The face of retail is rapidly evolving from traditional brick-and-mortar facades to electronic ones. While web businesses spend considerable effort in polishing Internet front ends with sophisticated graphics and animation, they must also give attention to back end fulfillment operations. Invisible to the web consumer, these operations encompass networks of manufacturers, warehouses, and distributors that shoulder the burden of filling orders and delivering products. Advanced Planning and Scheduling (APS) offers powerful tools for addressing the challenges presented to these networks by e-commerce.
For all its promise, the Internet presents web businesses with significant challenges, including:
Global marketplace potential: Geographic marketplace boundaries have been all but obliterated by the Internet. Web portals appear with the same clarity on laptops in Boston or Bangkok and data transmission technologies continually improve the speed at which on-line ordering activities take place. Though web businesses can choose to limit delivery areas, in doing so they ignore a significant avenue for growth. All that is needed is a means for making expanded distribution cost effective.
Large order volumes: Free access from every corner of the globe implies the potential for massive order volumes. Unfortunately, an increase in order volume is usually accompanied by a disproportionately large increase in the complexity faced by back end fulfillment resources. These complexities arise because companies must balance customer demand fulfillment with constraints of time, distance, resources, and cost.
Easy access to alternatives: Because the Internet dissolves physical distances, companies are faced with the sobering fact that competitive sites lie a mere mouse click away from their own. Web businesses that fail to satisfy customer expectations regarding product quality or delivery risk losing customers to companies that offer superior alternatives. In addition, the Internet serves to level the competitive landscape, allowing small unknown companies to take on established giants.
What is APS?
Broadly speaking, APS is a set of techniques that facilitate and/or automate human decision-making. More specifically, APS comprises methods for solving complex problems that arise in the operational infrastructure used to support customer fulfillment. The solution to these problems is usually a sequence, strategy, or configuration that results in the optimum material, time, or cost savings subject to a set of constraints. An example from everyday experience is the problem of getting dressed in the morning. If one specifies the goal of this problem as "a fully dressed person", then many possible solutions exist, however, constraints imposed on the dresser quickly reduce the number of acceptable outcomes. For instance, shoes are not put on before socks (sequence), the activity cannot take more than twenty minutes (strategy), fashion dictates that no one wear two ties (configuration), and so on. This problem is a simple example of linear programming, a mathematical technique for finding solutions of linear equations subject to simultaneous constraints. It is just one example of the vast array of problems that are addressed by APS.
In manufacturing and distribution environments, constraints are far more difficult to juggle because the problems are many times more complex. A few constraints come easily to mind, such as raw material availability, resource availability, and order due date compliance, but others arise from many sources, including:
Constrained market conditions
*
Impossible to support all market demands
*
Orders must be prioritized by key customers, region, technology, etc.
Fixed capacity requirements
*
Capacity cannot be added when operating at maximum levels
*
Must optimize existing capacity
Geographically dispersed manufacturing and distribution operations
*
Complicated supply chains
*
Operations can extend to multiple plants, warehouses, distribution centers and cross international borders
Discrete manufacturing characteristics
*
Short product, component life cycles place pressure on throughput
*
Component and sub-assemblies can also be sold as end products
Process manufacturing characteristics
*
Product grading and inverted BOMs (Bills of Materials) introduce complexity
*
Routings may visit operations iteratively
*
Setup procedures vary significantly by product
Product mix variability within manufacturing and transportation lead times
*
In-process material may be used for different end products
*
Product availability must remain flexible over time
Unlike the dressing experiment, problems in manufacturing and distribution that face these types of constraints rarely lend themselves to a perfect or optimum solution, since finding one would involve an inordinate amount of time and processing power. Instead, reasonably good solutions are made to suffice. APS routines usually employ a "back door" that enables them to stop searching for the optimum configuration, strategy, or sequence and offer a merely "good" one - or at least one that is better than could be found without APS.
Shortcomings of older planning and scheduling techniques, such as MRP, MRP-II, and CRP, used by many Enterprise Resource Planning (ERP) systems provide ideal opportunities for APS. While these techniques represented a significant improvement over older methods when they were introduced in the 1960s, many companies find they can no longer match increasing demands on manufacturing and distribution networks. The fundamental disadvantage of these approaches is that all fail to address real world constraints sufficiently. In general, these approaches:
*
Fail to respond quickly to changes in supply and demand
*
Do not enable management of priorities across products and channels
*
Rely on fixed lead times to calculate delivery dates, failing to take into account all relevant delivery constraints
*
Do not search exhaustively through BOMs or recipes to check the availability of components, sub-assemblies and alternate parts when quoting availability of a finished product
*
Do not allow for Available-to-Promise (ATP) visibility across multiple sites, resources, business units, and warehouses
APS addresses these issues much more effectively than other techniques. Properly implemented, APS can help back end fulfillment networks achieve reduced inventory levels, better utilization of resources, shorter order cycle times, and lowered operation and delivery costs
SOURCE:-
http://www.technologyevaluation.com/research/articles/advanced-planning-and-scheduling-a-critical-part-of-customer-fulfillment-15234/
For all its promise, the Internet presents web businesses with significant challenges, including:
Global marketplace potential: Geographic marketplace boundaries have been all but obliterated by the Internet. Web portals appear with the same clarity on laptops in Boston or Bangkok and data transmission technologies continually improve the speed at which on-line ordering activities take place. Though web businesses can choose to limit delivery areas, in doing so they ignore a significant avenue for growth. All that is needed is a means for making expanded distribution cost effective.
