What is Production Planning?

At its core, production planning represents the beating heart of any manufacturing process. Its purpose is to minimise production time and costs, efficiently organise the use of resources and maximise efficiency in the workplace.

Production planning incorporates a multiplicity of production elements, ranging from the everyday activities of staff to the ability to realise accurate delivery times for the customer. With an effective production planning operation at its nucleus, any form of manufacturing process has the capability to exploit its full potential.

You can find everything that you could possibly need to know about production planning on this page by selecting the relevant article from the summaries below or the menu on the left.

Project Planning vs. Finite Scheduling

It is not uncommon for people to assume that project planning and finite scheduling software perform the same task. You can see why this happens from a cursory review of these types of software, since both utilize Gantt charts to show visually the work that has to be completed. To illustrate the differences between project planning and finite scheduling let's assume that we are managing a civil engineering project to build a bridge. One part of the project is to construct the foundations for a bridge pier, and (with due deference to civil engineers) the tasks required could be: -

• Survey pier site
• Dig pier hole
• Fill hole with concrete

A project planning Gantt chart for this work would place each task or activity on a separate line, as shown below.

Project Management Gantt Chart

If we should need to edit this Gantt chart as the project progresses, the editing would take the form of moving the Gantt bars left or right to compensate for gains or slippage. There would be no point in moving the bars vertically, since each bar is an activity in its own right. This is typical of a project management view, where the bars represent the activities and the user allocates the resources required to perform the task as data attached to the activity.

In a finite scheduler we are generally working at a more detailed level than the above example, and we want the software to help us select the 'best' resource to carry out each task. In our example we may have several excavators which could be used to dig the hole, and the finite scheduler would help select the best one, based on the other work they are committed to, their work or shift patterns, the rate at which they can dig, etc. We may also need to take into account additional resources that are also required to perform the task. Our fastest excavator may require the large dump truck to remove the spoil, which is already in use elsewhere on the site. The finite scheduler will then decide whether it is better to wait for the fast excavator/large dump truck combination to become available, or whether to start the task earlier using a slow excavator with the available smaller dump truck.

To do this the Gantt chart in the finite scheduler is different to that of the project planner. Whilst we still have bars representing the tasks/activities/operations that we have to perform, each line or row on the chart now shows all of the tasks/operations that are to be carried out by a particular resource (excavator). Therefore in the finite scheduler Gantt chart each line or row is associated with a resource and will generally have many bars on it, whereas in the project planning Gantt chart each row represented an activity or task and generally would only have one bar on it.

Finite Scheduling Gantt Chart

When editing a finite scheduling Gantt chart it is a common requirement to move the bars (tasks/activities/operations) vertically this will allocate the operation to another resource, e.g. move the task from the fast excavator to the slow one. Moving the bars left or right will alter the time at which the operation occurs, just as it does in the project planning Gantt chart.

The Finite Scheduling Gantt Chart shows the chart for our pier foundation project. It also shows the interaction of the tasks in the pier project with other tasks that the resources are performing in the overall construction project. If the 'Move Spoil' task slips to the right, then it will impact on our 'Dig Pier Hole' task. If the delay is sufficient we may decide to move the 'Dig The Pier Hole' task to the slow excavator instead, however to get an on-time start we may also decide to move the lower priority 'Load Trucks' task from the slow excavator to the fast one. The edited finite schedule shows the result of these changes.

Edited Finite Schedule

In a manufacturing process changes in the resource/task allocation may occur frequently due to breakdowns, rush orders, customer changes, etc. The finite scheduler provides an invaluable tool for helping the production manager control the process, by giving visibility of the resource loading and early warning of orders that are running late, etc. The output from a finite scheduler will be work-to lists for each of the resources, indicating the operations that the resource has to carry out, and the sequence and time at which they are planned to take place. In summary the finite scheduler is allocating tasks to specific resources, rather than representing the relationship between the tasks.

