June 14, 2024

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Everyday Fashion

How “Co-Opetition” Can Help Overcome the Obstacle of Retail Overstock

How “Co-Opetition” Can Help Overcome the Obstacle of Retail Overstock

The retail industry has struggled to cope with the unprecedented changes in the last few years spurred by the pandemic, leading to a scarcity of goods and overstocking in many cases. Additionally, snarls in supply chains resulted in bulk purchases until inflation changed consumer shopping patterns. Failure to identify and forecast changes resulted in excess inventories and markdowns or write-offs. As a result of these bloated inventories, the retail sector loses over $50 billion each year. Also, the oversupply led retailers to offer discounts of up to 70 percent on furniture, electronics, apparel and workout equipment.

A recent report estimates that companies will spend around $300 billion on warehousing each year, and that amount is increasing as global supply chains and e-commerce become more prevalent. Excess inventory now exceeds 30 percent for many retailers, forcing them to resort to heavy discounting, which eats into their profits.

“Co-opetition” is, therefore, key to overcoming the retail overstock issue. The term co-opetition refers to cooperation between competitors. Although it sounds contradictory, it’s a very effective way to do business. In the era of globalization and technology, co-opetition will likely become increasingly important for retail companies. Through co-opetition, retailers can develop strategic plans to liquidate excess inventory quickly and efficiently while making profits for themselves and their partners. Without sharing confidential and strategic data with partners, retailers can build cohesive strategies, which can help all involved weather the storm and come out on the other side stronger than ever.

Trimming the Fat: How Can Retailers Reduce Overstocking?

Before exploring strategic partners that would fulfill the co-opetition option, retailers should try to sell off their excess inventory. Smart recommendation engines can be useful and provide unique avenues to explore for selling excess inventory.

Therefore, it’s recommended that retailers employ “a trifecta approach” using artificial intelligence and data analytics to reduce overstocked inventories. This three-phased approach can help prevent further accumulations, creating a more sustainable system.

Phase 1: Igniting the Engine

Insufficient inventory can result in stock-outs and lost sales, while excess inventory can result in write-offs. However, by implementing some internal measures using advanced analytics, retailers can get back on track within a few weeks. The first step is to rebalance inventory levels by identifying slow-moving items and adjusting stock accordingly. Retailers can also use ship-from-store capabilities instead of relying on central warehouses to fulfill online orders.

A second step entails implementing markdowns guided by data. Determining the right prices and timing for markdowns will help retailers clear out slow-moving items without eating too much into profits. The third step is to bundle products together based on analysis to maximize the network’s use of overstocked products. This can help to reduce overall inventory levels, while still providing customers with the items they want.

Phase 2: Building the Momentum

This is the phase where retailers should open themselves up to explore the idea of co-opetition. There are many examples of co-opetition in the retail industry. A notable example of this is the relationship between Best Buy and Amazon.com. In 2018, the two companies partnered to sell Fire TV Edition smart televisions in Best Buy stores and on Amazon’s website. This gave Best Buy access to Amazon’s huge customer base while also expanding Amazon’s potential audience for Alexa-enabled products. By collaborating with each other, these two retail giants have been able to reach a larger audience and boost their sales. This co-operation is a win-win for both companies, and it’s a great example of how co-opetition can be beneficial for businesses.

Despite evidence that collaborating in the supply chain can reduce inefficiency and result in mutual gain, parties don’t wish to collaborate if they have to share their proprietary information. The main reason for their privacy concern is that the party doesn’t want to lose its competitive advantage by giving away company secrets. With the advancement in data science and data engineering and the sophistication with which new-age digital platforms are built, it’s very much possible to build a platform where partners can be onboarded with the details of their excess inventory — SKUs, volume, and price — without revealing this information directly to their partners. Collaborative optimization algorithms can be applied to such problems in the supply chain, where secure multiparty computation is incorporated as part of the algorithm to preserve the privacy of the parties.

With these platforms in place, all collaborating partners can together sell off the pooled excess inventory. One partner gets the customer on its website and the other partner fulfills it and they both share the total profit in a pre-decided proportion. Through this, one partner doesn’t lose its customer, while the other partner is able to sell off the inventory which otherwise it wasn’t able to. And they keep switching roles dynamically until all of them sell their excess inventory.

Phase 3: Achieving Escape Velocity

This phase is all about being better prepared for the same situation in future. A predictive analytical solution is key to preventing future inventory pileups. By identifying the buying patterns and trends in customer behavior, retailers can make better inventory and pricing decisions. Additionally, these solutions help retailers source goods more efficiently, ensuring they always have the right products in hand. This involves:

  • Dynamic sourcing strategy: Helps retailers identify potential disruptions in sourcing and adjust their strategies.
  • Demand forecasting: By analyzing historical trends and patterns, retailers can anticipate future demand and prevent inventory shortages.
  • Real-time inventory optimization: Monitors sales data and customer behavior to ensure retailers never run out of inventory.
  • Advanced return policies and behavior-based pricing: Assess how customers interact with products and help retailers keep the right mix of items in stock and price them to maximize profits.

The Road Ahead

Overstock is a pressing issue for many retailers, but it doesn’t have to be an insurmountable one. By being agile in thinking, collaborating with peers, and making use of AI and data analytics, businesses can begin to take back control of inventory levels and keep the stock in the backroom moving towards the consumer.

Shubhankit Verma is the senior manager of supply chain practice at Tredence, a data science company that offers industry-specific data analytics solutions.