With the advent of the best
production technologies many firms are successful in reducing their production
costs to a great extent. The key factor of reducing the costs and to increase
the profit margin is to cut down the operations cost incurred in the whole
business transactions. One of the practices that have been widely adaptive is
predictive modeling of the demand and matching the supply chain networks
accordingly.
Companies don’t generate revenue
when the goods are stocked in the ware houses, thus maintaining a higher
inventory turn ratio is very important for any firm. Accurate forecasts allow a
business to position itself competitively, and advance notice gives the
business time to implement new strategies. Consequently, effective long-term
forecasting requires not just a finger on the pulse of current events but also
deep knowledge on the merging trends in any industry.
Even though forecasting is
important, the process of effectively delivering the desired results to your
customers is not limited to forecasting. It includes synchronizing supply and
demand, increasing flexibility, and reducing variability. A good supply chain
system is the one that enables a company to be more proactive to anticipated
demand, and more reactive to unanticipated demand. There are various techniques
that can be used for forecasting the demand and thereby generating the
appropriate supply. Some of these techniques are; qualitative analysis, time
series analysis, casual and simulation analysis.
Using multiple prediction models
to simulate a single data set can provide accurate results. Long term
forecasting is more difficult than short term forecasting therefore firms tend
to maintain an inventory that would allow them to meet the unexpected demand.
Finding the right balance between the two is very important. A firm has to
ensure that there is no inventory accumulation over the period of time.
Inventory buildup can be unprofitable for many businesses. For the products
that have a shorter shelf life, high inventory turnover ratio is a must.
One important case that truly
explains the importance of accurate forecasting and matching the demand with
the supply is the trouble that CISCO faced around the year 2000-2001. In spite
of being a key hardware manufacturer as the world may think, CISCO did not
manufacture its own products. It outsourced the manufacturing of their products
to various other vendors and focused more on the design aspect. The suppliers
CISCO contacted with further had commodity suppliers. This long chain of information
exchange and product manufacturing caused a significant problem for them.
According to the demand
projections made by the CISCO’s ‘sales-force’, they ordered large quantities to
lock-in supplies as the industry expected a boom and the resources would go
scarce at that time. They made long term
contracts with their suppliers for long term availability of the products. The
telecom industry then saw a major hit and the sales dropped. The predictive
models that had formed the basis for all supply chain decisions made by the
firm did not have the right inputs only to start with. The firm went through a
major crisis at that time, in the third quarter of 2003 there sales dropped by
30%, they had to write of the inventory worth $2.2 billion and had to lay of
8,500 employees as the profit margin per employee had dropped down from
$700,000 to $240,000. They lost on a lot of customers as they were not able to
deliver the products on time. Their inventory turnover ratio increased from 54
days to 88 days.
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