YARN INTELLIGENCE – DECISION MAKING IN YARN

MANUFACTURING ON THE AI PLATFORM

Presenting and communicating author: Debashish Banerjee – CEO of

Blackstone Synergy Consulting Group Limited, Nairobi, Kenya

db@blackstonesynergy.com

1. Introduction: The textile value chain is a complex process with several variables

forming the integrated grid and decision-making becoming increasingly complex in the

absence of a scientific model backed up by sound mathematical principles.

This paper serves to integrate the author’s experiences in trouble shooting in the textile

value chain over the years and resenting alternative decision models that can be

transferred into the software through the formulation of an algorithm. The entire initiative

is one of crystallizing a pricing equilibrium throughout the different cogs in the value

chain and enabling the stake holders to redistribute the profitability around rational

practices of sound decision making.

2. Conceptual points: The fiber properties going into the product mix are the beginning

of an intrigue since the variables in the process are not well defined when it comes to a

range of machinery settings and fundamental mechanical and electrical health of the

equipment in response to the routine rigors of fiber processing. Complications increase

at the time of online yarn quality evaluations when sudden realizations dawn in that the

products have been produced without a commensurate plan in place to control and

correct the anomalies in place. The objective of the paper is to transform the textile

value chain into an engineered process wherein predictions in the product profiling can

be done with an accuracy function of greater than 92% and process decisions can be

initiated way in advance to customize the solutions ahead of the value curve.

That in turn shall be the fundamental driving point for the textile value chain in

determining yielded value in the process.

3. Heuristic processes for sub-optimal solutions

3.1. Selection of the DFBL (depth first and breadth last) model for the textile value chain

determined by the following characteristics;

1) The fiber properties and the changes registered becoming the influence variables in

the grid.

2) The parametric options and the mechanical fundamentals like the vibration and

thermometric profiles as well as harmonics, CF (crest factors) and impedance profiles in

the electrical engineering context of the equipment in the process lines are the other

influence variables with varying degrees of strength.

3) The parametric outcomes have been classified into yarn process, the woven and

knitted structuring of the profiles and the coloration properties as well as tonal lift as a

variable of the composite parameter, the in-process performance outcomes in the value

chain and finally the product performance-quality-pricing equilibrium as defined by the

process costs as derivatives of the decision matrix.

3.2. The iterations are defined real time through a series of changes in the process that

have multiplier effects and real time feeding of the data is done to maintain the basic

fidelity to enable the researcher to home in on the key groups of influence with higher

weights in the matrix.

3.3Empirical evidence and domain expertise is the starting point of the creation of the

influence matrix but with data being fed in religiously and with the required academic

rigor, the software takes over in the evaluation programs through the heuristic

algorithms to arrive at sub-optimal influence groups with clarified implication sin the

value network.

3.3.1. Screen shot-1 is a cross-sectional data base on the AI sheet with the key sub-

variables listed out in the process based on empirical and domain expertise but the

mathematical probability weights of influence are updated real time by the software in

evaluating the data feed.

The left extreme column lists out the variables of influence from the process engineering

data while the lateral movement is on the parametric influences beginning with the

process itself as characterized by the in-process breaks and the tensile properties of the

fibers through the different stages.

3.3.2 Integrating the subsequent steps in the textile value process

Further sequencing of the progression leads us to evaluate the influences in the

downstream processes and the corresponding lift factors influencing the groups of

variables.

Irrespective of the available domain expertise at the disposal of an enterprise, the

algorithm evaluates mathematically the weights of influence rather independently and

hence comes up with the lift factor – the factor that collectively decides the influence

groups with higher probability of a trigger of changes in the outcomes.

Stronger the trigger, greater should be the propensity of the researcher and the process

engineer to tinker with the settings and the fundamentals to get the desired outcome at

a higher performance threshold.

The structure gets completed with the detailed views on the influence variables and the

corresponding relationship weights and the consequent algorithm – driven groups of

influences having higher trigger lift values – the clarified action plan in a unified

universal plane to initiate the actions on, tinker with parameters and finally get close to

sub-optimal heuristic solutions to the desired outcomes through the mechanisms on the

AI platform.

3.4. Determinants for parametric definitions:

1) The fundamental parameters influencing the matrix are vital for deciphering and

interpreting. The structure has to be in place to facilitate the decision making.

2) Parametric migration occurs on the lateral plane for an influence composite

especially when the algorithm-driven derivatives are allowed to over-rule the empirical

knowledge and the related domain expertise.

3) Iterations might converge into smaller parametric composites but the rela time data

feed is important to keep the software computing the right influence weights. Sampling

for the software triggered iterations need data fidelity although the algorithm does take

care of missing variables phenomena quite efficiently.

3.5. Influence nodes occur in three planes in the depth first mode.

1) The stochastic processes in the iterations yield the weights of influence and also

converge through certain redundant influences into singular points of influence from a

multiple domain.

2) Unknown pairing of data confluences do occur and take empirical knowledge by

surprises; however, more often than not the algorithm-driven evidence is powerful ina

practical world fed on real time data that have reasonably high fidelity match.

3) Fundamental lines of action need to b drawn around these group influences to enable

the stake holders achieve excellent process results within shorter timelines.

3.6. Series of influences and groups thereof.

1) The series of influences chronicled in the screen shots serves to help the reader

appreciate the fluidity of data structures and the links of influence therein to facilitate the

researcher and the process engineers to draw in the various groups of influences for

routine decision-making.

2) The paper strives to bring in cross-functional expertise in one platform to try and

converge actions with powerful triggers and structure process decision making in a

simplified model

3) Eventual empowerment of the stake holders in the value chain for undersanding the

influence grous is of vital importance so that the knowledge base becomes less esoteric

and more universal in the fundamental approaches.

4. Conclusions:

1) AI modeling links engineering data, process data and the end use dynamics

inclusive of in-process performances, the color characteristics and the various product

value fundamentals in one matrix that can help the process engineer and decision

makers predict outcomes of a group of corrective measures way ahead of the events.

2) The AI modeling helps improve on productivity real time by working on certain work

groups in a unified interference field to derive value spontaneously and get the desired

outcomes along with the process movements.

3) AI platform updates of the real time data base give invaluable insights for the

researcher to go for advanced simulation techniques to create an algorithm that can

eventually predict the drape and fall characteristics of apparels – something that

researchers have attempted over the years but have hitherto failed to crystallize owing

to lack of cohesion in the data structure as vouchsafed for in this paper through real

time modeling.

Blackstone Synergy as a corporate entity strives to forge alliances in this ongoing

research project to improve on the fundamentals of the strategies that can turn around

the fortunes of this industry as a cluster.