Uche Okongwu
email: u.okongwu@tbs-education.fr

Matthieu Lauras

Julien Franc

Jean-Christophe Deschamps

Impact of the integration of tactical supply chain planning determinants on performance

Keywords:

a b s t r a c t

In fulfilling customers' orders, one of the goals of tactical supply chain planning is to satisfy the customers in terms of delivery efficiency, delivery quantity accuracy and on-time delivery. These performance ob jectives can b e impacted b y the way firms plan each of the three phases of the supply chain: procurement, production and distrib ution. Though the link b etween each of these phases and supply chain performance has b een studied in extant literature, very few authors have considered all three phases at the same time. By adopting an integrated approach, this paper therefore aims to study the manner in which, taken together in one model, the planning determinants of the different phases impact on supply chain performance. It is important for managers to understand, from a holistic and integrated perspective, how a given comb ination of the planning determinants of the supply chain functions impacts positively or negatively on the performance of the supply chain. To carry out this study, this paper starts b y proposing an integrated framework that is b ased on the SCOR model and the customer order decoupling point (CODP), followed b y a five-step methodology for tactical supply chain planning. Then, using an analytical model and simulations, and b ased on a numerical example, it shows how the proposed methodology can b e applied in a given decision-making situation. Our results enab led to identify the worst and the b est comb inations of planning determinants.

Introduction

In the glob alised and highly competitive world of today, companies aim to achieve high performance through an effective and efficient management of their supply chains (SC). The Glob al Supply Chain Forum defined supply chain management (SCM) as "the integration of key b usiness processes from end users through original suppliers who provide products, services, and information that create value for customers and other stakeholders" [START_REF] Lamb Ert | Supply chain management: implementation issues and research opportunities[END_REF]. This definition allows us to state that the performance of a SC can b e leveraged through the effective and efficient design, integration, planning and control of the key b usiness processes. The Supply Chain Operations Reference (SCOR) model provides a processb ased framework, which incorporates five main process areasplan, source, make, deliver and return -that constitute a SC [START_REF]Supply-Chain Operations Reference: model overview[END_REF]. The SCOR model is considered to b e a powerful tool that can b e used to study and understand how performance variab les inter-relate and how to manage the trade-offs resulting from these relationships [START_REF] Bronzo | Planning, capab ilities, and performance: an integrated value approach[END_REF]. Apart from the return process area (which is an aftermarket process), the determinants of each of these process areas (or functions) can impact on the performance of the SC. The source, make and deliver process areas correspond respectively to the procurement, production and distrib ution functions. Ref. [START_REF] Li | Ensuring supply chain quality performance through applying the SCOR model[END_REF] studied and confirmed a positive relationship b etween supply chain performance and each of the five process areas, b ut did not investigate the individual or comb ined impact (on performance) of the determinants of these process areas.

Many researchers have studied the relationship b etween the determinants of three of these process areas (source, make and deliver) and supply chain performance [START_REF] Gunasekaran | Performance measures and metrics in a supply chain environment[END_REF][START_REF] Mungan | Manufacturing lot-sizing, procurement and delivery schedules over a finite planning horizon[END_REF]. But, despite the fact that academics and professionals have always thought that significant capacity adjustment expenditures and storage costs might b e avoided b y b etter planning [START_REF] Jones | Parametric production planning[END_REF], the impact of the planning process on performance has not b een sufficiently explored [START_REF] Lockamy | Linking SCOR planning practices to supply chain management: an exploratory study[END_REF]. The few studies that have b een done on this topic are generally limited only to the manufacturing or production function [START_REF] Wacker | Effectiveness of manufacturing planning and control systems on manufacturing competitiveness: evidence from glob al manufacturing data[END_REF][START_REF] Olhager | Manufacturing planning and control approaches: market alignment and performance[END_REF]. Moreover, most of the studies are b ased on one-to-one or one-to-few relationships. In other words, the authors study the impact of just one or two planning determinants on a few (or a single) performance measures. For example, Ref. [START_REF] Tsub One | The impact of lot-sizing and sequencing on manufacturing performance in a two-stage hyb rid flow shop[END_REF] looked at the impact of lot sizing and sequencing on manufacturing performance; Refs. [START_REF] Berkley | The effect of b uffer capacity and sequencing rules on single-card kanb an system performance[END_REF][START_REF] Akkerman | Influence of capacity-and timeconstrained intermediate storage in food production systems[END_REF] studied the effect of capacity and sequencing rules; Ref. [START_REF] Jamalnia | A simulation testing and analysis of aggregate production planning strategies[END_REF] studied capacity strategies with respect to total profit; Ref. [START_REF] Feng | Simulation and performance evaluation of partially and fully integrated sales and operations planning[END_REF] simulated performance differences b etween fixed and rolling horizon environments; Ref. [START_REF] Smith | User influence on the relationship b etween forecast accuracy, application and logistics performance[END_REF] analysed the effect of forecast accuracy; Ref. [START_REF] Teo | Setting planned lead times for a maketo-order production system with master schedule smoothing[END_REF] modelled the setting of planned windows and lead times; while Ref. [START_REF] Karim | An on-time delivery improvement model for manufacturing organisations[END_REF] studied the improvement of on-time delivery. These authors report the impact of one or two determinants of a given supply chain function (source, make or deliver) on one or two performance measures. Given that other authors have reported possib le trade-offs b etween different performance measures such as quality consistency, lead time, delivery reliab ility, cost, and flexib ility [START_REF] Mapes | Performance trade-offs in manufacturing plants[END_REF], our postulate is that a given comb ination of planning determinants from two or more supply chain functions would impact positively or negatively on various SC performance measures, thereb y creating a trade-off situation. Ref. [START_REF] Bahl | An integrated model for master scheduling, lot sizing and capacity requirements planning[END_REF] noted that, in material requirements planning systems, much was still left to the planner's intuition and experience in selecting appropriate capacity levels and lot sizes for components. Managers therefore need a decision-making support system that would enab le them to make the most optimal trade-off decision from an integrated perspective. In a nut shell, this paper aims to study how the integration of supply chain planning determinants impact positively or negatively on supply chain performance ob jectives.

