Classification Models For RFID-Based Real-time detection of Process Events in Supply Chain An...

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RFID• Asset Tracking

• Race Timing

• Inventory Management

• Tool Tracking

• Access Control

• Attendee tracking

• Supply Chain Management

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Classification Models For RFID-Based Real-time detection of Process Events in

the Supply Chain:

An Empirical Study

Transactions on

Management Information System

Publication Date: October 2014

by

VishnuTeja Thummanapelli

2671322

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Agenda

• Introduction to topic

• Prior Research and drawbacks

• Development of different Classifiers

• Results

• Critique

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Need for RFID in SCM

• Saves on Labor cost

• Less paperwork

• Saves time

• Bulk identification

• Affordable hardware prices

• Identification of objects without a line of sight

• Better analysis of data

• Better tracking of goods as they move from Supplier to Customer

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Architecture

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Flow of events

Warehouse person approaches the RFID

gate

Motion sensor recognizes him/her

RFID Reader starts scanning for

transponders/tags

Reading runs for 5 seconds - Gathering

Cycle

Collected pallet ID’s are sent to Warehouse

Management System

Visual feedback through signal light

person informs the system that the

loading is complete

Invoices are issued to the store

Shipping

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Problems in existing system

• Because of the range of RFID readers, B1, B2 and M are detected accidentally.

• Wrong invoices are generated

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Errors in reading pallets

True positive Read correctly Moved pallet

False positive Read unexpectedly Static pallet

False negative Not detected

Reasons for false-negative: • Water and liquids like shampoo can absorb radio waves.

• Dysfunctional tags

• Tags shielding each other

Reasons for false-positive: • Human error of placing the pallet in the read range

• Metals can increase the range of radio waves

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Low level RFID dataEach tag event has three types of informationRSSI (Received Signal Strength Indication)• How loudly the tag was heard by antennae

• Increases if closer, decreases when distant.

• dBm (decibel mill watts)

SinceStart• Time stamp relative to gathering cycle(5 seconds period)

• milliseconds

Antenna

• Which of the antennae read the tag

• Unique id of the antenna

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Sample of problem

This example is based on a case where- One tag is being moved x (Moved pallet)- Another tag was placed near by antenna ● (Static pallet)

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Previous researchStudy Approach

Brusey et al. [2003]

Method: Used Queue(First in, First Out) and Robotic arm removes the item after reading. Results: Other items are also scanned.

Bai et al. [2006] Method: Used multiple cycles. If the number of reads reaches a threshold, its classified as true positive. Results: False positives are increased.

Jiang et al. [2006] Method: More than one tag is used for the same object. Transmit N polls per second. Uses rotation and calculates. Results: Performance data is released, but the design is not revealed.

Tu and Piramuthu [2008]

Method: Multiple readers are used for the same tag. If both readers detects, its assumed to be present. Results: Just a simulation, but the design is not revealed.

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Comparison of previous and current researches

Weaknesses in previous research

• Previous researches ignored low level RFID data like Signal Strength.

• Additional readers and tags increases the hardware cost.

• Previous researches are based on lab tests and simulation results only.

Strengths in current research

• Signal strength plays a prominent role in current research

• Data Mining approaches are used to address false-positives.

• Massive real-time data is used (Source: METRO Group Inc., Germany)

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Types of RFID Reader portals

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Logical structure of tag event data - Conceptual Class diagram

Tag Event t = (RSSI, SinceStart, Antenna)Tag Occurrence T = {t1, t2,.……, tn}

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Finding meaningful attributes for Classifications

“meaningful” here indicates whether a particular attribute really helps in correct classification of moved and static pallets.

Sources for Attributes:

• Tag Occurrence

• Tag Event

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Attributes from Tag Occurrence level

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Aggregation functions are applied on Tag Occurrencelike

-Maximum

-Minimum

-Mean

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Attributes from Tag Event level

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• Tag Event forms a time-series, because they are ordered based on time.

• The idea: is to examine whether time series of a particular tag is similar to moved or static tag.

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k-NN (k-nearest neighbor Algorithm)

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Attributes development

3 types of attributes are used for the classification :

1. Domain attributes

2. Logical attributes

3. Time-series attributes.

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Classifier developmentClassifiers

Decision Stumps. Standard Classifiers

Logistic regression

Decision trees

Neural networks

Rule-Based Classifier

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Rule based Classifier

All the data that is not classified

Artificial attributes are generated based on domain attributes to improve efficiency of Decision trees Decision tree is generated

Classify the “moved” or “static”

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Classification performance• True Positives (TP) : number of moved pallets that were correctly classified as “moved.”

• False Positives (FP) : number of static pallets that were wrongly classified as “moved.”

• True Negatives (TN) : number of static pallets that were correctly classified as “static.”

• False Negatives (FN) : number of moved pallets that were wrongly classified as “static.”

• Derived metrics from the results

measure of the risk of incomplete shipments

measure of the risk of shipping pallets that were not ordered

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Decision stumps Classifier

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Standard Classifiers

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Rule-based customer classifier

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Critique

• Automated RFID systems like Conveyor belt based ones are available at the time of writing paper this paper, which would reduce the error rate. Those systems were not taken into consideration.

• Creating the Training data is a tedious process which may not be available readily for all the clients. So, the immediate results can’t be expected from a new client of the Warehouse Management system.

• Classifiers on the production systems are dangerous.

• So this classification model works well with the companies which has previous data.

• Which “Artificial attributes” are used aren’t mentioned in the paper.

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Conclusion

• Custom classifiers yield better results when applied to RFID systems in SCM.

• This approach can be applied to various other industries and models.

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Questions ?

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Thank you