Collaborative Demand and Supply Planning Networks - Enterprise Information Systems Design, Implementation and Management: Organizational Applications - page 501

Table 2. State of the art, challenges and requirements for future supply chain planning solutions

State of the art • Collaborative practices and methodologies have been developed for coordination between long term sup-
ply chain partners. Examples include Vendor Managed Inventory and Collaborative Planning, Forecasting
and Replenishment (Holweg et al., 2005).
• Predominant manufacturing planning and control techniques are based on centralized production control,
with infinite capacity, constant lead time and fixed routings in the single company (Alvarez, 2007).
• Decision support tools for network planning and execution are based on optimisation and simulation such
as Advanced Planning and Scheduling, Strategic Network Planning etc. (Turban, 2007)
Challenges and conflicts Existing collaborative practices and methodologies:
• Are not supported by low cost ICT-systems
• Have a single-company perspective, fail to provide collaborative planning for the network and fail to
include the non-hierarchical perspective
• Are restricted to centralized planning and control, and lack flexibility and real-time information.
• Are out of reach for most SMEs due to the centralized control, high complexity and high cost
• Assume centralized control in hierarchical networks and consensus between stakeholders
Furthermore, the tools does not:
• Address uncertainty and risk adequately
• Support a more diversified classification of tasks to enable improved planning results based on a more
holistic view.
• Enable effective human interaction in decision making
• Include presentation and real time information from network
• Support an “individual” real-time plan generation

Requirements 1. Planning and control techniques and principles with the capability to handle conflicting objectives,

dynamic networks and distributed decision making
2. Techniques for solving distributed and dynamic planning.
3. Full integration with dashboard solutions, allowing real-time decision support with advanced what-if
capabilities and graphical, interactive planning boards
4. Simple ICT-supported collaborative methodologies for non-hierarchical network collaboration which:
• Consider new manufacturing planning and control techniques for non-hierarchical networks
• Explicitly address operational (and selected tactical and strategic issues) as well as operational uncer-
tainties and risk, and cope with the dynamicity and complexity of manufacturing systems
• Automate routine tasks, leaving human schedulers to concentrate on exceptions handling

A neW suppLY chAin
pLAnning ApproAch

the bill-of-resources and bill-of-materials struc-
ture. Today's APS systems planning and control
capability depends on the ability to aggregate and
disaggregate plans and the data they are based on.
This makes the structure of the planning founda-
tion critical, unfortunately a simple linear disag-
gregation of information is not always possible
due to the nature of the planning foundation and
the multiple usage purposes of the information
contained herein.
We therefore present a relative simple solu-
tion based on individual ERP or APS plans in
the respective companies followed by improve-
ments in between partners. It is assumed that
requests from customers (customer orders) have
been accepted as far as possible by the ERP/APS
system, possibly based on customer priorities (se
below) in case of resource or materials shortage.

Whilst the planning and optimisation procedure
used in Advanced Planning and Scheduling sys-
tems is the current state-of-the-art for a single
company or a hierarchical supply chain/network,
planning among individual companies belonging
to several non-hierarchical networks require other
solutions. The reason for this is quite simple: an
APS system need to have a joint goal and clear
constrains for the planning engine to work, and
this is not obtainable when dealing with several
non-hierarchical networks. Furthermore, the hi-
erarchical demand planning approach is based on
the premise of aggregation and disaggregation
which is reflected in the master demand schedule
as well as the structures for capacity and material,