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    Final Paper1 Final Paper1 Document Transcript

    • NATIONAL INSTITUTUE OF INDUSTRIAL ENGINEERING (NITIE) MUMBAI ERP Assignment A Paper on “Advanced Planning and Scheduling from SCM perspective” Submitted to: Prof A. D. Raoot NITIE, Mumbai By- ANKUSH SETHI (01) AVANISH KACHHAWAHA (03) NIREN ATHAWALE (02) ERP Assignment Group 1 PGDIE 37
    • Contents  Executive Summary  Advanced Planning and Schedulling 1. What is APS 2. Scope of APS 3. Differences in Planning horizons 4. Planning and scheduling 5. Comparison of APS and ERP capabilities 6. Features of APS Theory and practice of APO in supply chain domain 7. 8. Implimentation Statergy  Conclusion Robert Bosch Case Overview 
    • Executive Summary For many manufacturers the demands of meeting rising customer expectations and lowering production costs in an environment of more products, more complexity and more choice is placing great stress on the effectiveness of their planning and scheduling processes. Organizations have already adopted ERP solutions with varying degrees of planning and scheduling capabilities. Yet, operations executives acknowledge that these same systems are becoming outdated, lacking the speed, flexibility and responsiveness to manage their increasingly complex production environments. They know too that as the business grows its operations, expands its product range and adopts a global manufacturing supply chain – they must seek alternatives to the current ways of doing things if they are to remain competitive and responsive to customer needs. APS is a new revolutionary step in enterprise and inter-enterprise planning. It is revolutionary, due to the technology and because APS utilizes planning and scheduling techniques that consider a wide range of constraints to produce an optimized plan:  Material availability  Machine and labour capacity  Customer service level requirements (due dates)  Inventory safety stock levels  Cost  Distribution requirements  Sequencing for set-up efficiency This paper also discusses the basic functionality of planning and scheduling in Advanced Planning and Scheduling systems (APS). Three basic planning options - concurrent planning (or unconstrained planning), constrained planning and optimization - are analyzed. The planning functionality is radically improved compared to MRP and MRP II. APS is relevant for production- organizations. Also distribution-organizations can benefit from implementing APS for supply chain management. The objective of this paper is to map the characteristics of advanced planning and scheduling systems and to find out the (use) fullness of these systems.
    • The origins of production planning and scheduling Traditional planning and scheduling systems originated in the 1960s with the advent of Material Requirements Planning (MRP) which evolved into Manufacturing Resource Planning (MRP II) and finally into Enterprise Resource Planning (ERP), where a financial component was introduced. In those days the demands placed on manufacturers were very different from today. The principle objective of planning was to synchronize all levels of production backwards from the customer due date, aligning all work orders according to date order and providing a target date for bought-in purchased parts. The primary inputs to the planning process were bills of material, bills of routing orders, inventory and work-in-process. With these planning and scheduling capabilities built into ERP systems, manufacturers were able to meet business demands as they were at the time. Since then, ERP systems have been refined, but they are still based on the same concepts and iterative processes as they once were. The planning engine in an ERP system is essentially the same today as it was in the 1970s. In an ERP system, a Master Production Schedule (MPS) is used to establish a plan for the factory, balancing off sales demand (orders, forecasts or some combination of the two) with inventory and planned supply. The resulting MPS plan becomes the input for the MRP calculation which explodes through all bills of material to synchronize manufacturing and purchased orders to the master plan. It is a proven and efficient process and has led to many documented benefits for manufacturers worldwide. Since the MRP calculation assumes infinite production capacity, additional capabilities have been added into the planning process including Rough Cut Capacity Planning (RCCP) and Capacity Requirements Planning (CRP). While it added something to the planning process, traditional CRP is a bit of a blunt instrument. The results tell the production manager whether the factory is over- capacity, even to the point of which work centers and work orders are causing the problem. But it is left to the production planners to juggle orders around to solve the problem and there are few, if any, ‘what-if’ scenario capabilities. Comparative costs of alternative plans are never considered and the calculations ignore other things that limit capacity such as labor skills, preferred production sequences, start-up and shut- down losses and industry-specific scheduling problems such as shelf-life. However, among the primary deficiencies of traditional ERP planning is the iterative nature demanded by the process and the time taken to make adjustments in the face of change. The planning processes of MPS, RCCP, MRP and CRP are all separate, sequential and iterative. It is a time-consuming task to investigate capacity shortfalls, make changes – often manually – and then repeat all the processes to re-check feasibility. Fundamentally, the systems identify problem areas rather than solving the problems. Overlay the frequency with which new orders are introduced into the plan and it is easy to see why yesterday’s reactive planning methods are no longer sufficient for today’s dynamic supply chain challenges.