In the same way that the management of companies involves decisions at the strategic, tactical and operational levels, supply chain planning also involves decisions at these three levels according to the time horizon -long term for strategic planning, medium term for tactical planning and short term for operational planning. However, only the tactical level planning will b e studied in this paper for the following reasons: (1) Although Ref. [START_REF] D'amours | Using operational research for supply chain planning in the forest products industry and paper industry[END_REF] argues that it is crucial for supply chain planning to integrate strategic, tactical and operational decision-making, the huge differences in planning horizons, as well as the difficulty of modelling make the integration (of the three planning levels) to b e unrealistic; [START_REF]Supply-Chain Operations Reference: model overview[END_REF] In recent years, the significance of planning and optimisation at the tactical level has b een recognised b y academics and practitioners as a competitive advantage for growing production-distrib ution firms [START_REF] Fahimnia | A review and critique on integrated production-distrib ution planning models and techniques[END_REF]; (3) Dealing with mid-range horizon, the tactical level forms a b ridge b etween the strategic and operational levels [START_REF] Schmidt | Strategic, tactical and operational decisions in multinational logistics networks: a review and discussion of modelling issues[END_REF]; (4) Tactical decisions concern issues surrounding the definition of the more-or-less generic rules for guiding daily operations [24, cited in 25] and these rules tend to satisfy the strategic ob jectives while respecting the capacities of the supply chain [START_REF] Ouhimmou | Furniture supply chain tactical planning optimization using a time decomposition approach[END_REF]; [START_REF] Gunasekaran | Performance measures and metrics in a supply chain environment[END_REF] The tactical level deals with measuring performance against targets to b e met in order to achieve results specified at the strategic level [START_REF] Gunasekaran | A framework for supply chain performance measurement[END_REF].

Today, it is commonly admitted that operations planning and control enab le firms to b e more competitive in many areas, such as quality, delivery, cost efficiency, and flexib ility [START_REF] Olhager | Evolution of operations planning and control: from production to supply chains[END_REF]. Moreover, given that this modern world competition is no longer b etween individual firms, b ut among supply chains [START_REF] Trkman | The impact of b usiness analytics on supply chain performance[END_REF], supply chain planning can b e considered to b e more effective than individual firm operations planning in securing a competitive advantage and improving organisational performance. Therefore, b y comb ining the SCOR model and the customer order decoupling point concept, this paper aims to develop an integrated framework and a fivestep methodology that is used to study the positive, negative and conflicting relationships b etween tactical supply chain planning determinants and supply chain performance. The paper is organised as follows. Firstly, b y reviewing the extant literature, we will clarify the notion of tactical supply chain planning determinants and performance measures. Secondly, b y discussing the manner in which the former impacts on the latter, we will formulate our research question. Finally, we will develop and present the integrated framework and the five-step methodology, and then apply them to a numerical example.

Definitions, literature review and research question

Given that many concepts and terminologies are defined in different ways b y different authors, we will in this section state the definitions that we have adopted from extant literature. We will first define supply chain planning and tactical supply chain planning determinants (TSCPDs), then the notion of performance measure (PM), b efore finally discussing the impact of TSCPDs on PMs.

Tactical supply chain planning determinants

Planning in any b usiness setup is done at three levels according to the time horizon: the strategic level for long-term planning, the tactical level for medium-term planning and the operational level for short-term planning [START_REF] Chopra | Supply chain management: strategy, planning, and operation[END_REF]. Depending on the complexity and life cycle of a product, planning time horizons vary considerab ly from one b usiness sector to another. For example, in the automotive industry, the strategic planning time horizon is ab out 5-7 years while the tactical planning time horizon is generally one year [START_REF] Bihlmaier | Modeling and optimizing of strategic and tactical production planning in the automotive industry under uncertainty[END_REF]. In the forest products industry, the horizon of strategic planning is expressed in decades while that of tactical planning is ab out five years for a forest management prob lem [START_REF] D'amours | Using operational research for supply chain planning in the forest products industry and paper industry[END_REF] and varies b etween six to twelve months for the production scheduling of pulp mills [START_REF] Carlsson | Supply chain planning models in the pulp and paper industry[END_REF]. Operational planning further details the tactical plan and generally focuses on activities on a day-to-day b asis [START_REF] Ivanov | An adaptive framework for aligning (re)planning decisions on supply chain strategy, design, tactics, and operations[END_REF]. Though the planning decisions at the three levels (strategic, tactical and operational) have b een conventionally considered in isolation from the other levels, the interrelation b etween them is very important in practice [START_REF] Ivanov | An adaptive framework for aligning (re)planning decisions on supply chain strategy, design, tactics, and operations[END_REF] and comb ining aspects of the strategic and tactical levels can make each far more valuab le than either would b e alone [START_REF] Sodhi | How to do strategic supply chain planning[END_REF]. However, the b ig difference in time horizons and the dispersed nature of supply chain configuration make this comb ination more complex and difficult to model. We have therefore chosen in this paper to consider only tactical supply chain planning determinants.

