January 1998 • Volume 8 • Number 1
Order Management Via Advanced Planning Systems
With advanced planning and scheduling systems, not only can we make reservations and plan for demand, we can also execute plans by simultaneously performing scheduling and sequencing of resources and materials.
By K. Cyrus Hadavi, Ph.D.
In any manufacturing industry, the core of the business is to take orders and deliver goods as promised in order to make as much profit as possible. On-time delivery of goods and the ability to perform real-time available-to-promise globally has become the competitive edge for almost all manufacturers ranging from consumer packaged goods to electronics, textiles and automotive. The recent trend in popularity of advanced planning and scheduling systems (APS) is an indication of the need for the critical support that they provide for the total order management (TOM) process. In addition to the obvious benefit of on-time delivery, the APS systems offer concrete and tangible value, such as reducing cycle times, minimizing inventory cost, and defining the right mix of production in order to maximize the profit (a process known as yield management).
The current focus on on-time delivery as the major competitive edge is providing the basis for revolutionizing the order management process, much the same as the airline industry experienced after the introduction of the on-line reservation system, SABRE. As most of us have experienced when we call for a seat reservation, the airlines' reservation system can respond to our request almost immediately and assign its existing capacity (or inventory) to our request (demand). This is the same as responding to customer requests in real time providing a "reliable" reservation of the capacity to a particular order or customer. Furthermore, airlines change their prices in an almost real-time fashion based on changes in demand and supply as well as the restrictions that apply.
We are now at a point where the same level of technology and responsiveness can be applied to manufacturing. With advanced planning and scheduling systems, not only can we make reservations and plan for demand, we can also execute plans by simultaneously performing scheduling and sequencing of resources and materials. The execution analogy in the airline industry would be the sequence in which passengers are seated — starting from the rear of the plane, with the high priority passengers (First Class) boarding first. In addition, commercial airlines have, for a long time, used yield management techniques in order to maximize their profit. Using such techniques based on the forecast, they decide how much of their capacity (say, in an airplane) can be allocated to First Class and how much should go to Coach Class. In a typical enterprise, this is analogous to controlling the mix of production and setting the production targets at each site.
Advanced planning and scheduling systems have so far proven to be a "change agent" and an enabler of superior practices. In addition, they have added much value by lowering inventory cost, improving customer service and minimizing cycle times. However, an even more fundamental and basic change is going on beneath the surface.
Understanding the Architecture
These systems are changing the role of the enterprise resource planning (ERP) systems as we know them today. A typical ERP system is designed as a suite of applications around a database (see Figure 1). A number of different applications communicate with each other through such a database. As an example, consider the interaction between a forecasting module, order entry module and planning module. Each one of these modules can make decisions and produce results independent of other modules. In the case of an order entry module, orders are entered and potentially sent to the planning engine for generating a completion date to be given to the customer. However, the response given by the planning engine may be unacceptable to the end-customer. One should, therefore, change the order entry parameters and send it back to the planning engine for a more satisfactory response. This process may continue back and forth until a satisfactory result is obtained. This iterative procedure of going back and forth between modules is not an ideal mode of operation. Furthermore, the transaction update time might be prohibitively long, preventing us from giving a real-time response to the customer. The basic problem is that the customer's constraints are not conveyed to manufacturing. Examples of customer constraints are: preferred delivery locations, minimum acceptable quantity, tolerated lateness, etc.
Figure1
In contrast, in a truly integrated environment, the entire logic and requirements of the customer's order would and should reside within the planning engine, making the logic of order entry part of the logic of the planning and scheduling engine. Figure 2 illustrates this concept.
Figure 2
Note that the function boxes in Figure 2 are purposely drawn in a concentric manner rather than sequentially in order to reflect that optimization rather than data is the goal. In a truly integrated environment, the planning engine will take into account all the "rules" that should be followed before an answer is given to the customer. Examples of such rules are: 50 percent of the orders must be on time, all of the products X, Y and Z must be shipped at the same time, or no more than 30 percent can be accepted using a lower grade substitute.
The existing order entry systems perform a vast amount of transactions requiring little or no intelligence. However, there is an additional component to the order entry that needs to communicate with the planning engine for capacity and material reservation, due-date quoting, cancellations, etc. To this end, mechanisms are needed for representation (expression) of the business rules and the ability to use the rules in order to provide a feasible answer for the end-user.
It should be noted that, as manufacturing capacity reservation gets closer and closer to airline reservations, it is inevitable that prices will be changing in an almost real-time fashion. Depending on the timing of the order and the quantity, prices can be quoted by the TOM component by consulting with the appropriate set of rules which would also reside in the TOM component. This architecture assumes the role of total order management as the central and critical function of the organization (order entry-shipping), and all the data that is needed by this central role for making intelligent decisions are then fed into this module without delay.
