Reducing operational friction
Updated: Sep 15
The clients were stressed and angry. IT problems were preventing them from accepting stock into their warehouses. They had just launched their online retail partnership with Ocado. There were months of careful planning and testing. But now they’ve hit this unexpected hurdle.
Many different types of problems related to product data were reported. But no one knew specifically what the underlying issue was. No one knew where to begin fixing it. Some people had partial answers, but no one had a complete understanding of all the problems. The picture was like a murky pond. A solution seemed far out of reach.
Here I describe how I helped the company find direction.
The process
The Product Manager and I knew we had to take action. Through our contacts in the client services team, we arranged interviews with all the clients who were affected by the problem. The severity of the problems meant relationships were at a low point. Being the interviewer in these high pressure sessions, I needed to be especially tactful and diplomatic.
In the interviews the clients were asked to walk us through the problems they experienced. Most of them described how the problem was affecting their operation, and gave a few examples of common issues. To understand further details and cover all issue types, we arranged additional meetings with employees who dealt with the problems daily. The last client we interviewed was probably the most interesting one. The interviewee was different to everyone we had spoken to thus far. He was calm and relaxed. Instead of complaining about how terrible the situation was, he shared with us his opinion about the broader context - the design of the backend, which proved to be a vital piece of the puzzle.
Analysing the research
Initially the large quality of data we had gathered was somewhat overwhelming. But as I analysed the data, distinct layers of problems began to emerge across different levels of the system.
User interaction layer
Process layer
Technical layer
Business layer
Strategic layer
They helped structure the findings and clarify how issues at different levels contributed to overall operational challenges. Let’s look at each one in turn.
User interaction layer
Supply chain staff struggled to create purchase orders and import product data. Error messages were difficult to understand, making it harder to identify and correct issues.
Process layer
Warehouse staff experienced difficulties accepting products into the system. Strict validation requirements meant that small issues could block the purchase order process, which fell short of their expectations.
Technical layer
The system does not surface errors when incomplete product data is uploaded. There were many types of data issues, which required different resolution approaches.
Business layer
When products are not accepted into the system, customers don’t receive the items they ordered. This has a significant impact on customer satisfaction and sales targets.
Product layer
The design of the backend introduced friction for clients importing and maintaining product data. Differences between source data and the product’s data model created additional overhead. Data quality issues were difficult to resolve efficiently, particularly when working with large datasets.
Identifying opportunities
Being a systemic problem, usually there is more than one solution. Several solutions may need to be applied to achieve the business objective. The analysis helped identify multiple opportunities and allowed the product teams to prioritise them and find the most effect combination.
User interaction layer
How might we make data import easier for users?
How might we make errors easier to understand and resolve for users?
Process layer
How might we reduce the disruption to the operation when there are data quality issues?
Product layer
How might we improve the design of the backend so that importing and maintaining product data is easier?
How might we improve the efficiency of bulk data correction?
Framing the problems as opportunities avoids locking into a solution too early, focuses teams on the problems, and provides them with the freedom to find the best solution.
Conclusion
It was a complex problem that was not well understood and caused considerable stress. But through careful research and analysis, clarity and direction were found. The product teams were highly engaged after they were presented the findings. The message was well received, generating strong interest and helping to create team alignment.
