Where do sales and supply chain teams collide?

20th Oct 2016

What comes first, the product or the demand? This question is the central dilemma of going to market. The likes of Apple and Dyson have built strong brands around their designs, using adjectives such as “magic” or “perfection” to describe their appeal.  However, such religiosity around product design isn’t an option for companies working in markets that are more saturated or where products are commoditised.

In these scenarios, the supply chain is integral to creating a positive customer experience. Metrics such as on-time delivery and quality can have an impact on repeat buys and expansion. In fact, Sirius Decisions has found that 80 percent of business-to-business buying decisions are influenced by both the direct and indirect experience of the sales process, while only 20 percent were influenced solely by price.

Creating common goals for a better customer experience

However, aligning sales and supply chain teams on objectives around customer experience is difficult. While leaders in both teams manage relationships with customers, their goals are very different. The teams also don’t interact or plan together in the same way that sales and marketing teams do.

This alignment challenge is compounded by the global nature of supply chains, which depend on multi-channel distribution, a mix of systems, and disparate reporting requirements. For companies with multiple business units or regional operations, achieving a single view of the customer across channels is a massive reconciliation exercise. Efforts to achieve a meaningful understanding of the customer can be futile.

How can management teams bridge those gaps successfully?

One approach is to bring teams together through common data. By using data sources from the two business functions, and providing visibility across both, it’s possible to gain a unified view of the customer experience, pre- and post-sale. While sales and supply chain teams run their own operations, neither exists in a silo, and some of the data will be useful to both. For example, teams should be able to answer questions like the following:

  • Where is demand greatest and in what regions?
  • What sales were booked at the end of quarter that need to be fulfilled?
  • What sales are close to closing, so we can plan availability on high-demand items?
  • How does customer satisfaction affect future buying behaviour?
  • Do customers interacting with post-sale support and supply teams provide repeat business?

Analytics on these patterns can help both teams get ahead of their objectives and enable management to understand how individual decisions across the business relate.

A data-driven approach has many advantages. Firstly, examining insights backed with analytics can help build consensus. If teams are taking contradictory positions, analytics provides a visualisation of actual performance and trends, rather than anecdotal evidence. However, the data has to be accurate and measured consistently. When metrics are defined differently in spreadsheets owned by separate stakeholders, the group will spend time arguing about the integrity of the figures, rather than focusing on what actions to take.

Secondly, it’s important to use data to rethink business goals in total. When teams are incentivised to act in opposing ways, they will pursue their own KPIs rather than consider the bigger picture. Using data to determine and measure common goals allows management to ensure that cross-functional teams work together to deliver results.

It’s also worth considering how redesigning metrics and models can drive performance. Rather than evaluating sales exclusively on revenues and supply chain teams solely on operations performance, consider measuring both teams on a customer impact metric. This approach requires examination of the end-to-end experience. For example, tracking delivery quality for premium customers is not traditionally a metric that sales teams follow, but they should be aware of delivery status to their best customers.

Delivering self-service analytics with governance

Using data can help teams collaborate and understand the impact of decisions across the customer experience. However, this information-sharing process has historically been flawed. When reports are distributed in a static format, decision makers can’t analyse data at the source to discover their own connections and correlations. Assumptions are perpetuated without getting to the root cause of performance drivers.

Self-service analytics, on the other hand, empowers individuals across the business to answer questions specific to their roles without relying on IT. However, when business users are enabled with desktop analytics, a mechanism for governing data and maintaining consistent business definitions must also be in place.

Rather than physically replicating data instances across regions and departments – a slow and manual process – virtually networked data and analytics leverage one enterprise data model and a universal set of definitions across all business units, regions, and data sources. The business has self-service without creating information silos, and new data sources and instances can be stood up automatically with fast time-to-value.

Looking ahead, supply chain and sales teams will be at the forefront of solving these collaboration challenges, because both teams are integral to the customer experience. For sales, strong customer relationships over time should lead to more sales and higher revenues. For supply chain management, improved processes will lead to shorter lead times, less inventory buffer, and fewer stock outs. While the operational goals for these two teams might be defined differently, sharing trusted data is essential for their collaboration. With this approach, they will achieve better results for their teams and for the enterprise at large.

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