than using the same sampling rate as a shortcut.\u003C\u002Fp>\n\u003Cp>The accuracy rate of inventory on reports, wave efficiency, and error causes should share the same source. If the on-site PDA has one set of numbers and the office Excel has another, improvement efforts will always get stuck in disputes over the figures. When compared with cycle counts: if the top cause of discrepancies in the same storage area is wrong shipment or receipt, and the top picking error is also taking the wrong item, it indicates that location and label management share a common root cause. Projects should be merged, rather than having separate mobilization meetings for warehouse management and picking.\u003C\u002Fp>\n\u003Ch2 id=\"先画一张差错饼图\">First, draw an error pie chart\u003C\u002Fh2>\n\u003Cp>If errors in the past thirty days cannot produce a classified pie chart, any procurement request using the 'upwave algorithm' should be suspended first — the algorithm amplifies the existing structure, including the existing chaos. Once the pie chart is ready, focusing on only the Top1 type for two weeks is usually more effective than launching five picking technologies at the same time. If Top1 is about picking the wrong item, prioritize isolation and scanning; if it’s about order mix-ups, prioritize container binding and wave reduction; if it’s a batch error, don’t talk about efficiency yet, talk about enforcement first. Two weeks later, if Top1 remains unchanged, first check the skip rate and exception permissions, then look into the personnel.\u003C\u002Fp>\n\u003Cul>\u003Cli>WMS Warehousing Solution: Wave Picking and Outbound Verification\u003C\u002Fli>\u003Cli>First-in, first-out enforcement\u003C\u002Fli>\u003Cli>Arrival Appointment and Receiving Rhythm\u003C\u002Fli>\u003Cli>Cycle Counting and Account Reconciliation\u003C\u002Fli>\u003C\u002Ful>\n        \u003C\u002Fdiv>\n        \u003Caside class=\"insight-toc\" aria-label=\"On this page\">\n          \u003Ch2>Contents\u003C\u002Fh2>\n          \u003Ca class=\"h2\" href=\"#复核台上一叠-看起来对-的周转箱\">A stack of 'looks correct' turnover boxes on the review counter\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#先给差错起名字\">First, give the error a name\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#波次是设计-不是越大越好\">Wave is a design; bigger is not necessarily better.\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#易混货架比-再仔细-便宜\">Easily confused shelves are cheaper than 'even more careful'\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#复核是风险阀门-不是二次劳动\">Review is a risk valve, not a second labor.\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#口径先稳住-再谈目标\">First stabilize the definition, then discuss the target.\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#系统抓手-任务结构-扫描拦截-复核策略\">System Handles: Task Structure   Scan Interception   Review Strategy\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#中断-插单与-万能跳过\">Interruptions, Queue Jumping, and 'Universal Skip'\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#先画一张差错饼图\">First, draw an error pie chart\u003C\u002Fa>\n        \u003C\u002Faside>\n      \u003C\u002Fdiv>\n\n      \u003Cdiv class=\"insight-cta\">\n        \u003Cp>\u003Cstrong>Want to apply this in your plant?\u003C\u002Fstrong>Resources has matching self-assessments and templates—or book a live demo.\u003C\u002Fp>\n        \u003Cdiv class=\"insight-cta-actions\">\n          \u003Ca href=\"\u002Fresources?from=wms#selftest\" class=\"btn btn-outline\">Self-assessment in Resources\u003C\u002Fa>\n          \u003Ca href=\"\u002Fcases#case-wms\" class=\"btn btn-outline\">WMS acceptance case\u003C\u002Fa>\n          \u003Ca href=\"\u002Fcontact?from=wms\" class=\"btn\">Get a demo\u003C\u002Fa>\n        \u003C!-- next-step:BEGIN -->\u003Ca class=\"btn\" href=\"\u002Fproduct\u002Fwms\">Product solution: \u003Cspan>JEDP-WMS Warehouse Operations\u003C\u002Fspan>\u003C\u002Fa>\u003Ca class=\"btn btn-outline\" href=\"\u002Fres\u002Fresources\u002Fkits\u002F24-wms-appointment-wave-picking.html\">Matching resource kit: \u003Cspan>Inbound appointment + wave picking checklist\u003C\u002Fspan>\u003C\u002Fa>\u003C!-- next-step:END -->\n        \u003C\u002Fdiv>\n      \u003C\u002Fdiv>\n      \u003Csection class=\"insight-faq\" id=\"faq\">\n        \u003Ch2>FAQ\u003C\u002Fh2>\n        \u003Cp class=\"insight-faq-sub\">Quick answers on this topic—still stuck?\u003Ca href=\"\u002Fcontact?from=wms\">Contact\u003C\u002Fa>\u003C\u002Fp>\n        \u003Cdiv class=\"faq-list\">\n        \u003Cdetails class=\"faq-item\" open>\n          \u003Csummary>Does every order need to be reviewed?