FRONTLINE
Supply Chain Planning Optimization: Just the Facts by Larry Lapide
Since the 1960s artificial intelligence (AI) research has sought to enable computers to replicate the thinking processes of the human brain. But many efforts have failed to produce useful results. One visible "big win" for the computer versus the human recently occurred when IBM's Deep Blue finally beat chess master Garry Kasparov. Can computers out-think people? Probably not! Deep Blue was able to beat Kasparov because the computer's logic was tailored to its opponent's way of thinking, based on his prior games and strategies. Thus, the computer logic was focused on tackling a particular problem area, not on general thinking processes. Ultimately, the computer won because it could dispassionately, tirelessly, and mechanically sort through many combinations of potential board situations extremely fast. Thus, the computer does not replicate the thinking process, but it can improve decision-making and strategy.
In the area of supply chain planning, advanced planning and scheduling (APS) vendors have put together solutions that help planners make better decisions. For planners, APS quickly analyzes the implications of alternative decisions. By performing what-if simulation analyses, APS systems provide information about whether plans are reasonable or if they, for example, exceed resource constraints or result in inadequate performance.
Recently, in a fashion similar to the AI efforts, there has been a trend to embed sophisticated optimization logic into APS suites to improve decisions of supply chain planners. If used successfully, this type of optimization promises to drastically improve a company's supply chain performance in a variety of areas:
This potential for improvement is generating a great deal of interest in supply chain optimization. Many established and startup APS vendors are now using the concept as a selling point, and in some cases, as a key marketing differentiator. While optimization methods have been around since post World War II--with the advent of operations research and management sciences--there has been only a marginal interest in applying these concepts to supply chain planning. Will companies embrace these newer versions of optimization technology? The answer is yes. What company would not want to optimize its supply chain? These solutions, however, are expensive. Do all buyers really understand how to apply these solutions and what they are getting for their money? Is optimization worth the cost? Does it really work? AMR addresses these questions by focusing on supply chain optimization technology from leading APS providers. The Report covers the following topics:
THE MARKET FOR OPTIMIZATION IS GROWING (BACK TO TOP)
Today's market dynamics have made supply chains extremely complex and planning more difficult. The following true story is a case in point:
ABC Inc., a small, 100-year-old agricultural seed company called in a consultant to work on a production scheduling problem. The consultant talked to upper management and found that over the last couple of years the company was having difficulty meeting increasingly diverse customer needs. The president, who came from a Fortune 500 company, seemed to understand the problem best. One night, he noted his production planning people were working later than usual and he asked them why. They said that they had been working to develop a plan that would meet marketing's forecasts, but they were not able to do it, despite working on it for a couple of weeks! Over the last few years, the business had become very competitive and the company's product line had expanded to several hundred items, making planning much more difficult. The president explained that, while these planners had over 20 years of experience, the complexity of the environment had exceeded their ability to do the production plan on paper and spreadsheets using the guidelines and rules-of-thumb that they had developed over the years. The president stated he wanted the consultant to develop software to help them schedule better. He was familiar with this type of software from his experience at the Fortune 500 company. The consultant said, "Of course, you will want the software to give the planners the optimal lowest cost solution." The president stated: "This would be extremely desirable, but just make sure it gives them a production plan that meets our marketing forecasts, as well as our production and distribution needs. Right now, it is of paramount importance that we generate realistic plans that satisfy our customer demand."
Manufacturers Are Showing a Greater Interest in Optimization (BACK TO TOP)
The situation at ABC, Inc., described above, has been happening for many years in all sizes and types of companies throughout the manufacturing industry. Customer demand and competition have made supply chain planning and scheduling more challenging and complex. A number of major trends have contributed to this increasing complexity:
These trends are contributing to an explosion in the number of entities that have to be planned for, driven by increases in the number of the following elements:
For many years manufacturers have been moving toward improved use of technology to support complex, diverse planning processes. Some, such as ABC, Inc., are doing it largely to maintain control of their operations in order to meet customer demand. Having already achieved control, many manufacturers are using APS technology to increase the productivity of planning processes and to lower supply chain costs.
