Every time I talk to supply chain professionals - whether in procurement, demand planning, manufacturing or delivery - one common theme keeps surfacing: the need for better analytics.
But what can business analytics do to enhance a supply chain? Supply chain managers face tough questions every day. Questions such as:
- Who are my best suppliers?
- How much do we as an organization spend on various commodities and materials?
- Can we consolidate our supply base without increasing risks to our supply chain?
- What would be the impact of adding another sales promotion on product profitability?
- Which of the various marketing strategies and sales tactics are most profitable?
- What will be the impact of changes in fuel prices, weather or a competitive promotion on product demand?
- How can we minimize our inventory carrying costs without affecting customer service?
- How can we identify product quality issues earlier to minimize our warranty claims?
- How much should we reserve for warranty costs?
Business analytics help supply chain professionals answer these questions by providing them data driven insights into demand, supply network vulnerabilities, operations and customer service requirements. Leading companies have repeatedly shown that by accessing and analyzing supply chain data from all pertinent sources, forecast accuracy can be improved, inventory can be optimized, emerging issues can be detected early enough to be addressed at the product-design level (rather than in the field), and fraudulent service claims can be identified and eliminated. With analytics, businesses get a complete picture of their operations that enables them to align their supply chain goals with the organization’s strategic business goals of improved profitability and increased customer satisfaction.
Take the example of a large manufacturer of wireless consumer products who was able to save US$4 million by using advanced analytics to detect product quality issues early. The company, set to ship 2.5 million units of wireless devices in a phased distribution, detected an issue with the product six weeks earlier than the shipment date because of warranty analytics. With the timely delivery of this information to its engineers and call center staff, the company was able to limit exposure to only 70,000 units and about 25,000 customers, which kept repair costs under $1 million. Had the company not detected the issue in time, more than 600,000 units would have to be serviced - affecting more than 500,000 customers at a cost of more than $5 million in repairs. By employing analytics, the company saved $4 million in repair costs and untold millions in brand equity.
So when the bottom- and top-line benefits are so significant, why aren’t more companies taking advantage of analytics?
I would say it’s a combination of many factors, ranging from limited analytical talent, siloed and incomplete data, to the limitations of current technological infrastructures. But, that is not the complete story. Industry studies show that while companies recognize the need for analytics, only a few are harnessing the benefits of available technology even to a moderate extent.
In fact, in a recent survey of more than 500 blue-chip companies, Accenture found that while more than two-thirds (71 percent) of respondents state that their organization’s senior management is “totally” or “highly” committed to analytics and fact-based decision making, about 40 percent of business decisions were still based on judgment rather than fact.
One of the biggest barriers to a wider adoption of analytics in the marketplace is the lack of education and misconceptions about the subject.
A lot of users, industry analysts and consultants have not fully grasped the difference between business intelligence (BI) and analytics. They continue to consider simplistic query and reporting and OLAP drill-down capabilities to be analytics, thus limiting themselves to traditional BI systems that provide simple alert, monitoring and dashboard capabilities. While BI tools are very important for answering questions such as what, when and where an event happened, they do not provide predictive insights that allow future business decisions to be optimized.
And that is where true analytics come into play. True analytical capabilities such as forecasting, data mining, predictive modeling and optimization provide businesses with an understanding of why something is happening, when it can occur again, what will be the future impact of decisions, so that outcomes can be optimized. To succeed in the current economic environment, businesses can no longer rely on traditional BI tools that only give you a view of the past. They must use advanced analytics to get better insights into the future to be proactive rather than reactive.
Another popular misconception about analytics is that only companies that employ doctoral-level statisticians can take advantage of advanced science. The reality is that solutions today are packaged in such a manner that even novice and intermediate-level modelers now can take advantage of advanced modeling techniques via point-and-click interfaces.
Earlier roadblocks of user resistance and cost of integration with existing technology are also no longer valid. Advanced analytical capabilities are now available via cost-effective channels such as software-as-a-service (SaaS), on demand, and from existing ERP and SCM systems using service-oriented architectures (SOA). Delivering analytics through a variety of channels means that users in organizations of all sizes can improve forecast accuracy, perform what-if analysis and optimize resources - all without ever leaving the comfort of familiar planning modules.
So what is stopping organizations? Something that many of you can probably relate to: the sunk costs.
Companies have expended so many resources in customizing and configuring existing SCM systems that they do not want to commit any further resources to new, better technology - even if the benefits over the existing system are substantial.
Before you resign yourself to the status quo, ask what makes better sense in the long run: Continuing to sink more money into maintaining an existing system that is already behind the times; or updating it with new, advanced technology that requires an initial outlay but provides the robust functionality required to survive in the new economy?