    • Planning and scheduling in the 21st century The planning and scheduling functionality of years past no longer provides the responsiveness and agility businesses need in order to prevail in today’s competitive, demand-driven environment. Today manufacturers must contend with globalization and meet steadily increasing customer demands for more choice, lower prices, faster delivery and higher quality. A typical production environment must now contend with increasing levels of product variation, faster new product introductions, shorter reduction runs, longer supply lines, and it must be prepared to work collaboratively with customers, suppliers and co-manufacturers. Manufacturers are responding by moving increasingly to demand-driven production strategies, employing a number of techniques to more closely align supply with customer demand, with techniques that vary according to the industry. These include make-to-order, configure-to-order, collaborative demand planning and other demand sensing methods. As the world of manufacturing has moved on, new manufacturing concepts such as lean manufacturing, six sigma, just-in-time, theory of constraints, agile manufacturing and demand- driven supply networks have arisen. A “lean” manufacturing strategy is favored by many executives who see simplifying their manufacturing as the best response to changing customer requirements. The simplification brought about by “going lean” usually changes the requirements for planning and scheduling, but even the leanest operations still need to plan ahead and schedule production resources and associated activities. So what new planning processes, techniques and technologies are employed to support modern manufacturing planning? Advanced Planning and Scheduling Systems (APS) 1.What is APS? From its humble beginnings in fast materials requirements planning (MRP) and constrained production scheduling programs, APS technology has blossomed into one the most important
    • advances in business applications. Its impact on the manufacturing planning and scheduling process is more revolutionary than evolutionary. For the first time, manufacturers have planning tools that can absorb vast complexities to produce optimal plans. More importantly, APS leverages the planner’s knowledge with responsive decision support tools rather than enslaving him or her with an endless barrage of exception messages. An APS system can function in a number of environments and types of complexity. When companies start to integrate within their organization an APS tool can be helpful, because the MPS-MRP-CRP planning process can take place simultaneously. An APS tool really benefits companies integrating with outside organizations. The APS tool can be helpful in dynamic environments, because it has the advantage of being really fast in recalculating the plans whenever necessary. Another benefit of this system is that it facilitates the combination of information of multiple sites and that it calculates an optimal plan for a complete supply chain. The key to understanding APS is that it is a new technology, not a rehash of 30-year-old MRP programs. In much the same way that the microwave oven revolutionized cooking and CDs changed the way we listen to music, APS technology is changing the way manufacturers plan. APS leverages the incredible advances in computer technology over the last 10 years. Generally, an APS application utilizes this memory capacity to store models or representations of the business environment against which it runs specially designed algorithms to solve for the best plan. Amongst the criteria that an APS will address in determining the optimal production schedule is: • Capacity of machines and labor • Labor skills– if only a few resources can operate a particular machine the systems will recognize this as a capacity limitation • Special tools • Material availability – if delivered materials are known to be due on a certain date, there is no point in planning production as if the materials would be available – which is what most systems do. • Production sequence – APS can recognize the optimal sequence of production (eg: “light- to-dark” or “can size”) which will minimize lost time due to change-over’s and clean- downs. This capability alone can boost productivity by as much as 25% without any investment in new equipment. The concept of optimization means that APS weighs the constraints and other business rules to find the optimal use of available material and plant capacity. This enables the business to meet such objectives as minimizing total cost (often from inventory and setup reductions) and maximizing overall plant operations to fill the most customer orders on time. 2.THE SCOPE OF APS APS as an umbrella technology embracing the following concepts: • Simultaneous consideration material and plant resources • Optimization algorithms that incorporate constraints and business goals • Leverage for memory-resident programs and databases to provide real-time plan and schedule creation with net change regeneration
    • • Real-time decision support • Real-time available-to-promise The scope of APS is not limited to factory planning and scheduling, but has grown rapidly to include the full spectrum of enterprise and inter-enterprise planning and scheduling functions (as shown in figure: 1) Execution Systems Shipment Scheduling Production Scheduling Transportation Planning Planning Manufacturing Planning Detail Distribution Planning Inventory Planning Supply Chain Planning Sales and Operation Planning Demand Planning Supply Chain Network Design Strategic Planning Seconds/ Hours/ Weeks/ Quarters Years Minutes Days Months Time Horizon Figure 1: APS solutions related to the time horizon • Strategic and long-term planning addressing such issues as the following: Which products should be made? What markets should the company pursue? How should conflicting goals be resolved? How should assets be deployed for the best rate of ROI? Currently, none of the major APS vendors offers strategic planning as part of its product suite. Time horizon: 2+ years • Supply chain network design: Optimizes the use of resources across the current network of suppliers, customers, manufacturing locations, and distribution centers. It is helpful for locating new facilities within an existing supply chain network and determining the optimal way to fulfill customer demand. What-if analysis can be performed to test the impact of closing or moving facilities on profits and customer service levels. Supply chain network design tools are often applied to optimize the balance between stocking