While strategic supply chain planning concerns capacity investments and facility locations [START_REF] Kauder | Strategic network planning for an international automotive manufacturer: b alancing flexib ility and economic efficiency[END_REF], tactical supply chain planning addresses allocation rules for resources, as well as usage rules that define production, distrib ution lead times, lot sizing and inventory policies [START_REF] D'amours | Using operational research for supply chain planning in the forest products industry and paper industry[END_REF]. It also deals with demand forecasting, production planning, supply planning, replenishment planning and transport planning [START_REF] Ivanov | An adaptive framework for aligning (re)planning decisions on supply chain strategy, design, tactics, and operations[END_REF]. According to Ref. [START_REF]Supply-Chain Operations Reference: model overview[END_REF], the SCOR Plan processes describ e the planning activities associated with operating a supply chain. This includes gathering customer requirements, collecting information on availab le resources, and b alancing requirements and resources to determine planned capab ilities and resource gaps. It also includes identifying the actions required to correct any gaps. In line with these statements, other authors define tactical supply chain planning as the process that captures information on market demand and inventories, and comb ines it with supply capab ilities and constraints to develop a plan for future volumes [START_REF] Kaipia | Effects of delivery speed on supply chain planning[END_REF]. This includes all the parameters associated with demand forecast, procurement of materials, transformation (making), and delivery of finished products to the customer. We refer to these parameters as tactical supply chain planning determinants (TSCPDs).

A review of the contrib utions of many other authors [START_REF] Gunasekaran | Performance measures and metrics in a supply chain environment[END_REF][START_REF] Wacker | Effectiveness of manufacturing planning and control systems on manufacturing competitiveness: evidence from glob al manufacturing data[END_REF][START_REF] Smith | User influence on the relationship b etween forecast accuracy, application and logistics performance[END_REF][START_REF] Kaipia | Effects of delivery speed on supply chain planning[END_REF][START_REF] Stadtler | Supply Chain Management and Advanced Planning: concepts, models, software, and case studies[END_REF][START_REF] Arnold | Introduction to material management[END_REF][START_REF] Handfield | Supply Chain Redesign: transforming supply chains into integrated value systems[END_REF][START_REF] Simchi-Levi | Designing and managing the supply chain: concepts, strategies and case studies[END_REF][START_REF] Chase | Operations management for competitive advantage[END_REF][START_REF] Slack | Operations management[END_REF][START_REF] Inson | Master production schedule time interval strategies in make-to-order supply chains[END_REF] enab led us to identify 12 generic TSCPD. These are planning horizon, frozen time fence, time b ucket, cycle time, non-frozen interval, capacity management policy, lot sizing, inventory management policy, Lead time, scheduling, sequencing, and

Planning determinants

Definition Reference

Planning horizon

The numb er of periods into the future taken into consideration in the planning system.

[9]

Frozen time fence

The interval within the planning horizon, where the timing and quantity of orders are not permitted to change in the next planning cycle.

[42]

3. Time b ucket A defined period, typically 7 days, wherein data is summarised for presentation in an MRP system.

[43]

Cycle time

The time b etween the b eginnings of two sub sequent cycles. [START_REF] Kaipia | Effects of delivery speed on supply chain planning[END_REF] 5. Non-frozen interval The interval which extends from the frozen time fence to the end of the planning horizon, where the timing and/or quantity of orders are permitted to change in the next planning cycle.

[42]

Capacity management policy

Capacity management policy defines how and when workforce size should b e adjusted. Workforce could b e varied, held constant b ut vary its utilisation b y overtime work or a shortened week, or held constant and produce to stock.

[35]

7. Inventory policy A statement of a company's goals and approach to the management of inventories.

[37]

Lot sizing

The division of the lot into sub -b atches that can b e transferred to the next operation as soon as the former operation has b een performed for all items.

[44]

Lead time

The time which elapses b etween the receipt of an order at the point of origin and the delivery of the goods at the point of consumption.

[5]

10. Scheduling The time or date at which activities are to b e undertaken. [START_REF] Gunasekaran | Performance measures and metrics in a supply chain environment[END_REF] 11. Sequencing Determining the order in which a manufacturing facility should process a numb er of different job s in order to achieve certain ob jectives.

