On the combination of Sensor Data in Supply Chain Automation...

On the combination of Sensor Data in Supply Chain Automation

Challenge and Research Agenda

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Presented byAkkaranan PongsathornwiwatAssistant ResearcherLogEn i4.0, SIIT, Thammasat University

Contents ❑ Automation: From manufacturing To supply chain

❑ Sensor in supply chain automation

❑Managing sensor data in digital supply chain: single or multiple data is useful for productivity and operational efficiency?

❑ On the combination of sensor data: trends and research agenda

❑What’s next in sensor and AI technology

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Automation: From manufacturing To supply chain❑Why is industrial automation important?

❑ Increase operational efficiency

❑ Increase productivity

❑ Increase resource utilizations

❑ Increase customer satisfaction

❑ Increase profitability

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https://images.app.goo.gl/DY8cr1PmYwfbBeEN7

Automation: From manufacturing To supply chain❑ There are THREE important industrial solutions for helping better operationalexcellence.

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Machining Solutions Measuring Solutions Factory automation & Robots Solutions

https://images.app.goo.gl/i543YiXcnndWtnYp9https://images.app.goo.gl/beAJ9fhH9MquVj2w8

Automation: From manufacturing To supply chain

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❑ The heart of three industrial solutions is the process of monitoring and control engineering.

❑ Sensor plays important role in detecting and collecting data for analysis in order to providewhat-if scenarios in targetrecognition.

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Automation: From manufacturing To supply chain

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❑ Taking advantages from automation in manufacturing to make a supply chain smarter. ❑ Increase visibility

❑ Increase transparency

❑ Increase predictive capability

❑ Increase adaptability

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Sensor in supply chain automation❑ The need of supply chain automation and tracking system.

❑ Case study: A big car assembly manufacturer in Thailand

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Sensor in supply chain automation

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Increase operationalefficiency throughautomation

Reduce repair costs andmaintenance downtimethrough better monitoring

Perform real-time inventory tracking with improved demand planningEnhance customer service

by connecting moreclosely to the customer

The smart sensor ecosystem

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Source: http://engineering.nyu.edu/gk12/amps-cbri/pdf/Intro%20to%20Sensors.pdf

Analyzing sensor data in supply chain automation❑ IBM’s supply chain teams proposed four stages of supply chain analytics

1) Descriptive analytics => Visualization

2) Predictive analytics => Predict events or future outcomes

3) Prescriptive analytics => what should we do

4) Cognitive analytics => deal with a human nature problem responding to the case that never happens in the past; for example, an organization answer complex questions in natural language — in the way a person or team of people might respond to a question.

Source: https://www.ibm.com/supply-chain/supply-chain-analytics

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Decision and Business IntelligentD

ecis

ion

In

tell

igen

ce

Business Intelligence

Optimization modeling: Planning

Visualization – Is the best happening?

Simulation modelling

Forecasting modeling

Statistical Analysis

Query/Drill down – Where exactly is the problem?

Standard report – What happened?

ERP – Record Transactional Data

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Industry 4.0 Maturity Levels

acatech Industrie 4.0 Maturity Index - A Multidimensional Maturity Model

ERP Optimization

How to combine sensor data for useful cases?❑ A single sensor may not be enough to derive a desired level of target estimation or hypothesis identification in supply chain applications.

❑ Therefore, multiple sensors are required to achieve a complete and accurate description of an environment or process of interest.

❑ The simple question is how to combine sensor data gathered for useful cases.

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How to combine sensor data for useful cases?❑ A generic framework for multiple data combination

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Sensor Data Unification and Assignment methods

Weight and reliability determination

Rule of Combination techniques

Frikha, A., & Moalla, H. (2015). Analytic hierarchy process for multi-sensor data fusion based on belief function theory. European Journal of Operational Research, 241(1), 133-147.

How to combine sensor data for useful cases?❑ A generic framework for multiple data combination

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Sensor Data Unification and Assignment methods

Weight and reliability determination

Rule of Combination techniques

Frikha, A., & Moalla, H. (2015). Analytic hierarchy process for multi-sensor data fusion based on belief function theory. European Journal of Operational Research, 241(1), 133-147.

Sensor Data-related Fusion AspectFig. Taxonomy of data fusion methodologies: different data fusion algorithms can be roughly categorized based on one of the four challenging problems of input data that are mainly tackled

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Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information fusion, 14(1), 28-44.