    • locations and transportation costs. Time horizon: 1+ years. • Demand planning and forecasting: Demand planning addresses creation of demand through promotions and external events. Demand forecasting uses statistical and time- series mathematics to forecast future demand from sales history. Demand forecasts are often considered unconstrained as they reflect what customers want, not necessarily what can be produced. Time horizon: 6 to 18 months. • Sales and operations planning (SOP): Loosely defined by most vendors, SOP is the process of converting the demand forecast into a set of operation plans for sales and manufacturing. This process may include the use of manufacturing planning or supply chain network optimizers to determine if forecast demand can be met. Time horizon: 6 to 18 months. • Inventory planning: Determines optimal levels and location of finished goods inventory to achieve the desired customer service levels. Essentially, it calculates the optimal level of safety stock. Time horizon: 6 to 12 months • Supply chain planning (SCP): Optimizes the use of manufacturing, distribution, and transportation resources (including materials) to meet forecast and actual demand. Generally, SCP works with aggregate-level resources and critical materials to develop a constrained production plan. SCP generally spans multiple manufacturing and distribution sites and may provide some level of supply chain synchronization. Time horizon: 3 to 6 months. • Available-to-promise (ATP): Determines whether a customers request date can be met and/or the next best date from existing inventory and production orders. A subset of ATP functionality, often called capable-to-promise, looks at available plant capacity and determines whether an order can be inserted into the schedule to meet the customer’s request date. In current APS products, ATP may be an explicit function or a capability supported by what-if analysis. ATP may take place at the SCP, manufacturing planning, or production scheduling levels. Time horizon: 2 days to 6 months • Manufacturing planning: Develops a master schedule constrained by material availability, plant capacity, and other business objectives. This is generally done for a single plant. Manufacturing planning may include a complete MRP explosion or work with only critical materials. The depth of material planning often depends on the complexity of the bill of materials and the desired replanning time. Time horizon: 2 weeks to 3 months. • Distribution planning: Determines the best deployment of finished goods inventory to meet forecast and actual demand. May consider actual transportation costs and material allocation requirements and support vendor managed inventory (VMI). Time horizon: 2 weeks to 3 months. • Transportation planning: Optimizes outbound and inbound material flow to minimize transportation costs and/or maximize the utilization of private truck fleets by consolidating shipments into full truckloads, when possible, planning routes and sequencing
    • delivery/pickup locations. It often uses current carrier freight rates in order to support lowest cost shipping calculations. Time horizon: 1 week to 3 months • Production scheduling: Determines the optimal sequencing and routing of orders on the plant floor based on detailed product attributes, work center capabilities, and material flow. Time horizon: 1 shift to 1 month • Shipment scheduling: Determines the optimal time and method to ship an order to meet a customer due date. Time horizon: 1 shift to 1 week • Intercompany collaboration: Provides the ability for planners to collaborate with customers and suppliers via the Internet in the development of the demand plan for the purchase of materials or synchronization of feeder plants. Time horizon: 1 day to 6 months Generally, the planning process is divided into these levels because they are performed by different parts of the organization at different times. From a practical standpoint, even today’s most powerful computers cannot simultaneously optimize all of these planning levels. Several years ago, as APS technology was evolving, some vendors gave prospective customers the impression that single-solver engines would be able to optimize entire supply chains. This has not happened, nor is it even possible or required at this time. In fact, none of the surveyed manufacturers had implemented all of these levels. Most manufacturers had implemented only one or two of these planning functions in APS technology, yet dramatic results were achieved in most cases. 3. Differences in planning horizons The enumerated solutions can roughly be divided into three levels of planning and scheduling:  Supply Chain Planning  Manufacturing Planning
    •  Production Scheduling Supply Chain Actual Planning Demand Production Plan Backbone System Inventory Manufacturing Balances Planning Master Schedule Production Work Orders Scheduling Due Dates Frequent interface points Other interface points Figure 2: Relationships of major planning functions with typical data flows 3.1. Supply Chain Planning This SCP group takes a forecast and looks at actual demand, after which a constrained operation plan for both manufacturing and distribution is generated. A multi-plant constrained master schedule is the output of the SCP process for manufacturing. To create this output the material availability’s and plant capacities are accumulated. For some industries, transportation requirements and set-up sequencing are considered as well. “SCP determines what should be made given the available resources to achieve business goals.” 3.2. Manufacturing Planning The output from manufacturing planning generally is a constrained master schedule for a single plant or a group of similar plants. This master schedule considers the constraints in a more detailed perspective than in SCP. In manufacturing planning a full MRP I/II explosion can be included in the process. “Manufacturing Planning determines how and when it should be made based on material and resource constraints to meet customer demand.”
