Historical demand patterns
can indicate future demand where the demand for the organisation's products or
services has been stable. In time series analysis, the fundamental assumption
is that future forecasts are based on past demand patterns. This works well in
mature markets where products or services have been available for a period that
has allowed historical demand data to build.
However, the only thing that
can be said for forecasts is that they are incorrect. It is rare for a forecast
to be 100% correct. However, forecasting is fundamental to the organisation's
manufacturing demand and inventory planning management procedures and
processes.
Organisations and their
suppliers work together to eradicate, where possible, the commercial risks of
long-term inventory and manufacturing demand planning by identifying and
eliminating issues such as products or services with long lead times or where
the price of products or services only becomes viable when long production runs
reduce prices to a sustainable level through economies of scale.
This is an issue where MRP
principles are used to place requirements for materials in advance, which takes
lead times into account. Lead times for two sequential MRP items with lead
times of 12 weeks each mean that inventory planning horizons could stretch
beyond 24 weeks if the second item can only be produced when the first item has
been manufactured.
Long lead times can mean
that purchase orders need to be placed so far in advance for these products or
services that demand becomes impossible to forecast with a high degree of
accuracy. This results in high levels of inventory being built up, which
increases the capital tied up in stock and the commercial risks of
obsolescence.
The longer the forecasting
period, the greater the commercial risks become. Most organisations carry out
forecasting at Stock Keeping Unit (SKU) level for no longer than a month in
advance or the length of their longest lead-time item.
Organisations plan at an
aggregate level for periods longer than this to measure the demand for the
whole organisation's products or services. They use this data to plan their
productive capacity and secure production time with their suppliers of
manufactured parts and sub-assemblies. Actual demand is confirmed near the
required delivery date by placing purchase orders detailing the product, price,
and delivery date needed.
The manufacturing industrial
sectors have developed tools such as Just In Time (JIT), materials planning
requirements (MRP i & ii), kanban and Vendor-Managed Inventory (VMI) as
ways of managing the demand for raw materials, parts, and sub-assemblies.
In some cases, the
commercial risks of inventory demand planning have been pushed back onto the
supply base, where suppliers are made responsible for planning the requirement
for the products or services that they supply in exchange for a commitment with
the parent organisation of a long-term supply partnership where mutual trust
and product development initiatives are shared. This is a typical example used
by the car industry.
Organisations that use just
historical demand as the primary input into their manufacturing and inventory
demand planning processes are placing themselves at a greater commercial risk
than those that use a mix of planning methods, three of which are:
- Qualitative: Using intuition or opinions as input
to planning processes.
- Casual: Using assumptions that demand is
strongly related to certain factors.
- Simulations: That combine causal and time
series methods.
Historical demand data is a
powerful source of information when planning manufacturing and inventory
resources and works well where the demand for an organisation's products or
services has been stable for many years and where the accuracy of historical demand
data can be relied on but to use just one planning method alone places a
greater commercial risk on an organisation, most organisations use two or more
methods of planning which decreases the commercial risks on relying on just one
method.
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