1. Case 
Ningbo aiken intelligent technology co., ltd. | digitalization of bakelite and final assembly workshop

Customer name: Ningbo Aiken Intelligent Technology Co., Ltd. (referred to as "Aiken")

Industry: Electronic Appliances

Enterprise Size: Medium Manufacturing Enterprise

Project Cycle:6 months

2. Customer pain points 
Before the digital transformation, Aiken faced the following core pain points:

Production pain points: the product variety is large, the batch is small, the traditional scheduling depends on manual labor, the production switch is time-consuming and the flexibility is insufficient, resulting in high risk of delivery delay, it is difficult to meet the rapid response to market demand.

logistics/storage pain points: material storage and transportation are highly dependent on labor, resulting in high material loss rate in the workshop, such as time-consuming material finding, untraceable material loss, opaque inventory management, etc, inventory backlog is serious.

Quality/traceability pain point: production process data collection relies on manual records, quality traceability system is missing, it is difficult to locate the source when products have quality problems, manual quality inspection efficiency is low and the leakage rate is as high as 8%.

Management/system pain point: the original system runs in isolation, cannot communicate with ERP, MES and other systems, the production status cannot be monitored in real time, and the data lag leads to low management decision-making efficiency.

3. Our solution 

For pain points, we focus on "digitalization and intelligence" and tailor-made solutions:

Deployment system:

Core system: Deploy MOM manufacturing operation platform + whole process quality traceability system to realize production planning, execution and quality closed-loop management.

Hardware configuration: Deploy smart sensors, data acquisition terminals (DCS), workstation smart tablets, RFID traceability equipment, etc. to build a real-time data acquisition network.

Implementation:

Phased implementation: push forward according to the path of "pilot verification → gradual promotion → full coverage" to ensure that workshop production will not stop production and reform;

Personnel training: Carry out training on system operation, equipment maintenance and digital management to improve the team's digital ability.

System integration:

open up MOM platform and aiken's existing ERP and PLM systems to realize real-time synchronization of orders, materials and production data and eliminate "information islands".

Quality traceability:

build a whole-process quality traceability system, from raw material warehousing to finished product outbound, each link binds batch codes, and records process parameters and quality inspection results in real time through intelligent terminals.

4. Project implementation effectiveness : After digital transformation, Aiken workshop has achieved significant improvement:

Efficiency category: scheduling efficiency improvement 20%, logistics transfer efficiency increased by 1.5 , and overall production efficiency increased by 10%.

Quality category: Product defect rate decreased 2%, quality inspection efficiency increased 2%, the missed detection rate dropped to 2%.

Cost category: Labor savings 7 people/year , material loss rate decreased 2%, inventory turnover increased by 15%.

Management category: delivery on-time rate increased from 70% to 85%, production data can be checked in real time, quality traceability coverage is 100%.

5. Case Highlights

Flexible production and capacity optimization: intelligent scheduling through MOM platform, real-time monitoring of capacity bottlenecks, support for rapid switching of multiple varieties and small batches, increase the synergy of the manufacturing process 30%, and significantly improve the capacity utilization rate.

Paperless quality inspection and traceability closed loop: adopts station intelligent flat plate instead of paper inspection records, and binds a unique traceability code for each batch of products to realize traceability of the whole process data from raw materials to finished products, quality inspection efficiency and accuracy are greatly improved, reducing quality personnel costs.

Phased transformation and data-driven decision-making: the project adopts a phased implementation strategy of "no shutdown" to ensure production continuity; After integrating multi-system data, the management layer can grasp the production status in real time through visual kanban, shorten the response time of abnormal warning by 50%, and realize data-driven fine control.