    • 3.3. Production Scheduling The goal of this group is to translate the output of the supply chain planning to an operational plan and work orders. Here is where the ultimate specification takes place on the basis of which the suppliers will deliver. The production departments produce and distribution receives and ships the products. APS supports the planner by continuously adapt or suggest adaptation of the planning and scheduling based on the recent information. Product scheduling is designed to produce the most efficient production schedule (where the throughput times are minimal, the output maximal and the costs are low). 4. Planning and scheduling An APS system uses the following planning and scheduling approach: 4.1 Advanced Planning This planning process is order-centric and the role of planning in APS is to determine what demands on the production system will be met over a given planning horizon. The input to the planning process includes information on manufacturing capacity and demand data. Demands may be of several types: customer orders, forecast, transfer orders (i.e., orders from other plants), released jobs, or replenishments of safety stock. Manufacturing system data includes bills of material, work center availability, part routings through work centers, and inventory (both on-hand and scheduled for delivery). The output from the planning process is a feasible plan, which provides release and completion times for every demand. Like MRP, APS takes into account the availability of materials. Unlike MRP, it also takes into account the capacity of work centers to process the material and satisfy demands. It is in fact often desirable for a plan to be somewhat tentative, since it covers a planning horizon subject to disruptions. Forecast may not be accurate. Deliveries may be delayed. Equipment may fail. Unexpected rush orders may be received. Therefore planning is not expected to be highly detailed. Individual machines may be aggregated into a work center with no determination of which will be used by a specific order. Setup times may be averaged since sequencing at this time is premature. Buffer times may be defined, especially prior to processing on bottleneck machines, to allow for possible disruptions. The end result is a “scheduleable”plan. 4.2 Advanced Scheduling The role of the scheduler module in APS is to produce a detailed list of operations specifying which orders are to be worked on at which work centers and at what times. The input to this module includes all demands to be satisfied, including the internal orders added by the planner module when an end item required a component to be manufactured. It includes the current material inventory levels as well as planned deliveries or purchased materials. It also includes the same manufacturing system data as that provided to the planner module but uses a more detailed representation of that data. Detailed information used by the scheduler module that is not pertinent to the planner module includes:
    •  Variable run times based on the machine and operator actually assigned.  Rules for selecting machines and operators based on skill sets and quality requirements.  Variable setup times based on the previous and next part characteristics such as part type, family, colour, width, etc. Rules for sequencing jobs at work centers, based on minimizing setup and other factors.   Allowable shift overruns. Rules for selecting from a list of prioritized jobs based on due date, slack, cost and other  factors. 5. Comparison of ERP and APS capabilities ERP Planning Advanced Planning & Scheduling (APS) Separate, iterative planning Single, holistic planning run in a single pass - faster processes for MPS, RCCP, MRP, CRP
    • Planning assumes infinite production APS uses capacity as a known limitation and plans capacity accordingly Planning assumes materials will be APS can operate to constrain production plan based delivered on time on material availability Plan assumes production on “primary” work APS will use all permitted alternatives of how a center or machine only product can be made and select the ones which optimize overall use of plant capacity ERP can “cost” a plan – but can’t suggest APS can use the costs of alternative production the lowest cost plan methods to recommend a cost-optimal plan Single site planning Multi-site, enterprise-wide planning Identifies problem areas and highlights them Solves planning issues by recognizing capacity and for users to solve material constraints Supports “available-to-promise” from Supports “capable-to-promise” to support accurate known stock and planned production order promising Limited or no scenario planning Full support of what-if scenario modeling and rapid solve times makes this an accepted practice Limited to planning only the production APS solutions can connect to multiple systems areas where the ERP system is installed inside and outside the organization for global planning No industry specific differences in Infor APS solutions dedicated to the industries for planning processes which they were designed 6 . Features Of APS An APS system has a number of features that enable it to be clearly differentiated from
    • traditional planning systems such as MRP I/II and DRP. 6.1 Concurrent planning In the traditional planning process, as in the case of MRP I/II and DRP, three main variables can be distinguished:  demand  materials (raw material and semi-manufactured articles)  capacity The traditional planning process is the so-called ‘waterfall approach’, in which the planning process is undertaken sequentially. It starts with an MPS, after which MRP I/II and CRP are performed. In case of ‘concurrent planning’, however, the three main variables are considered simultaneously. This results in synchronized, optimal planning for the chain as a whole, based on the most up-to-date data. 6.2 Constraint-based planning A second important characteristic of APS systems is that account is taken of the constraints present in an enterprise, such as capacity and materials. APS systems use these constraints to model the production and distribution environment. The performance that an enterprise can achieve is determined by the constraints. Various constraints can be identified as:  Material availability  Available capacity  Enterprise policy  Cost  Distribution requirements  Sequencing for set-up efficiency 6.3Speed The speed of planning is an important characteristic. Improvements in computer processing power and software design have lead to good response times. As a result, a customer can be informed about the delivery possibilities within a few seconds. The person in contact with a customer who wishes to place an order has a strong negotiation position since he has a picture of the possibilities that the company can offer the customer. If the company is not able to satisfy the customer’s wishes, he is immediately able to offer alternatives to the customer. Speed is also important during the planning cycle. Since all the links in the chain are now closely co-ordinated, delays in one link can have an amplified effect in the subsequent links. 6.4Preferences It is possible to indicate preferences in APS for purposes of strategic decision making. It is possible to regard certain customers as strategically important. In APS this is interpreted as a customer with a higher priority. These strategic customers must be considered as such throughout