Capacity Planning Overview As defined by the APICS Dictionary, “Capacity planning or capacity requirements planning is the function of establishing, measuring and adjusting limits or levels of capacity. The term “capacity requirements planning” in this context is the process of determining how much labor and machine resource is required to accomplish the tasks of production”. The Capacity Planning Decision Hierarchy Finite capacity scheduling uses basically the same data as capacity requirements planning but adjusts the schedule to ensure that the capacity required never exceeds a work center’s defined capacity limits in a given time period. The above figure depicts the hierarchy of capacity planning decisions that can be made within a planning and control environment. These range from long-term capacity decisions down to short-term shop floor monitoring and control tasks: Planning resource capacities over long time horizons. The rough-cut evaluation of capacity required by the master production schedule. Detailed capacity requirements of a particular production schedule. The use of finite loading procedures. The simulation of the use of alternate capacity plans. Monitoring actual outputs versus plan. The source of the loading data changes as you move down this hierarchy. While resource planning takes its capacity requirements from the business plan, rough-cut capacity planning uses the master production schedule as the source of its information. Capacity requirements planning and the remainder of these shorter-term planning modules take their loading data from the Material Requirements Planning output. Supply Chain Scheduling The term Supply Chain Scheduling (SCS) may be new to you, but I'm sure you will be aware of some of the other technologies that have been used to try and improve the efficiency of supply chains, such as Electronic Data Interchange (EDI), Supply Chain Management (SCM) systems, etc. So where, you may ask, does SCS differ from these other techniques, and why should you want it if you haven't found a use for EDI and SCM. Both EDI and SCM foster closer relationships between the companies within a supply chain, but it tends to be a one way relationship. For example EDI is, for our purposes, primarily a way for an OEM (typically a car manufacturer) to place orders on their suppliers. The ability of the supplier to produce the components is taken for granted, and for some the advent of EDI simply pushed the necessary stock of components back up the supply chain to the supplier. SCM goes a step further, but its major application areas have been in controlling stock in multiple warehouses for FMCG and food/drink manufacturers. In this role SCM attempts to maintain the required stock levels whilst deciding how and from where to fulfil particular customer orders. Again the assumption is made that the stock/capacity exists to supply the customer orders. So effectively both EDI and SCM are mechanisms for companies to place their demand (requirements) on their suppliers, without taking much account of the supplier's capacity. This is the wrong relationship for the vast army of companies who want to build on the classical business practice of inquiring when components will be available. For example, if I wish to buy some specialised bearings, which I know are not stock items, then I will ring the supplier to ask for a delivery promise. In effect I am asking the supplier to review his capacity and tell me when he can make my bearings. Now we start to see the fundamental difference between EDI, SCM, etc., and SCS. The former are about placing demand and delivery requirements on a supplier, whilst the latter is about reviewing the supplier's capacity and then determining the delivery requirements. SCS has the potential to improve supply chain efficiency for companies in the make to order sector, but to work in its most effective way, these companies will have to work more closely together. The closeness of the relationships could be measured on a scale ranging from loose to intimate, and to see the difference the type of relationship can make we will review the SCS requirements of Fred, the proprietor of the Acme Bike Company (ABC). Fred's company is an OEM in the supply chain hierarchy. He supplies finished products to us, the end users. Fred's process route is fairly simple, and is shown in figure 1. He manufactures the frames and wheels in-house, but buys in his saddles and the other raw materials. He also uses a sub-contractor to plate the frames. Fred has a loose relationship with his steel tube supplier(s), he orders large quantities to get good discounts, and buys to specification from one of a number of suppliers, going for the cheapest each time. For this relationship SCS will provide a mechanism to automatically send inquiries to the suppliers, and to receive and review their responses, but the improvement to the supply chain efficiency is small. Unlike the tube, the saddles are costly, specialised items with many variants, so Fred orders the saddles on a 'make to order’ basis against each sales order he receives. He always uses the same supplier, and has built up a close relationship. Because of this, the supplier is willing to make more of his schedule data available, and allows Fred's scheduling system to make automatic Available/Capable To Promise inquiries to reserve capacity in the saddle making process. This means that while Fred's planner is creating a schedule for his plant, the capacity of the saddle supplier is automatically checked and taken into account. In this way Fred knows that his schedule is achievable, without having to call his saddle supplier every time a new order arrives, or his own schedule changes. This significantly improves the supply chain efficiency for both companies. The relationship between Fred and Bert, the plating sub-contractor, can be described as intimate. They have worked together for years, and Bert offers Fred special rates if Fred can time his work so that it uses any 'spare' capacity caused by the 'ebbs and flows' in Bert's other work. To achieve this Bert allows Fred's planner full access to the plating schedule. The planner then schedules the frame plating jobs so