About the Problem Statement:

Sustainability is a top priority in Yusen Logistics as many customers are working towards public green initiatives. They are looking for service partners who prioritise corporate responsibilities as well.

About the Problem Statement:

The logistics supply chain generates waste at every turn. The types of waste include transport packaging materials, lashing materials, cushioning materials, cooling materials, and desiccant agents produced in logistics operations. Society is demanding that we reduce and promote recycling of these wastes.

Currently, our supply chain does not have a holistic, efficient and optimal waste management system and solution. Yusen Logistics hopes to reduce internal costs and prioritise ESG goals by optimising our supply chain through waste reduction and sorting technology.

About the Problem Statement:

Freight transport is a large contributor to emissions of CO2 and customers look for supply chain partners who prioritise Environmental, Social and Governance (ESG) goals. Customers often ask us to optimise logistics concerning cost and efficiency in their supply chains. Recently, we have been asked not only to optimise logistics but also to propose ways to reduce the environmental impact of logistics. In other words, they are looking for the best balance between logistics optimisation and environmental impact. To meet this new demand, it is necessary to visualise the CO2 emissions from air, ocean, and land transportation with transportation data.

For air and ocean freight forwarding, the estimated amount of CO2 emissions can be calculated through current shipment data from the organisation’s operations system.

Yusen Logistics owns its trucks for land transportation and works with many partner companies in many countries. The number of subcontractors is so large that it takes a considerable amount of time to collect the necessary data from all the subcontractors and a great deal of effort to organise the data. This is because each company has its transportation management methods and IT systems. Some of them even manage the data manually. In addition, the transportation data is handled in various foreign languages, which takes time to translate.

Green transport platform to implement shared resources, minimise empty transport legs & protect our planet.

Solutions that consider customer demands that may change rapidly (such as delivery destination, delivery window) to improve logistics efficiency.

A platform to synchronise different transportation, yard & warehousing systems to optimise resources & unlock efficiency with speed.

Objective:

• Improve vehicle build quality using vision systems, AI & Machine Learning algorithms.

Desired Outcomes:

• Delivery of visually defect-free vehicle to the customer.
• Inspection of fit and finish, painting defects, missing parts and mismatch of parts as per checklist.
• Quantifying in terms of AQI score as per the provided standard.
• Statistical analytics using the obtained data and escalation in case of deviations as defined.
• The system needs to be capable of connecting defects to the stage where the defect is happening and sending alerts/escalations.
• The system should be able to give results immediately after inspection, clearly indicating the zones and places where visual defects need to be corrected.
• The system needs to interact with the ERP system to understand the variant of the vehicle to inspect. 

Current Limitations:

• Skilled men are deployed in the line to inspect the finished vehicle and give dispatch clearance on every shift.
• Inspection needs to be done within the cycle time of <20 seconds on a moving conveyor.
• Inspection is subjective, and perception varies between inspectors and between shifts too.
• Data is manually recorded and not in a form for consumption or analysis.
• Data is entered manually in SAP.
• Occasionally, errors escape to the next stage due to monotony in the inspection.
• More information on existing system constraints:
• Type A and Type B errors – disagreement between man and machine
• Unable to cover all checkpoints (220) in < 20 seconds.
• A vehicle needs to be loaded in a particular orientation to get better images for giving ok and not ok decisions – loading difficulty to an operator to be avoided.
• Difficulty in getting repeatability in results due to allowed variations in fabricated parts.
• The camera cannot clearly differentiate minor dust particles (>1.5 mm) and colour shade differences.
• Some defects will come once in a while, and it is challenging to teach the system frequently.

Objective:

• To improve the transfer efficiency of robot painting.

Desired Outcomes:

• To improve efficiency by 30% through optimisation of the robot programme and spray parameters using vision system based AI/ML logic:
• The vision system fitted on the robot scans the painting jig and captures the image of the parts to be painted.
• Robot path to be generated using AI/ML algorithm based on the image data captured by the vision system.

Current Limitations:

• Two-wheeler parts are painted using a robot painting process, with the parts moving on a conveyor.
• Robot programme and spray parameters are developed by trial and error process based on the shape of the parts to be painted.
• It is an iterative process of modifying robot parameters and checking the paint film built on the parts after baking.
• This results in lower paint transfer efficiency – paint transfer efficiency is the ratio of paint deposited on the part to the total paint sprayed.
• Transfer efficiency for parts is measured by weight method, i.e. weight of the paint on the component to the total paint sprayed from the robot gun.
• The above process is highly skill-oriented (a skilled robot programmer is needed)
• Wide variety of parts to be painted in a single paint plant.
• Robot programmes cannot be optimised for each type of part, as it is a manual process of creating the robot programme.
• Trials and verification are time-consuming as paint thickness can be checked only after parts are baked in the oven.
• High skill requirement for engineers carrying out robot teaching for the painting process.

Objective:

• To achieve zero CO2 emission by having a solution for an oven that can bake the vehicle and not emit CO2, e.g. electricity or others.

Desired Outcomes:

• Real users are from the Toyota Manufacturing plants in each country.
• The main outcome is to be sustainable and achieving zero CO2 emission as part of company policy.

Current Limitation:

• Currently, the burners are still using gas, thus resulting in a high volume of CO2 emissions.

Objectives:

• Effectively managing solar energy collected over the weekends when the factory’s electric demand is low.
• To find a solution to manage those excess solar energies for further utilisation.

Desired Outcomes:

• Real users are from the Toyota Manufacturing plants in each country.
• The primary outcome is to be sustainable and have good excess energy management, which will, in turn, lead to cost reduction.

Current Limitation:

• No means to manage the loss of energy, which means lost cost.