    • the whole organization. This avoids a situation in which one sales organization regards a particular customer as strategic, while for another sales organization the same customer is unimportant. 6.5What-if simulation One of the first, and still most common applications for advanced planning and scheduling products, is decision support using the facility for what-if simulation. It is possible for various alternatives to be entered into the system and for the system to maximize company profit and/or minimize costs, subject to the condition that the order can be delivered on the date required by the customer. The planner can examine various scenarios under which the order is delivered and the system subsequently indicates the consequences of the various scenarios for existing orders. A graphical interface makes it easy for the planner to compare the various alternatives computed by the system, so that the most acceptable solution can then be chosen. The planner can ‘play around’ with the data, with the most acceptable alternative being chosen and used as new input. While all APS products can be used for simulation and what-if analysis, some vendors provide more complete facilities to compare plans and schedules. This ranges from the ability to have multiple copies of different plans visible for die-by-side comparison (such as ERP systems) to the ability to produce cost analyses of various planning options. 6.6Available to Promise (ATP) APS can be used to obtain a better insight into ATP. ATP represents a rolling balance of “unconsumed supply” (uncommitted portion of the inventory) over time. “Unconsumed supply” is inventory on hand, plus planned supply, minus existing commitments to customers. The ATP allows a company to see what inventory has not yet been allocated and what can be done with that inventory for potential customers in a specific period. When an ATP function receives an order, it slots the order for the day (or days) on which there is sufficient supply available to cover the order quantity. Based on the slotting dates, the function proposes a delivery date (or dates) to the customer. 6.7 Capable to Promise (CTP) CTP derives from the real-time APS engine a delivery date by adding a customer order in the system, where after this engine determines when the order is scheduled to be produced, by looking at available material and capacity. 6.8 Profitable to Promise (PTP) ATP and CTP only look at the possibility to deliver the order on time to the customer. It would be better to be able to accept the order based on the financial implications for the company. This is called profitable to promise. With PTP you can assure that the right customer gets the right order at the right time, which is most profitable to the organization. 6.9 Reliability This is the possibility of making promises concerning delivery times and delivery dates and also fulfilling such promises. It is possible to inform the customer of the ultimate delivery date. When
    • the customer places his order, the company gives the delivery date and has the possibilities to adhere to that promised date. 6.10 Optimization Optimization means generating the best solution to a specific problem. APS can be used to optimize both tactical and strategic business issues. At the tactical level the system can help to optimize sourcing, production and distribution plans. At strategic level APS supports in optimizing the network configuration. Different techniques can be used to solve the optimization problems:  Linear Programming  Genetic Programming  Theory of constraints  Heuristics 7. THEORY AND PRACTICE OF ADVANCED PLANNER AND
    • OPTIMIZER IN SUPPLY CHAIN DOMAIN Supply Chain Management technology of SAP is a comprehensive tool set which provides solution not only for Supply Chain Planning but also Supply Chain Execution, Supply Chain Coordination and Supply Chain Collaboration. Advanced planner and Optimiser (APO) and SCM SAP APO is a robust tool designed to enable your organization to achieve the self-contradictory goal of improving customer service while reducing costs. Supply chain execution: From the moment a shipment to a customer leaves your organization’s shipping docks, transactional data is being gathered through the use of enterprise requirements planning solutions, such as mySAP ERP. This information, whether it is about inventories, open customer orders, new product specifications, existing production schedules, or numerous other details, is fed into SAP APO to support ongoing planning. The combination of SAP’s real-time Core Interface Functionality (CIF) and liveCache planning model allows for continuous planning updates of this transactional and master data throughout the day. Supply chain Coordination: Using tools like SAP Event Management, updates to plans can be triggered by key events, such as a supplier’s production lot passing final quality inspection. While event management tools monitor critical pieces of the supply chain puzzle, other tools, such as SAP Inventory Collaboration Hub, gather inventory data across the entire supplier base. Looking forward in the supply chain, demand and inventory from a customer, or even a customer’s customer, is brought into the mix. Supply Chain Collaboration : Using out-of-the-box Web collaboration capabilities, SAP APO enables organizations to share demand and promotional plans in real time with customers, any of whom may view, comment on, or even edit the plan. SAP APO supports intercompany collaborative processes, such as vendor-managed inventory (VMI) and the Voluntary Inter-industry Commerce Standards Collaborative Planning, Forecasting, and Replenishment process (VICS CPFR), and it has been certified by the Uniform Code Council as being interoperable with other vendors. Continuous Cycle : As SAP APO assists your organization’s strategy to be continuously refined in the process of coordination, planning, and collaboration, it also enables near-term plans to be committed to execution, all in real time. Products scheduled for production are produced, packaged, and placed on a truck to turn a customer’s order into a shipment. That real-time information is then, of course, fed back into the overall plan.
    • Following figure explains relationship between all the modules of supply chain and APO. A supply chain optimisation problem Generally, optimization problems seek a solution where decisions need to be made in a constrained or limited resource environment. Optimized plans are generated based on plan objectives and constraints. The constraint-based rules are extended with some extra rules (titled decision variables and penalty factors). As the optimization is based on cost and profit the constraints might be overruled if this reduces the total costs. Decision Variables are within the planner’s span of control.  When and how much of a raw material to order from a supplier  When to manufacture an order  When and how much of the product to ship to a customer or distribution centre Constraints are limitations placed upon the supply chain  A supplier’s capacity to produce raw materials or components  A production line that can only run for a specified number of hours per day and a worker that must only work so much overtime  A customer’s or distribution centre’s capacity to handle and process receipts
    • Objectives and implicit objectives Objectives maximize, minimize, or satisfy something, such as the following: Maximizing on/time delivery  Maximizing profits or margins  Minimizing supply chain costs or cycle times  Maximizing customer service  Minimizing lateness  Maximizing production throughput   Satisfying all customer demand The implicit objective is maximized by minimizing the penalty costs for:  Late demand  Supplier capacity violation  Transport capacity violation  Any unused supply  Using alternate resources  Unmet demand  Resource capacity violation  Safety stock violation  Using alternate routings The following penalty cost factors are used explicit in relation to decision variables:  Late demand  Exceeding resource capacity  Exceeding material capacity  Exceeding transportation resource capacity The optimization process drives penalties out of the solution, tending to drive the most costly penalty factors out first. A high degree of accuracy in setting penalty factors is not as important as the relationship between penalty factors.
