1. 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

2. 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. 3.3.1. 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.

4. 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.

5. 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.

8. 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.