A Supply Chain (SC) is a system of organizations, people,
technologies, activities, information and resources involved in moving
materials, products and services all the way through the manufacturing process,
from the original supplier of materials supplier to the end customer.
Supply Chain Management (SCM) is about planning, coordinating, execution and control of
the organizational entities with their supporting resource (human, machines),
process and technology in moving material, products and services through
different interconnected system entities to the end customer.
Supply
Chain key objective is to ensure time and place utility at the lowest possible
cost: just enough at the right time and right place to satisfy customer demand
with a view to strive for maximum possible potentiality. It impacts directly the organization:
- Growth and its viability by ensuring efficient and effective supply chain aligned with marketing, sales and financial strategic plans
- Profitability by ensuring the lowest possible operating costs in delivering a balance between efficiency and effectiveness, minimizing/eliminating waste and errors. Optimum throughput will ensure financial inflows and reduce inventory levels
- Optimum inventory turns within optimum efficiency and effectives conditions: inventory turns to need to be associated to optimum inventory targets
- Optimum return on invested capital: striving for the highest possible potential within the efficient and effectiveness objectives: striving for optimum plant/equipment effectiveness: optimum return invested capital whereby long batch runs versus optimized batch runs will maximize throughput (batch size impacts return on invested capital, machine set-up due to change over reduces the return on invested capital)
Most
organizations have a hard time minimizing the discrepancies between their
capacity (what is possible or constraints) and the demands of their customers. These inefficiencies can have a
massive negative impact on an organization. In this competitive environment,
organizations cannot afford the luxury of being inefficient when it comes to
their capacity planning (potentiality) — the balancing
act of ensuring available capacity meets required capacity while maximizing the
return on an organization’s assets, minimizing costs, and effectively managing
risks.
An
organization’s supply chain must be flexible enough to accommodate fluctuations
in demand, while also guarding against investments in capacity or production
before it is needed. As a
result, most of them are under-utilizing resources or are unable to fulfil
customer demand due to complexity: this implies poor Supply Chain Governance in
terms of planning, coordinating and control achieving sub-optimum balance
between efficiency and effectiveness (the right things the right way).
The performance of the system depends on how well the parts fit together,
not how well they perform individually and the ‘whole’ system exhibits emergent
properties that are not to be found in its parts.
The Supply
Chain governance framework key objective is to always strive for maximum
potentiality and optimum efficiency and effectiveness:
- Potentiality: This is what Supply Chain governors should be doing by developing resources and removing constraints, although still operating within the bounds of what is already known to be feasible. A bottleneck (manufacturing or handling resource) is a resource with capacity less or equal to demand while a constraint is a limiting factor to organization’s performance, an obstacle to the organization achieving its goal. These are imposed constraint such as a policy or method (way of working) that influence system output.
- Capability: This is what system should be presently doing with existing resources, under existing constraints, if there is management focus to eliminate waste, increase output resulting in improvements in productivity and efficiency.
- Actuality: Current state of Supply Chain variables (output, Equipment effectiveness, forecast accuracy), with existing resources, under existing constraints
- Productivity: is the ratio of actuality and capability
- Performance: is the ratio of actuality and potentiality
Feasible and optimum planning should be based on capability while management effort
should strive for maximizing potentiality
(leverage capability). Key performance indicators should compare actuality to capability
and actuality to potentiality (what is possible).
A
bottleneck (resource) is a resource with capacity less or equal to demand while
a constraint is a limiting factor to organization’s performance, an obstacle to
the organization achieving its goal. A constraint can be a bottleneck, but a
bottleneck is not always a constraint.
Constraints
fall into four different groups:
- Constraints of foundation (where we are limited in something that is usually seen as a foundation element for success); internet selling does but provides the buyers to touch and feel. Policy decision such as aligning product development around a specific operating system (Nokia).
- Constraints of resource such as facilities, manufacturing resources, people etc.. A constraint related to manufacturing resource relates to what generates output.
- Constraints of method (where we are limited by having to do something in a certain way). Typically, entrenched business processes based on ERP system ways of working that influence effective exploitation of resources and time.
- Constraints of time (where we are limited in the amount of time we have to do something). Management Constraint; limitation of the system due to management capacity or management attention. Management has a limited availability to deal with issues and control in their system sphere. Capacity management implies how many different issues can be handled over a period of time. A fundamental aspect to viability relates to autonomy; the ability of operational units to operate in autonomy within predefined control criteria freeing up management effort to deal with more complex and serious issues.
