Redesigning Order-to-Cash for a better buying experience
Max Mara uses process mining to make more strategic, ROI-driven process improvements

Since it became one of the first fashion companies to embrace “ready-to-wear” designer clothing, Italy’s Max Mara Fashion Group has demonstrated a pioneering style that has helped it stand out in the global fashion market. Today, that same business model—focused on making high-quality fashion accessible to everyday people through a wide range of brands—is as successful as ever.

Almost by definition in the fashion industry, keeping an eye on changing trends and tastes is de rigueur for Max Mara to stay relevant and stay true to its brand pledge. That’s the job of designers, product managers and others whose focus is bringing the right mix of products to market.

But there’s also another side of Max Mara’s business model that’s essential to keeping its clientele coming back: a satisfying buying experience, whether it’s through one of the company’s 10 brand-specific websites or its more than 2,300 brick and mortar stores around the world. The embrace of digitalization and omnichannel marketing has been a huge part of the story.

For Max Mara, like many other companies, the pandemic’s arrival—and the changes in buying behavior it produced—accelerated a digital transformation that had already been underway. In fact, over the course of the pandemic, the digital share of business volume nearly tripled. As if crossing a threshold, Max Mara’s digital operations unit—established early in its digital transformation journey—recognized that the efficiency of its back-end operations would now have an even larger impact on customer satisfaction.

“If you imagine a ‘heat map’ of potential process improvements, our reddest zone would be the Order-to-Cash cycle, from order processing to fulfillment, payment and customer service,” explains Max Mara’s Head of Digital Operations. “And during the seasonal spikes in sales we experience [typically in July and December], those red zones get even redder.”

Process problems create bottlenecks, especially in the range of warehouse-based activities between picking-and-packing and shipping. In assessing their options, Max Mara’s digital ops team considered traditional process redesign approaches that relied on business intelligence (BI) systems and frontline insights from business analysts, process owners and other stakeholders to get to the bottom of process flow issues.

While the team saw these methods as a necessary part of process optimization, they recognized it was just that: only a part. “BI systems are valuable to point out symptoms of process problems,” explains the Head of Digital Operations, “but they’re not as capable at diagnosing their root causes, which is critical to solving them.” Max Mara’s more expansive vision was the ability to take targeted action, based on hard data. This means not only pinpointing suboptimal processes at a granular level—say, staffing patterns in a particular warehouse or the performance of a logistics provider—but also making data-driven projections of how specific process changes—whether it’s fixing a process flow or automating it—would impact key operational metrics.

But the digital ops team recognized that the sheer complexity of Max Mara’s digital operations made achieving this data-driven vision especially challenging. “We sell all around the world, and while the ‘front end’ of our order process is fairly standardized, the physical part of the flow—further down in the process stack—varies considerably by country,” explains the digital ops lead. “The same is true of our supporting systems like ERP and CRM, which have also been heavily customized for local needs.”

Reduced Resolution Time


Reduced customer service resolution times by  90% compared with manual approaches

Cost Savings


Reduced average cost per resolution by up to 46% by opening bottlenecks

Because process-related decisions need to be based on their ROI, they’re only as good as the data and process discovery models that underlie them. The strength of IBM Process Mining gives us the confidence to know where to take action, and what the business case of those actions will be. Head of Digital Operations Max Mara Fashion Group
How to improve Order-to-Cash through process mining

Max Mara recognized that to improve its Order-to-Cash (OTC) processes, it needed the ability to quickly and accurately identify not only where the problems were, but also which fixes would yield the highest ROI. The digital ops team saw advanced process discovery tools as the right approach. More specifically, they sought a process discovery tool that combined implementation flexibility with powerful, granular process modeling capabilities.

After looking at a range of options, the team selected the IBM® Process Mining solution, which they saw as providing “the most comprehensive foundation for data-driven process optimization.”


