Supply chain analytics and measuring performance in manufacturing

Kate Delimitros
- Technology - Nov 01, 2016

Moving the needle – supply chain analytics and measuring performance in manufacturing

Given the increasing requirements from customers for product availability and on-time delivery, manufacturing companies are under pressure to meet these demands while retaining margins. In an age of global presence, local demand, personalised offers, and omni-channel experience, businesses must optimise every aspect of their operations across both internal processes and external relationships with suppliers.

The value of analytics in supply chain performance

Improving the performance of a globally distributed supply chain depends on information. Decision makers need access to data in a way they can analyse without relying on IT. The information must also be up-to-date and consistent across the value chain, so that updates made in one process or region are accurately reflected across the enterprise.

However, different members of supply chain teams require different sets of data. A procurement or strategic sourcing team will be concerned with material costs and availability, as well as supplier performance, while the fulfillment team will strive for on-time delivery and availability of finished goods.

Both teams use data to track product and delivery priorities, but at a very different stage in the product lifecycle. Both teams also analyze granular information to make decisions minute by minute, whereas management needs an end-to-end enterprise view with top-level KPIs.

Getting to the right metrics

In order to evaluate the opportunity for improving access to information and analytics, a discovery needs to take place linking data flows to their business outcomes.  Starting point questions would be:

 

  • Who consumes the data?
  • What decisions do they make?
  • What are the ultimate business outcomes of these decisions?
  • How is their performance measured?
  • What pain points exist today that prevent improved performance?
  • What would be an ideal future state?

 

Based on this audit of analytics activity, it becomes possible to start putting together data more effectively. However, the central issue is not a question of simply providing visualisations. Key Performance Indicators (KPIs) must be determined that measure the overall business and have a financial impact.

Below these KPIs, each team or individual can work on a set of Key Value Indicators (KVIs). Whereas KPIs are set at the department or business level, KVIs are based on an individual’s goals and objectives. Setting the right KVIs can help employees optimise their own performance, which then rolls up to the department and greater enterprise working at maximum potential.

In the supply chain, most of the “low hanging fruit” process improvements possible through ERP and workflow automation have been realised. Evolving further to support a global market where unexpected weather or one celebrity tweet can dramatically impact demand requires a new level of orchestration across multiple teams and external parties.

By identifying and setting KVIs that support common goals, decision makers can collaborate to meet the requirements of the overall KPI goal. More importantly, rolling up KVIs to top-level KPIs ensures that the wider business goals are monitored and measured and everyday work is driving the desired strategic outcomes.

A good example would be a brand that pursues a premium pricing strategy as part of a tiered packaging model. Premium customers typically have higher service levels for on-time delivery. Perhaps an in-demand product needs to be kept in reserve for these customers who might order on short notice. In this case, maintaining safety stock is worth the extra cost, since the premium customers deliver high margins and have a significant lifetime value.

Of course, sometimes these customers aren’t worth the cost to serve. With analytics, you can uncover instances where a customer receiving white glove treatment may not be worth the total landed cost of expedited shipping and premium packaging.

For procurement and logistics teams, metrics for suppliers and component manufacturers are normally based on cost-saving targets. However, the cheapest supplier may not be the most reliable, so the KVI should balance supplier reliability against costs. A holistic view can identify whether a reliability mistake in one region is in fact a global trend; parts can then be shipped back to the original supplier proactively, instead of delivering a defective product.

Bridging the gaps with data across the ecosystem

Collaboration with external suppliers and customers can improve forecast accuracy, product availability, and time-to-delivery by putting everyone on the same plan, even as it adjusts. Embedding analytics in an external portal for suppliers or customers creates a mechanism for sharing up-to-date data with consistent metrics.

This approach reduces the manual work and error-prone activity of sending data files back and forth as email attachments (which is also not very secure). By taking a networked approach to analytics, the virtual instance used by external parties can be easily administered as a child space or clone from the enterprise master, with the appropriate security and provisioning rules in place.

A networked approach represents a shift towards more collaboration both inside and outside the organisation to achieve commonly defined goals. Instead of maximising one departmental KPI (for example production throughput) that may then lead to excess inventory on the balance sheet, KPIs across the business can be optimised by monitoring the relationship between inventory, product availability, and demand.  

Similarly, instead of negotiating with suppliers by taking a fixed position, share your interests to reach a model that allows both parties to be successful. Then monitor progress in a common dashboard. Processes may need to be measured differently, and teams within the business may have to collaborate more than they’re accustomed to, but the use of analytics will ultimately support value creation across the ecosystem.

Kate Delimitros is Senior Director of Product Strategy at Birst

 

Follow @ManufacturingGL and @NellWalkerMG 

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