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How Can Artificial Intelligence Assist DTC Companies With Inventory Management?

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DG

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May 23, 2022

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6 min read

pexels-tara-winstead-8386440 Source: Pexels

Artificial Intelligence (AI) emerges to have piqued everyone's interest. The percentage of investors buying artificial intelligence equities reflects this, and the number is steadily advancing. AI is witnessed as a secure port in logistics and inventory management disciplines after a long storm at sea. Large businesses confront significant issues in managing their warehouses and ensuring a smooth movement of goods in and out. To assemble your firm, you must consider several paramount elements that will affect inventory management. You constantly manage space optimization, inaccurate forecasting, customer happiness, and inventory levels when working with stock. These characteristics are unavoidable. It's impossible to keep track of everything without the suited technologies. Artificial Intelligence (AI) can assist your company in suitably supervising inventory. COVID-19 will significantly influence customer shopping behavior through 2022 for DTC enterprises. Many of the purchasing habits developed in 2020 will remain constant, despite the availability of vaccines and the removal of restrictions on social gatherings. Consumer behavior is altering, which needs a modification in how DTC brands accomplish business. A wider preference of solutions is now unrestricted thanks to developments in AI-based inventory management. In 2020, customers disbursed $861.12 billion online with US corporations, up 44% over 2019. Throughout the year, grocery businesses transitioned from exclusively depending on foot traffic to offering various delivery options to customers. Due to this huge transformation, supply networks, operations, and retailer gross margins were all put to the test. Businesses built strategic acquisitions in March (last year), anticipating increased ultimatum for specific categories and commodities as buyers fretted. To respond quickly to order spikes and uphold service standards while opposing rising costs, retailers must build a supply chain that is both resilient and agile.

Challenges Solved With AI In Inventory Management

Artificial intelligence in inventory management can eliminate inefficiencies in present procedures by offering a predictive approach and diminishing errors via automation. Inventory management procedures nowadays are fraught with hardships and inefficiencies. It is primarily carried out manually, which takes a protracted duration. Likewise, inaccuracy is a considerable risk, negatively influencing corporate operations.   Multiple enterprises have the challenge of resolving these issues without raising operating costs. Furthermore, how can you meet consumer demand while simultaneously guaranteeing that the appropriate product is available at a suitable time and location? By using an AI-enabled forecasting system, businesses may overcome these impediments and reap the benefits of artificial intelligence in inventory management.

Inventory management vends with various challenges to identify the most appropriate explanations. Let's go over each of them to get to the bottom of the problem. 

1. Challenge One: Carrying Too Much Inventory

An inferior inventory setup is one of the most rudimentary challenges in preserving an eCommerce business model. Industries must maintain track of inventories to deliver goods to an accurate place in real-time. The quality and quantity of the goods need to be tracked often to keep the customer's focus on the transaction. DTC merchants routinely overstock inventory to meet customer expectations and provide a smooth experience. This overstock increases storage and logistics costs while maintaining availability. When products deteriorate and expire, these can become even more formidable for grocery retailers. Using algorithms and machine learning, businesses utilizing AI to align business goals can diminish surplus inventory while enhancing customer satisfaction. As a result, supply planners may reasonably comprehend service and cost alternatives that can assist them in setting suitable stock levels and free up millions of dollars in working capital.

2. Challenge Two: Wrong Inventory Location

The lack of a method to track in-store products/equipment is the primary reason for this situation. Identifying a certain product for sale from inventory requires a significant amount of labor, and finding a product among thousands of goods is extremely difficult. Selecting the wrong material slows down sales and lowers consumer satisfaction. A business that runs out of critical supplies risks losing consumers. If a direct-to-consumer company claims to have it, it must be able to deliver it to the customer's selected location or channel. Stock placement errors resulted in increased markdowns at locations with excess inventory and missed sales at locations with no inventory. Many retailers' end-to-end inventory management procedures are labor-intensive and time-consuming. With an AI-enabled system, DTC organizations may transport goods between facilities and channels quickly and effortlessly. Data correction services and a location planning strategy are two basic automated techniques that help businesses analyze and resolve prior problems.