For effective
planning it is fundamental to achieve a plan that is both feasible and optimal
which will increase performance, reduce costs, avoid costly mistakes and boost
confidence among organization areas such as marketing, production and finance
and finally shareholders:
- Capacity planning — this implies achieving system balance (continuous balancing act based on feedback loops) by ensuring available capacity meets required capacity (demand driven capacity requirements) while maximizing the return on an organization’s assets, minimizing costs, and effectively managing risks.
- Flexibility – supply chain must be flexible enough to accommodate fluctuation in demand considering current investment in productive capacity.
- Feasible capacity plans can be executed within the realities of the business. They must not reflect unrealistic objectives for which organization will never be able to execute given all their important constraints. A feasible plan must account for the constraints, trade-offs, regulations, policies, and the financial requirements of a business.
o
Constraint:
a constraint is anything that limits an organization from being able to produce
more of what it strives for. Some constraint examples are labour, production
equipment, warehouse space, transport limitation and third-party supplier
capacity limits, way of working (process).
o
Regulations:
Organization such as the chemical industry, pharmaceutical and food industry
are heavily regulated from a compliance perspective when it comes to their
production. Production flow are impacted
by batch control, compliance and traceability: higher inventory levels to
off-set validation time (quality checks)
o
Financials:
what is produced (product), quantity that is produced (batch size) and quality
impacts the financial situation of the organization such as cashflow perspective.
If an organization fails to convert its output to income fast
enough, they could find
their survival being threatened. An organization may be able to physically increase
production by running over-time shifts, but this may not be practical from a
financial standpoint.
·
For
a plan to be feasible, it must be able to meet the output given the constraints
over a specified time period. A feasible plan represent reality, it must reflect
organization’s system constraints (bottlenecks decide the overall constraint),
and it the biggest point of failure when it comes to effective capacity
planning. This is where time granularity plays a fundamental role. Data
granularity for 24-month plan very different to data granularity for the next 60
days or two weeks plan.
The second fundamental aspect is to understand time
granularity. Time granularity is fundamental in effective planning. Time granularity
with respect to internal production and external sourcing planning impact influences planning in a
different manner:
· > External sourcing one has JIT supply within narrow
band, short term semi confirmed production requirements, medium to long term
sourcing requirement. Some external suppliers have long lead times due to shipment
methods (sea container) limiting planning flexibility
· > Production planning withing short horizon 0-3
weeks considers already scheduled firm orders work in process , scheduled
orders that are sequenced based on ideal setup matrix.
> 4 week to 2 months bottleneck resource
constrained but not sequenced optimized with capacity bucket equivalent to
shift size
> Medium term planning based on daily bucket time
granularity. As time granularity increases bucket definition size increases.
Next 3-6 months bucket definition is daily while longer time frame time bucket
can be weekly.
Backet constrained provides accurate enough and
feasible plan that considers capacity constraints.
The third fundamental aspect with respect to creating
an effective capacity plan is optimization:
to determine an optimum plan within
defined capacity considering time granularity.
Most organizations fail to create optimal capacity plans,
they just fail to integrate it with feasibility. MRP execution adopts infinite
capacity and its outcome is an unfeasible plan fuelling chaotic management.
Many don’t have adequate planning systems, or many have invested millions but
with poor or limited usage.
An optimal plan allows organizations to maximize or minimize
the objectives they are trying to meet such as satisfying demand within
different capacity constraints variants (shift definition) that have
corresponding cost implication.
SAP 4Hanna Advanced Planning now offer PPO optimizer . The optimizer profile which is identical
profile used in IBP Supply optimizations . Linear optimization, also known as
linear programming (LP), is a mathematical method for achieving the best
outcome in a model represented by linear constraints. This approach is
particularly important in today's dynamic business landscape where companies
aim to optimize efficiency and minimize costs to remain competitive. The goal
of linear optimization is to maximize or minimize a specific objective, such as
profit or efficiency, while adhering to defined constraints like resource
limitations or budget restrictions. The objective function represents the goal
that the optimization aims to achieve. The objective function, represented as a
linear function, can be used for minimization (minimizing costs) or
maximization (maximizing profit or maximizing efficiency). Cost factors such demand
delays, undercapacity utilisation, capacity expansion cost , production cost
and transport costs.
The key outcome is feasible plan considering defined bucket size constraints that is actually feasible. The feasible plan also creates critical dependant demand for sub-assemblies, components and raw materials.
The optimal and feasible plan should highlight demand
that cannot be satisfied and must be feedback to demand management. Demand
management must have a clear understanding of what can be realized and what is
not feasible in terms of products, markets, and timing).