[37]

12. Forecast accuracy A reflection of how close previous forecast are to actual demand. [START_REF] Smith | User influence on the relationship b etween forecast accuracy, application and logistics performance[END_REF] forecast accuracy. Tab le 1 summarises the definitions of these determinants.

Given that three of these TSCPDs (inventory policy, lot sizing and lead time) are applicab le to the source, make and deliver processes, we have a total of eighteen TSCPDs, which can b e grouped together under the four SCOR processes. Each of the TSCPDs is composed of two, three or more tactical options (or values) as can b e seen in Tab le 2. For example, the planning horizon could b e short (some weeks), medium (some months) or long (one or two years); material replenishment could b e b ased on a fixed order quantity, fixed period quantity or lot-for-lot policy; while sequencing could b e done on the b asis of earliest due date, first in first out, last in first out, shortest processing time, longest processing time or critical ratio.

Performance measures and metrics

A performance measure is a metric used to quantify the efficiency and/or effectiveness of an action [START_REF] Neely | Performance measurement system design: a literature review and research agenda[END_REF]. Generally speaking, effectiveness is defined as the extent to which goals are accomplished, while efficiency is the measure of how well the expended resources are utilised [START_REF] Mentzer | An efficiency/effectiveness approach to logistics performance analysis[END_REF]. One of the key goals of a firm is to sustain growth b y satisfying its customers. Therefore, from a b usiness and marketing perspective, effectiveness can b e defined as the extent to which customer requirements are met, while efficiency is a measure of how economically the firm's resources are utilised when providing a given level of customer satisfaction [START_REF] Neely | Performance measurement system design: a literature review and research agenda[END_REF]. In the supply chain management context, Ref. [START_REF] Gunasekaran | Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications[END_REF] reviewed the extant literature and identified seven performance measurement frameworks that are b ased on different criteria. The four most relevant of these criteria are (1) the b alanced scorecard perspective [START_REF] Kaplan | The b alanced scorecard: measures that drive performance[END_REF], which includes four areas -financial, customers, internal process, innovation and growth; (2) components of performance measures [START_REF] Beamon | Measuring supply chain performance[END_REF], which covers four categories -time, resource utilisation, output and flexib ility; (3) the location of measures in supply chain links [START_REF] Gunasekaran | Performance measures and metrics in a supply chain environment[END_REF], which is b uilt around five categories -planning and product design, supplier, production, delivery and customer; (4) decision-making levels [START_REF] Gunasekaran | Performance measures and metrics in a supply chain environment[END_REF], which is b roken into the three conventional planning levels -strategic, tactical and operational. Performance measures and metrics should b e directly related to a firm's strategy [START_REF] Maskell | Performance measures for world class manufacturing[END_REF], b ut one weakness of these frameworks is that they do not show how tactical and operational performance metrics can b e linked to a firm's strategic goals. Ref. [START_REF] Lynch | Measure up -the essential guide to measuring b usiness performance[END_REF] developed a performance pyramid (as shown in Fig. 1) that enab les to correct this weakness.

Supply chain management integrates supply and demand management within and across companies, and encompasses the planning and management of all activities involved in sourcing and procurement, conversion, and all logistics management activities [START_REF]Supply chain management terms and glossary[END_REF]. Based on this statement, the criteria for measuring the performance of a supply chain (SC) can b e grouped into three categories: those associated with the performance of the upstream activities of the SC (with respect to sourcing and procurement), those used to measure the performance of the internal SC (with respect to conversion or manufacturing operations), and those used to measure the performance of the downstream activities (with respect to delivery), as well as the logistics component of the SC, with the aim of satisfying the customer. The performance measures and metrics The aim of the delivery and logistics function is to ensure that the right product is delivered in the right quantity, at the right time, in the right condition (quality), to the right place, with the right documents, and for the right cost [START_REF]Supply-Chain Operations Reference: model overview[END_REF][START_REF] Aghazadeh | Improving logistics operations across the food industry supply chain[END_REF][START_REF] Bhagwat | Performance measurement of supply chain management using the analytical hierarchy process[END_REF]. Possib le corresponding performance metrics (or measures) are: delivery item accuracy for right product, delivery quantity accuracy for right quantity, on-time delivery for right time, quality conformance for right condition, delivery location accuracy for right place, documentation accuracy for right document, and delivery efficiency for right cost. Based on the performance pyramid (Fig. 1) of Ref. [START_REF] Lynch | Measure up -the essential guide to measuring b usiness performance[END_REF], we can say that these seven performance metrics constitute the quality and delivery performance measures that lead to customers' satisfaction, which in turn contrib utes to higher market and financial performance, thereb y achieving the firm's strategic goals in terms of efficiency and effectiveness. In support of this statement, some authors [START_REF] Anderson | Customer satisfaction, productivity and profitab ility: differences b etween goods and services[END_REF][START_REF] Rust | Customer satisfaction, customer retention, and market share[END_REF] suggest that customer satisfaction implies lower marketing costs, less price elasticity, and higher customer loyalty, which in turn lead to improvements in financial performance measures such as sales revenue and market share.

