Supply Chain Management and Advanced Planning

Supply Chain Management — just another short-lived management philosophy? The gains that have been realized when adopting

Supply Chain Management

(SCM) and Advanced Planning are impressive:

Hewlett-Packard cut deskjet printer supply costs by 25% with the help of inventory models analyzing the effect of different locations of inventories within its supply chain. This analysis convinced Hewlett-Packard to adopt a modular design and postponement for its deskjet printers (Lee and Billington 1995). In 2004 Billington et al. (Billington et al. 2004) gave an account of a thorough analysis of Hewlett-Packard’s inkjet cartridge supply chain. As a result savings of $80 million (in net present value) were achieved by a move of transocean freight lanes from air to sea despite an increase in supply chain inventory.

Car manufacturer BMW applied a strategic-planning model to its global production sites. By reallocating the supply of materials as well as the distribution of finished cars to the global markets it is expected that investments and costs for materials, production, and distribution will be reduced by about five to seven percent (Fleischmann et al. 2006).

Intel Corporation devised a suite of capacity models of production facilities along the semiconductor supply chain in collaboration with its key suppliers. Now, Intel has access to all the suppliers’ models but holds each in strictest confidence. These models may well be used for various planning horizons (next 5 years, next 9 months, or next 8 weeks). While Intel profits from a better exploitation of bottlenecks the suppliers’ bene-fits are more accurate requests and forecasts from Intel. Dollar savings of hundreds of millions are estimated for the suppliers and tens of millions for Intel (Shirodkar and Kempf 2006).

Swift & Company owns slaughter and processing operations at five plants in the US. An advanced scheduling and capable-to-promise (CTP) software solution was created which enables Swift to answer customer queries within seconds, i.e. to promise the shipment of an order-line-item quantity on the requested date given the availability of cattle and plant capacities over a 90-days planning horizon. The project’s return on investment in the first year of production was 200 percent (Bixby et al. 2006).

What is the essence of

Supply Chain Management

(SCM)? How does it relate to Advanced Planning? In which sense are the underlying planning concepts “advanced”? What are the origins of SCM? These as well as related questions will be answered in this chapter.

When starting an improvement process one has to have a clear picture of the structure of the existing supply chain and the way it works. Consequently a detailed

analysis

of operations and processes constituting the supply chain is necessary. Tools are needed that support an adequate description, modeling and evaluation of supply chains. In Sect. 2.1 several issues regarding supply chain analysis are discussed. Then, Sect. 2.2 presents modeling concepts and tools with a focus on those designed to analyze (supply chain) processes. The well known SCOR-model is introduced in this section. Building on these concepts (key) performance measures are presented in order to assess supply chain excellence (Sect. 2.3). Inventories are often built up at the interface between partners. As a seamless integration of partners is crucial to overall supply chain performance, a thorough analysis of these interfaces (i. e. inventories) is very important. Consequently, Sect. 2.4 gives an overview on inventories and introduces a standardized analysis methodology.

The SCOR-model presented in Sect. 2.2.2 is an excellent tool to analyze, visualize, and discuss the structure of the supply chain, and to reveal redundancies and weaknesses. It enables the formulation of structural changes and strategies to improve the performance of the supply chain as a whole.

Why planning? Along a supply chain hundreds and thousands of individual decisions have to be made and coordinated every minute. These decisions are of different importance. They comprise the rather simple question “

Which job has to be scheduled next on a respective machine?

” as well as the very serious task whether to open or close a factory. The more important a decision is, the better it has to be prepared.

APS have been launched independently by different software companies at different points in time. Nevertheless, a common structure underlying most of the APS can be identified. APS typically consist of several

software modules

(eventually again comprising several software components), each of them covering a certain range of planning tasks (see Rohde et al. 2000).

In this chapter we will focus on the long-term, strategic planning and design of the supply chain. Section 6.1 explains the planning situation and the problem setting. Section 6.2 outlines the formulation of the problem as mixed integer programming model and Section 6.3 describes the use of such models. Section 6.4 and 6.5 review the relevant literature and the software modules available in APS, respectively.

