Building Your Supply Chain Strategy: Retailers are at the Forefront of Customer-Centricity

How can manufacturers leverage retail’s leading practices?

In Part I of my Supply Chain Strategy series, I explained why the five tenets of High-Performing Supply Chains remain a great starting point to build your supply chain strategy. In this post I will discuss how manufacturing companies can leverage leading retailers’ winning approach to customer-centricity.

Why should manufacturing companies emulate retail’s customer-centricity? What can they learn from this approach? And how can manufacturers apply those lessons to manufacturing processes?

Retail faced the digital economy first. Being in direct contact with the digital consumers, retailers were the first to offer their products to consumers via websites to compete with new entrants such as Amazon. By creating these new channels, retailers had to deal with several waves of disruptive forces such as eCommerce, social media, mobile and big data.  That same nexus of forces (see figure 1) is now transforming manufacturing companies.

Big Data - AR 2

Figure 1: Nexus of Forces Shaping the Digital Economy, as defined by Gartner

Let’s illustrate how retailers adapted to the digital economy with a familiar example.  Prior to the digital economy, predicting which toys and video games would be hits during the Christmas season were nothing more than wild guesses. Therefore, responsiveness during the peak season was essential for retailers to react fast enough to a sudden bestseller.

Today, retailers prepare their strategy for peak season months in advance. Sifting through data, they define customer profiles and buying behaviors, as well as product demand trends. Potentially hot items from the gaming industry, for example, are determined very early by enriching enterprise data with advertising profiles from game producers, consumer web-browsing patterns, social media sentiment, and potentially even movie release dates. Based on this data, a predictive model is created to help identify expected trends for select items.

In addition, retailers try to understand from big data where the demand will occur. Data sources that may be leveraged for this include demographic data, customer transactions, shipping information, local buzz and insights from research.

The retailer now can proactively allocate inventory to various channels with a much higher level of accuracy than the competition. But demand profiling is not limited to channels. Retailers look at customer segmentation by defining customer profiles from purchase behaviors, presence on gaming forums or loyalty cards.

A customer profile typically contains shopping habits, order preferences (likely to preorder, sensitivity to incentives, etc.) and also preferred channels of communication (email, Facebook,…). With this information, retailers can target specific customer groups with specific offers via specific communication channels at specific locations. Big data also helps in bundling product offers based on past buying patterns or web-browsing patterns specific to a customer or customer group.

The advantage of using big data does not end with the start of the peak season. During peak season, retailers use these data sources to track, on a much more real-time basis than their competitors, how orders and inventory profiles match their original predictions. And by closely watching competitors’ pricing changes, they can change prices dynamically by the hour, not only sensing how well their forecast materialized, but giving the demand curve a new shape.

Supply chain professionals call the above competitive advantages ‘demand sensing and shaping.’ Supporting this is ‘Intelligent Fulfillment,’ which is characterized by a diversity of fulfillment models and predefined customer service priorities.

The key is that demand sensing and shaping and Intelligent Fulfillment are not limited to the retail industry. Depending on your industry, many sources of data can be leveraged to enrich demand signals and better sense or shape future customer demand:

  • Store-level Point of Sales data
  • Transaction log data
  • Loyalty card data
  • Causal data
  • Promotional data
  • Social media information

The challenge is not just to collect this data, but to make sense from the emerging patterns within a short time window, identifying correlations, simulating impacts, and discovering root causes. This knowledge is then used to automatically enrich the latest forecast.

JDA offers these kinds of capabilities, and via Flowcasting, is bridging the divide between the worlds of retail and manufacturing. This enables new forms of win-win collaboration based on prescriptive analytics and big data.

For more information, read Part 1 of this blog series – The Five Tenets of High-Performing Supply.

No Comments

Be the first to start a conversation

Leave a Comment