Schneider Electric's Batam smart factory: start with one line, cut machine downtime by 44%
Solution case studies are scenarios based on common industry problem patterns. They do not refer to any client and are not MarkasDev project portfolio items. Our client portfolio is published separately.
Industry context
This project belongs to Schneider Electric and its partners, not MarkasDev. Every figure below comes from openly published whitepapers and materials. The Batam smart factory was built and is run by Schneider Electric together with its technology partners, including AVEVA. We read a World Economic Forum (WEF) white paper produced with McKinsey, an official Schneider Electric press release, an AVEVA customer case study, and reporting by ANTARA, Majalah ICT, The ASEAN Post, and FutureIoT. All sources are listed at the end.
Schneider Electric's Batam plant has operated since 1991 and makes 11 lines of power and industrial automation products (AVEVA, 2020). According to ANTARA (18 November 2018), PT Schneider Electric Manufacturing Batam (SEMB) employs more than 2,900 people across three smart factories that make contactors, relays, variable speed drives, sensors, circuit breakers, and electronic boards for the domestic market as well as Europe, North America, China, India, and Asia Pacific.
In November 2018, Indonesia's Ministry of Industry named SEMB the Industry 4.0 lighthouse for the electronics sector under the Making Indonesia 4.0 roadmap, and the two signed a three-year memorandum of understanding to train and mentor other manufacturers (ANTARA). In April 2019 the plant received the "A National Lighthouse for Indonesia" award at the INDI 4.0 event (Majalah ICT). Then in July 2019 the WEF designated it an Advanced Fourth Industrial Revolution Lighthouse, one of the first two ASEAN factories in the Global Lighthouse Network alongside Petrosea in Tabang (Schneider Electric release, 9 July 2019, and The ASEAN Post, 12 August 2019).
Why this matters for plant leaders and operations managers in Indonesia: this is an old plant, not a new one, made more productive with data. Many Indonesian factories are in the same position, with machines that still run well while shop-floor decisions still depend on paper, phone calls, and people's memory.
Problem
The starting problem was not bad machinery. AVEVA notes the Batam plant was already running efficiently on Schneider's own EcoStruxure solutions. What was missing was visibility. The team wanted better oversight of operational performance, more detailed data on equipment failures to know how to tackle them, and one digital solution that spanned the several facilities on the Batam site.
At the global program level the challenge was stated more sharply: little visibility of shop-floor activity because of a lack of data, which delayed responses to problems on the line, plus legacy systems, bespoke software implementations, and manual paper-based processes (AVEVA, 2020).
The real cost sat in three places. First, machines that stop unexpectedly halt output and eat technician hours spent hunting for the root cause. Second, defects and scrap are a direct cost. Third, late deliveries erode the trust of export customers. The WEF also noted that informal communication such as phone calls and chat apps, and time wasted chasing people for data, were sources of inefficiency that the transformation reduced (WEF, December 2019).
The sources we opened do not give a rupiah figure for downtime losses before the project, absolute downtime hours, or the total investment in the transformation. That data has not been published.
Constraints
- A brownfield plant operating since 1991, so digitization had to happen while production kept running
- Many lines and facilities: 11 product lines and three smart factories in Batam with more than 2,900 employees
- Legacy systems, site-specific bespoke software, and paper processes that had to be replaced without hurting output
- The model had to be standardized across Schneider's global plant network, not built for one site only
- Suppliers were involved, so production data and demand forecasts had to be shared outside the plant
- The workforce needed new skills in data, digital tools, and problem-solving
- Limits of our evidence: most result figures were reported by Schneider Electric and its partners, without absolute baselines
Approach
Diagnose
This section is our analysis of how the Diagnose phase shows up in the Batam case. It is not a claim that MarkasDev did this work.