Large order volumes: Free access from every corner of the globe implies the potential for massive order volumes. Unfortunately, an increase in order volume is usually accompanied by a disproportionately large increase in the complexity faced by back end fulfillment resources. These complexities arise because companies must balance customer demand fulfillment with constraints of time, distance, resources, and cost.
Easy access to alternatives: Because the Internet dissolves physical distances, companies are faced with the sobering fact that competitive sites lie a mere mouse click away from their own. Web businesses that fail to satisfy customer expectations regarding product quality or delivery risk losing customers to companies that offer superior alternatives. In addition, the Internet serves to level the competitive landscape, allowing small unknown companies to take on established giants.
What is APS?
Broadly speaking, APS is a set of techniques that facilitate and/or automate human decision-making. More specifically, APS comprises methods for solving complex problems that arise in the operational infrastructure used to support customer fulfillment. The solution to these problems is usually a sequence, strategy, or configuration that results in the optimum material, time, or cost savings subject to a set of constraints. An example from everyday experience is the problem of getting dressed in the morning. If one specifies the goal of this problem as "a fully dressed person", then many possible solutions exist, however, constraints imposed on the dresser quickly reduce the number of acceptable outcomes. For instance, shoes are not put on before socks (sequence), the activity cannot take more than twenty minutes (strategy), fashion dictates that no one wear two ties (configuration), and so on. This problem is a simple example of linear programming, a mathematical technique for finding solutions of linear equations subject to simultaneous constraints. It is just one example of the vast array of problems that are addressed by APS.
In manufacturing and distribution environments, constraints are far more difficult to juggle because the problems are many times more complex. A few constraints come easily to mind, such as raw material availability, resource availability, and order due date compliance, but others arise from many sources, including:
Constrained market conditions
*
Impossible to support all market demands
*
Orders must be prioritized by key customers, region, technology, etc.
Fixed capacity requirements
*
Capacity cannot be added when operating at maximum levels
*
Must optimize existing capacity
Geographically dispersed manufacturing and distribution operations
*
Complicated supply chains
*
Operations can extend to multiple plants, warehouses, distribution centers and cross international borders
Discrete manufacturing characteristics
*
Short product, component life cycles place pressure on throughput
*
Component and sub-assemblies can also be sold as end products
Process manufacturing characteristics
*
Product grading and inverted BOMs (Bills of Materials) introduce complexity
*
Routings may visit operations iteratively
*
Setup procedures vary significantly by product
Product mix variability within manufacturing and transportation lead times
*
In-process material may be used for different end products
*
Product availability must remain flexible over time
Unlike the dressing experiment, problems in manufacturing and distribution that face these types of constraints rarely lend themselves to a perfect or optimum solution, since finding one would involve an inordinate amount of time and processing power. Instead, reasonably good solutions are made to suffice. APS routines usually employ a "back door" that enables them to stop searching for the optimum configuration, strategy, or sequence and offer a merely "good" one - or at least one that is better than could be found without APS.
Shortcomings of older planning and scheduling techniques, such as MRP, MRP-II, and CRP, used by many Enterprise Resource Planning (ERP) systems provide ideal opportunities for APS. While these techniques represented a significant improvement over older methods when they were introduced in the 1960s, many companies find they can no longer match increasing demands on manufacturing and distribution networks. The fundamental disadvantage of these approaches is that all fail to address real world constraints sufficiently. In general, these approaches:
*
Fail to respond quickly to changes in supply and demand
*
Do not enable management of priorities across products and channels
*
Rely on fixed lead times to calculate delivery dates, failing to take into account all relevant delivery constraints
*
Do not search exhaustively through BOMs or recipes to check the availability of components, sub-assemblies and alternate parts when quoting availability of a finished product
*
Do not allow for Available-to-Promise (ATP) visibility across multiple sites, resources, business units, and warehouses
APS addresses these issues much more effectively than other techniques. Properly implemented, APS can help back end fulfillment networks achieve reduced inventory levels, better utilization of resources, shorter order cycle times, and lowered operation and delivery costs
SOURCE:-
http://www.technologyevaluation.com/research/articles/advanced-planning-and-scheduling-a-critical-part-of-customer-fulfillment-15234/
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