that, where possible, they use the spare capacity. Such intimacy requires great trust between the parties, but brings huge rewards in the form of supply chain efficiency improvements. As well as using SCS to improve efficiency between his business and his suppliers, Fred has also extended it to his customers. His sales force carry WAP mobiles (yes, we have finally found a use for them), and by dialling in to the ABC WAP site they can make order inquiries and get delivery promises while sitting in front of the customer. The next step is to make the same facility available to selected customers from Fred's main web site, and, as figure 2 shows, all of these remote inquiries take full account of the saddle and plating capacity limitations. Fred is able to reap the rewards of SCS because of the relationships that he has built with his suppliers and sub-contractors. You can also use SCS to improve your process, but remember that it will require close relationships. Many companies regard their capacity data as commercially sensitive, so the best efficiency improvements will come to those who forge tight knit supply chains or clusters, where such data can be freely interchanged. Algorithmic Sequencing Algorithmic sequencing is one method for schedule construction. The sequencer uses an algorithm to select an order and places each operation for that order on the planning board. It repeats this process until all orders (and therefore, all operations) have been loaded. Each operation is loaded on its specified resource at the first available time slot that satisfies the constraints for that operation. The order-at-a-time construction process, sometimes called Order Based Method or Sequential Loading, is shown in following diagrams. In this example, we first load Order A, then Order B, and finally, Order C. Step 1: Load Order A Step 2: Load Order B Step 3: Load Order C In an order-at-a-time sequencer, selecting the sequence in which the orders are loaded onto the planning board controls the schedule. Once an order is selected, all of its operations are loaded in a straightforward, unalterable way. Hence, the key to the quality of the schedule that is generated by the algorithmic sequencer is the rule that is used for selecting the order-loading order. There are a number of very simple heuristic rules that can be used to select the sequence for loading the orders on the planning board. One rule that can be used for selecting the order sequence is to sort the orders by a priority value that has been assigned to each order. Other possible sorting criteria are earliest due date, earliest release date, and smallest remaining slack time. None of these rules are optimal in a mathematical sense. Each rule represents a different strategy and focus in planning the orders. For example, the due date-related rules (earliest due date, least remaining slack) focus on reducing the number of late orders, whereas the priority-based rule strives to complete the most important orders as soon as possible. In some applications, a given operation can be performed on two or more different resources. For example, a drilling operation might be performed on either of two drills. In such cases, the schedule in an algorithmic sequencer is first determined by the sequence of orders, then by the rule that is used to determine to which resource a given operation is assigned during the loading process. Again, simple heuristic rules (e.g., assign the operation to the resource that will complete the operation first) can be used to determine the schedule. Although an order-at-a-time sequencer typically loads each order working forward in time by starting with the first operation and continuing through to the last operation, (forward sequencing), it is also possible to use this same sequencing scheme but reverse the procedure to work backward in time (backward sequencing). In this case, the sequencer begins by loading the last operation for the order to finish at its due date. It then continues by loading the order's preceding operation to finish at the start time of the last operation. This process is continued, working backward in time, until the first operation for the order is loaded. At this point, the sequencer then selects a new order to load and repeats the entire process, again starting with the last operation and working backward in time. It continues in this fashion until all orders are loaded. The following diagram shows a schedule generated by backward sequencing Orders A, B, and C, where the vertical line on the right denotes the due date for all three orders. Backward Sequencing Chart for Orders A, B, and C The advantage of a backward algorithmic sequencer is that it always generates a schedule that has no late orders. However, the schedule may require start times that are infeasible; that is, the orders must start before the current time. In essence, a forward order-at-a-time sequencer fixes the start times for an order and determines the end times (which may violate the due dates), whereas a backward order-at-a-time sequencer fixes the end times and determines the start times. Although the idea of constructing a schedule that has no late orders is appealing, backward sequencing has some practical limitations, even in cases where a feasible solution is generated. Backward scheduling shifts all orders to the right on the planning board so that they start as late as possible while still meeting the due date. This means that there is no time buffer in the system and any disruptions that occur; (machine breakdowns, late material arrivals, etc.) will typically create late orders. In addition, by postponing the use of our available capacity and waiting until the last minute to start each order, we give up the opportunity to consider additional orders that may arrive later and need to be added to the schedule. For these reasons, many schedulers prefer a forward sequencing. There is also a tendency for backward sequencing to produce lower resource utilization. If you consider the Backward Sequencing Chart, a gap has been left between operations A-10 and B-20 on Resource 1. It is likely that the gap is too small to be filled by operations loaded later in the