Generally, companies are looking for planning solutions that consider major supply constraints, which leads them to constraint-based optimization. Supply chain planning optimization techniques and solutions attempt to accomplish the following tasks:
While a feasible, realistic plan is of paramount importance, an optimized plan is better. It is the need for realistic, optimized plans that is driving many manufacturers away from classic materials requirements planning (MRP)-based planning solutions, which do not consider supply constraints (especially material constraints) and frequently generate an unrealistic supply plan.
Vendors Are Embedding Optimization in Their Planning Applications (BACK TO TOP)
Consistent with this corporate trend toward greater need for supply chain planning technology, the APS market is increasing dramatically. While this has happened over the last decade, only within the last two to three years has optimization been widely incorporated into APS suites. Examples include the following events:
Enterprise resource planning (ERP) vendors have also noted the dramatic growth in the supply chain planning market and some have announced plans to add optimization functionality:
Renewed Interest in a Mature Market (BACK TO TOP)
Despite the recent flurry created by the APS and ERP providers, it should be noted that supply chain planning optimization technology solutions are not new. There has been a market for optimization solutions for over 30 years. The market has slowly evolved from toolkit-based products to a packaged application market. Early adopters of optimization technology tended to be quantitative analysts, usually with degrees in operations research, who worked in the corporate world. Many worked in process industries such as Chemical, Paper, and Steel. These early adopters used general-purpose optimization tools (e.g., linear programming packages) purchased from software vendors to develop custom planning tools that typically ran in a batch mode. Early optimization tool vendors include the following companies:
Few if any of the customized planning solutions developed by these early adopters dealt with large portions of a company's total supply chain. They usually focus on one important aspect of it. Some of the early applications dealt with specialized optimization problems:
As this market progressed, a few early supply chain planning vendors started to sell general-purpose optimization applications. These applications made it easier for corporate users to develop supply chain planning solutions on their own or working with the vendor's consultants. Two such vendors are Chesapeake Decision Sciences (New Providence, NJ) and Numetrix (Toronto, ON). As general-purpose optimization applications, these types of solutions allowed users not only to model specialized planning problems like trim, blending, and network flow, but they also allowed users to model more general planning problems, such as combining blending with production scheduling.
Despite some early success in the use of optimization, the market was relatively stagnant until recently. Advances in powerful computer technology have helped to accelerate the growth of the APS market. The technology has also allowed APS vendors to embed optimization into their solutions more seamlessly and transparently. This has made it easier for users to model their planning environment, even those users not trained in optimization techniques.
Today there are many popular APS solutions with embedded optimization. Despite this, the optimization aspect of the market carries a certain amount of mystery. Optimization is difficult to understand because of the jargon used by practitioners on both the user and vendor side. To many non-practitioners this is a very confusing, but seemingly intriguing and important area. The sidebar entitled "What is an Optimization Problem?" below should clear up some of the confusion. A short primer on the concepts and language behind optimization techniques and methods, it provides the context for the remainder of this Report.
Generally, optimization problems seek a solution where decisions need to be made in a constrained or limited resource environment. Most supply chain optimization problems require matching demand and supply when one, the other, or both may be limited. By and large, the most important limited resource is the time needed to procure, make, or deliver something. Since the rate of procurement, production, distribution, and transportation resources is limited, demand cannot be instantaneously satisfied. It always takes some amount of time to satisfy demand, and this may not be quick enough unless supply is developed well in advance of demand. In addition to time, other resources, such as warehouse storage space or a truck's capacity, may be constrained in meeting demand. An optimization problem comprises four major components:
Decision Variables are within the planner's span of control:
Constraints are limitations placed upon the supply plan:
Constraints in an optimization problem are either hard or soft. Hard constraints, such as the number of working hours in a shift or the maximum capacity of a truck, must be adhered to or satisfied. Soft constraints can be relaxed or violated. Examples of soft constraints include customer due dates or warehouse space limitations. Customer due dates can be changed or a product may be squeezed into a warehouse temporarily, making constraints less stringent. Most optimization problem formulations designate cost penalties if a soft constraint is not met. The penalties allow constraints to be weighted by importance. For example, missing a customer due date is a more important concern than cluttering a warehouse aisle.
