ABSTRACT
Workshop digitalisation (WD) provides a rich supply of real-time production data to achieve efficient manufacturing operations management (MOM). However, if data quality (DQ) cannot be ensured, manufacturing enterprises will not benefit from WD despite the resources invested. This study proposes a new method to evaluate the effect of WD from a data quality (DQ) perspective and identifies the root causes of WD failures at the operational level by information stream analysis based on an extended value stream mapping 4.0 (VSM4.0). The method is developed through a Design Science Research (DSR) approach and tested in an automobile parts manufacturer in China.
Nomenclature
This section provides the notations used in Section 4.2 | ||
ℝ | = | Represents a relational database scheme |
ℛ | = | Represents a relational scheme |
= | Represents an attribute of a relational scheme | |
= | Represents a domain of an attribute | |
Iℝ,Iℛ | = | Represents an instance of a relational (database) scheme |
t | = | Represents a tuple in an instance |
= | Represents a (tuple of) constant(s) or variable(s) | |
= | Represents a (tuple of) variable(s) | |
= | Represents a value assignment to a (tuple of) variable(s) | |
P, Q | = | Represents an atom |
θ | = | Represents a comparison operator |
Agg(•) | = | Represents an aggregation function |
exp | = | Represents an expression in an aggregation function |
fc | = | Represents a filtering condition in an aggregation function |
c | = | Represents a denial constraint |
f(•), w(•) | = | Represents a function |
Acknowledgments
This work was supported by the Projects of the National Natural Science Foundation of China under Grant 72188101; and the Chinese Institute For Quality Research.
Disclosure statement
No potential conflict of interest was reported by the author(s).