How to do business logistics in a post-pandemic world

As life gradually returned to normal operations, businesses had to begin addressing the damages even before the full relaxation of anti-coronavirus measures. The pandemic posed an intense stress test for many companies.

The extraordinary situation also highlighted what companies should prioritize within their processes. Above all, this is to ensure operational viability even during other unforeseen circumstances.

During the coronavirus crisis, two camps of businesses emerged. The first included operations that had to radically reduce or completely halt their activities. The reasons for a complete shutdown varied: fears of employees contracting the disease during work, lack of supplies, or a drop in customer orders.

Businesses whose commercial and operational processes allowed them to continue their activities belonged to the second camp. The e-commerce sector (e-commerce and online retail) experienced unprecedented growth. The Slovak e-commerce market saw a growth of up to 54% in March 2020.

The difference was most notable among trading and distribution companies dealing with fast-moving consumer goods and groceries. Increased demand in many companies reached levels comparable to seasonal peaks. The pressure from demand was felt not only in commercial processes but also in the increased burden on inventory management, warehouse logistics, and overall fulfillment.

On the other hand, the pandemic also revealed, in real-time, how fragile the network of global supply chains is. Traditional inventory management models were, in some cases, not agile enough to cope with the constraints brought on by the coronavirus crisis.

The coronavirus led to the greatest disruption of supply and demand since World War II. For this reason, digital technologies played a leading role during the pandemic. Their position and role will only strengthen in the post-pandemic world.

Data Not Just for Crisis Management

The crisis situation uncovered weaknesses in many operations and highlighted areas and functions that must be safeguarded against disruptions. At the very least, the state of emergency provided material for re-evaluating priorities.

Companies were able to identify with reasonable accuracy the processes and procedures they should focus on to ensure future sustainability without negatively impacting efficiency, productivity, and quality.

Moreover, the coronavirus crisis will have far-reaching and lasting consequences in the form of a deceleration of globalization. As a result, there will be an increased focus on regionalization and diversification of suppliers, which will lead to the adaptation of new business models and operational processes. Companies will find most of their answers in digitalization and intelligent automation.

In the case of supplier-customer chains, much discussion has revolved around "end-to-end" (E2E) visibility (tracking and data collection across the entire material flow) as a tool for proper supply management during exceptional situations, which also has significant relevance during standard operations.

In addition to creating transparency in logistics flows for flexible real-time management, strategic sourcing and sequential planning are coming to the forefront. These are inseparably tied to companies' access to the correct and adequate data and relevant insights derived from them.

The revision of corporate and operational strategies will inevitably involve rethinking data acquisition and evaluation procedures. These data will subsequently be used for further operations, including:

• automation of work management,
• resource allocation,
• and increasing the elasticity of processes.

Many businesses discovered as a result of the COVID-19 pandemic that they lacked timely access to the necessary data, negatively impacting their crisis management. While generating data is no longer an issue for most businesses, timely access to it and its proper utilization to obtain actionable insights were insufficient to ensure smooth operations during the emergency situation.

Partial digitalization, such as through ERP or other record-keeping systems, has resulted in the creation of data lakes (repositories of structured and unstructured diverse data in large volumes) in many businesses. Inefficiency is further compounded by the fact that individual processes within companies are often isolated from each other, both in terms of data and functionality.

To ensure appropriate responses to unexpected events, crisis situations, or the implementation of operational changes, it is essential to connect data and processes across the company hierarchy. Only then can interfunctional decision-making and management processes be ensured, enabling businesses to respond swiftly to external and internal stimuli without significant operational disruptions. This approach should already be incorporated into the new setup tailored to post-pandemic operations.

Managing Social Distancing and Effective Logistics Simultaneously

Businesses that continued operations during the coronavirus crisis had to adopt anti-epidemic measures to protect the health of their employees. At the same time, these measures ensured the continued functioning of the business, preventing further disruptions or shutdowns, which could also have been caused by employee absences due to health reasons.

Preventive measures, such as employee temperature checks at facility entrances or increased sanitation frequency for spaces, technological equipment, and tools, became part of emergency-mode operations.