Objectives
Objectives maximize, minimize, or satisfy something, such as the following:
Those unfamiliar with optimization are often confused about the difference between a constraint and an objective. Fueling this confusion, some factors can be formulated as either an objective or a constraint. For example, in most problems customer due dates are hard constraints. On the other hand, in resource constrained environments, while meeting customer due dates is important, it may not be possible. Therefore, the objective to meet customer due dates is expressed by maximizing customer service.
Models
Models describe the relationships among decisions, constraints, and objectives. These are often expressed in the form of mathematical equations. This is probably the most important but least understood part of an optimization problem. Generally, the model must represent the "real world" to the degree needed to capture the essence of the problem. It must represent the important aspects of the supply chain in order to provide a useful solution. For example, strategic planning typically uses aggregate models, which do not include every factor. On the other hand, operational planning uses models that include almost all factors and require detailed data.
Once an optimization problem is formulated, a solver determines the best course of action. A solver comprises a set of logical steps or algorithms embodied in a computer program to search for a solution that achieves the objective. A solver can develop three types of solutions:
Figure 1 represents an optimization problem with a generalized set of objectives to maximize. It depicts the different types of solutions that might be developed by a solver. While an optimum solution is intuitively appealing in most cases, it is unattainable in very complex problems. Unless a problem has a very specific structure (such as a linear programming problem), an optimized rather than an optimum solution is the best that can be generated. In some cases a solution may be a local optimized one (see Figure 1).
SUPPLY CHAIN PLANNING OPTIMIZATION FRAMEWORK (BACK TO TOP)
A supply chain planning environment can be described in terms of the supply chain's structure and the level of planning being supported. In general, the following holds true:
Supply Chain Viewed as a Network Model (BACK TO TOP)
As part of the planning process, the structure of the supply chain needs to be represented. This is typically done using a network model. A network model graphically visualizes a supply chain and is used to depict the parts of a supply chain being considered in the planning process. Figure 2 represents a manufacturer's supply chain. Usually referred to as a network representation, the nodes represent facilities that add value to the supply chain. Nodes occur from the sources of raw materials and intermediate products to the consumers of the finished products. The arcs or links connecting the nodes represent transportation lanes for materials, semi-finished, and finished products.
Planning Processes Have Three Hierarchical-Based Levels (BACK TO TOP)
Planning processes are typically subdivided into multiple hierarchical-based planning levels. Each level has a planning cycle that its processes follow. Currently, supply chain planning is usually done using three hierarchical planning levels:
Figure 3 depicts the scope of APS processes in relation to these planning levels. A description of each supply chain planning level and how optimization is used within it follows.
Strategic Level Planning--Supply Chain Network Design (BACK TO TOP)
To support supply chain design, optimization determines the location, size, and the number of plants, distribution centers, and suppliers. This level of planning includes sourcing and deployment plans for each plant, each distribution center, and each customer. It also considers the flow of goods through the supply chain network. Generally, supply chain network design is done infrequently (i.e., every few years) as companies do not need to add new plants or distribution centers on a routine basis.
Conceptually, supply chain network design can be thought of as determining the nodes and arcs within a supply chain network. Multistage production processes are usually handled by creating nodes for each major step in the process (to handle cases such as feeder plants). The concept of time is usually not a consideration in supply chain network design. Month-to-month or week-to-week changes--for example in demand--are typically inconsequential to supply chain network design decisions.
Supply chain network design products include the following:
Tactical Level Planning--Supply Planning (BACK TO TOP)
Supply chain planning at a tactical level is called supply planning and involves optimizing the flow of goods throughout a given supply chain configuration over a time horizon. Similar to supply chain design, supply planning develops sourcing, production, deployment, and distribution plans. But there are some major differences:
Some manufacturers and software vendors subdivide the supply planning process into two or more planning processes and hierarchies. For example, a first level of planning might involve the development of material and product flows within the supply chain network. This establishes optimized sourcing plans, which determine the suppliers for each plant and the plants/distribution centers that will replenish the distribution centers and customer stocking locations. This planning is typically done using aggregated product groups over longer periods of time (quarters or months). The second, lower level tactical planning might involve more detailed, optimized manufacturing master plans and distribution center inventory replenishment plans for individual SKUs over shorter periods of time (months or weeks).
