Optimizing distribution center operations using AI
According to a recent study, nearly 1.4 billion tons of food is wasted every year. The United States discards more food than any other country, at nearly 40 million tons per year. This not only leads to increased expenditure on treating wasted food, but it also wastes the water and energy used for production. Many food manufacturers and retailers struggle with this problem due to a lack of proper storage facilities, cold chains, proper food handling practices, and transportation.
From quality control to warehouse management, AI solutions can be applied to all stages of product distribution. These AI capabilities can help organizations analyze large volumes of data, better understand customer behavior, orchestrate relationships between different business functions, allow visibility into operations, and support better decision making.
What is a distribution center?
Simply speaking, a distribution center (DC) is a specialized warehouse that acts as a hub in the distribution network to receive products from suppliers and fulfill the demands of wholesalers and retailers. The efficiency of a distribution center plays a vital role in maintaining product quality or freshness, customer satisfaction, and delivering the right product to the right place at the right time.
These centers are a key part of the fulfillment process and managing them is often complicated as they store a high number of products for shorter durations. Distribution centers typically have a higher velocity as compared to the other warehouses.
Challenges for distribution centers
Operating a distribution center for perishable items like packaged food, fruits, medicines, etc. is challenging due to the need to constantly maintain a certain temperature for storage purposes. Poor distribution management for perishable goods may lead to quality issues, reduced nutritional value, trust issues with retailers, and bad customer experience. Here, we discussed some common challenges faced by distribution centers:
- Managing inventory: It’s crucial to store enough products in distribution center to meet the rising demands of consumers. Sometimes, order picking and delivering to the distribution centers will be delayed due to human errors or transportation inefficiencies, which leads to customer dissatisfaction.
- Maintain product quality: Manual product handling, improper storage space, poor temperature and other environmental factors may lead to reduced product quality. This could result in increased product waste and costs and failing to meet customer demands on time.
- Errors in loading and unloading trucks: A breakdown in accuracy and tracking of goods may occur due to incorrect receipts and purchase orders. This is often caused by human error when relying on manual data entry processes for identification and counting.
- Staff shortages: Warehouses and DCs struggle to keep the facilities adequately staffed as experienced workers retire and the younger workforce shows declining interest in labor-intensive, material handling careers. Another challenge associated with attracting skilled talents is location, so many of the companies seek to shift their operations to metros or bigger cities, where those workers live, leading to increased operating costs.
- Changing consumer demand patterns: Certain products have the same demand throughout the year, whereas others are more popular during a specific time of the year. If the distribution center is not optimized or capable of storing and delivering enough stock during peak times, it can create disappointment and frustration for customers. Also, it can lead to missed opportunities to engage shoppers, lost sales and revenue, and potential damage to a retailer’s brand.
- Growth in omni-channel purchasing and its impact on fulfillment needs: The growth in omni-channel retailing is likely to have a significant impact on retail networks and their logistics needs. Businesses strive to find solutions to design their supply chain network to fulfill customer demands across multiple channels in more timely and efficient ways.
How AI helps solve distribution center challenges
AI solutions have the potential to reduce cost and manual engineering time and efforts. AI can help distribution centers optimize product slotting, manage workforce, automate batching and sequencing, orchestrate automated robots, and manage transportation. With AI-based solutions, companies don’t need to spend the time and money to develop extensive in-house AI expertise. Instead, they can engage with partners, such as Neal Analytics, to build and customize AI solutions.
A recent survey carried out by Lucas Systems on 350 companies in the US and UK found that majority of them are already leveraging AI in at least one way within their warehouses and distribution/fulfillment centers.
DCs create a favorable environment for implementing AI, with the potential to drive significant operational gains. DCs are controlled environments for collecting and aggregating historical and real-time data, which is a key to effective AI.
AI and machine learning-based solutions help overcome the challenges faced by DCs and drive better results than traditional resource and inventory management approaches that rely on Excel, inherited best practices, or simple rules-based decision-making.
AI and ML capabilities can drive visibility into all aspects of the supply chain with greater granularity and apply methodologies that humans can’t mimic at scale using traditional approaches. AI solutions offer powerful optimization capabilities required for more accurate capacity planning, high productivity, improved quality, lower costs, while allowing safer working conditions.
Managing inventory and transportation at DCs
Designing and optimizing distribution center layouts and operations could be challenging in the real-world due to its dynamically changing parameters. But, with simulation model capabilities, organizations could potentially meet these challenges by modeling the structure, processes, and resources of a real-world distribution center with minimal cost and risks.
The distribution centers simulation by AnyLogic allows organizations to effectively manage transportation, maximize transportation loads, minimize risks, and provide detailed analysis. This model focuses on three principal operations: unloading, assembling orders, and loading.
- Truck unloading: Trucks loaded with the pallets arrive at the receiving dock at the distribution center. Then, the pallets are unloaded from the trucks and placed on racks at the receiving dock using forklifts.
- Assembling orders: When orders come in, the forklifts pick up the pallets according to the type of order to be shipped and place them at the loading dock in respective shelves.
- Truck loading: Once the orders are ready to ship, the trucks are assigned to the loading dock to receive those orders, and the pallets are loaded on to the trucks using forklifts.
The simulation model helps monitor the key metrics and goals, allowing us to understand how efficiently the process is operating. Also, the what-if capabilities of simulation allow us to increase or decrease the number of trucks or forklifts, loading capacity, number of orders, and many more to analyze the process performance.
How Microsoft Project Bonsai helps optimize DC operations
The Project Bonsai platform allows subject matter experts to design, train, and develop AI models to solve real-world business problems using machine teaching and deep reinforcement learning capabilities.
AnyLogic simulation and Project Bonsai together can help organizations analyze changes in product demands. The AI agent can be trained to manage various processes like producing enough products at manufacturing facilities, managing inventory at distribution centers to meet the changing demands, and optimize transportation in a more timely and efficient manner.
Neal Analytics has deep expertise in end-to-end AI model deployments, and we’re an AnyLogic partner! Contact us to hear how we can help you leverage AnyLogic for Microsoft Project Bonsai solutions.