The field of business analytics is evolving at a dizzying pace, with increasingly sophisticated predictive models available to help businesses anticipate and respond to changing global trends.
And, owing to its position as one of the primary drivers in a company’s cost structure and profitability, the business supply chain has naturally emerged as one of the key areas of focus.
Eliminating waste in a supply chain is a proven way to reduce operating costs, minimize inventory, improve inventory turns, enhance flexibility and, ultimately, optimise profitability
Successful supply chains are connected, scalable and flexible – able to respond efficiently to changes in customer demand. At their heart, they rely on accurate, complete and timely information. And, the demand for tools to take control, and make sound uses of this information continues to grow.
A Google Trends map for ‘supply chain analytics’ shows a steady growth in users searches since 2009.
A number of factors appear to be driving this trend.
We’ve seen the unyielding rise of globalisation, pricing pressures and ever increasing customer expectations. Throw in an uncertain economic climate, fluctuations in fuel costs and the re-shaping of many business sectors by the rapid growth of the digital economy, and you’re left with: an urgent need to reduce supply chain costs and risks on one hand, and the necessity to sustain business growth on the other.
Many businesses are clearly seeking answers, so what solutions are available to them?
The common focus areas for supply chain analytics are:.
– Supply Chain Visibility (SCV). Modern platforms offer a real-time, singular view of the supply chain – allowing all stakeholders to track and monitor progress. This, in turn, facilitates collaboration and a quick response to challenges as they occur.
-Planning and Forecasting. Enhanced forecasting models give businesses the foresight and tools to ensure the right resources are in place at the right time – meeting customer demand without building up excess inventory. Modern forecasting platforms are so sophisticated that they actually improve over time.
-Supply Chain Optimisation. With a consistent, single view, companies are able to optimise every conceivable area of their supply chain, from transport logistics, to inventory management and strategic product sourcing.
-Improved Working Capital. Businesses have increasingly been able to ‘free’ the cash tied up in their supply chains to strengthen those chains, minimise risk and optimise cash flow all at the same time.
In addition to the functionality provided by ERP platforms, there are some highly popular supply chain management tools that allow you to manage all these factors, including Contract Logix, ERP Mark 7 and Cin7.
It’s on everyone’s mind – but it’s far from ubiquitous
Accenture Research revealed that while companies are certainly aware of advanced supply chain analytics and its ability to improve business performance, its use is far from widespread. Indeed, only 4 in 10 companies have a true enterprise-wide supply chain strategy. The greatest concerns stopping companies from embracing its use include the requirement for large investment, and issues around security and privacy.
The companies who are using big data for supply chain analytics are achieving success in a whole range of areas, including but not limited to improved customer services, faster and more effective reaction time to supply chain issues, and huge increases in supply chain efficiency.
And, use of supply chain analytics almost universally leads to improved profitability, reduced costs and better management of risk. Businesses that are positioning themselves ahead of the curve are able to respond to a volatile global environment like never before.
Those companies not embracing the opportunities provided by supply chain analytics need to seriously question if the perceived costs and risks associated with implementing a supply chain analytics programme really do outweigh the costs and risks on not implementing one. If not now, then when? The clock is ticking.