Some of the software vendors that provide optimization-based supply planning solutions are listed in Table 1.
Table 1: Vendors With Optimization-Based Supply Planning Modules
Source: AMR, 1998
Operational Level Planning--Production Scheduling (BACK TO TOP)
At an operational level, supply chain planning can be viewed as supply scheduling. For a manufacturer, supply scheduling is essentially production scheduling done on a plant-by-plant basis. Production scheduling develops a minute-to-minute or hour-to-hour schedule for all of a plant's resource needs, including labor, equipment, and materials. Production scheduling optimally sequences orders into the manufacturing process. Generally, production scheduling is done frequently, potentially several times a day to account for changes to orders, machine failures, material shortages, and other plant disruptions. The process usually considers the lowest level of detail on plant routings, product bills-of-material, and changeover and setup times.
The following vendors provide some level of production scheduling optimization:
MODELS, DATA, AND SOLVERS ARE KEY ELEMENTS OF OPTIMIZATION (BACK TO TOP)
Models and data are two very important elements of supply chain optimization. A plan's usefulness greatly depends on the quality of both. If a planning process is based on a model that inadequately represents reality or if the data used in a model is wrong, the solutions developed by the optimization will not be meaningful or executable. The two elements go hand-in-hand for the following reasons:
Another important element of an optimization process is the solver, which solves for an optimized solution. The planning model determines the best solver. (See the sidebar "Optimization Solvers" on page 18 for a discussion of solvers). Together, data, models, and solvers represent key elements of optimization-based planning.
Good Quality Models Are Needed (BACK TO TOP)
A supply chain planning optimization process requires "good quality" models. For good optimization, the level of detail in the model must be appropriate for the planning level. For example, in deciding how much to make in a plant this month, the model does not have to consider the exact sequencing of orders (actual or forecast) placed on the plant. On the other hand, to decide on the plant schedule for a specific day, the model should consider the order sequence.
While models and data are extremely important to the usefulness of a plan developed using optimization, realistic, executable plans can sometimes be developed without detailed models and data. A common guideline in supply chain planning optimization is that detailed models are not needed for strategic planning but are needed for operational and tactical planning. For strategic and higher levels of tactical planning, a model could be based on aggregate data such as product groupings or regional demand. It is important for an operational model to be based on detailed data such as SKUs and customer orders.
Solvers Depend on Models Used (BACK TO TOP)
In addition to models and data, solvers are important since they generate the optimized solutions. Yet one of the most confusing aspects of optimization technology is the various methods or mathematical algorithms deployed in these solvers. Many of these methods were developed to solve problems with a specific model or mathematical structure, while others were developed to improve the computational speed of the solver. This led to esoteric names for these solvers and added to their "rocket-science" positioning. In the area of supply chain optimization, most methods can be grouped as one of the following types:
Generally, mathematical programming methods are used in solvers for strategic and higher levels of tactical planning. These methods generally work only for solving linear- and some integer-based models, commonly used in strategic levels of planning. Tactical and operational models are usually not linear and are much too complex to solve using mathematical programming methods. For this reason, heuristic methods are generally used in tactical and operational planning level solvers.
Genetic algorithms are used primarily in operational planning to consider a large number of possible solutions. The Theory of Constraints, a heuristic method based on work by Eli Goldratt, is another solver method commonly used in operational planning. Vendors that use solvers based on the Theory of Constraints include the following:
While not a formal optimization technique, exhaustive enumeration is predicated on using the computer to find a solution by looking at all possible alternative plans. This method proves useful in simple supply chain situations. Otherwise, this method is computationally intensive and slow to generate a solution. Distinction Software (Atlanta, GA), uses this optimization method for its manufacturing planning solution. Since the company focuses on mid-tier and smaller manufacturers, the exhaustive enumeration approach is feasible.