    • Example of optimisation Optimisation - Max On-time Delivery Labor constraint Machine constraint Demand Production Demand Production Demand Production Demand Production Demand Production 1 2 3 4 5 Figure 3 An example of an optimised plan in relation to on-time delivery. In the first three periods of this example, there is no difference between the optimized plan and the constrained-based plan. A backlog occurs in the third period because the hard machine constraint makes it impossible to meet the peak demand. However, production in the fourth time period has been increased compared to the CBP example. Recall that in the CBP example, some of the period 3 demand was backordered and not met until period 5. In the optimization the cost of labour overtime in the fourth period is balanced against the cost of carrying the backorder into period 5. If the backorder quantity is large, and if the customer is likely to accept a two period delay, and if the cost of overtime is relatively low, then optimization would suggest the solution in figure 3. 7.1 Optimisation framework As part of the planning process, the structure of the supply chain need to be represented. This is typically done using a network model which graphically visualizes a supply chain and is used to depict the parts of a supply chain being considered in the planning process.
    • Figure 4: Network representation of a Supply Chain Distribution Suppliers Plants Customers Centers Figure 4 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. 7.2 Optimisation solvers To APS developers, optimization is a systematic approach to improving the plan or schedule based on the constraints of the business. Some vendors attempt to achieve optimization by applying a single algorithm to a wide range of problems, while others maintain a library of algorithms or “solvers” which can be used in a trial fit approach. There are different techniques that can be used for optimization: 1. Linear programming – 2. Genetic Algorithms – 3. Theory of Constraints- 4. Heuristics
    • 7.3 A standard LP-model for optimisation Currently commercial SCP software assumes a rolling schedule concept, where each planning cycle a mathematical program is solved, either to optimality or some heuristics are applied. For uncapacitated SCP problems without lot sizing restrictions it is rather straightforward to formulate an LP model that fits in this rolling schedule context.
    • 8. Implementation Statergy To come to a successful implementation, it is preferable to choose a stepwise approach. The people inside the organization can see the results en get enthusiastic about the system. This will prevent that the project will take years before the results are visible. An example of a stepwise approach is to begin with the introduction of an APS-system over a couple of production-locations and to extend this in a next step. Table 1. Choices with the implementation of an APS-system Aspects Strategic choices Supply chain concept Organisation- managementconcept for supply chain management Commercial strategic policy Involvement suppliers and customers (chain integration) Productdesign Organisational culture Tactical choices Priorityrules; which customer gets precedence? Aggregationlevel in managing Integration APS with ERP Information architecture and datamanagement Customerorders dispatching/Customer service Operational choices Procedures and day-to-day decision making Office hours/attainability planning-department KPI’s in scorecards and reports
    • Linkin-pinfunction between central planning and local execution 8.1 Points of attention There are different aspects to be taken into account when implementing an APS: Supply chain management concept  The first pitfall is the lack of a strategic concept for supply chain management and the commercial strategic policy (for example the role of national sales organizations). It’s evident that the concepts also enclose the role of suppliers and customers (chain integration). Experience  APS is a rather new development where little experience has been gained. The development has not been completely evaluated, so one can encounter unforeseen problems. Nervousness  Continuous changes in the system should be avoided. These changes will result in nervousness in the organization, what of course is not good. When a customer is told that he will receive his order at date X, it is not right when the next day it is changed in delivery date Y. Human factor  At high level in the organization one knows how to work with APS en how the system will look. Instead of the lower organizational level where they don’t now this. These people need to get enthusiastic and motivated as well. Working with APS means managing from another central concept. Another point is the constant changes together with APS. A lot of processes and activities, like planning and the transfer of information go much faster now. One should take care that people don’t loose the overview in the organization and ‘drown in' the new working method. Complexity  Because APS is not in the last stage of development, it is still the question which cases APS can handle and which not. During the implementation there are new software-releases and also the hardware is improved already. Financial resources  The financial resources of an organization should be sufficient to complete an implementation. An implementation of an APS system throughout the whole chain of a big organization can cost around 50 million euro’s. A small implementation is possible from one million euro’s. Data accuracy  The actuality, availability and purity of the data is often a big problem. A characteristic of an APS is that planning problems are solved with a mathematical model. APS-suppliers suggest
    • that they offer an optimal solution. Those optimal solutions are based on submitted variables; not the whole chain with all its innumerable variables are optimized. When those predictions are not so hard, than a rather simple calculations gives much better results than a complicated optimization method. 8.2 Integration with existing systems An enterprise usually has a number of existing transaction-oriented systems, in which much data is stored in databases. The APS system extracts from these transactional systems all the necessary data, such as order status, new orders and other current production and distribution information. This information is obtained from the Bill of Materials, Bill of Resources and routings, present in the existing Figure 5. APS in relation to existing systems systems. Using this information, APS systems perform calculations to optimize the entire chain, after which the adjustments (after possible personal amendment by the planner) are returned to the existing systems. This is where the hidden strength of APS lies. Without first having to standardize all the transactional systems throughout the organization (with all the efforts that this involves) the first logistical improvements can be achieved by adding APS.