Quality

Impact of tactical supply chain planning determinants on performance

In extant literature, contrib utions on the impact of tactical supply chain planning determinants (TSCPDs) on performance are very diverse in scope and nature, and most often remain dispersed. Some studies focus on internal job shop performance metrics such as average lateness, average flow time, average tardiness, proportion of tardy job s, maximum lateness/tardiness, machine utilisation, and work-in-process inventory [START_REF] Chandra | Permutation flow shop scheduling with earliness and tardiness penalties[END_REF][START_REF] Randhawa | Job shop scheduling: an experimental investigation of the performance of alternative scheduling rules[END_REF]. Ref. [START_REF] Porteus | Optimal lot sizing, process quality improvement and setup cost reduction[END_REF] reported that reducing the lot size reduces the fraction of defective products. Ref. [START_REF] Manavizadeh | A multi-ob jective mixedmodel assemb ly line sequencing prob lem in order to minimize total costs in a make-to-order environment, considering order priority[END_REF] studied assemb ly line sequencing prob lems with the aim of minimising tardiness cost, as well as some other internal performance measures such as costs related to setup, production rate variation, operator idleness, operator error and utility worker. Regarding the authors that looked at the external performance measures, most of the studies focus on the impact of only one or a few determinants (from only one supply chain function -plan, source, make or deliver) on one or a few performance measures. For example, Ref. [START_REF] Galasso | Decision support for supply chain planning under uncertainty[END_REF] found that the lengthening of the planning horizon always reduces the glob al cost. Ref. [START_REF] Enns | MRP performance effects due to lot size and planned lead time settings[END_REF] looked at the relationship b etween lot size, lead time, inventory, and performance.

Moving a step further, a few authors have studied the impact of a comb ination of two or more planning determinants on performance measures, b ut the studied determinants still b elong to the same supply chain function (plan, source, make or deliver). For example, Ref. [START_REF] Krajewski | Kanb an, MRP, and shaping the manufacturing environment[END_REF] reported that simultaneously reducing setup times and lot sizes was found to b e the most effective way to cut inventory levels and improve customer service. Here, b oth setup times and lot sizes b elong to the make (manufacturing) function. After studying the effects of four determinants (capacity, storage time, scheduling and sequencing rules, all b elonging to the make function) on the performance of a specific two-stage system, Ref. [START_REF] Akkerman | Influence of capacity-and timeconstrained intermediate storage in food production systems[END_REF] concluded that, contrary to common sense in operations management, the Longest Processing Time sequencing rule is ab le to maximise the total production volume per day. Ref. [START_REF] Barut | Revenue management in order-driven production systems[END_REF] investigated the effectiveness of a tactical demand-capacity management policy to guide decisions in order-driven production systems and found that the dynamic capacity allocation procedure produces higher profit compared to a first-come-first-served policy. Looking at the demand side of a supply network in a configure-to-order environment, Ref. [START_REF] Ngaya | The impact of demand uncertainty and configuration capacity on customer service performance in a configure-to-order environment[END_REF] studied the impact of three factors (demand skew, demand variab ility, and configuration capacity) and ob served that all the three variab les individually and interactively influence customer service performance.

Ref. [START_REF] Inson | Master production schedule time interval strategies in make-to-order supply chains[END_REF] studied eight factors (non-frozen interval policy, planning horizon length, frozen interval length, re-planning frequency, cycle length, vendor flexib ility, demand range and demand lumpiness) and arrived at the conclusion that vendor flexib ility and its interactions with Master Production Schedule design factors are the most significant drivers of system performance in two-stage supply chains. Though this study considers determinants from different supply chain functions, it investigates their impact only on internal performance measures. In this paper, we are rather interested not only in comb ining determinants from different supply chain functions, b ut also in studying their impact on external performance measures as defined in Section 2.2.

Therefore, b eyond the aim of looking at the impact of one or a few TSCPDs on one or a few performance measures, our study constitutes a significant contrib ution given that it adopts an integrated approach that explicitly analyses the impact of many TSCPDs from different supply chain functions on many (most important) external performance measures. With reference to supply chain planning in the forest products industry, Ref. [START_REF] D'amours | Using operational research for supply chain planning in the forest products industry and paper industry[END_REF] stated that procurement, production and distrib ution activities are always b ounded b y the trade-offs b etween yield, logistical costs and service levels. Based on this statement, we formulate our research hypothesis as:

Different combinations of tactical planning determinants from two or more supply chain functions (plan, source, make or deliver) would impact differently on a given set of external performance measures.

This hypothesis can b e reformulated as a research question in the following manner:

What combination of tactical supply chain planning determinants would enable to optimise supply chain performance, given the possible trade-off between different performance measures?

Methodology and numerical application

To address our research question, this paper starts b y proposing an integrated framework that incorporates the determinants of all the four SCOR process areas (plan, source, make, and deliver), as well as the customer order decoupling point (CODP) concept. The CODP is defined b y some authors as the point in the goods flow where forecast-driven production and customer-driven production/delivery are separated [START_REF] Giesb Erts | Dynamics of the customer order decoupling point: impact on information systems for production control[END_REF]. This framework is shown in Fig. 2.

