Addressing Port Governance s Big Data Challenges

Transcription

1 Addressing Port Governance s Big Data Challenges Brandeis Marshall, Purdue University Regena Scott, Purdue University Edie Schmidt, Purdue University Bruce C. Hartman, University of St. Francis Christopher Clott, University of St. Francis

2 Introduction Freight data analysis stakeholders (just ground transportation) include but not limited to: port authorities, 3PLs, trucking companies, shippers, and recipients within local, regional, state and federal governments Each stakeholder has unique data needs with respect to its accuracy, dependencies, detail granularity, and characteristics

3 Introduction Each freight stakeholder has maintained their data independently leading to... Missing and unknown data entries leading to appropriate data cleaning Inconsistent data units needing proper conversions Data element identification Data semantics reconciliation concerns requiring a unified data dictionary

4 Introduction What are the needs of ground transportation s freight data needs? What data model/design is appropriate considering freight data analysis stakeholders? An agile and standardized freight data system designed of effective decision making. Yeah, right! Agile and standardization growth is restricted by challenges in supply chain, port governance and, of course, Big Data

5 The Role of Supply Chain Supply chain improvement modes from Lam and van de Voorde, PLs typically focus on the operational details with customer service - to match and manage perceptions with expectations Inventory is virtually untouched at the strategic, tactical and operational levels

6 Port Governance & Policy Port regionalization (load centers + priority corridors) Further development of inland nodes Intermodal terminal building/expansion connection with a port terminal through rail, barge or ground transport support and organize freight transit via logistical activities Supply chain members, infrastructure providers and governments can assist in furthering development such as, distance spanning jurisdictions, structured negotiations, contract and performance guarantees, tax increment financing, etc.

7 Big Data Big Data = Volume+Velocity+Variety Datasets with sizes too large for current tools/infrastructure to adequately capture/manage/process/analyze in a reasonable timeframe e.g., WalMart handles 1M transactions/hour - importing into a 2.5 PB DB (150+ times larger than Library of Congress) MapReduce/Hadoop Map Step: split queries for parallel distribution amongst nodes Reduce Step: gather results and delivered

8 Big Data + Freight Big Data Analysis Pipeline (CRA) Missing fields or erroneous readings support data integration and modeling (e.g., default values) quality assessments and standards

9 Big Data + Freight Big Data Analysis Pipeline (CRA) Lack of transparency minimize data element description confusion across resources standardize data dictionary across stakeholders

10 Big Data + Freight Big Data Analysis Pipeline (CRA) Data cleaning data extraction/annotation phase completion times are extended format conversion issues limit the enabling of data aggregation

11 Big Data + Freight Big Data Analysis Pipeline (CRA) Lack of access to databases private databases may be accessible for a cost data representation, modeling and interpretation need to consider public and private stakeholders to enhance human collaboration

12 Freight Data Architecture Data sources and processes for proposed NFDA

13 Freight Data Architecture Freight data entities Zone: freight gateways, states, regional locations Origin: original facility of freight shipment Destination: final facility recipient of freight shipment Source: initial gateway/freight facility Sink: intermediate gateway/freight facility Commodity: shipment of goods (weight and measurements included) Vehicle: mode of commodity s transport Route: path-planning (directions) including checkpoints

14 Freight Data Architecture

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