18. Descriptive Analytics for the Supply Chain at Bernard Chaus, Inc.

Katherine Busey and Callie Youssi

In the midst of a severe economic downturn, Bernard Chaus, Inc., a women’s apparel manufacturer, invested in a new business intelligence tool and database. They were designed to deliver descriptive analytics on supply chain performance. The tool was delivered “in the cloud” to company staff on a self-service basis. In just a matter of weeks, the firm had improved visibility throughout its supply chain, with almost immediate payback in the form of significant cost savings and closer customer relationships. Key to the successful implementation was extensive prior consultation with business unit leaders, who outlined the required data and functionality.

The Need for Supply Chain Visibility

As consumers reined in their spending during the financial crisis of 2008, apparel retailers struggled to control inventory, manage discounting, and maintain margins. Retailers began to press suppliers such as Bernard Chaus to assume more responsibility for analyzing sales trends, recommending markdowns, and adjusting shipments to reflect this data.

Like most of its peers at this point, however, Chaus relied on simple weekly phone checks with buyers and factories to determine manufacturing and replenishment plans. But this information was far less detailed than needed—and even potentially misleading. A retail buyer might report that a particular dress style was selling well, for example, while the reality was that just one size (of six) in one color (of three) accounted for the bulk of the sales. Without the ability to drill down to the specific SKU, the manufacturer might produce and air-freight more of every style variant, rather than sending more of the one hot seller and recommending early markdowns on the slower-selling items.

“We knew something was wrong, but we didn’t know exactly where.” —Ed Eskew

Chaus required greater visibility in both supply chain directions: back to its factories and shippers and forward to its wholesalers and retail buyers—all of whom were investing in data warehouse and communications infrastructure. To stay competitive, Eskew realized that the firm had to invest in a structured, automated feed of time-sensitive production, freight, and sales data—something only software could handle—as well as a business intelligence toolkit to “slice and dice” that information in support of smarter decision-making.

Before selecting a software vendor, Chaus’s CIO did his organizational homework. Eskew understood that providing more complete supply chain visibility meant that acquiring and channeling production and sales data in and out of the enterprise was now his key task. But rather than rushing to a purchase decision and unilaterally delivering it to his users, he followed three deliberate steps:

1. Understand the business, and build personal trust. Eskew turned first to his business unit presidents and their “worker bees” and asked them these questions:

• What information do you need for improved supply chain visibility?

• When do you need this data? From whom should it come?

• In what format?

• What data can you supply?

• How do your cutting, prototyping, ordering, and shipping processes work now? How would better data impact them? Where could you trim costs based on deeper and more timely data? Is there a better way to do any of these things?

• Can you suggest a vendor’s solution? If so, why? What features are critical?

Most important, Eskew was simultaneously building one-to-one trust throughout this research. Not only did he personally appreciate organizational processes at a more detailed level, but he also established greater confidence and credibility in his subsequent decision-making.

“Unless you know exactly what’s going on, you lose control—and that’s expensive.” —Ed Eskew

2. Obtain executive backing. Eskew had to ensure that the firm’s other C-level executives were onboard. In some companies, convincing top management to invest in new technology during a recession is difficult or impossible. But Eskew, with a decade’s tenure at Chaus, knew that his CEO and CFO were aware of the challenges facing the company—including those related to lack of adequate supply chain visibility. He also knew that they never shied away from tough decisions.

As Eskew made the case for analytics, they also learned how implementing a data-rich approach would affect decisions: Everything from seasonal sales forecasting to communications with factories in China would undergo a major shift. Like the divisional presidents, all senior executives agreed that Eskew should lead the software selection process and implementation rollout.

3. Set prioritized criteria for the solution. Any software buyer can quickly come up with many criteria—ease of use, training, accessibility, service, and more—but Eskew knew that these were his top selection filters:

• An ability to house and support historical data in an easily retrievable manner

• Low startup costs compared with competitors

• Experience in apparel, retail, or a similar industry

Only when a vendor passed these screens were other criteria even considered. For Eskew, the selection wasn’t about the fastest servers or nice-to-have bells and whistles. Instead, his focus remained on providing accurate and timely information about point-of-sale, synchronized production scheduling, freight management, factor financing, and other vital business variables.

“We knew we had to streamline our way back to profitability.” —Ed Eskew

With organizational backing and solution criteria established, vendor selection and implementation progressed rapidly. Over a couple of weeks, Eskew was able to narrow down the field to two potential vendors, including SKYPAD, a retail analytics and business intelligence platform from Sky I.T. Group. Also, after being impressed by a conference demonstration from QlikView, Eskew learned that SKYPAD closely interfaces with QlikView’s database back-end. SKYPAD also “scrubs” and normalizes the wide variety of data types that Chaus encounters across its supply chain, from Excel spreadsheets to EDI transactions to Visuality style-image email exchanges between supplier and retailer. After a bit more due diligence, the vendor decision was a quick and straightforward one.

In the fall of 2008, only three weeks after selecting SKYPAD and well in time for the spring 2009 retail season, Chaus had a “plain vanilla” but functional model that revealed which SKUs were selling (and not selling) in specific locations—an immediate improvement over the previous manual process.

Eskew decided to launch with a bare-bones implementation to start seeing results and to learn what worked in the organization. Since then, based on feedback and suggestions from users, he has gradually but continually added options, data interchange formats, and user interface enhancements.

Analytics Strengthened Alignment Between Chaus’s IT and Business Units

Today, Chaus holds weekly business unit meetings where the focus is on identifying new drivers of cost reduction. In a time-sensitive, competitive business like Eskew’s, the volume and content of data never stop changing, nor does the potential for learning from experience. Thanks to close collaboration with IT throughout the initial implementation process, Chaus’s business units have embraced SKYPAD as their go-to tool for gaining visibility into sales trends, operational efficiencies, demand planning, and predictive what-if analysis.

Because they helped “spec” it, the user community understands what SKYPAD is and what it can do. They now come to Eskew and his team to “figure things out,” so the trust pays continuing benefits.

Eskew credits analytics with dramatically boosting the company’s linkage between IT and the business units. On a five-stage Strategic Alignment Maturity Model scale where 1 = initial/ad hoc, 2 = committed, 3 = established/focused, 4 = improved/managed, and 5 = optimized, Eskew estimates that Chaus has gone from a 2.5 level of maturity to more than 4.0 in just two years.

“Our goal is to become ever more responsive to what these tools can tell us.” —Ed Eskew

With the new data in hand, Chaus managers can now confidently assist retailers in properly timing their item markdowns, which minimizes their end-of-season inventory. In subsequent seasons, too, retail buyers have good reason to trust Chaus’s order-level recommendations. (Some stores have even turned seasonal inventory management entirely over to vendors like Chaus.) Because Chaus increasingly uses data to extend lead times, less money is spent on expensive, last-minute air freight from China.

Chaus’s analytics investment produced virtually instant payback. Although Eskew initially established metrics to measure the firm’s ROI, the payback was so quick that he soon stopped counting. Chaus’s ramp-up costs were approximately $15,000, and SKYPAD’s hosting charges—which vary based on customization and service requests—total about $50,000 annually. In contrast, Eskew estimates that Chaus has already saved well in excess of $1 million in reduced markdowns, better sell-through, and freight costs. “I can’t quantify the total value,” he says.

Because SKYPAD lets customers choose either hosted service or in-house implementation, Eskew knew that he could start on a monthly “cloud” basis and decide later to bring everything in-house. However, his experience with SaaS has been so positive—not to mention the absence of any new hardware investment—that Chaus plans to stick with the hosted approach.

“Information without action is overhead.” —Ed Eskew