Abstract—This paper discusses the hidden connections
among customers and products across several organizations
that may not otherwise be linked. The research includes several
business performance measurements along with customer
behavior and purchase patterns. It links inventory levels of
supplied materials to final product shipments and products or
sub products ordered by various customers. This enables
detection of hidden and unknown connections between
customer behaviors in different geographic locations and in
various stretches of time. The discovery of hidden lag times
between seemingly unrelated customers’ buying patterns, and
hence, market behavior, is also demonstrated. This allows for
model development for predicting market dynamics across the
business and multiple variables. The model will be enable
forecasting, with some degree of accuracy, market segment
behavior and business dynamics, as triggered by various
changes in the business environment. One of the greatest
benefits realized was the ability to cut lead times, which meant
businesses could meet customer demands sooner than the
typical lead times, providing an edge over competition and
making this a selling point by itself.
Index Terms—Big data, operations management, enterprise
resource systems, decision analysis.
The authors are with the School of Business, American Public University
System-Charles Town, WV, USA (e-mail:
ahmed.kamel@mycampus.apus.edu, kirwin@apus.edu).
[PDF]
Cite: Ahmed Kamel and Kathleen Irwin, "Obscure Business Intelligence OBI: Predictor Analytical
Strategies for Maximum Business Performance and
Market Dynamics," Journal of Economics, Business and Management vol. 3, no. 2, pp. 292-296, 2015.