sequencing process, resulting in lower utilization. If the work had been forward sequenced the gap would have been at the end of B-20 (i.e. added to the as yet unused pool of capacity), making it available to other operations of any duration. It is also possible to use an algorithmic sequencer in a bidirectional mode. In this case, we select an operation somewhere in the middle of the order and schedule the remaining operations using forward sequencing and the preceding operations using backward sequencing. This is useful in cases where we have an available time slot on a critical, highly utilized resource and we want to assign an operation to this critical resource and then load its upstream and downstream operations around this operation. We can accomplish this by fixing the operation on the critical resource and bidirectional sequencing the remaining operations for this order. In summary, an algorithmic sequencer is a simple and fast method for loading a set of orders on the planning board. The schedule is completely determined by specifying the sequence of orders and a rule for selecting between resources. Simulation-Based Sequencing Simulation-based sequencing provides an attractive alternative to algorithmic sequencing by providing a simple, yet very flexible method for constructing a schedule. In general, a simulation-based sequencer can also produce any schedule that is produced by an algorithmic sequencer. However, a simulation-based sequencer can also consider many schedules that cannot be considered by an algorithmic sequencer. Hence, the simulation-based sequencing approach, sometimes referred to as a Resource Based Method or Parallel Loading, can create better schedules than an algorithmic sequencer, but this would depend very much on what you consider to be a 'better' schedule. In contrast to the algorithmic sequencer, the simulation-based sequencer selects and loads an operation at a time. By loading individual operations rather than entire orders, the simulation-based sequencer has finer control over the way the operations are loaded onto the planning board. This operation-at-a-time loading is the key to the added flexibility in schedule generation using the simulation-based sequencer. A second fundamental difference between the algorithmic and simulation-based sequencer is that the simulation-based sequencer constructs the schedule in a single time pass by moving forward from one event time to the next. The simulation-based sequencer begins at the current time and loads all operations that can start now. Note that these operations don't come from a single order as in the case of the order-at-a-time sequencer, but can be taken from the entire set of orders. Once all operations that can start at this event time have been loaded, the simulation-based sequencer advances time to the next event e.g., the first completion time for any operation on the planning board. In this case, a busy resource has just changed to idle; hence, the simulation-based sequencer attempts to load additional operations at this new event time. The simulation-based sequencer continues in this fashion, advancing time forward to the next event time and loading additional operations as resources become idle until all operations have been loaded. The following set of figures illustrates steps in the simulation-based construction process. In each of the figures, the vertical line denotes the current value of simulated time. The first step in the simulation-based sequencer is to load all operations that can begin at the current time. In this example: Step 1: Load Operation A-10 and B-10 Step 1: Operation 10 for Order A (A-10) can be loaded on Resource 1 and Operation 10 for Order B (B-10) can be loaded on Resource 2. Step 2: Load Operation C-10 Step 2: The simulated time is advanced to the ending time of Operation A-10, and Operation C-10 is then loaded on Resource 1. Step 3: Load Operation A-20 Step 3: Simulated time is advanced to the end of Operation B-10, and Operation A-20 is loaded on Resource 2. Step 4: Load Operation B-20 Step 4: Simulated time is advanced to the end of Operation C-10, and Operation B-20 is loaded on Resource 1. Step 5: Load Operation C-20 Step 5: Simulated time is advanced to the end of Operation B-20. Since no further operations remain to be loaded on Resource 1, simulated time is advanced to the end of Operation A-20 where the final operation, C-20, is loaded on Resource 2. In contrast to the algorithmic sequencer, the simulation-based sequencer only moves time forward. It only advances time forward once all operations that are to be loaded at that time have been loaded. Hence, the simulation-based sequencer temporarily stops time and examines the entire order set, advancing to the next event time once all operations have been loaded. The algorithmic sequencer, on the other hand, is constantly moving back and forth in time as it loads all operations for each order. It starts at the current time and goes forward in time loading all operations for this first order. It then goes back again to the current time and again moves forward in time loading all operations for the second order. It continues in this fashion, moving forward and backward in time, until all orders are fully loaded on the planning board. Hence, the algorithmic sequencer fixes an order and works across time, advancing to the next order once all operations have been loaded. In the Preactor 400 APS simulation-based sequencer, we control the schedule using dispatching rules that select the next operation to load. Typical rules select the next operation with the shortest setup time with the shortest processing time with the earliest due date with the lowest value of critical ratio in a preferred sequence etc... Although the loading concepts employed by the Simulation Based Sequencer provide great flexibility in schedule generation, the effective use of this flexibility is influenced by the dispatching rules chosen to select the next operation for the resource from the queue of work waiting to be processed. Preactor 400 APS employs both standard and customized dispatching rules and this is discussed further in the section dedicated to Preactor 400 APS. There