Theoretical Foundation❑ Fusion of imperfect data

❑ Probabilistic fusion

❑ Evidential belief reasoning (DSET)

❑ Fusion and fuzzy reasoning

❑ Hybrid fusion approaches

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Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information fusion, 14(1), 28-44.

How to combine sensor data for useful cases?❑ A generic framework for multiple data combination

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Sensor Data Unification and Assignment methods

Weight and reliability determination

Rule of Combination techniques

Frikha, A., & Moalla, H. (2015). Analytic hierarchy process for multi-sensor data fusion based on belief function theory. European Journal of Operational Research, 241(1), 133-147.

Some promising research issues❑Most fusion systems are optimistic in that they assume that all sensors are reliableand pay more attention to uncertainty modeling and fusion methods.

❑ The performance of the fusion system is, however, highly dependent on sensor performance and adaptability to the operating environment as well as ability to estimate the reliability of each sensor readings (pieces of evidence).

❑ Two issues that need to be considered❑ The data derived from multiple sources (signals or humans) is usually imperfect (imprecise,

uncertain, and even conflicting). ❑ The imperfection and unreliability of sensor data are often attributed to technical and noise

(environmental noise, presence of unknown targets, meteorological conditions, etc.) factors.

❑ It needs an index for quantifying sensor performance and weighing readings.

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Some promising research issues❑We can then treat such issues in Multiple Criteria Decision-Making Problem (MCDM).

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A Hierarchical structure for the weight evaluation of sensor BOEs, proposed by Frikha & Moalla (2015).

multiple conflicting decision factors

Ongoing data fusion research ❑ Automated fusion

❑ Belief reliability

❑ Fusion evaluation❑ Evaluating the quality of input data to the fusion system

❑ Assessing the performance of the fusion system

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Analyzing sensor data in supply chain automation❑ IBM’s supply chain teams proposed four stages of supply chain analytics

1) Descriptive analytics => Visualization

2) Predictive analytics => Predict events or future outcomes

3) Prescriptive analytics => what should we do

4) Cognitive analytics => deal with a human nature problem responding to the case that never happens in the past; for example, an organization answer complex questions in natural language — in the way a person or team of people might respond to a question.

Source: https://www.ibm.com/supply-chain/supply-chain-analytics

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Our research interests❑ Cognitive analytics => deal with a human nature problem responding to the case that never happens in the past; for example, an organization answer complex questions in natural language — in the way a person or team of people might respond to a question.

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Problem formulation

❑ Linguistic assessments represent uncertain and vagueness of information due to human’s ability.

❑ Linguistic values are totally different from numeric ones.

❑ A computing with word (CW) methodology is necessary!

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Data set

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Data set

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Data modelling ❑ How to model the uncertain linguistic assessment?

❑We reformulate the Dempster-Shafter theory of evidence for modeling the data.

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Data representation

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Data representation

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Future Directions ❑ It is not effective to define a unique linguistic term set to be used by alldecision-makers (Herrera et al, 2016; Jiang et al., 2017).

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Future Directions ❑ To extend a further study to application feedback stage in order for continuous improvement (Wu et al., 2012).

❑ So we aim to gather more experts’ opinions in order to build a software-based decision support system (DSS)

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Further readings 1) Zhang, Y., Liu, Y., Zhang, Z., Chao, H. C., Zhang, J., & Liu, Q. (2017). A weighted

evidence combination approach for target identification in wireless sensor networks. IEEE Access, 5, 21585-21596.

2) Frikha, A., & Moalla, H. (2015). Analytic hierarchy process for multi-sensor data fusion based on belief function theory. European Journal of Operational Research, 241(1), 133-147.

3) Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multi-sensor data fusion: A review of the state-of-the-art. Information fusion, 14(1), 28-44.

4) Sentz, K., & Ferson, S. (2002). Combination of evidence in Dempster-Shafer theory (Vol. 4015). Albuquerque: Sandia National Laboratories.

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What’s next in sensor and AI technology

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Cyber space

Physical space

IoT

Smart Machine

Smart Machine

Total Synchronize

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AI Cloud

AI Planning and AI Scheduling with Production Simulation

AI Planning

AI Scheduling

ProductionPlanning/Scheduling

GD.findi Production Simulation

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Final remarks!

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Any questions or suggestions is welcome!

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