Inference of Product Quality by using RFID-enabled Traceability Information

A study on the US pharmaceutical supply chain

Tatsuya INABAKeio University/Auto-ID Lab. Japan

IEEE RFID 2009@Orlando

May 2, 2009Page 2

Agenda

• Background

• Requirements of the model

• Case study : the US pharmaceutical Industry– Why US pharma supply chain?– Model– Evaluation– Observation

• Discussion and Future research

May 2, 2009Page 3

Background

• Trend companies provide traceability info to consumer– Simple effect

• Products w/ traceability info > Products w/out traceability info

• Consequences of too much (raw) traceability info– Too complex to understand– As a result, avoid “intuitively” low quality products (e.g., long

travel history)– Cause problem on SCM practice

• Current premise: products on shelf are sold• Problem: products with long travel history may stay on the shelf

May 2, 2009Page 4

Background (cont’d)

• New problem under fully traceable world!– Try to promote RFID to make SCM more efficient.

BUT…unwanted side effects??

• Necessity of model to provide a simple index– Out from complex traceability information– Easy to understand quality and easy to choose products

• Enable companies to operate supply chain as it is– Not item-level inventory management but SKU-level inventory

management

May 2, 2009Page 5

Requirements of the Model

• Incorporate elements that affect product quality– Company names, conditions, IDs, contractual info

• Stochastic, Not deterministic– Causal relationship is known deterministic, but not in this case– Elements stochastically impact quality of products

• E.g., Difference b/w quality of fruits shipped by air and by sea

Quality Index = f (names of companies, conditions of storage,IDs of other products, contractual information, . . .)

Use Bayesian Network as a stochastic model and apply the model to the US pharmaceutical supply chain

May 2, 2009Page 6

Bayesian Network (BN)

• BN is a stochastic model with acyclic graph– Consists of variables (nodes) and arrows that connect variables

(parent node to child node)– Direction of arrow show casual relationship– The extent of impact from a parent node to a child node is

expressed as a set of probabilities called Conditional Probability Table (CPT)

C D

EB0.8 0.2

b

eb

0.6 0.4

0.01 0.990.2 0.8

ebee

b

EB P(A | B, E)

A

CBT

May 2, 2009Page 7

Why US Pharma Supply Chain?

• Example of fully traceable world

• Item level tagging to prevent drug counterfeiting– Studies regarding ILT to prevent counterfeiting by government

and industry– Studies to mandate exchanging drug traceability information

(called pedigree) in some states– Virtually all the individual prescription drug traceability info is

available in those states

May 2, 2009Page 8

Why US Pharma Supply Chain? (cont’d)

• Example of complex traceability information

• Complex drug supply chain– Tier-structured wholesaler– Three 1st tier wholesalers buy drugs from manufactures and 2nd

tier wholesales, the same trade in 2nd tier wholesalers – Repackaging process makes the supply chain more complex– Companies avoid to buy long travel history drugs (intuitive

purchase decision!)

May 2, 2009Page 9

Illustration of US Pharma Supply Chain

• Complicated layered trade of the industry

ManufacturerManufacturer

Tier-1 wholesaler

Tier-1 wholesaler

Tier-2 wholesaler

Tier-2 wholesaler

Tier-3 wholesaler

Tier-3 wholesaler

RetailerRetailer

Repackaging process makes the situation more complex…

May 2, 2009Page 10

Model: Elements to Affect Quality

• From current counterfeit cases– Involvement of 2nd and 3rd tier wholesalers– Involvement of small repackagers

• From pedigree doc exchange process assessment– Electronically exchange pedigree w/exception

• Small retail pharmacies are allowed to use paper-based pedigree– Product return from paper-based pedigree retail pharmacies

• Malicious parties tamper paper based pedigree and return fake drug with tampered pedigree

May 2, 2009Page 11

Model: Development of BN Graph

• Selection of nodes– Nodes are selected based on observed important characteristics

of real drug trades

• Graph development– Graph is developed based on the real counterfeit cases from

both government and industry reports

Repackage Paper-based pedigree

Tier-2Wholesaler

Tier-3Wholesaler

Return(paper-based)

Return(non paper-based)

Product Fraud

May 2, 2009Page 12

Evaluation

• Simulation with a hypothetical scenario– Real traceability data is not available– Traceability data is generated from a hypothetical scenario– Data of fraudulent products is included based on the counterfeit

reports

• Two sets of data for CPT and evaluation– A set of traceability data is needed for CPT calculation– The other set of traceability data for evaluation