Optimization solvers use and are named for the different methods or algorithms deployed to find solutions. These methods can be grouped into four categories:
Mathematical Programming Methods
Mathematical programming methods are used for problems that can be modeled with equations that describe the constraints and objectives. Mathematicians have proved that if a problem can be described using certain sets of equations, then an optimum solution can be computed following a prescribed algorithm or technique. This is in contrast to other methods that search for an optimum but offer no guarantee that it can be found. The most commonly used mathematical programming technique is linear programming (LP). This method works only if all the constraints and a single objective can be expressed as linear equations (i.e., a linear equation looks like this: (sum)Xijk - DjZkj = 0). If this holds, the optimum solution that either maximizes or minimizes the single objective can be generated. LP assumes that the decision variables can be expressed as regular, continuous numbers. If some decisions can only be expressed as an integer or whole number, LP does not work. For example, if the decision is to incur a production changeover or setup, this can only be expressed as a "yes" or "no" or mathematically as a "0" or a "1."
To handle this, mixed integer programming (MIP) was developed. This method also only works if all the equations are linear. In contrast to LP, however, while an optimum solution can be generated, it may take too long. The good news is that MIP does have a way to tell how much better an optimum solution would be if it could be generated.
Other mathematical programming methods include dynamic programming and nonlinear programming. However, these methods are not often used in supply chain planning optimization.
Heuristics
Heuristic methods are predicated on trying to improve a known feasible solution following prescribed steps. Heuristics do not guarantee that an optimum solution can be found, nor do they determine how much better an optimum solution might be. As an illustration, a simple heuristic for maximizing an objective might follow a three-step approach:
While this heuristic method might not lead to the optimum, solutions will usually get better or stay the same. Simply put, heuristics are based on the logic a reasonable person might follow in looking for an optimum.
Some scheduling optimization solutions use heuristic logic based on the Theory of Constraints (TOC) espoused by Eli Goldratt. These methods focus on critically constrained resources or "bottlenecks" to develop a schedule. The TOC approach revolves around a drum, buffer, and rope concept. First, TOC uses the critically constrained resources to develop a master plan or drum that the plant or system "beats to" or to which the pace is set. Buffers, such as work-in-process inventories and surplus time in the schedule, are put in place to ensure maximum utilization of the critically constrained resources that ensure they do not sit idle. Lastly, all non-critically constrained resources are "tied" together according to the drum, creating so-called ropes that pull work through the system.
In addition to TOC, there are many types of heuristic methods that are proprietary knowledgeware of the vendors. Some of these are based on known, published approaches such as Simulated Annealing and Repair-Based Scheduling methods.
Genetic Algorithms
Genetic algorithms are predicated on a biological selective breeding concept of survival of the fittest. The methods attempt to find an optimized solution from a large set of possible solutions by comparing them and selecting the best ones of the group. The ones that survive this test are then mutated or crossbred to establish another set of solutions. This search method continues testing from generation-to-generation for some duration of time, thereby developing a reasonably optimized solution. These methods work well when a baseline schedule or plan exists. For example, sequencing a number of orders through a single assembly-line operation to maximize on-time delivery or to minimize changeover is an optimization task where genetic algorithms could be used.
Exhaustive Enumeration
Not always considered a formal optimization technique, the exhaustive enumeration method evaluates all possible combinations of decisions to find the best combination. This method is used when there are relatively few decision-variable combinations to consider. For example, a job shop with 1 machine and 10 orders to sequence would generate 3.6 million potential combinations for evaluation. While this is a lot for a human to handle, it is an easy task for a computer.
VENDORS ARE MOVING TOWARD MORE HOLISTIC OPTIMIZATION (BACK TO TOP)
Generally, optimizing each piece of a plan in isolation from other plans does not guarantee that optimization is achieved for the total planning process. For example, developing an optimal production plan in isolation from distribution does not guarantee that the total production/distribution plan is optimal. Vendors are addressing this concern in the following ways:
Each of these vendor trends attempts to provide users with a more holistic approach toward optimization. They each address planning processes that may be somewhat subdivided but that support optimized plans across the entire supply chain. Each trend is discussed below.