The value of an optimal and feasible plan is also a critical
feedback to demand management:
·
> > Unconstrained
demand versus constrained demand by different capacity variants and cost
implication
· > This
then enable marketing to have clear view demand profiles versus supply profiles
and their impact on feasibility and profitability
For an organization to be able to generate optimal and
feasible plan it requires effective Supply Chain Governance Framework (SCGF) and
supporting planning systems (IT architecture) that can generate optimal plans
and provide clear performance guidance in terms of what it has planned versus
actual outcome. This is fundamental for learning and adaptation.
Most organization do not have effective governance
in place, most have poor understanding in dealing with constraints and most have close
to zero initiative to maximize potentiality.
Effective Supply Chain governance must co-exist
with system governance (the whole system): it is of no value to having an
effective SCGF in place while the system is a disaster. Nokia Supply Chain
governance might be effective, but it will not change the destiny of Nokia due
to its disastrous governance.
The SCGF must be supported by smart Supply Chain
Planning System (SCPS) that are able to generate optimal plans within the capacity
constraints which are feasible. The SCPS must be able to generate plans that
are aligned with time granularity (planning rolling waves) and respective
feasibility:
- 1 to 4 year constrained plan by product family/market/facility based on expected demand and facility constrains, projected investment projection (shut down, new facilities or consolidation)
- 12 months constrained and optimal plan (network plan) by product/facility/supplier as per demand requirements (forecasting and marketing initiatives) matched by key resource capacity variants (shifts) which is fundamental for shift planning (increase or decrease)
- The selected 12-month plan (rolling wave review every 3 months) driving sourcing and procurement rolling into the production plan
- The time granularity for actual production: example 3 month window (qty based on lot size rules for batch size and rough-cut scheduling) and detailed scheduling production plan 3-4 weeks within exact constrains, routings (operations and BoM assignment) and sequence optimization
·
To
absorb demand flexibility and protect detail scheduled plans the use of rule
based available to promise systems (gATP)
can deal with flexibility without disturbing feasible and optimal plans. This
will then ensure customer delivery is met in full and on time and must where required
not fulfill demand or rather postpone demand that is not covered within the
demand characteristics used by optimum and feasible plan.
Many organizations have invested millions in Supply
Chain Planning Systems and ERP, some with success while others with dismal
results.
Fundamental to understand that ERP system utilize MRP
logic which generates chaotic planning results: it cannot generate feasible and
optimum plans resulting in a chaotic situation. Some organization have heavily
invested in Supply Chain Planning System such as SAP APO that not only have MRP
type deterministic heuristics but also optimization toolset to enable optimum
planning such as linear programming that permit defining objective function to
maximize profit or service levels.
Majority organizations have failed to utilize SAP
APO toolset correctly such as the SNP
optimizer which utilizes linear programming techniques to achieve feasible and optimum plans with
corresponding scenario as per defined objective function. Many do not feedback
to Demand Management constrained supply: marketing assumes demand will be always
matched. The SNP optimizer is a critical
toolset to be able to create scenarios with different capacity variants and
their corresponding cost implication.
Many have made substantial investment SCPS with
poor result not due to system capability but due to lack of adequate and effective
Supply Chain Governance Framework in
place that is able to manage potentiality, system complexity, system dynamics
feedback that causes system imbalance or sub-optimum outcome (less than ideal
efficiency and effectiveness). Many fail to plan with the correct data set that
considers time granularity and where data granularity flows to different data
sets: Sales and Operation Plan that flows into Masters Production Schedule (MPS)
network plan, MPS that flow into detailed
production plan by site by product , that flows into detailed scheduled
production plan.
Many have invested in Sales and Operation Planning System (ideal for 1-4 year planning
rough cut) but without an effective integration to SCPS that is able to use output
of S&OP time granularity plan to a more detailed supply chain network plan
which feasible and optimum plan in detail over next 12-month horizon.
This is critical for feedback, without feedback tools such as S@OP and demand management
are of little value.
Feedback is critical to provide a view of what has occurred
(actual) versus what was planned in terms of feasible (constrained) and optimum:
this is critical to ensure that what is planned in terms of feasible and
optimum is realized and is critical for learning and adaptation in order
carry-out corrective action.
Finally, many have invested million in reporting
data warehouse system but in general they are ‘so what’ performance indicators
reducing the ability to true learning, understanding, and adaptation. Inventory
KPI or on-time delivery, or backorders, equipment effective KPI in isolation
provide limited value.
Concluding remarks:
Creating feasible (constrained) and optimum plans
will leverage performance, but it requires effective Supply Chain Governance
Framework and effective Supply Chain Planning Systems in place.