Anti-epidemic measures, as part of occupational health and safety (OHS), naturally extended to work performance. Distribution centers, retail, and wholesale warehouses with less automated processes, where employees could not avoid contact while performing their duties, faced the challenge of maintaining social distancing among workers.

A potential solution in such situations lies in WMS/WES (Warehouse Management System/Warehouse Execution System) systems, which autonomously manage stocking and picking procedures to ensure the most efficient execution of these tasks.

Picking methods are typically chosen based on several key criteria, such as:

• overall volume of goods,
• turnover rate,
• types of SKUs,
• warehouse layout,
• and order structure.

If social distancing requirements are added to these criteria, the suitable picking methods narrow down to three primary types:

• Zone Picking:
  This method is most suitable for warehouses with a wide and diverse range of products. Different SKUs are distributed into separate zones, with each zone typically assigned to specific operators responsible for picking items within their zone. Once the operator has picked the items from their zone, the order is sequentially passed to the next operator in the subsequent zone. Zone picking enhances efficiency as each warehouse worker is trained in a specific zone, minimizing unnecessary movement outside their area and reducing interaction with other staff. However, this method requires a consolidation workstation, also managed by the WMS/WES system. The disadvantages include uneven workload distribution, such as when the turnover of a specific product type significantly increases.
• Wave Picking:
  This method involves grouping several types of SKUs into a shared picking list for a specific warehouse worker. Picking cycles are organized into "waves" conducted during work shifts in designated time slots. Wave picking is primarily used in warehouses where orders are tied to specific deadlines, such as those dependent on carrier pickups or contracted delivery terms. Since it is a sequential picking process, new orders cannot be picked until the current wave (sequence) is completed.
• Cluster Picking:
  This is a specialized form of batch picking, based on consolidating multiple orders into a single batch using a picking list. In cluster picking, the WMS/WES system generates picking lists that group orders into "clusters" based on similarities in ordered items. This allows workers to pick multiple items from a single storage location, reducing the amount of walking between different warehouse sections. Moreover, the system generates lists to ensure that workers' picking routes do not overlap. Cluster picking is characterized by fewer movements and higher volumes of picked orders. Effective cluster picking relies on sophisticated intelligent algorithms that generate picking lists based on an analysis of incoming orders, item availability, and the current workload of warehouse staff.

The use of WMS/WES systems in inventory or warehouse logistics management is not only effective from a labor management perspective. These systems contribute to the overall optimization of warehouse processes and supply chain management, including increased productivity and automated quality control. They also reduce error rates and ensure synchronization of picking cycles to meet specified delivery deadlines.

In addition to minimizing the interaction of working staff in warehouse spaces, the deployment of WMS/WES systems brings another undeniable benefit in the digitalization of overall paperwork (waybills, returns, protocols, transactions between the company ERP system). These warehouse management systems contribute to the automation of administrative and financial processes.

Intelligent warehouse and inventory management systems also significantly shorten the training process for new employees. Newly hired workers do not need to become familiar with the specifics of various SKUs, warehouse zones, or workflows in detail. The system holds all the necessary information and provides it to employees through mobile devices, guiding them through individual warehouse positions during their tasks.

The method of managing social distancing in warehouses without negatively impacting productivity can also be applied to manufacturing operations. Modern MES/MOM [Manufacturing Execution System/Manufacturing Operation Management] systems enable real-time management of production processes and operations even in facilities with lower levels of technological automation. They also help maintain leaner material flows thanks to the dynamic operational control these systems provide

Data Transparency and Adaptability of Supply Flows

A long-established tool is Kanban, a lean management method popularized in the engineering industry as part of the just-in-time (JIT) supply concept. The traditional Kanban system uses cards and boards to support smoother supply processes for production workstations and lines.

This system has also undergone digital transformation and is now used to support and accelerate supply flows between suppliers, manufacturers, and warehouses. Given its optimization capabilities, Kanban has found applications outside manufacturing operations, such as in e-commerce, distribution, and retail supply.

Digital Kanban or e-Kanban allows for effective process management without requiring direct interaction between employees. Inventory levels at specific positions (or production workstations) can be monitored using various sensors.