May 2, 2009Page 13

Hypothetical Scenario for Evaluation

• Fraudulent cases assumed in the evaluation– Pattern A: Through repackage process by tier-2/3 repackager– Pattern B: Through return process of retailers that use paper-based

pedigree– Pattern C: Through tier-3 wholesaler handling

Pattern CPattern C

ManufacturerManufacturer

Tier-1 repackager

Tier-1 repackager

Tier-2/3 repackager

Tier-2/3 repackager

Tier-1 wholesaler

Tier-1 wholesaler

Tier-2 wholesaler

Tier-2 wholesaler

Tier-3 wholesaler

Tier-3 wholesaler

RetailerRetailer

Retailer(Paper-pedigree)

Retailer(Paper-pedigree)

RetailerRetailer

Pattern APattern A

Pattern BPattern B

May 2, 2009Page 14

0

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Simulation Result (Pattern A)

• Inferred probability ranges 6 – 99 %• Fake drug’s highest authenticity probability is less than 90%• “Products whose authenticity level is above 90 % is authentic.”

– Percentage of false positive (Judge fake drug as authentic): 0%– Percentage of false negative (Judge authentic drug as fake): 2.4 %

Inferred authenticity

Authentic

90 %

Fraudulent

May 2, 2009Page 15

0

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Simulation Result (Pattern B)

• Inferred probability ranges 6 – 99 %• Fake drug’s highest authenticity probability is less than 70%• “Products whose authenticity level is above 70 % is authentic”

– Percentage of false positive (Judge fake drug as authentic): 0%– Percentage of false negative (Judge authentic drug as fake): 1.1 %

70 %

Inferred authenticity

Authentic

Fraudulent

May 2, 2009Page 16

0

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Simulation Result (Pattern C)

• Inferred probability ranges 6 – 99 %• Fake drug’s highest authenticity probability is less than 80%• “Products whose authenticity level is above 80 % is authentic”

– Percentage of false positive (Judge fake drug as authentic): 0%– Percentage of false negative (Judge authentic drug as fake): 2.8 %

80 %

Inferred authenticity

Authentic

Fraudulent

May 2, 2009Page 17

Observation

• Benefit of the model– Successfully infer the authenticity level– Enable to decide threshold with zero % false positive and small

% false negative• Need an operation to check the drug authenticity with other method

whose authenticity level is below threshold (e.g., ingredient analysis)

• Beneficial to both consumers and companies– No need to be selective as far as inferred probability is above a

certain level– Companies could run their supply chain with SKU level

granularity

May 2, 2009Page 18

Discussion and Future Research

• Effectiveness of the stochastic model– Be useful for consumers when choosing products– Help companies to run their supply chain with product traceability

info

• Future research– Approach would be reasonable but operation needed to carefully

designed to provide product quality indices• Model• Data to be used• Patterns to infer the product quality

– May need regulation and standards for quality indices providing process

May 2, 2009Page 19

Summary

• In this research…

– Identify a possible problem that is caused by increasing traceability information to both consumers and companies

– Propose and evaluate a model by using Bayesian Network to provide a simple quality index out from traceability information so that consumers can judge product quality with the simple index not traceability information

May 2, 2009Page 20

References

[1] M. Roberti, “EPC reduces out-of-stocks at Wal-Mart,” 2005[2] RFID Journal, “Tesco deploys Class 1 EPC tags,” 2005[3] R. Koh, E. W. Schuster, I. Chackrabarti, and A. Bellman, “Securing the pharmaceutical supply

chain,” Auto-ID Labs White Paper Series, 2003[4] P. Jones, “Networked RFID for use in the food chain,” in ETFA ’06. IEEE Conference on

Emerging Technologies and Factory Automation, 2006., 2006[5] B. Patkai, L. Theodorou, D. McFarlane, and K. Schmidt, “Requirements for RFID-based

sensor integration in landing gear monitoring - a case study,” Auto-ID Labs white paper 2007[6] M. Ogawa and M. Umejima, “Traceability system for disclosure – a case of consumer

empowerment strategy,” Auto-ID Labs White Paper Series, 2005[7] United States Food and Drug Administration, Combating anticounterfeit drugs A Report of the

Food and Drug Administration, 2005[8] S. Russell and P. Norvig, Artificial Intelligence : A Modern Approach, 2002[9] United States Department of Health and Human Service, Industry: Development &

Distribution, Stakeholder Meeting, 2004[10] EPCglobal Inc., Pedigree Ratified Standard, 2007[11] Norsys Corporation, http://norsys.com/[12] The United States Food and Drug Administration Med Watch[13] Pfizer, http://www.pfizer.com/[14] Eli Lilly, http://www.lilly.com/[15] CVS Pharmacy, http://www.cvs.com/.

May 2, 2009Page 21

Questions