A Move Toward Synchronized Concurrent Planning (BACK TO TOP)
Figure 4 is a graphical representation of a synchronized sequential supply chain planning process. In this process, demand planning is done first. The resulting forecasts are used as input to the distribution planning process (including planning for inventory, transportation, and warehousing). The distribution plans developed by this process are then used as input to the manufacturing planning process. Lastly, the output of this process is used as input for the procurement planning process. The planning process is synchronized, since any change in "downstream" plans is automatically reflected in the "upstream" planning processes. From a product perspective, Manugistics' supply chain planning suite was the first major solution providing this type of synchronized sequential planning approach.
The synchronized sequential approach works well in integrating supply chain planning and in moving it to a consumer demand "pull" environment. It has, however, two problems from an optimization perspective:
Some optimization processes solve these two problems through "synchronized concurrent" planning (See Figure 5). In synchronized concurrent planning, the demand, distribution, manufacturing, and procurement plans are jointly or simultaneously developed. All constraints along the supply chain and optimizing objectives, such as cost or profitability, are considered within the planning process. Many vendor applications have moved or are moving toward this planning approach. For example, Manugistics' Supply Chain Navigator provides synchronized concurrent planning.
Planning Levels Are Being Synchronized To Ensure Optimality (BACK TO TOP)
In most optimization frameworks, optimal solutions generated at higher levels in a planning hierarchy are used as starting points or constraints on the optimization taking place at lower levels. In a planning environment where optimization is a goal, plans at different levels may need to be synchronized to ensure that the supply chain is continually operating in an optimized fashion.
Figure 6 depicts the difference between architectures that do and those that do not have synchronization functionality. Disjointed plans and data levels do not ensure that optimization is achieved. To ensure synchronization and consistent optimization across hierarchical planning levels, vendors use several approaches:
In a telescoping planning horizon, "time buckets" vary over time (typically increasing):
This approach helps ensure synchronization along the time dimension for the different planning levels. For example, Thru-Put Technologies' Resonance product allows the user to define a telescoping planning horizon for viewing results. Other vendors with telescoping planning horizons include Chesapeake and Numetrix.
Vendors also address the synchronization issue by using a common data structure for all planning levels. This common data structure ensures that aggregated data is always derived from and synchronized with detailed data--possibly down to the lowest level of base data (e.g., ERP transaction data). If needed, these solutions also allow a planner to build models using detailed data. For example, if a lower-level work task or portion of a plant's routing is always a major bottleneck, a user can include it in higher level plans to ensure that the solution is optimal and feasible. For example, Paragon Management Systems uses a common data structure approach within its applications; Ortems has a data structure with aggregated data derived from detailed data.
Some vendors address the synchronization issue by incorporating functionality to monitor and control the degree of synchronization among planning levels. As a planner works with the system, if a lower-level plan gets significantly out of synch with higher-level plans, the higher-level plans are regenerated. Paragon Management Systems has designed its APS product suite to ensure synchronization among planning levels in this way. Numetrix's Collaborative Enterprise Network product takes a slightly different approach. The application monitors the degree of synchronization among plans and sends a planner an alert message when they are out of synch.
Real-Time Planning and Execution Is a Goal (BACK TO TOP)
As computers get more powerful, companies are shrinking planning cycles and achieving real-time planning and execution. In turn, this moves them toward achieving real-time optimization, as well as reducing the need for hierarchical planning processes with long cycle and lead times. A reduction in these planning cycles would lead to the following improvements:
Up to now, reducing planning cycles with the use of applications has been limited by the speed at which an optimized plan can be generated. While some manufacturers have moved from planning in a batch mode, many have not. Applications are getting closer to generating optimized plans in real-time.