    • APO’s primary elements are:  Supply chain cockpit An intuitive and configurable graphical user interface to manage and optimise the supply chain. It consists of a highly intuitive, graphical interface that acts as the top enterprise planning layer covering all planning areas such as manufacturing, demand, distribution, and transportation. All employees in the Plan -> Source -> Make ->Deliver cycle of supply chain management can use it to their advantage.  Demand planning It Provides advanced forecasting and demand planning tools that enable companies to capture changes in demand planning signals and patterns as early as possible. There are 3 types of forecasting methods. 1) Staistical forecasting predicts future demand based on historical data. 2) Casual Forecasting: Multiple Linear Regression (MLR) enables you to include causal variables (like climatic conditions, price, advertising) in the forecasting process. MLR investigates the historical influence of these variables on demand to produce a forecast. 3) Composite Forecasting: This combines forecasts from different individual forecasts (statistical or causal forecasts) for a particular brand, product family, or product. Each individual forecast is based on the same historical data but uses a different technique. The underlying objective is to take advantage of the strengths of each method to create a single quot;one numberquot; forecast. There are 5 types of demand planning tools in APO 1. Promotion planning : In APO Demand Planning, one can plan promotions or other special events separately from the rest of your forecast. For e.g. millennium, or repeated events such as quarterly dvertising campaigns, trade fairs, trade discounts, dealer allowances, product displays, coupons, contests, free-standing inserts, as well as non-sales-related events such as competitors' activities, market intelligence, upward/downward economic trends, hurricanes, and tornados. 2. Life Cycle Planning : A product's life cycle consists of different phases: launch, growth, maturity, and discontinuation. In this process, one can model the launch, growth and discontinuation phases. 3. Collaborative Demand Planning: Collaborative Demand Planning between manufacturers and their distributors allows both partners to streamline their work processes and ultimately benefit from a more accurate forecast, better market transparency, greater stability, reduced inventory, and better communication. 4. Charactristics Based Forecasting: In SAP APO Demand Planning one can create a forecast based on the characteristics of configurable end products. Characteristics-Based Forecasting allows you to forecast many different variants of the same product and react swiftly to changes in market demand. Orders can also be placed with your suppliers for assemblies and components in a timely fashion.
    • 5. Kit planning: : As well as planning demand for a product, one can also forecast dependent demand at different planning levels by exploding bills of material. This can be used for a kit that consists of several finished products (that can also be sold separately).  Supply network planning and deployment SAP APO Supply Network Planning integrates purchasing, manufacturing, distribution, and transportation so that comprehensive tactical planning and sourcing decisions can be simulated and implemented on the basis of a single, global consistent model. Supply network Planning uses advanced optimization echniques, based on constraints and penalties, to plan product flow along the supply chain. The results are optimal purchasing, production, and distribution decisions; reduced order fulfillment times and inventory levels; and improved customer service. The Deployment function determines how and when inventory should be deployed to distribution centers, customers, and vendor-managed inventory accounts. It produces optimized distribution plans based on constraints (such as transportation capacities) and business rules (such as minimum cost approach, or replenishment strategies).  Production planning and detailed scheduling Production planning enables the planner to create feasible production plans across the different production locations (also with subcontractors) to fulfill the (customer) demand in time and to the standard expected by the customer. Detailed scheduling delivers optimized order sequences that can be released for production. Solvers simultaneously take into account constraints and costs to schedule the optimized order sequence. Thus both the functions ensure the smooth and optimal flow of materials and resources on a plant-by- plant basis. Production planners have advanced tools to create optimised, feasible production schedules.  Transportation Planning: 1) Collaborative Shipment Forecasting: Based on Internet enabled planning books, one can exchange and adjust forecast information between your customers and your carriers. 2) Load consolidation: SCM provides different possibilities to consolidate deliveries and orders to shipments. It is possible to combine orders based on rules and strategies, based on optimization logic in APO. During Load Consolidation, SCM will consider multi- dimensional capacity constraints of the resources. This function is provided by APO.
    • 3) Mode and route optimization: The goal is to create least cost transportation plan while guaranteeing the customer service. 4) Carrier Selection: APO offers four options to do a carrier selection. Priority, freight cost, Business share, Freight exchange are the four options ensures the correct carrier selection. 5) Collaborative Shipment Tendering The assignment of a carrier to a shipment needs to be confirmed by the carrier (tendering). Rewards of planning: 1. Reduced costs 2. Improved customer service 3. Increased responsiveness 4. Improved productivity 5. Improved efficiency 6. Increased return on assets (ROA) 7. Increased revenues, 8. Faster return on investment (ROI), Again, specific customer financial benefits will vary, but the following ranges can be used as a guideline: • Increased ROA (3%-5%) • Reduced inventory carrying costs (5%-10%) • Reduced lost sales-revenue uplift (3%-6%) • Reduced administrative expenses (5%-10%) • Reduced transportation costs (10%-15%) In essence, SAP APO 4.0 is the latest step in the continuing evolution of supply network management. The demand planning and supply network planning capabilities it provides result in an enhanced visibility across the network. All stakeholders can now work from a single, accurate demand plan. Efficient and optimal planning for production, warehousing, and transportation is the rule of the day. You can meet the dual challenge of improving service and controlling costs, and ultimately reach the pinnacle goal: increasing profits.  Global Available to Promise Utilises a global, multi-level, rule-based strategy to match supply with customer demand. It also performs multi-level bill-of-materials and capacity checks in both real-time and simulation mode to enable delivery commitments for customer orders.
