Production companies need to ensure that their short,
medium and long term production and capacity needs are met to ensure lasting
business success in their industry.
Short and medium term decisions are integral in planning as they will affect
future capacity constraints like labour and inventories which will also effect ability
to meet demand.
Aggregate operations planning is vital to any major
manufacturing or service company, it
ensures that accurate demand forecasts are matched with adequate capacity. A key features of this plan is ensuring that
planners make accurate decisions on output rates of employment levels and
changes, inventory levels and changes, and backorders (William J
Stevenson, 2011)
. Without aggregate planning there would
be chaos in production and demand would never meet capacity and vice
versa. Imagine going to a bank and it
never having enough tellers to service its large amounts of customers. The European
Journal of Operational Research has published a journal article that not only
address the above mentioned issues, but takes into account the environment by
having a 'green' supply chain. Green supply chain management
(GSCM) is unique in that historically the two paradigms of supply chain
management and environmental concerns have been in head on collision with each
other (Al-e-hashem,
Baboli, & Savzar, 2013) . Studies have actually shown that the state of
the environment can be easily attributed to the most visible aspects of supply
chains, aspects of mode choice include equipment, fuel choice and the type of
transportation. Below are some examples
of big companies that have benefited from GSCM:
"–$15 billion footwear manufacturer Nike decided to remove a toxic compound from its core “Air” shock
absorption technology. The company says the environmental innovation did more
than reduce waste; it was fundamental to a breakthrough alternative that
allowed designers to insert full-sole-length Air in its new shoe, the AirMax
360."
"–To secure its 500,000 farmers a
living wage and retain a skilled labour pool, Starbucks pays its farmers 42% more than
the going commodity price of Arabica coffee beans. The company has also created
fair trade standards that exceed government standards and hired independent
auditors to verify compliance."
" –Through its Zero Waste
initiative, $312 billion retailer Wal-Mart has so far saved 478.1 million
gallons of water, 20.7 million gallons of diesel fuel and millions of pounds of
solid waste. Through its 100% Renewable Energy program, the company expects to
reduce energy consumption by 30% at all of its new stores in seven years."
Like any aggregate plan all inputs and outputs in
the process need to be taken into consideration, the information on all outputs
and inputs needs to be accurate to ensure forecasts are realistic as possible(
Appendix A1). Time horizons for
aggregate planning usually ranges from 2-12 months, once a time horizon has
been set and necessary information has been acquired its important to choose
the strategy that best fits your organization. A strategy should be chosen on one of two
options; demand oriented options or Capacity oriented options.
Capacity Oriented Options
|
Demand Oriented Options
|
· Leveling strategy
|
·
Influencing demand
|
-Changing inventory levels
|
-Sales, discounts, rebates, promotions
|
·
Chase strategy
|
·
Back ordering
|
-Hire or lay off workers,
overtime/idle time
-Subcontracting or part time workers
|
-Take orders, ship later counter
seasonal products
|
A levelling strategy focuses on maintaining a steady
rate of inventory which involves using sales discounts and back orders to meet
demand. A chase strategy matches outputs
to demand and keeps little to no inventory, although inventory costs are
reduced there are high labour costs.
Most operations today will use a mix of the above two strategies in
order to develop the most cost effective plan. Another option is using a linear programming
model which is a mathematical representation of the aggregate planning problem (William J
Stevenson, 2011) .
Variables are assigned to each of the inputs or output of an aggregate plan and
are made into a linear equation. These
equations can be entered into linear programming software to find the lowest
cost feasible solution (William J Stevenson, 2011) . The above addressed article found this
function to be best suited to green aggregate planning. Key considerations were maximizing profits
and minimizing changes in workforce levels, inventory investments and
backorders (Al-e-hashem, Baboli, &
Savzar, 2013) ;
which makes sense considering most large scale production operations today
utilize just-in-time (JIT). A key list
of variables developed and linear equations specific to green aggregate planning
can be found in ( Appendix B1).
Demand
flexibility is the capability of a production system to adapt efficiently to
changing market conditions. The need for demand flexibility in aggregate planning
is driven by fluctuations in market demand (Sillekens, Koberstein, &
Suhl, 2010) .
Given the many variables that are
accounted for using these equations it is important that your plan is set up to
be flexible. Fluctuations can occur for
many reasons but seasonality is the main issue for this, nearly all products
have issues of seasonality in at least one its market. Think of it this way you wouldn't try and use
a snowmobile during the summer!
With increases in information technology used in
logistics and forecasting optimizing aggregate planning using a linear
programming model has become the industry norm (Chen & Huang, 2009) . It allows organizations to easily track each
input that goes into the production process this decreases the amount of variability
and margin for errors while reducing costs. In addition to rising fuel costs
becoming some of the highest costs for production it's important that a plan is
in place to ensure maximum efficiency in production. The concept of green supply chain management
is new but is something that will become increasingly important in the future
not only on a cost level but the well being of the plant and its people.
Works Cited
Al-e-hashem, M.,
Baboli, A., & Savzar, Z. (2013). A stochastic aggregate production
planning model in a green supply chain: Considering flexible lead times,
nonlinear purchase and shortage cost functions. Europian of operational
Research , 26-41.
Chen, S.-P., & Huang, W.-L. (2009). A membership
function approach for aggregate production planning. International Journal
of Production Research , 6-10.
Hochman, S. (2007). Green supply chains. Retrieved
2014, from www.forbes.com: http://www.forbes.com/2007/04/20/green-supply-chains-logistics-cx_sho_0420amr.html
Sillekens, T., Koberstein, A., & Suhl, L. (2010).
Aggregate production planning in the automotive industry with special. International
Journal of Production Research , 1-20.
William J Stevenson, M. H. (2011). Operations Management.
Toronto: McGraw-Hill Ryerson.
Appendix A1
Appendix B1