To do this, some vendors, such as i2 Technologies and Ortems, use memory-resident planning applications loaded into and executed in main memory. This speeds up the application substantially. Other vendors, such as Manugistics, have designed applications to use both memory and hard disk space. Generally, both approaches achieve the goal of faster plan generation. Neither approach works better in all planning environments. For example, memory-resident applications are faster as long as the planning problem can fit into memory, but they quickly degrade in performance on large problems that also require hard disk space.
APPLICATION ARCHITECTURES DIFFER SUBSTANTIALLY (BACK TO TOP)
A variety of design issues must be considered to develop the architecture or framework of a supply chain planning optimization application:
Vendors have addressed these issues in a variety of ways, many times depending on a specific vendor's history and market focus.
Applications Need To Balance Flexibility and Built-In Functionality (BACK TO TOP)
Optimization depends heavily on the ability of the application to model real world issues. This can be done with built-in modeling functionality, such as predefined models for setting safety stocks. Rather than built-in functionality, an application may provide flexibility with general-purpose modeling capability that allows users to create models--such as defining safety stocks models. Applications need to provide users with a balance between built-in capability and flexibility.
Some vendors provide general-purpose modeling applications that allow users the flexibility to optimize across a broad range of decisions. These products allow users to tailor the optimization to their own environment, letting them optimize decisions uniquely important to them. Chesapeake Decision Sciences' MIMI Planning and Scheduling is a general-purpose modeling application. Other companies providing these types of applications include CAPS Logistics, i2 Technologies, and Numetrix.
To provide some level of built-in functionality, many of the general-purpose modeling application vendors are developing or offering templates. These are semi-custom applications, created using their general-purpose modeling applications. Templates reduce implementation configuration efforts. For example, i2 Technologies is building templates that are specific to industries such as Semiconductor, High Tech, and Metals. In addition, CAPS Logistics now markets Supply Chain Designer and Supply Chain Coordinator, strategic and tactical supply chain planning applications built with its toolkit.
Other vendors offer built-in capabilities rather than general-purpose modeling application functionality. While these applications limit the ability to optimize unique decision environments, they minimize the implementation configuration efforts for specific supply chain problems. For example, Manugistics has recently added optimization capability to its product suite tailored for consumer products and distribution-intensive companies. While this provides less flexibility to optimize, it reduces configuration efforts. Logility is developing similar optimization capability.
Users Need To Maintain Control of the Solutions (BACK TO TOP)
Generally, optimization solutions must be understood and controlled by a user. Optimized solutions are frequently not reasonable or executable and need to be adjusted by the planner. Remember, these are decision-support, not decision-making systems! In fact, one APS vendor claims that around 40% of software development efforts are spent solely on enabling user control functionality.
The major purpose of user control is the inability to enter all details into the optimization model. For example, an optimized solution may suggest that a customer's due date be pushed out because it is critical in achieving a low-cost solution. The manufacturer, however, may have already pushed out the due date for the customer's last three orders. This would be an unacceptable solution!
To ensure that optimization applications provide reasonable, executable solutions, most vendors provide graphical user interfaces (GUIs) to facilitate manipulating data and modifying solutions. This functionality includes the following:
Many graphical planning boards allow users to change a variable (e.g., a date or order) and immediately see the impact on the objectives and assess new constraint violations.
Vendors also give users control over the solution by allowing them to incorporate unique constraints or rules into the model. For example, a planning application might allow a user to specify that customer due dates can be relaxed by one or two days, or the user may approve the maximum level of overtime. While this is useful functionality, some users are tempted to abuse it by changing the objective. This is conceptually an invalid approach to an optimization problem. Solutions that are not reasonable are best changed by altering the constraints.
Some vendors allow users to control the progress and performance of the solver method. This is usually done by allowing the planner to set a time limit on the solver and then pause. This lets the planner evaluate the solution so far. If it is good enough, the user can finalize the plan. If it is not, the user can have the solver search longer for a better plan.
Many Vendors Use a Mix of Third Party and Internally Developed Optimization Components (BACK TO TOP)
Embedded within vendor solvers are components, submodules that perform the underlying optimization logic. Some vendors use optimization components from third party vendors to shorten development time. This approach also allows the vendor to focus ongoing development resources on the planning application while the third party takes on the responsibility for improving the optimization component.