As can b e seen in Fig. 2, there are three supply chain configurations -make-to-stock (MTS), make-to-order (MTO) and assemb le-to-order (ATO). In the MTS system, the deliver process is downstream of the CODP and therefore demand-driven, while the source and make processes are upstream of the CODP and therefore forecast-driven. In the MTO system, the make and deliver processes are downstream of the CODP and therefore demand-driven, while the source process is upstream of the CODP and therefore forecastdriven. The ATO system is a hyb rid system that is mid-way b etween the MTS and MTO systems. In this system, the deliver process and part of the make process (the final assemb ly of products) are downstream of the CODP and therefore demand-driven, while the source process and the other part of the make process (the fab rication of semi-finished products) are upstream of the CODP and therefore forecast-driven.

Based on this integrated framework we propose a five-step decision-making methodology that would enab le the planning manager to optimise SC performance b y identifying the b est comb ination of TSCPDs. The five steps are describ ed as follows.

Step 1: Identify all the planning determinants of the four SCOR process areas (plan, source, make and deliver) and shortlist the most important, taking into consideration the type of production system (make-to-stock, assemb le-toorder or make-to-order). Then, define the values of the selected determinants b ased on the characteristics of the supply chain.

Step 2: Identify all performance measures that are in line with the firm's strategic goals and b usiness environment, and then determine the key performance metrics with respect to the ob jectives of the supply chain.

Step 3: Identify and choose a modelling/simulation method. See

Ref. [START_REF] Fahimnia | A review and critique on integrated production-distrib ution planning models and techniques[END_REF] for a comprehensive review of planning models and techniques that are applied to different supply chain configurations.

Step 4: Develop the optimisation (or decision-making) model, run simulations, and perform some sensitivity analyses.

Step 5: Identify the strongest and weakest comb inations of planning determinants b y analysing their impact on performance. Then, choose the b est (optimal) comb ination of planning determinants.

To illustrate how the proposed methodology can b e applied to a given supply chain configuration, we will start b y describ ing the supply chain that we studied, b efore presenting the five steps.

The supply chain

In their review of integrated production-distrib ution (P-D) planning models and techniques, Ref. [START_REF] Fahimnia | A review and critique on integrated production-distrib ution planning models and techniques[END_REF] grouped the identified models into seven categories:

1. Single-product P-D models. 2. Multiple-product, single-plant P-D models. 3. Multiple-product, multiple-plant, single or no warehouse P-D models. 4. Multiple-product, multiple-plant, multiple-warehouse, single or no end-user P-D models. 5. Multiple-product, multiple-plant, multiple-warehouse, multiple-end user, single-transport path P-D models. 6. Multiple-product, multiple-plant, multiple-warehouse, multiple-end user, multiple-transport path, no-time period P-D models. 7. Multiple-product, multiple-plant, multiple-warehouse, multiple-end user, multiple-transport path, multiple-timeperiod P-D models.

The case study used in this paper can b e likened to category 3 (multiple-product, multiple-plant, single or no warehouse P-D models) of their classification. In essence, it is a two-stage and two-product supply chain in a make-to-stock environment where production is planned b ased on demand forecasts (see Fig. 3). The two product groups (shelves and tab les) are produced and delivered to end customers. The product structures of the two product groups are simple, with one component at each level. Each production plant in the supply chain makes just one component. The first plant (sawmills) transforms and delivers wooden parts (tab le legs, tab le trays and shelf b oards) to the assemb ly plant that constitutes the second and final production stage for the shelves. After assemb ly, tab les are shipped to a third plant for the painting operation. The sawmills, the assemb ly plant and the painting shop are assumed to collab orate in a dyadic supplier-customer relationship. Each plant is considered to b e a single resource with limited capacity. Regarding the supply of raw materials, we assume an ideal situation -constant, regular, and without shortage.

Transportation is required b etween any two plants. Activities and resources needed for transportation are lumped together such that each transport operation is characterised b y a given capacity (limiting transfer throughput) and a lead time. Each plant is solely responsib le for managing its processes -procurement, production, storage and delivery. We assume that the lead time for the transmission of information is zero.

Supply chain

Source

Application of the proposed five-step methodology

Step 1: Identifying and selecting planning determinants

The framework shown in Fig. 2 serves to identify and select the most relevant determinants for a given supply chain configuration. In this framework, the customer order decoupling point (CODP) is used to distinguish three supply chain configurations: make-tostock (MTS), assemb le-to-order (ATO) and make-to-order (MTO). Ref. [START_REF] Rudb Erg | Mass customization in terms of the customer order decoupling point[END_REF] defined the CODP as the point in the value-adding material flow that separates decision made under uncertainty from decisions made under certainty concerning customer demand. Based on this definition, we can say that the most relevant tactical planning determinants for a given supply chain configuration depend on the one hand on the type of production system -MTS, ATO or MTO (see Ref. [START_REF] Olhager | Strategic positioning of the order penetration point[END_REF] for detailed information on the factors that determine the strategic positioning of the CODP) and on the other hand on the factors of uncertainty emb edded in the supply chain and its environment.

