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April 26, 2023

Modernising order picking : An interview with Smart Robotics CTO Heico Sandee

The future of warehouse and distribution logistics is here

With the rise of e-commerce, warehouses, and distribution centers face constant pressure to improve efficiency and meet customer demands. Traditionally, parcel picking is a time-consuming and error-prone process that results in delayed shipments and dissatisfied customers. However, robotics and automation offers a solution. Using robotics is one of the most popular ways to automate order picking and warehouses can improve efficiency and capacity by automating order-picking. Heico Sandee, CTO at Smart Robotics, is sharing his insights into how automation and robotics in the warehouse optimises the fulfilment process.

CTO of Smart Robotics Heico Sandee
CTO of Smart Robotics Heico Sandee

1. What latest technological developments are being implemented to assist with order picking in warehouses?

Examples of robotic solutions include pick and place systems like small item pickers, big parcel pickers, palletizing and depalletizing solutions. These automated solutions offer reliability, accuracy, and greater speed against which warehouses are able to perform picking tasks.

Self-learning robots are becoming more of a reality for the Logistics industry. Automated solutions that have the ability to teach themselves could mean that smart warehouses will have an even more diverse range of robotic tools to choose from for improved pick and place stations.

Stationary pick and place robots will become more versatile in terms of the items they’re able to handle. With the advantages of AI, robots are becoming more sophisticated in how they balance and measure items. In the near future, a single pick and place robot will be able to handle a wider range of packaging material, including unpacked goods. Fine-tuned motion planning - handling fragile items with more care, regulating picking speed with more precision, etc. - greatly contributes to improving parcel picking speed and efficiency. In turn, this could improve the efficiency of loose load stacking and unloading of load carriers.

2. What automated systems are helpful in order picking?

Industrial robots that take over the handling of big and bulky, or heavy to lift parcels, can significantly improve warehouse efficiency by upping the speed against which orders are sorted, cutting back on parcel sorting time.

Cobots (collaborative robots) can easily support employees’ capacity and improve the speed against which tasks are completed. Essentially, the throughput of parcels and order sorting improved, whilst employees have more time to implement their critical skills where they are needed more than in repetitive pick and place.

Self-teaching robots require less resources dedicated to oversight and management of the system. And as technology becomes smarter, the systems become more engaging and interactive - the easier robotic systems are to interact with, the faster they’ll be embraced by the Logistics industry. This is because the opportunity is opened up for anyone to take responsibility for overseeing the robot, with no need for advanced robotics knowledge.

Adopting easy-to-use robotics will amount to more accurate and high-quality work. Automated systems rarely make mistakes and are very precise, making them a great tool to support manual pick and place labour. They can sort through and pick a greater quantity of items in a short amount of time, not to mention working against a constant speed with no breaks or time off.

The easier these solutions are able to integrate with existing warehousing infrastructure, the quicker they’ll be embraced across Logistics. In the long run, not only will employee morale improve, warehouse safety, throughput, capacity, and ROI will all benefit.

3. What are the risks to staff when carrying out order picking tasks?

Repetitive tasks are not only mundane, they also place great strain on employees’ mental, psychological, and physical health. Tasks like order picking expose employees and the organisation to an array of problems such as:

  • Physical, mental, and psychological health hazards

Repetitive order picking tasks can lead to a poor mental state. Employees are likely to become disheartened, desperate, depressed, and more likely to change their jobs. As there is almost no creative stimulus in order picking, employees’ critical thinking skills deteriorate if there is no other daily incentive or outlet for it to be practised.

Next to the decline in mental and psychological health, ergonomic risk factors from order picking impose a biomechanical stress on the worker. Static or awkward posture, repetitive wrist movements, and contact stress - especially when happening in combination - are most often associated with the occurrence of Musculoskeletal Disorders (MSDs). When left unaddressed, in the long-run, MSDs can cause irreversible defects. The health decline might not be noticed at first, however, when symptoms start to show, there’s most likely already a degree of damage done.

  • Repetitive task can damage high-level productivity and negatively impact capacity

With the gradual deterioration of both physical and mental health, poor manual handling can negatively impact productivity and capacity. When employees are worn down physically, a poor mental state shortly follows. This increases the risks of superficial injuries and long-term health issues. In turn, productivity is hindered which leads to capacity issues stemming from handicapped work performance.

4. What are the extended risks of staff having to carry out mundane order picking tasks?

The implications of repetitive and strenuous tasks don’t stop at the deterioration of employees’ mental and physical health. If left unaddressed, the effects of carrying out mundane order picking tasks without a creative or stimulating outlet causes damage to a facility’s employee morale that hurts culture and employee engagement.

