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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