The Diagnose work visible in public sources starts from a business question, not a technology. Schneider's release says the goal was to reduce maintenance costs while increasing overall equipment efficiency. The Smart Factory Program then mapped six pain points: shop-floor agility, process efficiency, asset performance management, empowered operators, reliability, and energy efficiency (AVEVA, 2020).
From that map came five main use cases that the WEF later recorded: Maintenance 4.0, digital performance management tools, Quality 4.0 for defect reduction, Quality 4.0 for traceability, and an integrated supply chain. Each use case had one clear indicator, such as machine downtime, operational efficiency, scrap cost, customer complaints, and supplier service rate.
For your plant the Diagnose questions are similar: which machines stop most often and what does an hour of stoppage cost, what data already sits in PLCs and panels but is never read, and which shop-floor decisions still wait for a paper report. See our Diagnose approach for mapping this before buying hardware.
Deliver
This is also analysis. Deliver means building in stages with results that can be measured at each step.
The pattern was staged and started small. According to Xavier Denoly, Country President of Schneider Electric Indonesia, deploying EcoStruxure on one electromechanical production line took less than a month and paid back in under six months thanks to a 46% reduction in wasted material and up to 17% fewer maintenance hours (Majalah ICT and The ASEAN Post, 2019). In general, payback on IIoT investment at Schneider's plants ranged from under six months to two years.
The technology layers went in one after another. Smart sensors and connected devices read machine condition. AVEVA Insight aggregates and visualizes real-time performance data in the cloud, AVEVA Enterprise Asset Management handles asset performance, and AVEVA Discrete Lean Management was co-developed to digitize lean practices on the shop floor (AVEVA, 2020). On top sit Manufacturing Control Tower dashboards for plant managers, augmented reality for operator training, and planning and scheduling tools that give an end-to-end view of demand (Majalah ICT and the Schneider release).
The supply chain was connected too. The WEF describes one communication portal for all suppliers, real-time production data sent to suppliers, demand forecasts for inventory planning, and QR-code inventory tracking. Schneider's release also describes the plant as a test bed for machine learning, AI, and predictive maintenance.
Reasonable acceptance criteria for a similar project (our analysis): the share of critical machines whose data is read automatically, time from alarm to technician arrival, failure prediction accuracy, and downtime before versus after. See also systems & API integration and AI implementation.
Operate
This is also analysis. Once the system is live, the Operate phase decides whether the results last.
Batam built an organization for that. The WEF notes a digital transformation department with a rotating project manager, typically in the role for one year, who works on road-map projects with subject-matter experts, line leaders, and managers in the relevant departments. Wiranata Suganda, Plant Director for the Batam Smart Factory, explained that real-time performance tracking from "shop floor to the top floor" enables digital escalation and stopping the line when there is a quality issue (AVEVA, 2020).
The cultural effect was recorded as well. According to the WEF, data transparency increased trust in the data and among workers, while going paperless cut informal communication and time wasted chasing data. The plant also became a showcase: more than 150 customers and partners from several countries had visited (The ASEAN Post, 2019), and the memorandum with the Ministry of Industry covered training for the food and beverage, electronics, automotive, mining, and textile industries.
In the Operate framework, the routine work is keeping sensors and connectivity healthy, reviewing prediction model accuracy, following up escalations, securing the OT network, and making sure the servers and environments that hold the data stay available.
High-level architecture
This flow is our simplification of the December 2019 WEF white paper, the 2020 AVEVA case study, and the Schneider Electric release, not an architecture we audited. Component names such as AVEVA Insight, AVEVA Enterprise Asset Management, AVEVA Discrete Lean Management, and Manufacturing Control Tower come from those sources. AI and machine learning are only described as a test bed in the Schneider release, and we found no result figure attributed specifically to an AI model. Network design, OT security, and ERP integration details are not described in the sources we opened.