\u003C\u002Fsummary>\n          \u003Cp>High-risk clients, expiration dates, high value, and frequent historical errors are recommended for full inspection or dual review; low-risk items can be spot-checked, but the proportion and sampling method must be auditable and not predictable on site.\u003C\u002Fp>\n        \u003C\u002Fdetails>\n        \u003Cdetails class=\"faq-item\">\n          \u003Csummary>How to choose between seeding and picking methods?\u003C\u002Fsummary>\n          \u003Cp>Stable order mix and concentrated SKUs favor put-to-wall (batch\u002Fsow); high-mix low-volume often favors pick-to-order (or a hybrid). Decide from pilot error and labor metrics, not slogans.\u003C\u002Fp>\n        \u003C\u002Fdetails>\n        \u003Cdetails class=\"faq-item\">\n          \u003Csummary>Can paper-based picking achieve high accuracy?\u003C\u002Fsummary>\n          \u003Cp>It can reach a certain level, but it is difficult to trace back to the person and the time, and it is also hard to intercept immediately in case of misclassification\u002Fmistakes. The value of the system lies in interception and record-keeping, not just printing tasks.\u003C\u002Fp>\n        \u003C\u002Fdetails>\n        \u003Cdetails class=\"faq-item\">\n          \u003Csummary>How to set the accuracy target?\u003C\u002Fsummary>\n          \u003Cp>First stabilize the statistical criteria (row level, including in-warehouse findings and customer rejections), then backtrack the target based on claims and line stoppage costs; announcing the target first and then changing the criteria is equivalent to inflating the numbers.\u003C\u002Fp>\n        \u003C\u002Fdetails>\n        \u003Cdetails class=\"faq-item\">\n          \u003Csummary>Will accuracy and FIFO conflict with each other?\u003C\u002Fsummary>\n          \u003Cp>No. Using the wrong batch is itself a type of error; use verification to ensure compliance, use waves and storage locations to improve efficiency, and do not turn off verification to speed things up.\u003C\u002Fp>\n        \u003C\u002Fdetails>\n        \u003C\u002Fdiv>\n      \u003C\u002Fsection>\n      \u003Csection class=\"insight-related\">\n        \u003Ch2>Related reading\u003C\u002Fh2>\n        \u003Cdiv class=\"insight-related-grid\">\n          \u003Carticle class=\"r-card\">\n            \u003Cdiv class=\"r-tags\">WMS · Warehouse\u003C\u002Fdiv>\n            \u003Ch3>Why does the nearest pallet always ship? FIFO needs hard blocks, not wall posters\u003C\u002Fh3>\n            \u003Cp>FIFO failure is usually not because the warehouse is uncooperative, but because the recommendation can be easily bypassed. It should be designed with three levels of enforcement: reminders, verification, and locking, with exceptions requiring authorization and review; inventory management and batch mixing prohibition are prerequisites, and are linked with wave picking...\u003C\u002Fp>\n            \u003Cdiv class=\"r-meta\">2026-04-23 · ~9 min\u003C\u002Fdiv>\n          \u003C\u002Farticle>\n          \u003Carticle class=\"r-card\">\n            \u003Cdiv class=\"r-tags\">WMS · Warehouse\u003C\u002Fdiv>\n            \u003Ch3>Three trucks at 9am, empty dock all afternoon? Appointments must consume unload capacity, not a remarks field\u003C\u002Fh3>\n            \u003Cp>Receiving congestion often occurs because appointments are only registered but do not occupy resources. Create a closed loop of occupancy—check-in—closure by linking time windows, docks, unloading capacity, and ASN; integrate tardiness, no-shows, and urgent queue-jumping into rules and performance; prioritize strategic appointments on peak days...\u003C\u002Fp>\n            \u003Cdiv class=\"r-meta\">2026-03-26 · ~8 min\u003C\u002Fdiv>\n          \u003C\u002Farticle>\n          \u003Carticle class=\"r-card\">\n            \u003Cdiv class=\"r-tags\">WMS · Warehouse\u003C\u002Fdiv>\n            \u003Ch3>Books never match floor? Without count grain and variance reasons, more counts only show chaos sooner\u003C\u002Fh3>\n            \u003Cp>The discrepancies between accounts and actual inventory primarily occur in daily receipts and deliveries, not because of insufficient inventory counts. Starting from year-end full inventory fatigue, it explains how ABC and the discrepancy rate distribute frequency, how storage location\u002Fbatch\u002Fmaterial granularity is frozen, and how reason codes drive corrective operations...\u003C\u002Fp>\n            \u003Cdiv class=\"r-meta\">2026-07-02 · ~10 min\u003C\u002Fdiv>\n          \u003C\u002Farticle>\n        \u003C\u002Fdiv>\n      \u003C\u002Fsection>\n    \u003C\u002Fdiv>\n  \u003C\u002Farticle>",1793937990847]