When morale starts to suffer, high business process costs in labour, employee error, and reduced productivity follows suit. Research studies have shown that depression alone is costing employers $31 billion to $51 billion per year in lost productivity. The World Economic Forum reports that by 2030, the global costs of mental health problems will total over $6 trillion. Adding to the costs is the factor of high employee turnover rates.

5. What are companies doing to ensure the wellbeing and health & safety of warehouse staff when order picking?

Robotic solutions are needed to take over every day repetitive and strenuous tasks to benefit existing floor workers' morale and overall health. To maintain good mental wellbeing, many facilities have started to automate manual pick and place activities. However, before switching over to automation, many warehouses can start by improving basic environmental factors to upgrade pick and place working conditions, such as temperature regulation, noise level reduction, and better lighting. Practical warehouse safety guidelines can also be implemented - encouraging employees to take regular breaks and to stand up or stretch.

It’s important to take into consideration that automation and robotic solutions aim to improve the overall health and safety of existing employees. Wellbeing in the workplace relies on constant infrastructure improvement to take pressure off of manual labour. In the long run, investing in automated solutions will free up resources for better investments in employees’ critical skills development.

6. How is AI and machine learning changing the way in which order picking happens?

In robotics and manufacturing, a few design elements come into play. There’s constantly new code being developed and enhanced technologies being made available to build and program smarter robotic solutions. There are three elements to take into account when designing an automated system, namely vision, motion, and task planning. Currently, it’s an advanced practice to balance these elements against one another, but it’s likely to become common practice as tech is being simplified for easier programming.

  1. Vision Planning: Currently in the industry, AI is becoming more widely adopted for perception, and more specifically for the processing of RGB camera data. We’ll see robots become equipped more and more with ‘touch’ sensing, in which sensor data will be processed with reinforcement learning (a particular form of AI).
  2. Motion Planning: AI will be applied in making the movement of pick and place robotics more human-like, smoother, and more efficient, currently this concept is mainly grounded on academic research.
  3. Task Planning: In task planning, a focus will go towards bringing the autonomous intelligence to the next level in the robot’s capability to respond to exceptional situations or simply to behave more effectively.

All three of these elements continue to become more sophisticated in isolation and together they’re resulting in robotic solutions that are highly advanced in their capabilities, yet easily adoptable to execute everyday warehouse activities.

7. What is the key to efficient order picking?

Accounting for over 50% of fulfilment centre operations and 55% of warehouse operating costs, order picking is arguably the most important activity in fulfilment centres, so an effective order picking system is crucial. There are several types of order picking systems warehouses can implement to boost productivity and accuracy, batch picking, multi-batch picking, wave picking, etc. The point is, efficient order picking can only take place if an automated solution matches the characteristics of the picking system your warehouse relies on.

It’s important to select the right automated materials handling system for the warehouse’s needs, and to adjust the systems as SKUs and volume increase. This contributes to maximising parcel throughput efficiency and upping warehouse capacity. Understanding the assortment of automated technologies available on the market is already a good step to improve pick and place efficiency. Exploring and implementing the right smart technologies for specific needs is key. But first, be familiar with all the automated picking technology options available to make informed decisions.

Also remember to create a balance between manual and automated labour and make pick and place stations comfortable for existing staff members. To optimise order picking, start by organising basic processes in the warehouse, like facility flow, establish a functional receiving process, utilise equipment such as bins, totes and dividers. Next, identify key pick and place stations that can benefit from automation. Prioritise these stations to install good-to-person technologies. More ergonomic workplaces reduce fatigue and minimise strains. To support the basic efficiency of order picking, adding cushioned floor mats at locations where staff will be stationed on their feet for long periods of time is one improvement to physical health. Next, investing in an automated storage and retrieval system (ASRS), pick and place cobots, or industrial picking robots can be a viable crossover to automated support systems.

8. What types of mobile technologies (scanners, wearable technologies etc) are available and how can this impact order picking?

Mobile technology has been causing major shifts in logistics operations around the world due to the widespread adoption of IoT tools in warehouses. Mobile tech touches virtually all aspects of logistics, including warehousing and inventory management, transportation, fulfilment, customer service, and even the retail experience, both in-store and online.

Radio-frequency identification (RFID) tags are now widely adopted across warehouses to improve track and trace of parcels. An RFID system consists of a tiny radio transponder, a radio receiver and transmitter, and uses electromagnetic fields to automatically identify and track tags attached to objects. The radio frequency, so to say, allows people and items to communicate. This type of technology greatly speeds up both manual and robotic order picking.

Neil Hodgson-Coyle
Neil Hodgson-Coyle
Editorial chief at TechNews180
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