Mobile terminals are also used for inventory handling, recording stock movements through scannable material identifiers (barcodes, QR codes, RFID). Deploying these technologies results in the dematerialization and automation of data flows, creating transparency in inventory management.

The increasing affordability of sensors and mobile terminals makes e-Kanban a suitable synchronization and coordination tool for real-time in-house supply processes, even for small and medium-sized enterprises (SMEs).

 

In addition to enabling remote communication between warehouses and production workstations, digital Kanban ensures timely and accurate inventory supply management while reducing excessive stock at individual workstations.

In larger operations with more complex supply routes and cycles, where material handling is often performed using tuggers, digital Kanban becomes part of the Milk Run supply system. Digital technologies transform the traditional Milk Run system into a flexible demand-driven supply model (a transition to pull strategy material flow management).

By digitally connecting warehouses, Milk Run tuggers, and production workstations (pickup points) through a cyber-physical platform, such as Smart Industry systems and solutions, it is possible to create an automated management system for internal material flows. The system manages supply cycles for individual production workstations according to real-time needs, coordinating inventory management, internal logistics, and production processes.

In the virtual space, warehouse positions, tuggers, and production workstations are assigned intelligent autonomous agents—digital twins (software bots). These digital twins communicate and synchronize with each other to meet set goals—in this case, ensuring the correct material supply to production workstations when needed.

Digital twins allow multiple processes to run concurrently, enabling businesses to achieve effective just-in-time supply. Incoming customer orders are evaluated in real-time, while intelligent autonomous agents continually map material resource availability and production process progress to ensure efficient material replenishment in production. This prevents production stoppages due to missing materials or material delivery to the wrong workstation.

Automated production intralogistics management eliminates the need for physical handover of protocols or other documentation, as all communication occurs exclusively digitally (via mobile terminals). Given the workflows, there is no direct contact between logistics and production employees.

The management system automatically evaluates individual operations, ensuring data flow and information processing for relevant logistics and production personnel, as well as management staff. Digital Kanban and dynamic Milk Run supply systems automate not only intralogistics processes but also administrative operations integral to material flow management (intelligent management systems are typically integrated with company ERP systems).

Streamlining internal material flows with digital Kanban and agile supply management using Smart Industry systems introduces greater adaptability to unplanned or emergency circumstances in logistics processes. Dynamic management also enables quicker responses to operational constraints, such as those that arose during the COVID-19 pandemic.

Post-Pandemic New Normal in Logistics

The coronavirus crisis has accelerated the digitalization process and the adoption of new technologies in many sectors. A survey by the National Association of Manufacturers revealed that over 53% of manufacturing companies anticipate changes in operations and functioning, with 35.5% of companies stating they are still experiencing supply chain disruptions.

Dynamizing supply processes and strengthening the agility of logistics flows will not only be achieved through digitalization but also through increased automation. On one hand, this applies to decision-making processes (crisis management, demand forecasting, subcontract planning), which require fast and reliable data management.

On the other hand, it also applies to technological processes themselves, where automation ensures real-time production management and autonomous adjustments to current factors. According to the Thomasnet platform, necessary coronavirus-related measures have led to a 147% increase in industrial automation in North America compared to the previous year, with a 20% increase from the previous quarter. Automation levels are expected to grow further as the full consequences of the pandemic unfold.

The stress test of corporate processes caused by the pandemic will lead to a review of operational and business models—not just out of concern for additional waves of infections but also due to other unpredictable events that could threaten production and supply processes, thereby disrupting business and operational continuity.

The new norm in post-pandemic operations will not primarily consist of anti-epidemic measures but rather digital transformation and intelligent automation. These already provide tools and procedures for immediate responses to various crisis situations.

The market will also be shaped in the near future by events other than health crises, such as geopolitical events, economic conflicts, and the consequences of climate change. Companies will need to adapt their processes, business, and operational models to these varied crisis scenarios, ensuring sufficient flexibility and adaptability.

One of the key findings of the pandemic’s aftermath is that businesses will not be able to function in the coming period without an operational and properly configured digital enterprise ecosystem.