    • CONCLUSION Within the last three decades there have been rapid changes in the way products and services are developed, manufactured and distributed. This is caused primarily by altering market conditions, including quickly growing product diversity, the need for quick and accurate response times, high quality and flexibility in delivering new products, and speed of innovation. Advanced information technology concepts are indispensable in providing answers to these challenges. Quite simply, APS is a planning revolution. APS will do to MRP what the PC did to typewriters. It’s a technology that leverages the planners knowledge with the tremendous capabilities of today’s distributed computing. For manufacturers, the question is not whether APS is necessary, but how soon it should be implemented. Thus far, the results have been great, even at companies that by their own admission had lousy planning processes. Early adopters of APS have dramatically improved the competitiveness of their companies with only limited implementations. With well financed vendors quickly expanding product suites to reach new industries and further stretches of the supply chain, no manufacturing or distribution concern is beyond the onslaught of a competitor armed with APS. APS sounds like the key for supply chain management: transparency, it fits over the existing systems, it optimizes and it offers control and acceleration. But one has to question what it is all about: the co-operation between humans in the organization, co-operation with customers, founded choices about the logistical concept, controlled processes and procedures and the information systems as support. APS will support the people with taking the complex decisions. With planning, scheduling, and collaboration tailored to specific industries and industry standards, a unified architecture, and an open technology framework that ensures flexibility and interoperability. As an integrated solution, mySAP SCM also eliminates many of the costs associated with piecing together and supporting a collection of best-of-breed or disparate systems. Measurable benefits typically include reduced overall project costs, as well as reduced IT maintenance and support costs. Advanced software-packages will never succeed in eliminating humans. There will probably never be a magic computer that solves everything on its own. On the contrary, because computer systems take over the repetitive work of humans, those people can devote themselves to more intelligent tasks
    • Robert Bosch Case Overview This case study presents the strategic issues faced by the CIO’s of the information technology division of the corporation, Robert Bosch GmbH, and its US subsidiary, Robert Bosch USA. The corporation has traditionally followed an international/multidomestic strategy and used multiple information systems at its plants/divisions/business sectors. Due to higher pressures to cut costs, increase interchangeability of products among the many plants worldwide, and fulfill customer requirements, the corporation was moving to a global strategy. Dr. Eggensperger, CIO of the corporate IT division of Robert Bosch (QI) acknowledged that there were some challenges in implementing SAP R/3 worldwide. QI had standardized on use of SAP R/3 during 1995 and it had ended up with many locations in Europe implementing SAP R/3 systems non- uniformly. In order to rectify this, QI focused on standardizing the IT-systems for use in every plant location using a top-down approach. During 1999, the Board of Management of Robert Bosch GmbH stated that the current implementation of information systems was not acceptable and did not meet corporate requirements. In order to fulfill the requests of the Board, the CIO of QI considered the following strategies for implementation options: standardize the ERP approach within the entire Bosch group, implement a domestic ERP approach for each country, or implement product-division based ERP approaches within the global operation. In addition to the requirement for a corporate strategy, there were requests to modify the IT infrastructure in several domestic markets. Don Chauncey, the CIO of the US Operations (RBUS) had to decide whether to continue with the current variety of information systems or to shift to a uniform SAP R/3 system for all the divisions of RBUS. In order to meet the current business needs, many different information systems developed by multiple vendors were implemented throughout RBUS. Some of these systems were not functioning well and there was pressure on Don to make changes. For example, the software supplier of the financial system announced that they will support the current AS/400 system only until the end of 2001. The Human Resources (HR) department recommended that PeopleSoft will be their choice to address the payroll/human resource needs. Changes were also required in the logistics
    • area at the plant level. During August 1999, Don proposed implementing SAP R/3 in the Financials (FI), Human Relations/Payroll (HR), and Logistics at the plants in North America. This proposal was put on hold by the executives who headed the North American Operations Committee (NAOC). They asked for additional information on the project cost drivers. Don, as the CIO of the domestic level organization, was faced with the decision to either stay with the multiple information systems or champion implementing SAP R/3 throughout RBUS. He was unsure which of these information technology (IT) solutions would be most effective in coping with the growing and changing business of RBUS. Salient Features of the Case: This case lays emphasis on the following points: (a) Importance of the alignment of business strategy and the IT strategy in a company at both corporate level and country level. (b)To identify the issues faced by RBUS in moving to a standardized corporate-wide information system such as SAP R/3. (c)How a ERP system can be implemented in many ways with resulting benefits and problems. (d)If an ERP package is not implemented correctly, it might lead to many different ERP systems existing within an organization that do not communicate with each other.