Supply chain analytics: How to move from insights to outcomes

Supply chains powered by advanced analytics enable faster, more profitable decisions, build resilience, and streamline processes. The result is a shorter journey from data to insights, insights to actions, and – most vitally – actions to tangible outcomes.

A recent Gartner survey found 87% of supply chain professionals plan to invest in resilience within the next two years, with 89% wanting to invest in agility. These capabilities will help them adapt to structural changes, such as the shift to e-commerce, as well as heighten their ability to sense and respond to the unanticipated changes in supply and demand that the disruption from COVID-19 continues to cause.

All supply chain operations – from forecasting demand and planning production to scheduling logistics and assessing supplier risk – are based on data and analytics. The challenge is to transform this information into actionable insights that improve business outcomes. This is only achievable when we reimagine entire operating systems rather than introducing standalone technology solutions. But some companies have made this leap by:

• Embedding end-to-end orchestration – to gain visibility of even the most complex supply chains and more democratized decision-making
• Applying domain intelligence – combining supply chain expertise and an understanding of their specific business needs
• Adopting an outcomes-based approach – focused on transformation that brings tangible results across the entire supply and demand ecosystem

For the companies that can harness the power of advanced analytics, outcomes include increased revenues, reduced costs, and diminished risk, as well as opportunities to scale. Take, for example, the surges in demand and disrupted logistics we witnessed at the start of COVID-19. Consumer goods companies that could act on real-time data and insights were able to secure scarce supplies, replan production schedules, and keep deliveries flowing. Shelves were stocked, customers were happy, and brand loyalty didn't take a knock.

We worked with a multinational household cleaning brand to increase supply chain agility during COVID-19 in the face of soaring demand. By overhauling its operating model across planning, processes, and technology, it now has a single platform for the critical unification and visualization of planning processes so it can monitor the on-time fulfillment of store orders.

The company has generated $135 million in recurring annual inventory savings and reduced write-offs by 70%. The intelligent automation of many planning-related tasks means they can happen five times faster, with 100% accuracy, as well as save millions of dollars in resource time and costs.

The goal of supply chain management (SCM) is to get goods from point A to point B and into the hands of the customer when they need them. A whole range of issues crop up along the way, whether it's a lack of critical parts, labor shortages, or sudden shifts in consumer behavior that make forecasts obsolete. But transparent analytics across the entire supply chain help spot these disruptions early and solve problems at speed.

Removing business and data silos and sharing insights at every point of the supply chain will build agility and resilience, even during rapidly changing circumstances. But despite these advantages, only 36% of companies currently describe their supply chain as having achieved external integration or end-to-end control.

Even digitally advanced supply chains currently struggle to operate as a single, connected ecosystem. Workers often focus on their own functional goals rather than having KPIs that support the entire value chain.

However, investing in an augmented intelligence approach transforms supply chains into true competitive differentiators. By sharing data, insights, expertise, and digital assets, operations and decision-making become democratized and optimized.

For example, a production team may have a frozen, four-week horizon for manufacturing a certain product. If the sales team asks it to speed up production to fulfill demand from its biggest customer, the teams can agree to change the operational plan if they both know manufacturing capacity and the impact on other customers.

Across the supply chain, employees become invested in outcomes, rather than just their own responsibilities. And leaders, freed from having to be involved in every decision, can focus their attention on embedding analytics insights into decision-making and managing change.

Enterprises have a massive opportunity to unlock valuable insights from data – both from within their four walls and from suppliers, customers, and other stakeholders.

Applying advanced analytics to this data to identify demand drivers and new market trends makes forecasts more accurate.

We worked with a global beauty brand to get a better understanding of customer demand drives in its duty-free outlets. It knew its forecasts could be more accurate if its data included external data such as macroeconomic variables and social listening. We designed an analytics framework that combines internal supply chain data with external data such as social media buzz, travel trends, economic conditions, and even weather events. Now, forecast accuracy for SKUs that make up 80% of duty-free revenue is up by 13%. Better product availability means happier customers too.

Growing supply chain complexity across markets and countries as well as multiple data streams and partners make effective SCM more challenging than ever. Sudden demand fluctuations put pressure on the entire ecosystem and can lead to increased risk and revenue leakage.

However, with the help of a digital twin – the virtual model of a process, product, or service – enterprises can model the outcomes of different supply chain decisions and their impact on all parts of the business. With the twin harnessing data from across the supply chain ecosystem, SCM leaders can forecast the outcomes of a wide range of what-if scenarios. These could include the impact of new tariffs, reducing the number of SKUs, or closing production lines. Using the digital twin to act out these scenarios enables the business to predict costs, risks, and impact on revenue, while also building resilience.

Supply chain digital twins are a key way to deploy augmented intelligence and empower better and faster decision-making. By virtually recreating production schedules and inventory volumes and locations, employees have the transparency and connectivity they need to implement change – for both quick fixes and long-term strategies.

We worked with one of the world's leading food and beverage manufacturers to identify the right locations for investment in its vast supply chain network and maximize the value of its five-year investment plan.

With 80+ warehouses, 8,000+ SKUs, numerous internal and external partners, and 600,000+ annual shipments, the manufacturer needed advanced analytics to process the enormous volume of data it gathers every day. Through actual and predictive modeling, we quickly identified inefficiencies for which we could make immediate improvements by reconfiguring the distribution network and realigning manufacturing to support the changes. Scenario testing also enabled the company to prioritize investment opportunities, such as separate networks for fast- and slow-moving products.

Overall, we identified 3% savings in production and 5% in distribution by:

• Optimizing production
• Identifying the optimal location for new lines to meet increased demand
• Implementing alternative customer sourcing
• Redesigning the network

To accelerate the journey from data to insights, insights to actions, and actions to outcomes, companies must prioritize digital transformation that enables the full value of analytics. This includes:

• Implementing end-to-end orchestration – invest in skills and technology, or a vendor service, that enables a fully integrated, transparent view of the supply chain ecosystem
• Creating a culture of digital literacy through augmented intelligence – bring machine capabilities and human expertise together and free up leaders to promote digital assets and reskill workers
• Harnessing predictive analytics to reduce risks – analyze multiple internal and external data points to improve risk and demand modeling accuracy
• Democratizing decision-making to enable scalability – a transparent, shared overview of data, insights, and digital assets makes it easier to pinpoint and solve problems, even before they happen
• Using digital twins to build resilience – a digital model of the supply chain allows SCM leaders to test what-if scenarios, adapt at speed, and ensure they're ready to face uncertainty and change
• Data and digital capabilities to design innovative solutions to be executed at scale

Success requires a strong focus on supply chain outcomes, building a solid data foundation, and the right operating model to turn analytics insights into outcomes.