In MTS systems, b ased on the forecast of future orders, finished products are produced in advance and kept in inventory, which is then used to fulfil customer orders. This implies that forecast accuracy and end-product availab ility are determinants that would have a significant impact on the customer order fill rate. In MTO systems, given that production is triggered upon the receipt of a real order from the customer, the primary challenge is how to meet the promised delivery lead time [START_REF] Teo | Setting planned lead times for a maketo-order production system with master schedule smoothing[END_REF][START_REF] Manavizadeh | A multi-ob jective mixedmodel assemb ly line sequencing prob lem in order to minimize total costs in a make-to-order environment, considering order priority[END_REF]. In this case, meeting the promised lead time will largely depend on the procurement and capacity management policies [START_REF] Teo | Setting planned lead times for a maketo-order production system with master schedule smoothing[END_REF][START_REF] Rafiei | Capacity coordination in hyb rid make-to-stock/make-toorder production environments[END_REF]. Regarding ATO systems, part of production (the final assemb ly) is triggered upon the receipt of a real customer order, while the other part (procurement and the fab rication of semi-finished products) is carried out b ased on

Internal supply chain uncertainty

End customer demand Irregular purchases or irregular orders from final recipient of product or service.

Demand amplification Amplification of demand due to the b ullwhip effect. Supplier Supplier performance issues, such as quality prob lems, late delivery, etc.

Parallel interaction

Parallel interaction refers to the situation where there is interaction b etween different channels of the supply chain in the same tier.

Order forecast horizon/lead-time gap

The longer the horizon, the larger the forecast errors and hence there is greater uncertainty in the demand forecasts.

Chain configuration, infrastructure and facilities E.g. numb er of parties involved, facilities used or location, etc.

External uncertainties Environment E.g. political, government policy, macroeconomic and social issues, competitor b ehaviour.

Disruption/natural uncertainties E.g. earthquake, tsunamis, non-deterministic chaos, etc.

Source: Ref. [START_REF] Simangunsong | Supply-chain uncertainty: a review and theoretical foundation for future research[END_REF].

forecast. Therefore, key determinants in ATO systems would include the key determinants of MTS and MTO systems.

Regarding the degree of uncertainty of the supply chain, Ref. [START_REF] Simangunsong | Supply-chain uncertainty: a review and theoretical foundation for future research[END_REF] reviewed the extant literature and identified fourteen sources of uncertainty, which they divided into three groups -internal organisation uncertainty, internal supply chain uncertainty and external uncertainty -as shown in Tab le 3.

The further the CODP is positioned downstream the more the value-adding activities would b e carried out under uncertainty. On the contrary, the more the CODP is positioned upstream, the more the value-adding activities would b e carried out under certainty [START_REF] Rudb Erg | Mass customization in terms of the customer order decoupling point[END_REF]. It follows that the tactical planning determinants to b e considered in a given supply chain configuration would depend on the factors of uncertainty that are downstream or upstream of the CODP. For example, in MTS systems, tactical planning determinants would b e sub ject to most of the internal organisation and internal supply chain uncertainty factors since the procurement, fab rication and final assemb ly activities are upstream of the CODP. However, the selection of the relevant planning determinants would depend on the degree of uncertainty, as well as on the consequences of the uncertainty.

Regarding the supply chain that is used as a case study in this paper, we identified six tactical supply chain planning determinants (TSCPDs): two (frozen time fence and cycle time) from the plan process area, two (capacity management policy and sequencing) from the make process area, and two (end-product availab ility and forecast accuracy) from the delivery process area. End-product availab ility and forecast accuracy are chosen since the studied supply chain is an MTS system. For the make process area, we chose to simulate capacity management policy and sequencing since each manufacturing plant is considered to b e a single resource with limited capacity. Limited capacity would lead to either shortage or excess inventory following an increase or a decrease in demand forecast. In this case, finding the right sequencing in a multiproducts system would b e a crucial issue. Consequently, cycle times will depend on sequencing, and to ob tain the b est capacity management performance, the frozen time fence has to b e optimised. We note that no determinant is chosen from the source process area since we assumed that raw materials are supplied in an ideal situation -constant, regular, and without shortage.

Frozen time fence is simulated b y considering different values (T = 5 or 10 periods) for the period of replenishment T. In this case, the decision making process is dynamically depicted according to the principle of rolling planning horizon. Two cycle times (long and short) are simulated. The transportation lead time stated in Tab le 4 represents values corresponding to a long cycle time. In this case, transportation cost is assumed to b e low (0.03). A short cycle time is 50% of the long cycle time and implies reducing the transportation lead time b y 50%. The transportation cost is therefore higher (0.1).

Two capacity policies are considered for each partner -the "constant output rate" strategy where the production rate is equal to the average demand over the planning horizon, and the "chase demand" strategy in which manufacturing capacity is varied according to demand. We note that the first strategy entails reducing the initial production cost (80% of the nominal cost given in Tab le 4) whereas in the second strategy, production cost is increased (150% of the nominal cost). We studied two sequencing rules: first-in-first-out (FIFO) where b acklogs are processed first b efore the most recent or incoming customer orders, and last-infirst-out (LIFO), which gives higher priorities to the most recent orders.

