Forget guesstimates, analytics software can crunch big data to improve supply chain efficiency, as Lindsay Clark reports
A crystal ball would be useful to any business wanting to revolutionise supply management. Seeing the future could tell managers what customers will want next month, allowing suppliers to prepare. A little clairvoyance could also show how suppliers will perform, helping avoid disruption.
Tarot cards aside, there are technologies available to businesses wanting to predict patterns in supply and demand. Increasingly powerful analytics software can exploit a growing number of internal and external data sources to create predictive models which can help slash supply chain waste and avoid risk.
The trend is attracting increasing interest from supply chain organisations, says Ray Eitel-Porter, managing director, analytics lead, Accenture. “We see a lot of interest in big data in the supply chain area,” he says. “There is a growing awareness of the value of data. You hear people talking about data as the new oil and data as a strategic asset. This is coming from not just the consultants, but senior leaders within business.”
Although supply chain and procurement professionals have long been accustomed to accumulating and acting on data, the application of analytics to a greater range of internal and external data can create dramatic improvement in supply chain efficiency, Mr Eitel-Porter says.
One client selling mobile phones achieved a 30 per cent improvement in accuracy of demand forecasts, he says. “That has a dramatic impact on the business. It translates into a reduction in stock of 21 per cent. The company can reduce working capital without reducing fulfillment to customers.”
Using analytics to forecast demand can also help lower supplier costs, so they can offer buyers better prices. European online retail and catalogue giant Otto, which owns Freemans catalogues, uses neural network technology, originally developed for nuclear research at the CERN particle accelerator, from software firm Blue Yonder. Vast amounts of historical data detailing sales, product features, website positioning and catalogue positioning, and external data such as weather, are combined to produce predictive models that improve forecasts.
The retailer estimates it has the potential to save at least €10 million each year with more accurate demand forecasting. It avoids under and over-stocking, and also allows buyers to create bigger orders, cutting the cost of transporting secondary orders and allowing suppliers to lower cost. Otto gets a better price without damaging supplier margins, says Michael Sinn, the company’s vice president, category management support.
You hear people talking about data as the new oil and a strategic asset
Tesco, the UK’s largest retailer, used a big data approach to analyse how sales of different food items responded to weather conditions. It built a computer model which took account of store location, product characteristics, recent weather history and weather forecasts to better predict demand, saving around £6 million by avoiding under or over-ordering. Applying similar analytics to distribution depot operations, shelf-life discounting and special offers has created about £100 million annual improvement in supply chain efficiency, says Duncan Apthorp, Tesco’s programme manager, supply chain development.
While Marks & Spencer is also reaping the benefits of big data analytics to forecast demand over the coming days and weeks, the retailer is predicting supply patterns in the long term. Using a mass of global data sources, such as forecasts for labour costs in China, it is modelling the cost-efficiency of its supply chain years into the future.
“The system is looking at where our suppliers are and where are they likely to be in the next few years, looking at demographic trends in places like China. We have always done scenario modelling, but now we are running models more frequently and using more external data,” says Emile Naus, head of logistics strategy at M&S.
Meanwhile, businesses are using analytics to improve strategic procurement. Over the past year, global machinery manufacturer Caterpillar has been bringing together data from across enterprise applications to do “deep-dive analytics” on supplier performance, says chief procurement officer (CPO) Frank Crespo. Citigroup, one of the world’s biggest banks, has been increasing its investment in procurement analytics to “shift the information advantage from supply chain to us”, says Scott Wharton, the bank’s chief procurement officer.
While no prediction can be perfectly accurate, procurement and supply chain professionals are exploiting the burgeoning growth in data sources and performance of analytics tools to help prepare for what lies ahead.