3. Challenge Three: Uncertainty About The Amount Of Required Inventory 

Managing a large inventory using manual paper-based techniques will not help your company develop. As sales volume and inventories rise, lack of digitization and ineffective inventory management processes will only deliver unsatisfactory outcomes. Due to unexpected demands, customers may be disappointed; opportunities may get debilitated; or inventory may be overstocked, not to mention the additional costs of meeting the unexpected demand.  Successful inventory replenishment needs to anticipate demand changes rather than react to them. Each company's inventory replenishment process is unique in its manner. Supply chain planning software can assist you in meeting revenue targets and selecting the optimal inventory combination through:
  1. Optimization for omnichannel
  2. Supported bills of distribution and bills of materials
  3. True lead time 
  4. Forecasting by season
  5. Automation of inventory strategies
Source: Pexels

How Artificial Intelligence Aids Inventory Management

Data Mining And Information Conversion Into Solutions

Artificial intelligence is exceptionally reasonable in the field of data mining. AI systems can gather and evaluate data in real-time to transform it into actionable information. As a result, introducing artificial intelligence into the inventory management system allows the company to change more quickly and find better solutions to the problems. DTC businesses may better understand their customers' expectations by gathering, aggregating, monitoring, and analyzing each client's data and interests. It allows them to develop more successful strategies, predict customer requirements, and deliver adequate products.
  • Inventory Replenishment Planning

When it comes to fulfillment forecasting, predicting demand is more difficult than projecting supply. Because each data set has uncertainties and anomalies to account for, data science is critical in anticipating allocation. Both supply and demand statistics can show anomalies, as both are prone to rapid fluctuations. People's decisions concerning how and when they purchase items must be considered in this customer-oriented paradigm. Inventory management solutions that use artificial intelligence to assess consumer fulfillment preferences and purchasing activities might help retailers increase stock levels.
  • Stock Management Safety

Businesses used to maintain their inventory at a set level or percentage. It entails establishing a minimum expense point for walk-in sales not included in eCommerce or other fulfillment methods. In today's ever-changing consumer demands and multichannel engagements, generalized information is no longer sufficient. Inventory levels are dynamically adjusted to utilize and adapt to incoming demand. By automating inventory rebalancing, we can bypass two major issues: 
  1. Due to overpromising and underdelivering on inventories, brand loyalty has dwindled.
  2. Overspending might result in a financial loss. Companies employ predictive rules to access inventory in stores and warehouses to meet seasonal customer and profit demands.
To achieve profitable omnichannel outcomes, DTC organizations must be able to proactively balance fulfillment costs against service to optimize return on investment, improve customer experience, and increase recurrence purchase behavior.
  • Estimated Arrival Time

To meet and exceed customer expectations, DTC merchants must comprehend the quantity and location of inventory. In today's highly competitive market, the ability to communicate with clients and the projected product delivery time is becoming increasingly valuable and necessary. With the help of improved quality of these models, DTC businesses must be able to stimulate and evaluate the performance of each calculation. Regardless of cost or time, each decision will be based on the company's priorities.

Conclusion

Artificial intelligence in inventory management makes warehouse, stocking, and other associated activities more automated. It can assist with physical tasks such as transferring and tracking items and more complex situations requiring increased knowledge to ensure error-free planning or client demand projection. Using an AI may be an unnecessary expense if the firm is small. As the volume of operations and data expands, manually managing inventory management processes and safeguarding the company from errors becomes increasingly complex.   If you still have any suspicions about what's most appropriate for your company, contact our experts for a free consultation. Our competent and experienced team at Saffron Edge will assist you in determining the ideal solution for your needs. To contact us right now, please go to Saffronedge.