This paper provides preliminary discourse on buzz words about Industry 4.0 and Society 5.0. This discourse focuses on the lens of Condition Based Maintenance (CBM) and Machine Learning of Artificial Intelligence (MLAI). To some extent several companies have embarked Industry 4.0 and Society 5.0 within Internet of Things (IoT) technology. Through the wave of IoT Technology, Industries are adopting automated machinery. Predictive maintenance (PM) is indispensable not only toward the machines” vitality and longevity purpose, but also toward the human error reduction. This paper elaborates its discourse of Industry 4.0 and Society through the lens of CBM and MLAI. The mentioned Machine Learning, in this paper, refers to research methodology, as methodological frameworks. Those frameworks comprise several phases, which are: 1. Equipment Analysis; 2. Data Evaluation; 3. Data Selection and Process; 4. Modeling; 5. Decision Support Model Evaluation. The MLAI techniques are based upon the identification of behaviour patterns. This identification comprises datasets that exclude mathematical models or prior historical knowledge. The discourse in this paper intertwines CBM process and MLAI through data cleaning and processing, features stratification and extraction, model stratification and validation. This paper elaborates two renowned maintenance approaches which are preventive and corrective maintenance. Discourse in this paper focuses on corrective action, known as predictive maintenance (PM), or condition based maintenance (CBM) within Reliability Centered Maintenance (RCM). CBM is chosen as the most desirable strategy, as it involves the intervention as the consequence of the machine breakdown. It also provides cost savings toward spare parts consumption, and optimizes production.

This paper conveys the theoretical perspectives of sustainable industrial systems through Strategic Laboratory Equipment Industry, within scope of PT. Promedika Sejahtera (PPS). PT Promedika Sejahtera is a company engaged in the laboratory equipment industry, that need its strategic touch on its Strategic Laboratory Equipment. Products sold by the company are Oxygen, Medical Equipment, Ultraviolet System, Activated Carbon Filter, Pempers, and Underpad. Products that exist in the company are not in their own production in the production from abroad. PT Promedika Sejahtera merely sell the laboratory equipment. Yet, the company has various types of consumers namely hospitals, hospital cooperatives, pharmacies, factories, and water equipment RO shops. In this paper, strategic laboratory equipment industry refers specifically to inventory within the supply chain management perspectives. This paper constitutes collaboration with other papers on Economic Order Quantity (EOQ), Re-Order Point (ROP), and Safety Stock (SS), that convolutes Forecasting, ABC Analysis, and Partial Least Square (PLS). Yet, this paper refers to its focus merely on Forecasting, Demand Pattern, Exponential Smoothing and EOQ. As part of conclusion, this paper conveys several highlighted remarks. Those remarks refer to ABC Analysis. The grouping of goods using ABC Analysis Method is divided into three groups: A with 80% priority, group B with 20% priority and group C with 10% priority. The number of goods in group A is 12 items, group B 18 item type and group C are 30 items of goods. Forecasting demand required by the Company for 2018 based on priority group A can be done using the Double Exponential Smoothing (Holt’s) Method because the pattern pattern possessed from the previous year’s sales data has no seasonal but has a random trend. The results of these calculations can be seen from the table of existing research results. Ultimately, Future research is deemed indispensable within novelty purpose for the industrial system sustainability within Strategic Laboratory Equipment Industry.

One of the important activities to achieve a sustainable palm oil company is by improving the sustainable supply chain management, especially being in line with the lean and green SCM concepts. The lean concept aims to reduce costs and to increase the effectiveness of the supply chain. While the green concept tries to ensure that the ongoing process keeps bringing good effect on the environment. In designing the performance measurement of the sustainable supply chain through the lean & green supply chain management approach, this study includes a formulation for Key Performance Indicators (KPI) and grouping of the lean and green indicators which are integrated with the balanced scorecard (BSC) perspectives. It obtained 28 KPIs which consists of 15 lean supply chain KPIs and 13 green supply chain KPIs. Based on overall weighting, the highest priority KPI is the company’s total revenue and total operating costs, followed by the total cost of the supply chain (all of them are from a financial perspective). The weights of three KPIs are 0.103, 0.078 and 0.072 respectively.

This paper conveys the theoretical perspectives of sustainable industrial systems of On board Unit (OBU) in Electronic Toll and its transportation industry. It proposes kernel density analysis of artificial intelligence approach to industry 4.0. Regulation for technology developments in artificial intelligence and robotics are deemed as one of beneficial yet structurally neglected domain. This domain refers to human perspective on augmenting automation. This regulation was emphasized in 2017 by the European Parliament report level. The mentioned regulation comprises attention in Indonesia transportation industry indicating positive innovation domains in term of safeguards and regulations are needed. Prior, current and posterior trends of the Internet of Things, Industry 4.0, and Physical Internet constitutes the results of the data development and understanding. Therefore, the topical framework of automation and robotics are triggered by these developments. The mention triggers has impacted the most important wide range implementation of industries in the future. Electronic Toll Collection (ETC) system is 40% higher in cost efficiency than Manual Toll Collection (MTC) system. In this situation, Indonesian government has already issued full swing policy implementation on non-contact freeway toll collection system by the end of 2018. Structural Equation Modelling (SEM) is capitalized to proceed to data processing. Result of this research shown the driver's characteristics that significantly affect willingness to pay an on-board unit are education expenses, distance and frequency. The average value of driver willing to pay an on-board unit was 225.781 IDR and factors that affecting values of the willingness to pay an on-board unit are expenses and distance.

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