Advanced Planning and Scheduling: A Critical Part of Customer Fulfillment

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Advanced Planning and Scheduling: A Critical Part of Customer Fulfillment
S. McVey - December 10th, 1999

Drivers from E-Commerce

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

APS Territory

APS can address a variety of problems in different areas of the fulfillment network. Four key areas are described below (see Figure 1).

Figure1 - Application Areas in Customer Fulfillment
  • Demand management

    Successful customer fulfillment begins, in many cases, with the ability to make accurate predictions of customer demand. Forecasting involves making assumptions about the marketplace and attempting to predict customer acceptance for a company's products. By superimposing the effects of seasonal factors, promotions, new product introductions, economic cycles, competitive actions, and other events, companies seek to build a demand profile that can be acted on by procurement, manufacturing, and distribution operations. Forecasting algorithms, ranging from basic extrapolation to neural networks, which take into account business events, are usually classified under demand management. Demand management is an important venue for APS more because it provides a point from which the rest of fulfillment activities follow. .

  • Production planning

    Production planning determines the blueprint for meeting customer demands over long periods of time. Plans specify the product to be ordered or manufactured, its required quantity, any planned raw material sourcing or procurement activities, and other information. Usually, companies decide to allocate available materials and resources to those customers' orders that will maximize their profitability, fulfill previous inventory agreements, or aid the development of preferred business partners. Production plans usually take into account these allocations, or material and capacity set aside with particular business objectives in mind. Industry frequently determines the scope of production planning. For discrete manufacturing, production plans typically deal with material availability only. For process manufacturing, resource availability plays a larger role. Though most companies selling products over the Internet do not manufacture widgets, their suppliers or their supplier's suppliers most definitely do. Visibility across all links in the supply chain is becoming increasingly necessary for web businesses to respond to customer orders quickly.

  • Production scheduling

    Production scheduling differs from production planning primarily in regard to time horizon and granularity. Where production plans map out expected material flows by week, month or even year, production schedules focus on smaller intervals of time: day, hour, and even minute. Production scheduling frequently involves horizons of less than two weeks, although industry is the determining factor. Generally, volatile businesses tend to have shorter scheduling horizons as longer ones are usually wasted effort because of order cancellations. A good example of algorithms used for scheduling is capacity balancing. Capacity balancing seeks to ensure that no resource (production line, mixing vessel, kiln, packaging station) is loaded beyond its available capacity. This is not as straightforward as it may at first appear, since there may be more than one alternative resource for offloading tasks, movement of tasks scheduled on an overloaded resource may be restricted by upstream tasks, or due dates can be violated if tasks are postponed. Since these problems involve a high degree of complexity, algorithms usually employ heuristics, or "back doors", that allow them to return a feasible answer within a reasonable time.

  • Logistics

    Logistics involves planning the flow and storage of finished goods from warehouses to distribution centers and end customers. Transportation time, methods and costs are the primary trade-offs that APS seeks to balance in order to arrive at the optimum distribution plan. One famous problem in logistics concerns the plight of the travelling salesman who must visit a number of cities scattered across the country while minimizing the total distance traveled. Like scheduling problems, the logistics problems seek to find a good solution quickly given the constraints. As in the case of production planning, web businesses, while not involved in the actual shipping of goods, can be held responsible by disgruntled customers when their order doesn't arrive on time, making logistics a priority consideration.

  • Available-To-Promise (ATP)

    No discussion of APS is complete without mentioning ATP. ATP is not a specific functional area like demand planning or production scheduling but is a feature that provides visibility into the aforementioned areas. ATP refers to a quantity of finished goods, work in process (WIP), raw materials, or some combination of the three that is available for promising to customer orders. ATP figures also incorporate the results of allocation routines that limit supply to particular customers. Global ATP extends the reach of this allocation to facilities and warehouses around the globe. Although vendors profess to offer these capabilities, advances in data sharing technologies and security are just beginning to make global ATP a reality.

Shortcomings of APS

Though APS can help web businesses satisfy high customer expectations, there are serious consequences associated with packages that prospective users should understand. Two of the most insidious are level of representation and algorithm visibility.

  • Level of Representation

    APS packages require that users create an electronic model of their fulfillment network that represents how the business operates at a functional level. Though the software provides the basic development tools for constructing these models, it is often difficult to know just how detailed they should be. A single model designed for a large network is often unable to sufficiently represent important business process details. On the other hand, highly detailed models that attempt to replicate every nuance of an organization's workflows usually fail because of data integration and usability issues. Success of APS hinges on striking a balance between the two extremes.

  • Algorithm Visibility

    Most vendors do not allow users to "peek behind the curtain" and observe how their APS algorithms work. While some, like SAP and i2 Technologies, license solver technologies from third party vendors such as ILOG, they usually augment the acquired techniques with others developed internally. Many subtleties of planning algorithms do not become evident until late in the implementation phase when detailed testing takes place. For instance, few users realize that i2's Rhythm Factory Planner will automatically schedule low priority orders in advance of more critical orders if they are significantly overdue.


Advanced Planning and Scheduling (APS) has received new relevance with the advent of the Internet marketplace. E-tailers like are successful not so much because of their glittery graphics, but because of their back-end fulfillment and customer service capabilities that enable them to satisfy customer demands. As heavier "brick and mortar" manufacturers move towards e-commerce, APS will continue to play an important role in the invisible back end network of manufacturing and distribution. According to Ray Hood, CEO of EXE Technologies, "as companies attempt to transform their traditional stores into e-businesses, they often overlook the complexity and cost associated with having an efficient back-end order fulfillment system." Armed with APS, web businesses can respond well to challenges resulting from these complexities and prosper in the new global marketplace.


MRP (Materials Requirements Planning): A planning method that schedules on-hand materials and/or procurements to external demand.

MRP-II (Manufacturing Resources Planning): Successor to MRP, this method attempts to factor in capacity constraints.

CRP (Capacity Requirements Planning): A detailed procedure that checks capacity against production plans produced from MRP.

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