Forecasting and Demand Essay example

Submitted By Rose86
Words: 964
Pages: 4

The Supply Chain Says You're Wrong If You Believe These Assumptions
The time has come to rethink supply chain performance and challenge decades-old assumptions that unnecessarily restrict supply chain efficiencies and constrain the economy.
Jan. 4, 2013Robert F. Byrne, president and CEO, Terra Technology

Supply chains are the backbone of the global economy. Weak economies require efficient supply chains to prosper on thin margins; whereas robust economies require healthy supply chains to support growth. Accurately predicting demand is one of the largest factors influencing supply chain performance because it affects decisions that drive between 80 to 90 per cent of the costs in the supply chain, including procurement, manufacturing, distribution and inventory. Billions of dollars have been invested in upgrading communication and computing infrastructure with the latest technology, yet most of the industry still relies on planning mathematics developed decades ago.
For years, the industry achieved incremental gains in forecast accuracy through the use of increasingly complex statistical models. While these improvements might have been perceived as “good enough” in the past, this is no longer the case.
Today we live in a fast-paced world where manufacturers and retailers face risks stemming from uncertain economic, climate and political pressures. It is a connected world where shopper preferences are increasingly shaped by mobile technology and social media. The time has come to rethink supply chain performance and challenge decades-old assumptions that unnecessarily restrict supply chain efficiencies and constrain the economy. Let’s look at some assumptions we’ve long assumed to be correct.
Assumption 1: Protecting Service in Volatile Times Requires More Inventory
People quickly learn that revenue depends on product availability, so the tendency to raise safety stock levels is a natural reaction to market volatility. Yet this is at odds with shareholder pressure to free cash flow and improve return on working capital by reducing stocks. The answer is to ensure availability without increasing inventory, or better yet, while reducing inventory.
Manufacturers are using new mathematics designed for volatile markets to analyse current supply chain data, gain visibility into shifts in demand and quickly respond to new market expectations. For example, during the H1N1 “swine flu” pandemic several years ago, one tissue manufacturer detected a corresponding lift in demand as the virus spread across the country and raised production to ensure on-shelf availability in affected markets at a time when competitors stocked-out.
Whether shifting schedules for underperforming items or stepping up production to capture a lift in sales, an accurate view of future demand provides the means to manage optimal inventory levels to balance business needs.
Assumption 2: History Repeats Itself
Today’s volatility has rendered past shipments an increasingly poor predictor of future demand. A recent study encompassing roughly one third of all North American consumer packaged goods traffic found that forecast error has increased since 2009, with weekly error rates now at 53%. In response, leading manufacturers are turning from statistical time-series systems built on the assumption that history will repeat, to pattern recognition systems based on the certainty that data is dynamic.
To sense changes in demand patterns, these systems require much more data. Fortunately, there is no shortage of current data in the supply chain. What have been missing are the applications to systematically use this data to improve supply chain efficiencies and achieve better business outcomes.
By sensing demand, manufacturers can now predict future sales in tune with rapidly changing market dynamics, giving them the insight to plan and build what they know will sell instead of what they hope will sell. Findings from the above study show that companies that sense