The leading optimization component vendor is ILOG. The company offers three optimization components including market-leading LP/MIP acquired from CPLEX. The CPLEX solution is embedded in many applications including those from the following vendors:
ILOG's other optimization components are general-purpose planning and scheduling components called ILOG Planner and ILOG Scheduler. Fygir has embedded the ILOG Planner within its FIT planning and GRIP scheduling products.
Sunset Software Technology (San Marino, CA) is another component vendor. Adapta Solutions (Hawthorne, NY), has embedded this company's LP/MIPS functionality into its planning suite.
Many application vendors exclusively offer optimization components that are internally developed, as they believe that they have a core competency in optimization, especially for planning environments in their target markets. For example, Ortems uses proprietary heuristics based on "graph theory" in its SRP product, which focuses on production scheduling and the lower levels of tactical planning. Similar to Ortems, Thru-Put uses its own proprietary methods based on the Theory of Constraints. The company believes that these methods provide greater optimization benefit for the complex manufacturing environments it targets (such as Automotive and Industrial Product manufacturers).
Most application vendors use both internally developed and third party optimization components. These vendors usually have a broad suite of supply chain planning products. For example, i2 Technologies offers a full suite of optimization products that use internally developed components based on proprietary heuristics (including those based on Constraint Anchored Optimization and Simulated Annealing methods), genetic algorithms, and third-party LP/MIP components.
Some Vendors Provide Multi-Objective Functionality (BACK TO TOP)
Some vendors allow planners to define multiple objectives. For example, both customer service and costs can be optimized together--despite the fact that two or more objectives cannot be optimized at the same time. Maximizing customer service is not likely to minimize costs; quite the opposite usually holds true. To accommodate planners that want to strike a balance among competing objectives, some vendors provide solvers that find an optimized solution using the objectives in priority sequence. i2 Technologies offers this type of solver using a "hierarchical" LP/MIP algorithm.
Another way in which applications allow planners to use multiple objectives is through the use of weights. This is accomplished by creating a single objective that is a combination of the different objectives, weighted by a number. For example, a customer service objective might carry a weight of 90 (out of a possible 100), while a cost objective might carry a weight of 70. The solvers in these applications optimize a single, composite weighted objective. ProMira, recently acquired by Manugistics, provides this functionality, incorporating a graphical slidebar interface to make it easy for a planner to set the weights.
Some Applications Handle Special Optimization Problems (BACK TO TOP)
Some special planning environments involving trim and blending optimizations cannot be accommodated by many supply chain planning applications. Some vendors have incorporated such capability to be integrated into their supply chain planning suites.
For example, integration of trim optimization in the Paper industry can lead to substantial benefits by reducing waste paper from cutting operations. Chesapeake and IBM both have special trim optimization functionality to support Paper manufacturers as part of a total supply chain planning optimization application.
Blending optimization is used in the Fruit Beverage and Oil industries, where raw materials are limited and prices are volatile. Materials must be efficiently blended to create products that meet specifications. Fygir, Chesapeake, and STG have products that can incorporate blending optimization into the supply chain planning application.
OPTIMIZATION USAGE GUIDELINES (BACK TO TOP)
While the use of optimization in supply chain planning is extremely appealing, the technology may not be for everyone. Rather than by employee headcount reduction alone, these solutions are justified by such supply chain benefits as the following:
Though there are no hard-and-fast rules for manufacturers deciding whether to purchase optimization technology, AMR offers the following guidelines:
CONCLUSION (BACK TO TOP)
The guidelines above and in this AMR Report will hopefully reduce some of the mystery surrounding optimization, and they should help buyers during their technology selection and implementation processes. The research for this Report has led to the following conclusions:
To summarize, optimization technology offers great promise for improving a manufacturer's supply chain performance. But understanding it will take a great deal of work and great effort on the part of users. AMR expects, however, that its use in supply chain planning will continue to grow, since the benefits should far outweigh the efforts.
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