Abstract: In the past decade, artificial neural networks (ANNs)
have been regarded as an instrument for problem-solving and
decision-making; indeed, they have already done with a substantial
efficiency and effectiveness improvement in industries and businesses.
In this paper, the Back-Propagation neural Networks (BPNs) will be
modulated to demonstrate the performance of the collaborative
forecasting (CF) function of a Collaborative Planning, Forecasting and
Replenishment (CPFR®) system. CPFR functions the balance between
the sufficient product supply and the necessary customer demand in a
Supply and Demand Chain (SDC). Several classical standard BPN will
be grouped, collaborated and exploited for the easy implementation of
the proposed modular ANN framework based on the topology of a
SDC. Each individual BPN is applied as a modular tool to perform the
task of forecasting SKUs (Stock-Keeping Units) levels that are
managed and supervised at a POS (point of sale), a wholesaler, and a
manufacturer in an SDC. The proposed modular BPN-based CF
system will be exemplified and experimentally verified using lots of
datasets of the simulated SDC. The experimental results showed that a
complex CF problem can be divided into a group of simpler
sub-problems based on the single independent trading partners
distributed over SDC, and its SKU forecasting accuracy was satisfied
when the system forecasted values compared to the original simulated
SDC data. The primary task of implementing an autonomous CF
involves the study of supervised ANN learning methodology which
aims at making “knowledgeable" decision for the best SKU sales plan
and stocks management.
Abstract: The running logs of a process hold valuable
information about its executed activity behavior and generated activity
logic structure. Theses informative logs can be extracted, analyzed and
utilized to improve the efficiencies of the process's execution and
conduction. One of the techniques used to accomplish the process
improvement is called as process mining. To mine similar processes is
such an improvement mission in process mining. Rather than directly
mining similar processes using a single comparing coefficient or a
complicate fitness function, this paper presents a simplified heuristic
process mining algorithm with two similarity comparisons that are
able to relatively conform the activity logic sequences (traces) of
mining processes with those of a normalized (regularized) one. The
relative process conformance is to find which of the mining processes
match the required activity sequences and relationships, further for
necessary and sufficient applications of the mined processes to process
improvements. One similarity presented is defined by the relationships
in terms of the number of similar activity sequences existing in
different processes; another similarity expresses the degree of the
similar (identical) activity sequences among the conforming processes.
Since these two similarities are with respect to certain typical behavior
(activity sequences) occurred in an entire process, the common
problems, such as the inappropriateness of an absolute comparison and
the incapability of an intrinsic information elicitation, which are often
appeared in other process conforming techniques, can be solved by the
relative process comparison presented in this paper. To demonstrate
the potentiality of the proposed algorithm, a numerical example is
illustrated.