The HK$100M Manufacturing+ Scheme: Why Smart Production Starts with Data
The HK$100M Manufacturing+ scheme funds smart production upgrades, but AI forecasting only works when your data architecture is ready. A data lake built first is what turns the subsidised funding into a working forecasting capability.
Your operations run on speed, but your back-office processes run on people. As production volume grows, the team is managing data across more systems than when you started. Often, the bottleneck shifts away from the physical assembly line and becomes the manual coordination of fragmented data.
To help local industries modernise these workflows, the Hong Kong Government launched the Pilot Manufacturing and Production Line Upgrade Support Scheme (Manufacturing+) on 18 November 2025. It is a two-year pilot scheme under the Innovation and Technology Fund (ITF), with a total earmarked envelope of HK$100 million. While traditional upgrades often focus on physical machinery, this scheme is designed to fund smart manufacturing technologies, including data and software infrastructure as well as relevant equipment and training.
How the Manufacturing+ Scheme Works
The financial mechanics of the scheme reduce the risk of a digital upgrade.
The matching ratio and funding ceiling. The government provides a maximum grant of HK$250,000 per enterprise for one approved project. This funding is provided on a 1:2 matching basis, meaning public funding covers up to one-third of the total approved project cost.
The qualifications. To be eligible, your company must be registered in Hong Kong, not listed or government-subvented, and have operated a legitimate manufacturing operation or production line within the territory for at least one year.
Fundable scope. The grant covers external technology consultancy, smart manufacturing software and services, and related equipment and integration work.
Exclusions. The scheme excludes normal business operating costs such as premises rental, staff salaries, and general office IT, such as PCs and office software suites.
The Infrastructure Comes First
The goal of smart manufacturing is predictive automation: systems that forecast demand and generate production schedules. But you cannot run AI forecasting models on messy, disconnected data.
Before introducing machine learning, an enterprise needs a data lake. This is the data architecture that extracts fragmented information from different sources, such as POS systems, standalone payment terminals, and B2B wholesale spreadsheets, cleans it, and normalises it into a single structured source of truth.
The Bakery Case
Consider the operational reality of large-scale food production and commercial bakeries. Their core constraint is latency. The extended proofing cycles of artisanal sourdoughs, for example, can require up to 18 hours of advanced preparation. Because their sales data is trapped in separate retail POS terminals, delivery aggregator apps, and wholesale channels, managers are forced to reconcile it by hand to guess tomorrow’s production volume.
The first step to upgrading this production line is not installing AI. It is building the integration layer. Once a data lake is engineered to pull and structure that fragmented data, the business is in a position to deploy AI forecasting models. Unlike a software subscription, the data lake is a data foundation the business owns. Every forecasting or reporting layer added later draws from the same foundation.
From Fragmented Data to Smart Production
The architecture above shows the path from raw, fragmented operations data to an automated production forecast. The point of the build is not innovation for its own sake. It is to remove manual reconciliation work. The scheme’s fundable scope covers technology consultancy, smart manufacturing software and services, and integration work, which are the building blocks of this architecture.
Before You Apply
Projects approved under Manufacturing+ must typically be completed within 12 months, using the scheme’s dedicated e-procurement system. Applications run year-round through HKPC, the scheme’s implementation partner.
Before committing to a software build or navigating the procurement portal, the useful first step is understanding what the build requires: which data sources feed the lake, how they are cleaned, and what forecasting the structured data can then support. The scheme funds the work. The decision that determines whether the funding pays off is whether the data foundation is designed before the AI is bought. That is the question to settle before an application goes in.