Black Week: Optimize picking for peak times

Black Friday originated in the US and was intended to boost retail sales at the start of the Christmas shopping season. Black Friday falls on the Friday after Thanksgiving, which is always celebrated on the fourth Thursday in November.

Black Friday has now also established itself in many other countries, including Germany, and has been extended to Black Week. The discount and sales week runs from the Monday before Black Friday to Cyber Monday. Today, Black Week is one of the most important sales and promotional periods in e-commerce, during which both online shops and brick-and-mortar retailers generate enormous sales.

Like other peak seasons, Black Week poses enormous challenges for logistics and warehousing. Companies have to cope with a flood of orders in a short period of time – especially in picking and delivery.

Typical questions for warehouse optimization on Black Friday

• How can companies make order picking more efficient?
• Which warehouse systems are suitable for peak times in e-commerce?
• How can processes be automated?

Challenges in manual warehouses

In order to cope with the sharp increase in orders, the processes in the company and warehouse must be easily scalable. In a manual warehouse, this primarily means that additional employees and possibly picking stations must be provided. Here, temporary workers are often hired through temporary employment agencies or student services. However, these workers must be trained quite quickly, which contributes to the frequency of errors in manual warehouses and manual picking.

• Additional employees and picking stations required
• Use of temporary workers or student services
• High error rate due to short training period

Advantages of automated processes

The advantages of automated processes and warehouses include fast order processing, higher throughput, lower error rates, and easy scalability. Manual warehouses also operate according to the man-to-goods principle, which places a heavy strain on employees. Automatic warehouses operate according to the goods-to-person principle (as do manual flow racks), which is much more ergonomic and reduces the strain on employees. However, due to the wide range of products and the small number of orders in some cases, the e-commerce sector is not well suited to full warehouse automation. Partial automation of processes using assembly and picking robots, cobots, and driverless transport systems (AGVs) can often be more promising for warehouse optimization in this area. 

• Speed and higher throughput
• Lower error rate
• Scalability and ergonomics (goods-to-man principle)

Minimizing picking times (base time, travel time, picking time, dead time, distribution time) through organizational and technical measures plays a major role in the efficiency of the picking process. In goods-to-man systems, travel time is naturally zero. RFID transponder technology is also increasingly being used in picking. One reason for this is automation and digitalization within the framework of the Industry 4.0 standard. Special picking methods such as pick-by-light, pick-by-voice, pick-by-terminal, pick-by-MDE, and pick-by-point® are being introduced into picking. Pick-by-light and pick-by-voice systems in particular offer great potential for optimization and improved ergonomics for order pickers. Pick-by-voice is a paperless, voice-controlled picking method. This solution allows order pickers to focus entirely on picking. It also leaves their hands free for the picking process. The WMS transmits the picking orders via a voice client, and the picker confirms the removal, which is reported directly back to the WMS. This eliminates time-consuming searching, reduces error rates, and optimizes picking performance. 

If you use storage systems based on the man-to-goods principle, you should arrange the goods according to the size of the demand. Fast-moving items must be stored as close as possible to the picking stations. In general, it is also worthwhile to optimize inventory and use predictive sales planning (see below). Items should also be organized into product classes to eliminate long searches. If warehousing is already organized and controlled with a WMS, the system usually also takes care of route optimization for efficient picking. 

Single-stage picking

Various methods of order picking are used in warehouses: order-oriented serial or parallel (single-stage picking), series-oriented parallel (two-stage or multi-stage picking). Manual or automated picking systems are also used. 

Two-stage picking

Serial-oriented, parallel order picking is particularly suitable for large numbers of orders (more than 1,000) with few items (1 to 5) – i.e., especially for e-commerce. With this solution, identical items from different orders are grouped into series and processed in parallel in different storage zones. This drastically reduces the number of times an item is accessed and also the distance traveled during the picking process. Multiple orders are grouped into one overall order, then picked based on the items and only later distributed and packed based on the orders. This makes it possible to optimize the picker's picking routes for each item. Other factors such as storage area, weight, volume, and quantities are also taken into account. Of course, the items can also be delivered according to the goods-to-man principle or picked by picking robots. After picking, an efficient sorting process is necessary so that the respective item can be returned to the corresponding order . However, the required sorting and distribution systems (sorters) are quite costly. The efficiency of two-stage or multi-stage picking is significantly higher than that of single-stage picking. 

There are software solutions with artificial intelligence (AI) for inventory and scheduling optimization to adjust warehouse stocks to fluctuating demand. Based on sales figures from previous years and using statistical methods, the optimal order quantity for a specific period is automatically calculated (predictive planning & Forecasting). Seasonal fluctuations are included in the forecast, with spontaneous changes in demand being smoothed out by algorithms. If a very high level of service is to be achieved, ensuring the immediate execution of a large number of orders, the safety stocks in the warehouse must be correspondingly large. In general, this enables a company to prepare much better for peak periods. 

Advantages of AI-supported planning

• Use of historical sales data
• Taking seasonal fluctuations into account
• Algorithms compensate for spontaneous changes in demand
• Ensuring high service levels through sufficient safety stocks

This allows companies to optimally adjust inventories and prepare specifically for peak seasons such as Black Friday.