6 Ways How to Power Up Fresh Food Distribution and Cold Chain Management


6 Ways How to Power Up Fresh Food Distribution and Cold Chain Management

Fresh food logistics and order fulfillment require the most sophisticated processes and state-of-the-art technology to meet the highest standards of speed, quality, and precision. We look at six possibilities to boost fresh food order fulfillment and cold chain management utilizing Industry 4.0 strategies and intelligent semi-automation.

Among the most complex types of supply chains are fresh food distribution and pharmaceutical logistics. Fresh food distribution requires the most sophisticated processes and state-of-the-art technology to meet the highest standards of speed, quality, and precision, particularly because of the sensitive nature of the transported goods and the conditions for handling, storing, and transporting them.

In combination with the more extreme changes in consumer behavior, increasing consumer demand for services has a profound impact on fresh food logistics management. Businesses are continually innovating and scaling order fulfillment processes with digital technology for this reason.

Pandemic ebb and flow have impacted global and local supply chains in a significant way during this tumultuous period. The coronavirus pandemic is further compounded by other factors that directly affect supply—from climate change to mercurial consumer behavior patterns. Logistics nodes are also facing additional pressure due to the shortage of skilled labor.

The omnichannel model made it possible for e-commerce and retail to respond to the challenges, which led to new opportunities. Businesses and warehouses have to constantly innovate to prevent disruption in food chains.

#1 Expand Business Intelligence

A standard process of warehousing and distributing fresh food requires stricter hygiene measures as well as slotting inventory into different cooling zones. Not solely due to the pandemic event, fresh food is further restricted to prevent contamination of goods and the spread of infection among employees. An extended period of a temporary shortage of labor caused by illness can lead to delivery problems, downtime, or product expiration due to a reduction in quality. All of these are entirely preventable.

As a result of the pandemic, the just-in-time (JIT) operational strategy was altered as well. Supply chain disruptions and warehouse outages have caused delays and even missed deliveries during the pandemic.

These incidents have raised concerns about the lack of visibility of ongoing operations and the status of individual orders, inaccurate data on stocks and inventory transfers, and unprepared planning to prompt changes in customer orders. These can also be linked to a lack of flexibility in business processes and related sustainability of operations during crises. Having accurate and up-to-date data enables enterprises to make better business as well as logistics decisions.

Monitoring processes in real-time,

  • such as order planning,
  • order picking status,
  • bestsellers (or least-requested items),
  • SKU turnover,
  • and returns in process,

helps reduce the response time. It also improves the quality of both tactical and strategic decision-making to effectively meet the needs of retail and end users (B2C) and enterprise clients (B2B).

A major shift in consumer behavior has also been triggered by the pandemic. A significant impact has been felt on the food sector as a crucial source of essential products. Data and analysis allow the company to identify emerging trends in a timely manner, thus allowing it to adapt in terms of business model and operational processes.

Food retailers, such as those dealing with fresh produce, might have foreseen that food service and hotel industries’ corporate clients would represent a very small share of customers. As a result, they began focusing on retail orders and end users.

The types of goods offered and the composition of orders also reflected the transition from B2B to less complicated, smaller-item orders in larger volumes. Most of the pandemic habits will change to the normal post-pandemic routine, and seasonal goods will also enter the planning, which companies must adapt to.

#2 Accelerate Order Picking with the Right Data

The sensitive and perishable nature of stored and transported goods in the food industry requires businesses to pay attention not only to the correct storage conditions but also to the expansion of warehouse capacity. That applies throughout the year, regardless of seasonal peaks.

It is not uncommon to see outdated information on the availability of products, incorrectly marked locations of items, as well as an inability to find the products. A combination of these factors can also result in an increase in incorrectly picked orders, especially for clients with priority supplies, where fast dispatch and delivery of shipments are highly encouraged. It is not only detrimental to the reputation of the company when orders are delivered incorrectly, but it also generates additional costs when they have to file a complaint and pick up a repeat order with the right goods.

It is for this reason that inventory management is one of the most crucial processes in fresh food warehousing. A correct inventory level data prevents the sale of goods that are currently unavailable or are otherwise limited. Moreover, insufficient inventory management can lead to a slowdown in the picking and assembly process, which, in the worst case, can lead to mass cancellations of orders and a loss of revenue.

Currently, modules of WMS systems devoted to inventory planning and order management have a significant impact on warehouse optimization. Through timely notifications, WMS systems can automatically monitor inventory levels and prevent them from falling below a critical level, especially for strategic items and bestsellers. In the same way, WMS systems regulate the upper limit for stocks, preventing unnecessary oversupply that could take up space for relevant products.

#3 Enable Process Elasticity for Growing Variability

The use of predictive analytics is not limited to business planning. The techniques can also be utilized for inventory optimization. Algorithms allow us to determine what goods are most frequently bought or that are also picked up together.

In this way, the types of goods that most often appear in orders can be selected to be warehoused as close as possible to assembly locations based on the recurring patterns of consumer behavior. Additionally, storing goods with the highest turnover in a flow warehouse makes it possible to increase strategic availability and eliminate the time spent picking in the warehouse.

With dynamic inventory management, businesses can adjust inventory slotting in real-time and match bestsellers accordingly. Therefore, warehousing and supply can flexibly respond to seasonal growth as well as unplanned fluctuations or sudden changes in customer habits. A flexible approach is becoming essential not only in the food industry but for order fulfillment in general as well.

Demand for certain types of goods also peaks and falls outside the already known and predictable seasonal peaks, including sharp and unexpected changes in demand. Ideally, a warehouse or distribution center needs to be flexible and scalable in order for the increasing turnover of specific types of goods not to unexpectedly overwhelm picking processes and slow down the sending of completed orders.

