Insights

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Random failure period / h(t) near constant: low and steady risk.

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Wear-out failure period / h(t) increasing: material fatigue causes risk to climb again.

Figure 1: Bathtub Curve — Different causes of anomalies at different time points

This theory offers a direct reminder for service center management: operational oversight cannot look at a single time-slice figure alone; the same WIP or TAT anomaly means something entirely different depending on where it falls on the bathtub curve — without complete records, it is impossible to judge which stage the failure is at. This is the raw data foundation behind the wareconn Electronic Dashboard: service data, records collected from the application process and repair services during the warranty period, can be obtained directly from the aftersales warranty system. It mainly includes five categories: product warranty data, warranty application data, failure environment data, repair service data, and service cost data. The table below lists field examples for these data types:

Table 1: Repair Service Data Examples

The wareconn Electronic Dashboard decomposes these four indicators into eight visual modules, each with built-in anomaly detection rules, so managers no longer need to flip through reports page by page; the system directly flags signals that deviate from the norm.

Table 2: Overview of the Eight Modules of the wareconn Electronic Dashboard

Figure 3 is a real-world case of the Electronic Dashboard: the top-right "Workstation WIP" decomposes WIP by workstation, and the noticeably high WIP at station VI1 is a classic bottleneck signal; "WIP (By Inventory Days)" decomposes by inventory days, and the group with a high proportion over 91 days is the target for priority investigation into asset stagnation risk. The bottom-left "Inbound/Outbound" module is a real-time presentation of throughput metrics, and a persistent noticeable gap over several weeks is the anomaly signal to capture.

Among the eight modules, four directly correspond to the four key indicators, while the other four are auxiliary perspectives extended from the core indicators, used to answer why an anomaly occurs, not just that it occurred:

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Inbound/Outbound Failure Statistics: Throughput trend analysis; a persistently widening gap between inbound and outbound can provide early warning of rapid WIP accumulation; a sudden spike in monthly failure numbers often represents a major systemic flaw in the preceding stage.

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Warranty Application and Case Closure: Gauges repair pressure across service sites; if application volume consistently exceeds closure volume, the backlog of pending cases will explode in the short term.

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Inbound/Outbound: Reflect the overall throughput of the service center; if inbound significantly exceeds outbound for several weeks, it indicates the warehouse will face an overstock risk.

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Repair Statistics: Sort repair codes and locations from largest to smallest; if a location that usually has few failures suddenly rises to first place, it represents a concentrated anomaly failure; cross-referencing the adverse judgment result can also distinguish whether the part itself is flawed or if the root problem remains unresolved due to blind part replacement.

Worth noting is that these eight modules do not apply a single anomaly judgment logic. Inventory-type metrics care about "whether it is stuck," throughput-type metrics care about "how fast it is moving," and the two problems inherently require different statistical logics:

Table 3: Comparison of Inventory-type and Throughput-type Judgment Logic

Taking WIP stuck at the Assy test workstation as an example, this embedded cycle runs fully:

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Plan: The system already records inventory days by workstation, so no manual tracking is needed.

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Do: The dashboard automatically compares WIP quantities across workstations, no need to wait for end-of-month aggregation.

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Check: As soon as a workstation quantity is anomalous, the system directly flags that workstation as the primary bottleneck, allowing managers to quickly pin down the problem.

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Act: Further investigate the specific cause — parts shortage, complex issue, or operational errors like scanning omissions.

The entire process does not require daily report checks; the dashboard itself is a continuously operating PDCA loop, and the cycle is shortened from monthly to near real-time:

Table 4: Comparison of Traditional Monthly Reports and the Electronic Dashboard

This shift in management can be summarized as management by exception: managers no longer need to check every workstation and every indicator for normality, only handle the anomalies flagged by the system. Normal operations are left to automatic system monitoring without manual intervention; attention shifts from carpet-bomb checking to precision strikes, and the management pace moves from monthly to real-time. For service center managers, this means the same manpower can manage more workstations and more customers without sacrificing response speed.