Typical outcomes
Every figure below comes from a source we opened. The figures were reported by Schneider Electric and its partners and then published by the WEF, without absolute baselines, so read them as percentage changes within each scope.
| Indicator (Batam) | Result | Source |
|---|---|---|
| Machine downtime (Maintenance 4.0) | down 44% in one year | Schneider release, 9 Jul 2019, and WEF, Dec 2019 |
| On-time delivery | up 40% | Schneider release and WEF |
| Operational efficiency | up 12% | WEF and AVEVA |
| Employee engagement | up 5% | WEF and AVEVA |
| Scrap cost (Quality 4.0) | down 40%, for some critical machines | WEF and AVEVA |
| Customer complaints (traceability) | down 24% | WEF |
| Supplier service rate | up 70% | WEF and AVEVA |
| Supplier administration time | down 85% | WEF |
| One electromechanical line | wasted material down 46%, maintenance hours down up to 17%, payback under 6 months | Majalah ICT and The ASEAN Post, 2019 |
Additional figures from The ASEAN Post (12 August 2019): manufacturing defect rate down 31% and non-quality or scrap cost down 25%. The same outlet reported downtime down 40%, which differs from the 44% in the official release and the WEF paper. We use 44% because it comes from the primary sources.
Energy. AVEVA cites 21% energy savings "at some sites" and 377 tons of CO2 reduced across Schneider's whole smart factory network. Those are not Batam-specific figures, so we do not present them as Batam results.
Recognition. The plant became the Ministry of Industry's national Industry 4.0 lighthouse and a WEF Advanced 4IR Lighthouse. The WEF Global Lighthouse Network had 44 sites as of 6 January 2020.
What we did not find: total investment, cost savings in rupiah, absolute downtime hours, and results data after 2020.
Typical or expected outcomes, not claims about MarkasDev project results.About this analysis
This page is compiled from open sources and is not a MarkasDev client project. We did not interview Schneider Electric or AVEVA, did not visit the plant, and did not access raw operational data. Most figures come from Schneider Electric and its partners and were then published in the WEF white paper prepared with McKinsey. The smart factory page on Schneider Electric Indonesia's website could not be opened from our network, so figures that appear only on that page are not used.
Why this case matters for Indonesian industry
Making Indonesia 4.0 names five pioneer sectors: food and beverage, textiles and apparel, automotive, chemicals, and electronics. According to the then Minister of Industry, this group accounts for 65% of exports, 60% of GDP, and 60% of the industrial workforce (ANTARA, 18 November 2018). The Batam case shows that a plant in Indonesia can stand alongside the best factories in the world, and that the path can start from an old plant.
Reading the case with Diagnose, Deliver, Operate
Three questions in order. Which machines and processes cost the most when they fail, and what data is already available? That is Diagnose. Which line becomes the pilot, with what indicator, and when is it judged a success? That is Deliver. Who watches the sensors, prediction models, and escalations every day? That is Operate. For other case studies in this sector, see the Manufacturing sector. To start a conversation, see IT consulting or contact us.
Sources
Accessed 5 October 2026 (WIB).
- World Economic Forum with McKinsey & Company, Global Lighthouse Network: Insights from the Forefront of the Fourth Industrial Revolution, white paper, December 2019.
- Schneider Electric via PR Newswire, Schneider Electric's Batam Smart Factory recognized by the World Economic Forum as a Fourth Industrial Revolution Lighthouse, 9 July 2019.
- AVEVA, Schneider Electric's Smart Factory Program: A lighthouse for digital transformation in manufacturing, customer case study, 2020.
- ANTARA, Kemenperin cari pabrik pintar untuk rujukan Industri 4.0 (in Indonesian), 18 November 2018.
- Majalah ICT, Smart Factory Schneider Electric Raih Penghargaan "A National Lighthouse for Indonesia" (in Indonesian), April 2019.
- The ASEAN Post, Showcasing IIOT to ASEAN and beyond, 12 August 2019.
- FutureIoT, WEF recognizes Schneider's Batam smart factory for 4IR innovations, 4 July 2019.