Insight

Supply Chain Transformation

While nearly all retailers understand the need for supply chain transformation, there is less consensus as to where to focus efforts in order to have the most significant impact. Which levers are the most critical? How can they be identified? Once they are, what steps should be taken that have a cumulative positive impact across the value chain and customer experience that builds in flexibility and agility?

While every situation is unique, there are three key areas that nearly all retailers can benefit from as it relates to supply chain optimization:

Demand planning—the ability to accurately plan and predict product needs across both digital and physical channels—is one of the most crucial components in a modern supply chain. Effective demand planning allows organizations to optimize service and inventory levels, thus reducing the need for excess inventory and minimizing costly markdowns. In addition, demand planning is instrumental in optimizing costs through tangential operations like workforce planning, transportation, warehouse allocation and vendor management.

Demand planning consists of three main areas:

• Demand shaping: Creating demand through interventions such as promotions, events, marketing and other customer-facing tactics
• Demand forecasting: Predicting future demand
• Demand sensing: Identifying and reacting to short-term (hourly or daily) fluctuations

In the modern retail landscape, demand planning is most accurate when a business considers both internal and external data to inform predictive models. Internal metrics may include historical sales numbers, ad spend, digital traffic and in-store footfall. The business may also consider external factors, such as consumer trends, weather, and holiday spending. The insights generated from these models can be used to develop sales predictions which can then be tracked in real time. While accurate demand planning takes some of the guesswork out of inventory, this capability also helps organizations improve agility and flexibility.

For example, an accurate demand planning tool will take seasonal weather and local climate into account when selecting inventory and determining product levels. However, if an area experiences unexpected extreme weather, such as heavy snow and ice, the system will detect upticks in related purchases, such as shovels and rock salt and restock inventory before it runs out. On the other hand, this system will identify sluggish sales on other items and enable the business to consider promotions, offers or other advertising efforts to help generate interest before drastic markdowns need to be taken.

One leading grocer had an 80 percent success rate in its current demand forecast, which meant that items were not selling as predicted 20 percent of the time. This resulted in unnecessary overstocks and costly out-of-stocks, squeezing the bottom line from both ends. An AI-based demand forecasting system with machine learning was able to produce a more accurate picture of inventory needs, reducing the error rate to just over 5 percent. This represented a 75 percent improvement over the current system and resulted in significant savings for the organization.

Using this method, one of our clients was able to reduce the forecasting processing time from seven days to three hours through the use of cloud-based tools. In another engagement, we were able to reduce the time to upload forecasting data from 550 million records in .csv format from 10 minutes to 2.7 seconds through the use of big data techniques.

The second way to improve demand forecasting accuracy is through the use of fast data analytical tools to sense near-term (hourly, daily) demand. This approach may incorporate metrics such as:

• Intraday activity data, including footfall, point of sale numbers and inventory levels;
• Intraday competitor data, like dynamic pricing and promotions; and
• Intraday external events, such as temperature changes and traffic congestion.

Given the growing importance of omnichannel fulfillment, it is critical for retailers to modernize their order management and inventory solutions. One way to do so is by leveraging microservices to provide a real-time view of inventory across the retailer’s supply chain. This helps optimize inventory while also reducing the need for markdowns, increasing customer conversion, and building retention.

For example, with global inventory visibility, if a customer walks into a store and finds an item out of stock, the manager can instantly refer to the inventory view and provide options for fulfillment based on in-transit goods, additional nearby stores, or online orders. Looking to digital channels, a near real-time inventory solution enables retailers to provide choices at the time of purchase and nudge customers towards more profitable “win-win” fulfillment options.

Alongside global inventory visibility, multi-echelon inventory optimization enables retailers to optimize inventory levels against attributes like cost, shelf-life, profit margins and space. It enables companies to automatically calculate the right inventory levels at all tiers, including national, regional and local levels, thus enabling companies to have the optimal working capital investment.

In conclusion, demand planning and global inventory management are two of the most significant drivers of cost optimization within the supply chain. While there is a significant variety in the way retailers can plan—including demand forecasting, shaping and sensing—nearly all rely on the use of big data, predictive modeling and advanced technologies, such as AI and ML. So too is the foundation for a modern global inventory management system. As such, organizations must invest in these technologies and build their internal capabilities to ensure they have the resources to optimize demand planning efforts and tap into this powerful growth opportunity.

Fulfillment – the process of receiving, packaging and shipping orders – has long been the cornerstone in every retail organization. However, as consumers take their purchases online, many organizations are struggling to adapt and expand traditional supply chains in order to serve customers when and where they are. This not only requires the rethinking of the fulfillment design but also finding ways in which fulfillment could be made more profitable for the retailer without compromising the customer experience.

In today’s digital age, retailers need to design their fulfillment strategy and channel mix to both optimize costs and increase speed, choice and convenience for customers. In many cases, this will include re-engineering traditional physical channels to enable true omni-channel fulfillment, including leveraging the existing store network to reduce the cost and increase speed of last mile fulfillment of digital orders. Retailers must also re-think how to increase and scale in-store pick up, which not only takes last mile costs out of the equation but also increase store footfalls and the ability to cross sell/up sell.

In order to reduce both the cost of returns to the business and the length or the return cycle, retailers must build a competitively advantaged return capability that optimizes products, the purchase experience and the cost of the return—all while maximizing customer value.

While it’s important to focus on improving returns process efficiencies and putting products through the re-commerce cycle more quickly, the use of data/AI to predict return and fundamentally address reasons for returns is equally important.

Using AI and ML, retailers can identify which items are experiencing a high rate of returns and determine reasons why. For example, size/fit is amongst the largest contributor to apparel product returns. Investments in developing the right product recommendations for size/fit at the time of purchase could help reduce returns. Retailers can also cross reference returned and non-returned items, thus having a better sense of size/fit both as it relates to the product catalog and the personal profile of each customer. Retailers can use this information to make adjustments, such as improving the product description, adding a sizing guide or upgrading the imagery.

Data/AI-driven insights about their customers can also enable retailers to address some of the most challenging and complex drains on the returns process, such as compulsive and impulsive shoppers and net-loss customers.  By using technology, retailers can develop models that predict real-time return probability during customer checkout. The retailer can then decide whether to intervene based on transaction characteristics, the customer profile and customer lifetime value (CLV). This technology can also help eliminate fraud, as it can detect with great accuracy customers who abuse or circumvent the return policies.

Unfortunately for retailers, there is no single “magic bullet” for returns optimization. Instead, success will be a cumulative effort as organizations refine internal processes to enable greater levels of efficiency and influence customer behavior through a range of incentives, perks and policies. To build momentum within the organization and demonstrate results, retailers should consider beginning return optimization efforts with one of the “quick wins”—those areas that offer high impact with relatively low complexity.  Over time, when put together, the efficiency gains will compound, enabling greater speed and cost reduction, while also serving as a valuable example for the business.

For many retailers, the shift to digital channels is happening far faster than they expected. In less than a decade, e-commerce sales are expected to quadruple, accounting for 40 percent of total revenue. This means that retailers must find ways to make this channel more profitable now, in order to remain viable in the future.

The biggest takeaway is advent of e-commerce has fundamentally changed the retail supply chain. But, at the same time, few retailers have made substantial changes to the business to support these new needs.  Further, the supply chain is no longer just a cost play, it also focuses on the customer experience. To reinvent the supply chain, businesses will need to be forward-looking, relying on new technology and operating models as opposed to past experience and legacy solutions.  

In today’s digital world, supply chains must move beyond a cost center or back office function. Instead, they should be viewed as a growth engine that can enhance the customer experience through improved choice, speed, convenience and quality.