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CSOWM: Logistics Technology, Data, and Analytics

CSOWM · Work through your exam, one topic at a time.

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Logistics Technology, Data, and Analytics

Logistics technology, data, and analytics tests the systems that plan, execute, and measure logistics: WMS, TMS, ERP, visibility platforms, data capture, integration, and KPI analytics. Expect questions on OTIF, master data, EDI versus API, and the limits of dashboards.

Core rules

  • ERP is the system of record for orders, inventory, and finance; WMS executes warehouse work; TMS plans and executes transportation. Integration and master data determine whether the systems agree.
  • Data capture uses barcodes, RFID, sensors, and mobile devices. Accuracy at the point of scan is more valuable than a sophisticated dashboard built on bad data.
  • EDI is a structured, batch-oriented standard for trading partners; APIs are more flexible and real-time but require governance and security.
  • Visibility and control towers combine event data across carriers, suppliers, and facilities. They support exceptions, not just tracking screens.
  • KPIs should be defined with numerator, denominator, scope, and owner. OTIF, perfect order, fill rate, inventory accuracy, and cost per order are common.
  • Analytics maturity runs from descriptive reporting to predictive and prescriptive models. A digital twin is only useful if the underlying process and data are modeled correctly.

Worked example

A facility ships 1,000 orders. 950 are on time, 920 are complete, and 900 are both on time and complete. OTIF = 900 / 1,000 = 90%. Multiplying 95% x 92% = 87.4% assumes the two events are independent, but they are not; the actual intersection is 90%. If 30 of the 900 also had damage or documentation errors, perfect order = 870 / 1,000 = 87%. The reasoning shows why metric definitions and the intersection matter.

Traps that cost marks

  • Multiplying on-time and in-full percentages when the events overlap.
  • Treating OTIF as on-time delivery alone, or perfect order as OTIF alone.
  • Building dashboards on inconsistent master data and blaming the analytics.
  • Assuming EDI and API are interchangeable; they differ in latency, cost, and governance.
  • Buying technology to fix a process problem that the software will only automate.