The target of SCM is to fulfill the (ultimate) customer demand (Ch. 1). Customer demand does either explicitly exist as actual customer orders that have to be fulfilled by the supply chain, or it does exist only implicitly as anonymous buying desires (and decisions) of consumers. In the latter case, there is no informational object representing the demand.

The main purpose of

Master Planning

is to synchronize the flow of materials along the entire supply chain. Master Planning supports mid-term decisions on the efficient utilization of production, transport, supply capacities, seasonal stock as well as on the balancing of supply and demand. As a result of this synchronization, production and distribution entities are able to reduce their inventory levels. Without centralized Master Planning, larger buffers are required in order to ensure a continuous flow of material. Coordinated master plans provide the ability to reduce these safety buffers by decreasing the variance of production and distribution quantities.

The planning process that determines how the actual customer demand is fulfilled is called

demand fulfillment

. The demand fulfillment process calculates the first promise date for customer orders and — thus — strongly influences the order lead-time and the on time delivery.

1

In today’s competitive markets it is important to generate fast and reliable order promises in order to retain customers and increase market share. This holds particularly true in an e-business environment: Orders are entered on-line in the e-business front end, and the customer expects to receive a reliable due date within a short time period.

Assuming that the master plan has been generated, we can now derive detailed plans for the different plants and production units. In the following we will describe the underlying decision situation (Sect. 10.1) and outline how to proceed from a model to a solution (Sect. 10.2). Some of these steps will be presented in greater detail, namely model building (Sect. 10.3) and updating a production schedule (Sect. 10.4). Whether Production Planning and Scheduling should be done by a single planning level or by a two-level planning hierarchy largely depends on the production type of the shop floor. This issue will be discussed together with limitations of solution methods in Sect. 10.5.

An indispensable part of an ERP system, Material Requirements Planning, also plays an important role in APS, because it

generates replenishment orders (production orders) for uncritical components and parts (operations) in a multi-stage production environment (Sect. 11.1 and 11.2) and

provides access to a transactional ERP system and thus can initiate the execution of orders.

The typical tasks of purchasing are to analyze procurement markets, to negotiate the terms of trade with potential suppliers and finally to select suppliers and to place replenishment orders. Here, we are interested in the way APS can support the selection of suppliers and the decisions on order sizes, taking into account the specific cost functions of suppliers, which often allow for quantity discounts (Sect. 11.3). This may apply to input materials for production, indirect materials and articles of merchandise.

Transport processes are essential parts of the supply chain. They perform the flow of materials that connects an enterprise with its suppliers and with its customers. The integrated view of transport, production and inventory holding processes is characteristic of the modern SCM concept.

A strong

coordination

(i. e. the configuration of data flows and the division of planning tasks to modules) of APS modules is a prerequisite to achieve consistent plans for the different planning levels and for each entity of the supply chain. The same data should be used for each de-centralized planning task and decision. APS can be seen as “add-ons” to existing ERP systems with the focus on planning tasks and not on transactional tasks. In most cases an ERP system will be a kind of “leading system” where the main transactional data are kept and maintained. The data basis of APS is incrementally updated and major changes on master data are made in the ERP system. This task will be called

integration

of APS with ERP systems.

The preceding chapters deal with planning processes within one

planning domain

, e. g. an enterprise (demand planning, master planning) or a factory (production planning). The term

planning domain

constitutes a part of the supply chain and the related planning processes that are under the control and in the responsibility of one planning organization. However, the quality of a plan and the quality of the decision-making process that is based on that plan can often be improved by considering additional information that is beyond the scope of the individual planning domain.

Supply Chain Management aims at improving competitiveness of the supply chain as a whole, by integrating organizational units along the supply chain and by coordinating material, information and financial flows in order to fulfill (ultimate) customer demands (Sect. 1.1). Supply Chain Management projects range from functional improvements on the IT level to large-scale change programmes. Functional improvements might be the introduction of a new forecasting method or the adjustment of the master planning optimization profile. Examples for larger SCM projects are the optimization of the supply chain network, the redesign of the planning processes or the adjustment of the business strategy based on SCM concepts. In either case, the goal of SCM projects is to improve competitiveness of the supply chain.

