A hybrid business process optimisation method incorporating quantifiable interdependence analysis and smart data

This research presents an answer for the central issue of business process optimisation by integrating quantitative process interdependence investigation. The process interdependence characterises how various functions of a process rely upon the exhibition of others. Authoritative execution and functions are intensely subject to the relationships that can be sorted as pooled, sequential and reciprocal. For a production supply chain, the most widely recognised sort of relationship is sequential, and its quantifiable investigation can permit to distinguish the functional and cross-functional reliance factors that show up during the execution of changes in measure capacities for advancement. The cross-functional factors can have both a negative and positive effect on proficiency and profitability. The recognisable proof of cross-functional impacts and changes as per the reliant connections of sub-processes can diminish the disappointment rate and increment cost-effectiveness by killing the superfluous advances and limiting mishandling of assets. Interdependence in business processes is one of the most significant challenges to quantify and can provide actionable insights based on data. Process interdependence is generally analysed in a qualitative manner (through interviews and surveys) that increase the risks of overlooking crucial parameters. This research presents a new hybrid optimisation method called the KHB (Khan-Hassan-Butt) method that incorporates quantifiable interdependence analysis using structured data.

KHB method incorporates radical and incremental improvements through established management principles, i.e., business process re-engineering and business process management. The validity of the KHB method is accepted with the use of the Witness Horizon 22.5 simulation package, and it has been implemented in two different production line case studies. In both case studies, the process interdependence was identified using structured data collected from the shop floor using process interdependence algorithm and filtered with the data filtration process. The data filtration process transformed the structured data into smart structured data by filtering large data sets into actionable information. This information was used in the simulation software for optimisation purposes.

For case study 1 (ACME valve production line), the KHB method increased the productivity by 22.93% and decreased the production cost by 20.96% compared to published literature using an expert mechanism and bottleneck approach. In case study 2, the KHB method was implemented and validated in an international garment manufacturing company. The output showed an increase of 19.78% in productivity and a 9.78% decrease in production cost. The results show that the KHB method can have a significantly positive impact on the flexibility, problem identification, cost reductions and profitability of a business process.

The KHB method can be used to identify the quantitative process interdependence in any business process regardless of their interdependence types (sequential or reciprocal). The KHB method is capable of identifying every interdependent factor that has an impact on process functions and uses them for effective decision making to optimise the process and maximise productivity.