Much has been said about the effect that past years' challenges have had on the way companies do business. As managers adjust to a marketplace where permanent volatility and accelerating globalization are facts of life, they must acknowledge that what was effective yesterday may not be tomorrow. For supply chain professionals, the need to anticipate and adapt has never been more critical to high performance.

The data revolution has brought valuable resources to supply chain managers with the tools and inclination to harness enormous quantities of data being captured every day across their organizations. Using an integrated framework that employs quantitative methods to derive actionable insights from data is becoming such a key differentiator between the masters and the majority. Through predictive analytics, decision-makers have the tools to keep their supply chain fit, agile and competitive. In the new economy, insight based on hindsight is, quite simply, not good enough.

Understanding analytics

Earlier this year Accenture conducted a comprehensive survey of supply chain challenges and the characteristics of those professionals that have achieved high performance. The survey found that companies still struggle with visibility over what will affect them at either end of their supply chain - on both the demand and the supply sides. Most respondents still lack the information technology, linkage of systems with suppliers and customers, and common processes that are essential for dynamic supply chains.

These findings are in tune with wider analytics-based research conducted by Accenture in 2009. In a survey of 500 UK and U.S. blue-chip organizations, two-thirds of all respondents cited "getting their data in order" as an immediate priority. Longer term, the top objective for more than two-thirds of executives is to develop the ability to model and predict behavior and actions to the point where individual decisions can be made in real time, based on the analysis at hand.

Almost 40 percent of companies in this analytics survey believe that their current technological resources and systems significantly hinder the effective use of enterprise-wide analytics. Forty-five percent of the respondents said that data is housed in isolated parts of the organization, while more than half reported that analytical talent - an increasingly vital resource - is housed separately from the relevant organizational data.

Learning from the masters

While many organizations say they already do analytics at some level, this is typically limited to descriptive analytics (understanding "what" happened and "why").

By contrast, the "masters" in our supply chain survey are deploying prescriptive analytics to understand the "now what?" - moving from "what happened?" to "what's the best that can happen?" As well as being more than four times as likely to achieve minimum accuracy levels of 80 percent in their demand forecasting, they are twice as likely to rate their ability to shape demand as "good" or "excellent" - resulting in up to 50 percent less finished goods inventory than their competitors.

Five analytics-enabled objectives

In today's "new normal", supply chain directors and managers must be equipped with 20:20 vision into their operations. Based on our experience, organizations are typically already focusing on one or more of the following analytics-enabled objectives:

1 Supply chain visibility and cost to serve

Achieve a descriptive view of the most important supply chain processes through KPIs, reports and balanced scorecards, as well as understanding the cost to serve each customer segment.

2 Demand forecasting and inventory optimization

Using analytical capabilities and tools to segment and assign the best forecasting profiles to improve predictions of future sales, and incorporating critical business intelligence to adjust baseline forecasts and ensure accurate inventory level projections.

3 Network optimization

Periodically performing Total Cost of Ownership assessments of their supply chain network (suppliers, plants, distribution centers, transportation modes; customers' service levels, etc.) to identify and adopt the optimal supply chain combinations for each customer segment.

4 Predictive asset maintenance

Improving uptimes, performance and availability of assets by predicting when maintenance or new parts are required in order to avoid unplanned downtimes.

5 Spend analytics

Understanding how much the company is spending on different procurement categories (and with which suppliers).

While some companies are already becoming proficient in a few of the above areas, it is less usual for an organization to have implemented them on an enterprise-wide basis with sustained results over time. A suite of analytics capabilities, aligned with the company's strategy, will be a pre-requisite for competitiveness from now on.

Looking to the long term

Longer term, the priority objective should be to drive towards building cross-departmental/supply chain analytics capabilities that enable rapid reactions, as well as the ability to predict what comes next. Examples of these could include supply chain risk assessment and mitigation throughout the chain, and after-sales profit optimization to understand costs and maximize customer satisfaction and repurchases. Additionally, companies need to be able to adapt rapidly by, for example, switching suppliers or manufacturing capacity. This means providing insights needed to understand and optimize the total cost to serve - on a dynamic "on demand" basis, instead of conducting occasional project-based assessments.

Whatever the preferred course, there is no doubt of the need for analytics-driven insights. Companies that thrived in the wake of previous downturns were those that used data-derived insights made by informed decision-makers to produce competitive advantage. This time around, companies that use analytics to determine "what happened" in hindsight will fall behind those using predictive analytics to succeed and shape "what is to come." 

Source: Accenture Analytics