Advanced Planning Systems are a relatively new software technology. One of the first Advanced Planning Systems was OPT that was implemented end of the Eighties (Schragenheim and Ronen 1990; Silver et al. 1998). OPT is based on the

Theory of Constraints

(Goldratt 1990), stipulating that the constraints of a production system have to be represented in detail in a planning system in order to exploit and to control its performance.

A successful implementation of the selected APS is the obvious goal of any organization that has decided to go for a supply chain project. The first section of this chapter details an approach to ensure the success of supply chain initiatives based on the experience of several realized projects. In the second section, an APS implementation project will be considered from a modelling point of view.

This chapter will introduce the APS used in the case studies from

AspenTech, i2 Technologies, Oracle and SAP

: aspenONE, Six.One, EnterpriseOne, and Advanced Planner and Optimizer (APO). As these tools regularly consist of a multitude of software modules and special add-ons, only a brief survey without claiming completeness can be given. Furthermore, different lines of business can use different modules of an APS. It is also possible to use an APS only partially, e. g. without modules for scheduling or only using modules for demand planning and demand fulfillment. For each individual case the composition of modules has to be evaluated and selected (see Chap. 16).

In the chemical industry final products of one producer act as input material to the production process of the following producer (i. e. the customer of the first producer). The following producer may be also a chemical company, further refining and processing the input chemicals, or it may be a producer of some other products, such as textiles, food, pharmaceuticals, etc., using the input chemicals as ingredients for their final products. As production lead times in the chemical industry are usually longer than the order lead times, chemicals are — in most cases — produced in make-to-stock mode. Thus, after production, the final products are pushed into a distribution network and stored in distribution centers. The structure and operational parameters (e. g. safety stock levels) of the distribution network are directly influencing the performance of the chemicals supply chain.

The computer industry is a typical example of a

material constrained

supply chain. The main bottleneck of demand fulfilment is the availability of the electronic components, e. g. disk drives, processors, memory etc. This case study is based on an actual APS implementation project at a large international computer manufacturer. Four modules of the APS system by i2 Technologies are implemented, supporting the demand planning process, the mid-term supply planning process, the short-term supply planning process and the order promising process. The following case study describes in detail

the computer assembly supply chain, the product structure and the assembly process (Sect. 20.1),

the scope and objectives of the APS implementation project (Sect. 20.2),

the planning processes being supported by the APS system, i. e. demand planning, operational planning, order planning, order promising and the integration of the applied i2 planning modules with the existing SAP R/3 system (Sect. 20.3), and

results and lessons learned from the APS implementation (Sect. 20.4).

The oil market is a worldwide market. Due to an increasing demand of the fast growing countries like China and India, the oil market has been changing to a strong emerging market. Due to these effects the prices of raw material and finished goods have extremely increased and are strongly volatile. Faced with very complex production techniques and high investment costs for enlarging production capacities a European company needs a very high level of integration in planning and scheduling in its supply chain to survive in the world market.

Competitive advantage in the pharmaceutical industry is driven by first class research and development and by optimised supply chain operations. Harmonised SCM processes, systems and organisations will lead to reduced inventories, increased capacity utilisation, reduced order lead time, less obsolescences and lower IT system maintenance costs. Critical decisions can be made faster resulting in an improved customer service level. Based on common, standardised data, error rates are reduced and most importantly, full FDA CFR 21 part 11 and GMP compliance can be guaranteed and sustained.

The purpose of Demand Planning is to reduce uncertainty about what will be sold to the customer in the future. Improving forecast accuracy leads to economic benefits such as cost cutting by reducing safety stocks and increasing sales by avoiding stock out situations. The case presented describes a Demand Planning implementation using mySAP SCM at an international company in the process industries.

Everyone knows the situation: You go shopping in your favorite supermarket and all the items on your list are available, except for one. Therefore, you have to drive to the next store and hope that you can get the product there.