are disadvantages in using a simulation-based sequencer too. An example would be use a dispatching rule which minimizes lateness, such as 'critical ratio', where selection of operations would be based on the remaining operation time compared to the delivery time. In this situation, if capacity is a problem, a simulation-based sequencer would tend to minimize total lateness of all the orders but you would have a higher number of late orders (i.e. a large number of slightly late orders). An algorithmic sequencer using the due date priority option would produce fewer late orders (i.e. a small number of very late orders). In this situation the user could focus attention more easily on those orders that are predicted to be late. An algorithmic sequencer can also control some process parameters more effectively than a simulation based sequencer. For example in many food and process industries it is often a requirement to control the amount of time that work waits between process steps (food can spoil). Because an algorithmic sequencer loads a complete order at a time it can easily check if the intervals between the operations are correct, and adjust them if required. The simulation-based sequencer has less control because when it loads the first operation it does not know when the next operation will be loaded. Later, when the next operation is loaded, it may not be possible to move the first operation because other work has been loaded in between. In general, a simulation based sequencer using First In First Out (FIFO) as the dispatching rule, will generate a schedule with higher resource utilization and a shorter overall schedule span than an algorithmic sequencer, but at the expense of higher work in progress. Supply Chain Management The Supply Chain describes the succession of suppliers, manufacturers, storage facilities, distributors and customers that allow products to be ordered, produced and delivered. The Supply Chain Each level of the supply chain is described as a ‘tier’. There can be several tiers beneath the final supplier; some of these may also be suppliers to other companies operating in other supply chains. Supply-Chain Management (SCM) is another aspect of Advanced Planning and Scheduling. It administers the flow of supplies, logistics, services and information through the supply-chain, from suppliers, manufacturers, sub-contractors, stores and distributors to customers and end-users. It involves business strategy, information flow and systems compatibility. Benefits that can be accrued from supply-chain management include: Improved visibility of information between suppliers and customers: quicker response to changes in demand. Shared knowledge: reducing waste and inventories, improving product quality and services throughout the chain. Development of a longer term “learning network” for the benefit of customers, suppliers and individuals. The environment in which SCM can be applied is changing. For many years manufacturing companies have been striving to improve their efficiency to remain competitive. These improvements are characterized by: A move to ‘Make to Order’ production. Sub-contracting non-core activities. Smaller manufacturing batches caused by an increase in product variants. Lower inventories. The net effect of these changes has been to oblige companies within informal supply chains to become more dependent on one another. For example, a specialist bicycle manufacturer has decided to reduce their inventory costs by buying in the saddles they require on a ‘make to order’ basis. In addition they have decided to sub-contract the plating of the frames to a specialist. Now, when they wish to bid for an order, they must first check on the capacity available in the saddle supplier and the plating sub-contractor, since both have a direct affect on their delivery lead-time. These closer relationships mean that companies within a supply chain have a natural tendency to be involved in bids for new orders. Initial applications of SCM were developed around the use of Electronic Data Interchange (EDI), the electronic exchange of purchase order etc. While EDI fosters closer relationships between the companies within a supply chain, it tends to be a one-way relationship, driving demand from the end user back through the supply chain, without taking the true current capacity of the supply chain into account. For example EDI is, for our purposes, primarily a way for an OEM (typically a car manufacturer) to place orders on their suppliers. The ability of the supplier to produce the components is taken for granted, i.e. capacity and materials are assumed to be available. EDI works best in vertically integrated supply chains, particularly where a large percentage of a supplier’s turnover (>25%) comes from one OEM client. Other SCM solutions have order transfer facilities, but go a step further by controlling stock in multiple locations. In this role SCM attempts to maintain the required stock levels whilst deciding how and from where to fulfill particular customer orders. It is, therefore, only applicable to large companies, typically producing in high volume, making largely to stock and shipping from multiple warehouses. A fundamental problem with many existing technologies is that they are mechanisms for companies to place their demand (requirements) on their suppliers, taking only a cursory account of the supplier’s capacity. For the majority of SME manufacturers who make up loosely knit and may be transient supply chains, such technologies do not work. They are in a Make to Order not Make to Stock environment, and they want to co-operate in their supply chains by exchanging capacity data, rather than having requirements forced on them. For example, today, if you wish to buy some specialized bearings, that you know are not stock items, then you ring the supplier to ask for a delivery promise. In effect you are asking the supplier to review their capacity and tell you when they can make the bearings. Therefore SCM solutions should consider the availability of materials and resources throughout the supply chain in both “Make to Stock” and “Make to Order” environments and this is the basis of the Supply Chain Server.