In addition, warehouse logistical elasticity helps operational processes remain sustainable and meet delivery deadlines even in cases of sales channel expansion—entering into new territories or retargeting or updating customer segmentation. Consequently, current WMS / WES systems are already adapted to the real-time operational model of inventory, warehouse, and orders management, allowing food production and distribution companies to react to market changes more quickly and accurately.

#4 Start Adopting Food Industry 4.0

Transporting fresh goods requires careful storage and handling of supplies, careful checks and inspections, a regulated environment, and adequate packaging. To ensure order processing is accurate and timely, managing picking processes and warehouse throughput efficiently is imperative.

Keeping strict parameters and maintaining the high quality of fresh food requires that the flow of goods be optimized. The nature of fresh food storage and distribution needs process management automation, even though working with the correct data helps speed up decision-making and improve the quality of decision-making.

Fresh food is supplied in a series of unique orders, where individual SKUs and quantities are not usually repeated. This means that picking cannot be optimized through conventional standardization. Further, orders from end consumers, businesses (in the food and hospitality industry), and supermarkets are being prepared simultaneously for weights, cartons, and pallets, which further complicates the narrowing of picking processes.

The fresh food distribution center must therefore be prepared for picking highly individualized assignments. This type of handling of a large number of preferences and requirements in different values and numbers becomes difficult to perform manually. Delivering fresh fruit and vegetables within 24 hours from home growers no longer appears to be an advantage as much as a norm. With the growing volume of orders, the conventional technologies are becoming increasingly unreliable in the context of inventory management and picking.

In a warehouse, the most common technology is IoT, which is essential for semi-automating or automating the operation through the WES system (Warehouse Execution System). Aside from automation technologies, such as storage systems (AS/RS), pallet conveyors, and sorters and balers controlled by a WMS/WES system, key algorithms are also used for optimizing inventory slots and enterprise resource management in general, combining picking strategies and optimizing operations across the shop-floor.

#5 Permit Process Parallelism for Synchronized Order Fulfillment

The WMS system excels at managing inventory and automating warehouse management. However, the WES system provides more extensive integration options, including the management of transportation equipment and handling technology. Additionally, the WES system's enhanced integrability and dynamic scalability make it more suitable for the management of food logistics.

A digital twin technology, which can be part of a WES system or an advanced WMS system, is becoming increasingly important for the effective supply chain management of fresh food. The digital twin can create a virtual replica of a warehouse, material flow, or supply chain, with digital counterparts for goods, materials, and handling technologies that have their own intelligence and decision-making abilities.

Enterprise resources can not only make independent decisions based on current situations and act on those decisions in the physical world of the warehouse or distribution center. Their interaction also allows for process parallelism in the supply chain, especially when it comes to order fulfillment.

Process parallelism refers to the fact that processes related to order picking occur simultaneously but have mutual influence in different parts of the warehouse or material flow. The same principle also applies to enterprise resources and sequences in which they are executed in order to maximize the order fulfillment performance.

Pre-shipment orders are completed in a manner that allows items to be picked in the main or buffer warehouse to be shipped in the correct order and without delay once they have all been picked.

Depending on customer preference and time priorities, each item is assigned to the appropriate warehouse operator or automated picking technology. Picking lists are generated according to these criteria and the resources available at the current time.

Through the use of digital twin technology, the WES management system creates a sequence of activities across the enterprise, allocating resources according to priorities. This is coordinated in real-time to avoid duplication or unplanned downtime.

#6 Improve Performance and Agility with Predictive Management

Predictive operations management is a critical feature of supply and logistics automation, as it contributes to process parallelism and streamlines order-picking and material flow operations.

Planning and projections are usually accomplished with predictive analytics. Today's WES platforms use digital twins and IoT to predict outcomes in short time frames. This is particularly useful when setting up picking procedures during one work shift based on the number of orders received and the volume of enterprise resources available.

Using predictive management in warehousing and order picking operations enhances the performance of mass customization and parallelization. This can help maximize the number of picked orders, expand the warehouse flow, and ensure error-free picking of diverse forms of packaging and orders—the so-called hybrid order picking. The deployment of hybrid order picking strategies shortens the time slots for picking fresh food and orders, as well as reduces the length of the cold chain.

WES systems are deployed in food manufacturing and distribution to automate or semi-automate order picking processes and improve distribution center management within a broader digital transformation strategy.

Other processes in food supply and demand can also benefit from IoT technology, digital twins, and data analysis. The first mile can be digitalized through harvest prediction, monitoring, and food quality control. Generally, food processing is increasingly automated, especially through production automation (MOM/MES systems) and efficient ERP systems.

Distributors and logistics companies use intelligent automation to elevate supply chain analytics, cold chain management, warehouse logistics automation, and order picking and transportation management system (TMS). The last mile is also being digitized, particularly

  • omnichannel and D2C (direct-to-consumer, logistics),
  • demand management,
  • forecasting,
  • and reverse flow management.

Digital twin technology is part of the intelligent warehouse automation strategy, which includes process parallelism and predictive operation management. Combining these processes allows for increased efficiency in picking processes and fresh food inventory management. As a result, it increases the adaptability of logistics operations.

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Case Study | Accelerated Fresh Food Order Fulfillment

The trading and logistics service company Hortim has decided to enter a new era of intelligent logistics. The company chose to implement a state-of-the-art operations management system for warehousing and intralogistics for its newly built fresh food distribution center.

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Thinking about digital transformation of fresh food distribution or cyber-automating cold chain order fulfillment?

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