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 casual 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 dangers on
relying on just one method.
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