Analyzing Production Schedules

More powerful, low cost computing systems and software development tools that allow creation of fast, easy-to-maintain software have driven the cost of sophisticated software down with no compromise in performance. For manufacturing, the most visible example of enhanced price/performance software is finite capacity scheduling (FCS) systems. As with any tool, the real value in understanding what the results have to say.

So What is FCS Exactly?

Finite capacity scheduling emerged as a response to the limitations of infinite capacity scheduling that is common to all MRPII systems. The basic problem in MRPII is that the production plans lack realism since they are produced under the assumption that all resources have an infinite capacity to perform work. As any production manager knows, the real world does not work this way, and many of the problems of resource underutilization, excessive inventories and work-in-progress, and job lateness relate back to the inaccuracy of the production plan.

FCS itself is not a new idea, and for many years versions of FCS emerged to enhance the accuracy of the production plan to better manage inventory, resources, and customer satisfaction. The first systems were simple, involving the measure of the primary resource to do work, and either scheduling forward from the current date for new orders (single constraint finite forward scheduling - FFS). Multiple constraints were later added to provide more accurate modeling of the production resources. About the same time backwards and bi-directional scheduling began to appear in some packages.

Then the explosion of different production philosophies appeared to challenge the American ideas of MRP and mass manufacturing, notably just-in-time and theory of constraints. Variations of FCS systems emerged, some committed to supporting one of the three competing philosophies. This forced the innovative FCS companies to face the reality that to remain flexible, the new FCS systems would need two basic improvements:

1. Model any production philosophy and
2. Extend the model with rules that capture the nuances of the competitive model of the client as it related to scheduling. To achieve these goals, the new FCS systems had to support job-based, resource-based, and event-based modeling. By applying any one or a combination of these three views of the production floor, a FCS model can produce the accuracy that adds value to the business goals of the enterprise.

The Value of FCS

The value of FCS comes from the accuracy not obtainable in tradition infinite capacity planning. With this accuracy, raw material levels can be in sync with demand from the production floor, customer service levels improve since throughput is predictable and due dates are more reliable, work-in-progress reduced, and better resource utilization means hidden capacity is uncovered. These are strategic goals, but there is also a very important operational improvement as well.

Figure 1: Time-Cost Plot for Manufacturing

Figure 1 shows that for a given need to produce goods (or provide a service), there is an optimal point in time and cost where goals are met. In manufacturing there are two means to advance the delivery time: Expedite the order, or create WIP to have excess products to deliver against. One obvious drawback with WIP is that the value-added is not recovered quickly; therefore the manufacturer has lost the time-value of the money. What is not obvious is that expediting costs much more than WIP, because of the ripple through impact of moving other orders around (which is to say pushing orders later in delivery) and otherwise disrupting the optimal point for all the other orders, accumulating a much greater financial impact than creating excessive WIP.

It is difficult in the real world, where the product-quantity mix can change daily, to determine these optimal points, which is precisely where the FCS has the greatest value. A good FCS can compute scheduling solutions that set these points for all orders, within the context of the production philosophy and the business goals to be achieved. However, the scheduling solution cannot exist in a vacuum, and the output of a scheduling system needs to provide useful data in order to fulfill its other role as a decision support tool.

Analyzing Schedules

One of the primary advantages of a FCS system is that it offers the user a 'what if' tool to try different methods of scheduling or different dispatching rules, routings, or constraints that are in use. This is because a truly interactive FCS system is not a 'black box' that just gives the 'result' of a scheduling run. It provides in addition a mechanism by which, with the aid of analytical tools (which in its very simplest form is to put the data in front of an experience scheduler), alternative schedules can be generated and compared. There are a number of ways by which scheduling systems offer tools to display and analyze the results of scheduling runs. These include:

• Gantt Charts and Schedule Performance metrics
• Order Trace Charts
• Due date Compliance
• Bottleneck Identification
• Job Analysis
• Material Allocation