Identifying Unwanted Conditions Using Lower Boundaries on Individual Control Charts in the Context of Supply Chain Economic Resilience of Cities in Indonesia

Abstract

This study presents the unwanted conditions determination. The economic resilience model without taking into account the level of disruption and unwanted conditions is unrealistic model. The Objective is to determine unwanted conditions as a key criterion in determining the economic resilience status of a city. This study used data on Concern variables group and Control variables groups from website of Central Bureau of Statistics Indonesia. These data covered all 514 cities in Indonesia and are observed for a 5-year period from 2014 to 2018. The data is useful to develop a statistical model that can explain well the pattern of relationships between concern variables and control variables. Piecewise linear regression is applied to identify statistics model between Pc and Z, Lower Control Limit (LCL) for variable Z using Individual control Chart is applied to determine the unwanted conditions.  We obtained that the control variable, Z is the ratio between the original income of the region (PAD) with the number of poor people in a city and the concern variable is income per capita, Pc of a city. Piecewise linear regression with breakpoint 126,255,066 can explain well the pattern of relationships between Z and Pc variables. The equation is: Pc = 26,660,263+0.28Z, R-square = 70.48%. LCL value is.1.884.059.5 so all cities that have a Z value below 1.884.059.5 fall into the unwanted condition area and after careful examination is obtained percentage of cities classified as do not have economic resilience , PER =28%. Cities that fall into unwanted conditions are defined as cities that cannot bear receiving economic shocks.