This case study deals with a project which has been finished in its first version in the process industry in 2000 with a quite early release of APO PP/DS and which has been further improved by new releases of SAP APO with their additional functions and the integration of more parts of the supply chain. It was the first APO PP/DS project that managed to keep up with the difficult scheduling requirements in the field of the chemical and process industries.

In a highly dynamic market environment, the required planning quality for the entire supply chain can increase to such an extent that it can be reached with fixed planning cycles only at the very high cost of frequent planning. If demand or the raw materials market develops differently from what is anticipated, a readjustment of the entire supply chain becomes necessary to re-attain maximum planning quality and hence profitability. In a highly dynamic environment, an event-controlled synchronization of the production and sales planning in place of the timetable-controlled synchronization can ensure the necessary increase of planning quality and limit planning expenses to the necessary minimum. This study describes the planning scenario of a chemical industry company which has fully integrated the demand planning, master planning as well as production planning and detailed scheduling; its results are used as quotas in the availability check of customer requirements (see chapter 9). This integrated planning system is carried out, in whole or in part, event-controlled as a function of market trends.

The preceding chapters have shown the different steps of introducing an APS in industry, starting with an analysis of a given supply chain, its redesign and subsequently modeling the supply chain from long-term to short-term decision levels. The integration of all planning tasks relating to the order fulfillment process will result in a new era of enterprise wide and supply chain wide planning. Thereby, an APS will not only yield improvements on the three crucial factors of competitiveness, namely costs, quality and time, but it will also allow for

making processes more transparent,

improving flexibility, and

revealing system constraints.

Widely available information from all over the supply chain results in a

transparent

order fulfillment process. It enables companies and supply chains to provide customers with accurate information about the order status and provides alerts in the case that an unexpected event causes the delayed delivery of an order. However, before this happens a decision-maker can find and check alternative ways to fulfill the customer’s order, either by a shipment from another warehouse or another production site or by offering parts of the next higher grade. Additionally, transparent processes will reduce waste along the supply chain, because waste, e. g. resulting from excessive inventories or resources with low utilization rates, will be recognized quickly and measures for its improvement may be introduced.

In Chap. 28 we will show how demand planning can be done when seasonality and trend are given. For a comprehensive introduction to forecasting in general the reader is referred to Hanke and Wichern (2005) or Makridakis et al. (1998).

Linear Programming

(LP) is one of the most famous optimization techniques introduced independently by Kantarowitsch in 1939 and by Dantzig in 1949 (Krekó 1973). LP is applicable in decision situations where quantities (variables) can take any real values only restricted by linear (in-) equalities, e. g. for representing capacity constraints. Still, LP has turned out to be very useful for many companies so far. LP is used in APS e. g. in Master Planning as well as in Distribution and Transport Planning. Very powerful solution algorithms have been developed (named solvers), solving LP models with thousands of variables and constraints within a few minutes on a personal computer.

Many optimization problems of the type arising in scheduling and routing (see Chaps. 10 and 12) are of combinatorial nature, i. e. solutions are obtained by combining and sequencing solution elements. When solving such problems to optimality, the number of solutions to be examined exponentially grows with the problem size. For example, for

n

solution elements

n

! different sequences exist.

Constraint programming

(CP) represents a relatively new technique for computing feasible (and optimal) solutions to combinatorial decision problems like those typically arising in scheduling and routing (see Chaps. 10 and 12). In the mid eighties, it was developed as a computer science technique by combining concepts of

Artificial Intelligence

with new programming languages. In the meantime, it has received considerable attention in practice as well as in the

Operations Research

(OR) community, in particular, since it has successfully been included into commercial software systems (e. g. ILOG OPL Studio). The basic idea of CP consists of providing an integrated framework for formulating and solving decision problems based on a single programming language. For the latter purpose, generalized solution procedures are included within CP systems, the application of which can be controlled by the user. Hence, in contrast to classical OR techniques such as mixed integer programming, the user of CP does not only specify the decision problem to be solved but also determines how the search for corresponding feasible solutions should be performed.