Today, those efforts are run by the process optimization Competence Center established as part of the eight-month implementation of IBM Process Mining. The Center’s head explains: “We provide technical solutions according to the business’s needs, and we act as a pivot entity, coordinating analysis and implementation between the business, our implementation partners, and our internal IT infrastructure.” The Competence Center played a critical role in the Process Mining project. A Business Analyst at the Center says, “Together with our business side, we performed a deep-dive analysis of our existing Order-to-Cash flow across our complex, multi-software, multi-data source scenario.”

The qualitative insights arising from this review are important because they define the lay of the land for process redesign efforts. But ultimately, process decisions—whether it’s changing a flow or automating it—require real, actionable data from the processes themselves. And that, according to the Head of Digital Operations, is where the IBM Process Mining solution bridges the gap. “Because process-related decisions need to be based on their ROI, they’re only as good as the data and process discovery models that underlie them,” he explains. “The strength of [IBM Process Mining] algorithms, along with the breadth of enterprise application data feeds it can ingest, gives us the confidence to know where to take action, and what the business case of those actions will be.”

Consider, for example, a case where order lead time in a particular geography is known to be higher, and the suspected root cause is the warehouse pick-and-pack flow. By running relevant ERP, CRM and other data through the IBM Process Mining models, members of the CoE team can not only corroborate that hypothesis, but also pinpoint unexpected process impacts that are compounding the problem. “In some cases, we knew there was a bottleneck due to process deviations,” says the digital ops lead. “But we were surprised at just how complex the flow was, and how few orders in the warehouse actually followed the ‘happy path’ process flow. That data-driven insight enabled us to design a more appropriate and effective fix for the problem.”

Making strategic investments in process automation will be critical to delivering the high quality digital experience customers have come to expect. With IBM Process Mining, we’ve gained a powerful tool to identify where automation will have the highest payoff, both for our customers and for our business going forward. Head of Digital Operations Max Mara Fashion Group
Smart automation decisions for highest ROI

A big part of coming up with the right process change is knowing what the impact of that change will be—before it’s implemented. The built-in simulation capabilities of the IBM Process Mining solution mean process designers can test the likely impact of changes in terms of key metrics such as lead time and staffing requirements. The same granular, self-contained qualities of the model can also reveal whether a particular change can have unanticipated impact. “We’ve seen how relieving a bottleneck at one point in the flow can cause another to develop at some other point,” says the digital ops lead. “By revealing these impacts out via dynamic modeling, [IBM Process Mining] has enabled us to take a more holistic approach to process optimization.”

In one noteworthy case, the digital ops team wanted to understand how proposed changes in the processing of customer post-sales support inquires would affect bottlenecks during so-called “high load,” when volume was reaching seasonal peaks. Using IBM Process Mining, they were first able to identify the more repetitive parts of the process flow that would best lend themselves to automation. By simulating these changes—including the automation of key process flow segments—they were able to demonstrate up to a 90% decrease in customer service resolution times, along with a 46% reduction in the average cost per resolution.

While today Max Mara is in the relatively early stages of process automation, the Head of Digital Operations expects it to become a much more prominent element of the company’s digital ops strategy in the near future. And he sees IBM Process Mining as an essential tool in mapping that journey. “Making strategic investments in process automation will be critical to delivering the high quality digital experience customers have come to expect,” he explains. “With IBM Process Mining, we’ve gained a powerful tool to identify where automation will have the highest payoff, both for our customers and for our business going forward.”

The Competency Center lead concurs. “We believe that the clear success of this project will make it the first of many across a range of our business units around the world,” he says.

Max Mara Fashion Group logo
About Max Mara Fashion Group

Based in Reggio Emilia, Italy, Max Mara (link resides outside of is a global fashion company established in 1951. With 41 companies and over 5,500 employees, Max Mara operates in 105 countries. Family-owned, the company was among the first fashion companies to focus on up-market ready-to-wear clothing. Today, the company operates 10 brands worldwide.

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