The question, therefore, is: How do we know what data is needed? The basic aim of this architecture is to ensure that the decision-making system is not waiting or searching for the needed data. The role of total order management is to use the appropriate information to produce the right decision in a timely manner. In order to guarantee this we need to have intelligent client processes (ICP) that can gather the information for TOM on an as-needed basis. These processes act as intelligent agents, making sure that they have the information that is needed for the TOM engine to make a decision. The nature of such processes is outside the scope of this article. However, an example will illustrate the function of such ICPs. Assume a promotional order is being input into order entry and an APS application. As a result, the system has to search for availability of material in order to know when it can be delivered. An ICP, specializing in the needed material, can respond to TOM and assure its availability. Thus, APS will engage in the planning task knowing that the purchasing ICP will get the needed material. The purchasing ICP then provides ATP information for the planning engine in much the same way as the planning engine provides ATP information for the customer or sales person. At the same time, the planning engine communicates the preferences to the purchasing ICP. In one instance, due date might be of highest priority; in another instance, the cost could be of significance. The purchasing ICP will use this information to decide which vendor to use and how urgently the components are needed.
Based on this information, the purchasing ICP will send a message to supplier ICPs to get the best deal for the planning engine. The quantity, level of urgency and priority is then broadcast to every supplier ICP. Each supplier ICP then evaluates the message and prepares a reply to be sent back to the purchasing ICP. The purchasing ICP, after receiving all the replies, will decide on the best bid or combination of bids. The final candidates are then identified for delivering the needed components (in a more conventional architecture, the list of final candidates would be sent to the ERP's purchasing module).
Using this approach, the planning engine can immediately react to the new order and provide real-time ATP across the whole enterprise without having to communicate with (and possibly wait for) every one of its suppliers. This strategy is illustrated in Figure 3. Note that the strategy modeled is far beyond electronic data interchange (EDI) or a simple broadcast to all the suppliers. Rather than simply communicating messages, each ICP contains local intelligence. For example, the purchasing ICP can decide between a number of supplier ICP bids as to which is the most cost-effective combination and communicate that to the planning engine. Furthermore, the ICPs are used as engineering client processes (for updating the routings), as inventory client processes, and so on. The ICPs may also reside on the customer side, feeding orders into the order entry and APS components.
Figure3
Case Study
A worldwide manufacturer of personal computers faces aggressive competition from overseas. As expected, the company has major product revisions every six months and minor revisions almost every month. A large portion of the finished goods cost is the components. The major competitive edge for the company is on-time delivery and minimizing cost (inventory and production) and cycle times.
Given the cost of components, elimination of obsolete and excess inventory seems to be one of the more significant cost reduction opportunities. This can be done in a number of ways: 1) examine the projected excess and obsolete inventory on an on-going basis (as the demand fluctuates) and decide which purchase orders could be canceled; 2) develop a methodology by which one can decide how to make use of the existing on-hand inventory so that optimal results are obtained. For example, if there are a lot of disk drives for which no demand exists, then what other less expensive components are needed in order to make effective use of the disk drives in the form of a salable product? Both of these scenarios may be handled using the planning engine depicted above. However, both also assume a demand that is based on some kind of forecast. Obviously, the forecast is subject to fluctuation. A more dynamic scenario follows.
A salesperson places a large order for customer XYZ and requests a shipment date. It so happens there was no plan for this order and no previous forecast was made. The customer would like to know when (expected partial shipments) and at what price it can be delivered. This information is needed in real time. In an ideal situation, a response is given within seconds. However, in the absence of on-hand (or on-order) availability of components, one would have to check with the suppliers about their delivery schedules and their prices, check availability of capacity and then decide on the price. This process could take days, especially when a number of feeders, consumer plants and subcontractor sites are involved. Just waiting for response from the suppliers could take days. Finally, trying to coordinate all such information for the final answer can be a tedious process. Very often the methods used are inaccurate and unreliable. The objective of the architecture described earlier is to ensure that one can respond with a reliable and realistic answer checking both material and capacity availability, understand the choices and, finally, having made a promise, execute the plan as promised.
With total order management, the planning engine is designed precisely to deliver the above result. As soon as an order is entered into the planning engine, the engine will check with the appropriate purchasing agents regarding the availability of components. Each ICP returns a reliable schedule of deliveries for the needed components and the associated cost. This information, together with the capacity information, is then used to plan delivery of the order. The delivery could be a partial schedule indicating dates on which partial deliveries can be made. The emphasis is on delivery, not just on shipment — in other words, different methods of transportation such as Federal Express or trucks are also considered. Based on the delivery schedule, the Pricing ICP will deliver the expected prices for each shipment. This information is then funneled back to the salesperson or the customer in real time. Note that the planning engine contains capacity and inventory information about each plant and the existing commitments that have already been made. This information is used in order to determine the viable delivery dates for the original order.
In most organizations, the planning cycle times are generally significantly higher than the actual physical cycle times. Furthermore, forecasting tends to be very inaccurate. The only weapon that one has for neutralizing bad forecasts is through faster planning time (ideally zero-planning time). The architecture described here delivers fast response to surprises and the ability to perform "what-if" scenarios so that different options can be investigated. Thus, through more frequent and real-time planning, one can constantly adjust for demand fluctuations and absorb the inevitable shocks.
K. Cyrus Hadavi, Ph.D., is president of Los Angeles-based Paragon Management Systems Inc.
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