01 / THE PROBLEM

The hidden complexity before machine avaliability

In the summer of 2020, 24-year-old software engineer Rashiq Zahid was unable to buy a McSundae in Berlin twice, once via a touchscreen kiosk and once through the McDonald’s app, so he decided to act.

Zahid built a bot that checked roughly 14,000 U.S. locations every 30 minutes by attempting to add a McSundae to an online order. Successful attempts appeared as green dots on a map, while failed ones appeared as red dots. The site quickly went viral: The data showed that 9.3% of U.S. McDonald’s ice cream machines were unavailable, rising to 20% in Seattle and 19.57% in New York City.

Sites like McBroken made downtime visible from the outside. A red dot tells the team that revenue is being lost, but not why it happened. Operators need the next layer of intelligence from the inside: why the machine stopped selling, what risk level the fault carries, and what action should follow.

01 The limits of availability status

A soft-serve machine can become unavailable for multiple and interconnected reasons. When those states are compressed into a binary flag, teams still rely on manual inspection to identify the root cause, consuming significant time, labor and operational uncertainty.

Binary status · Root-cause gap · Manual inspection

02 The fragmented machine data protocols

As operators scale across mixed equipment fleets, fragmented protocols can make machine data difficult to interpret. Without a normalized operating layer, real-time signals lose consistency, turning real-time operational signals into fragmented integration problem.

Mixed fleets · Protocol fragmentation · Normalized layer

03 The gap to verify recovery

Fault detection is only the first step. Without a closed loop from machine status to action and verification, teams cannot confirm recovery, leaving service continuity exposed to operational risk.

Fault detection · Recovery validation · Service continuity

02 / OUR APPROACH

Strengthening machine reliability through connected operations

VendSolution turns soft-serve equipment from a standalone vending unit into a connected, serviceable and recovery-aware operation. The platform brings device signals and operational data workflows into one normalized layer. Instead of stopping at isolated dashboard alerts, each machine state is interpreted in context with a clearer path to action. This helps operators move beyond isolated alerts toward faster decisions, establish an efficient machine-health workflow across unattended retail fleets.

03 / EXAMPLE

Why Chia Feng needed a connected service layer

Although our client’s service model already supports basic repair management, as the company expanded into soft-serve equipment, the operational challenge became more data-intensive: each machine carried 68 variables, 7 operating modes, and 7 alarm-code references, leading to problems in managing order outcomes and recovery conditions manually.

Helping to scale equipment service with connected operations

• ‍Machine data at operating depth

‍Unified device-level variables into one backend view, giving operators clearer visibility into each service interruption.

• ‍Fault logic tied to service decisions

‍Classified machine faults by operational risk and service impact, helping teams prioritize response across multiple machines.

• ‍Remote maintenance and recovery control

Turned fault insights into remote actions, reducing unnecessary dispatches and speeding up recovery.

• ‍Order-aware exception handling

‍Linked machine status to selling, fulfillment and refund decisions when equipment issues affect orders.

04 / THE RESULT

Turning machine faults into faster operation decisions

With VendSolution, machine availability becomes more than a binary status. VendSolution is commited to help operators gain a connected view of their business and properties. The system we developed reduces dependence on blind onsite inspection and gives service teams a more consistent way to manage uptime across soft-serve equipment fleets. The result is a more traceable operating model: fewer unknown failures, faster fault confirmation and more consistent service decisions across the fleet.

05 / SUMMARY

Making soft-serve operations visible, diagnosable, and recoverable

Ice cream downtime is often treated as a hardware problem. In practice, it is an operating-system problem across machine data, transaction outcomes, maintenance decisions and field-service execution. VendSolution connects these layers so operators can move from blind inspection to informed diagnosis, from isolated alerts to prioritized action, and from fault records to verified recovery.

For operators, the value is not only knowing that a machine is down. It is knowing why, what to do next and whether service has been restored.

About Chia Feng Foods

Chia Feng Foods Co., Ltd. is a well-established Taiwanese foodservice beverage solutions provider with over 30 years of experience and strong influence in Taiwan’s HORECA and commercial beverage market. With NT$50 million in paid-in capital, around 100 employees, and operations in Taipei, Taoyuan, and Taichung, the company serves a broad customer base across restaurants, cafés, hotels, catering operators, and beverage channels. Its one-stop capabilities in beverage R&D, branded ingredient supply, equipment rental, training, and maintenance, together with partnerships with major brands such as Nestlé, Coca-Cola, Calpis, and BUNN, reinforce its leading market position in Taiwan.

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