Renny Pradina Kusumawardani

Analysis of production planning in a global manufacturing company with process mining

Purpose The purpose of this paper is to present the result of using process mining to model the production planning (PP) process of a manufacturing company that is supported by enterprise resource planning (ERP) systems. Design/methodology/approach This paper uses event logs obtained from the case company’s ERP database. The steps for this research are planning process mining implementation, extraction and construction of event log, discovering process model with Heuristic Miner and analysis. Findings Process model obtained from process mining shows how the PP is actually conducted. It shows the loop in materials requirement planning and create plan order process. Furthermore, the occurrences of changing plan order date and production line indicate the schedule instability in the case company. Further analysis of the material management (MM) event log shows the implication of production plan changes on MM. Continuous change in the plan affects material allocation priority and may result in a mismatch between production needs and the materials available. Research limitations/implications The study is only conducted in a single and specific case. Therefore, even though the findings provide good insight, the use of solitary case study does not imply a general result applied to other cases. Hence, there is a need to conduct similar studies on various cases so that a more generic conclusion can be drawn. Practical implications The result provides insights into how the current company’s policy of adjusting the production plan to accommodate changing demand impacts their operation. It can help the company to consider a better balance between flexibility and efficiency to improve their process. Originality/value The paper demonstrates the use of process mining to capture the real progression of PP based on the data stored in the company’s ERP database, which give an insight into how a real company conducts their PP process, the implication of schedule instability on MM and production. The novelty of this research lies in the use of process mining to attest to the schedule nervousness issue at a process level.

BencanaVis visualization and clustering of disaster readiness using K Means with R Shiny A case study for Disaster, Medical Personnel and Health Facilities data at Province level in Indonesia

The open data movement has led us into immensely useful applications and innovations for decision making, both for individual citizen as well as government. This study aims to create a web application called BencanaVis which provide innovative visualization of disaster government open data using Shiny, a web framework from R programming language. The datasets being used are available from Indonesian National Disaster Management Authority agency (or BNPB), the official Indonesian Open Data government portal and the Indonesian National Statistical Bureau (or BPS) website. We create three types of scenarios or experiments for the dataset. After that, we normalize the data using min-max use normalization. Then, we employ PCA (principal component analysis) to reduce feature dimensionality. Furthermore, we apply K-Means clustering techniques and calculate the cluster validity using Sum of Square Error (SSE), Davis-Bouldin Index (DBI), Dunn Index, Connectivity Index and Silhouettes Index. The cluster member from optimal number of k are then being analyzed to create a score for disaster readiness. We shall analyze this disaster readiness using the scoring produced by weighting the attributes values with weights from the AHP methods. Furthermore, we provide two visualizations; they are 3D scatter plot and cluster distribution using leaflet library from R. There are two other visualizations provided in the web application use heatmap and streamgraph library. The heatmap visualization shows the pattern distribution of all attributes and streamgraph visualization which refers to stacked area chart shows the number of 21 types disaster which recorded from BNPB data in 16 years during the year 2000 – 2016.