Materials or end product availab ility (or inventory policy) is expressed in terms of safety stock. Two cases are tested: with or without safety stock. Without safety stock means that the parameter SS r p is set to zero. Otherwise, the safety stock is evaluated with the formula: SS r p = K r p √ D, where K is a safety factor implied b y Quantity of product p delivered during period t from PUr to PU r ′ q r,r ′ p,t Quantity of component p received during period t at the PUr from PU r ′ y r t Added capacity needed in the PU during period t the specified service level (ab acus), r p is the standard deviation of the demand in the resource r, and D represents the replenishment lead time. D can b e transportation lead time for materials or production lead time for end products. Finally, two situations of forecast accuracy are simulated: a fairly accurate forecast where actual demand varies only slightly with respect to forecasts and a low confidence forecast where actual demand is sub jected to huge variations compared to the forecasts.

Step 2: Identifying and determining performance measures and metrics

In Section 2.2, we identified seven performance measures for an order fulfilment process: delivery item accuracy, delivery quantity accuracy, on-time delivery, quality conformance, delivery location accuracy, documentation accuracy and delivery efficiency. In the case of the supply chain that we studied, four of these measures (delivery item accuracy, quality conformance, delivery location accuracy, and documentation accuracy) are considered to b e second-order performance measures since a high or low performance on these parameters would impact the other three measures. For example, the delivery of defective or wrong products at a wrong location and with incomplete (or wrong) documents would negatively impact on cost and on-time-delivery of the right products. We have therefore simulated only three measures (delivery efficiency, delivery quantity accuracy, and on-time delivery), which we consider as first-order measures. These three performance measures are mentioned b y Ref. [START_REF] Steinrücke | Tactical planning in supply chain networks with customer oriented single sourcing[END_REF] as the primary goal of supply chain planning. In this paper, they are defined in the following manner:

• Delivery efficiency is measured in terms of profit margin. An efficient system aims to minimise cost, thereb y maximising profit. In this study, we normalised this metric b y taking the highest profit as 100% and then calculating the others with respect to it. • Delivery quantity accuracy is measured in terms of the percentage of ordered quantities that are delivered within a given time frame.

SR p,r ′ ,r = H l r,r ′ p,t H d r,r ′ p,t (1)
Its value goes from 0 to 100%, the latter b eing the b est. 0% means that nothing is delivered on time and 100% means that all the ordered quantities are delivered on time. We will refer to this metric as "Completeness". • On-time delivery is measured as the Normalised Average Delivery Time (NADT) of the total quantity that is delivered.

NADT p,t,r ′ ,r = AL r,r ′ i=0 AL r,r ′ -i AL r,r ′ l r,r ′ p,t+i d r,r ′ p,t (2)
where AL r,r ′ is the acceptab le delivery lateness of customer r ′ from supplier r. For all quantity delivered on time (on the due date), AL = 100% and for those delivered after the period of acceptab le delivery lateness, AL = 0. For example, let us consider an order of 10 units of a product to b e delivered on day 1 (due date), with a maximum acceptab le lateness of 3 days b eyond the due date. If 3 units are delivered on day 1, 3 on day 2, 1 on day 3, 1 on day 4, and 2 on day 5, on-time delivery is equal to 60%. This is ob tained b y calculating the NADT as follows:

NADT = [(3 × 1) + (3 × 0.75) + (1 × 0.5) + (1 × 0.25) + (2 × 0)] 10
× 100% = 60%
Being normalised, the value of NADT must b e b etween 0 and 100%, the latter b eing the b est. A value of 100 means that 100% of the ordered quantity was delivered on or b efore the due date; a value close to 100 signifies that a high percentage of the ordered quantity was delivered on or b efore the due date and/or that most of the delivery was done (quickly) on the first day after the due date; while a value close to 0 signifies that most of the delivery was done (lately) on or after the last day of the period of the acceptab le delivery lateness.

Step 3: Choosing a model In the operations management literature, there are two types of models:

• Analytical models (deterministic or stochastic), which are used to study the b ehaviour of a supply chain using a mathematical approach [START_REF] Ganeshan | A taxonomic review of supply chain management research[END_REF]. • Simulation models, which are used in analysing complex systems whose state changes with time and/or in a random manner [START_REF] Angerhofer | System dynamic modelling in supply chain management: research review[END_REF] and where analytical models are not easy to apply since they require strong assumptions in order to reduce complexity.

Given the holistic and integrated (complex) nature of our study, we will use the simulation approach to study the impact of the tactical planning determinants on performance. However, we will first use a deterministic analytical approach to model the system b ecause analytical models make it easy to see the time horizon as a set of time periods, as used in planning approaches especially for rolling horizon experiments. In our model, the simulation of the planning decision-making process performed b y each partner (also called Production Unit and noted PU) in the supply chain is done b y solving a generic linear programming model.

Step 4: Model development and simulation

The proposed model defines optimal plans under storage, production and transportation capacity constraints. Prior to formulating the mathematical model that enab les to determine an optimal plan, the following assumptions are made: (1) each PU is associated to one resource r and manufactures a set of products p, (2) each resource has an inb ound inventory of components and an outb ound inventory of semi-finished or finished products, (3) suppliers of suppliers are considered as very reliab le, with complete and on-time deliveries, and (4) b ackorders concern only products delivered to end customers. The notations used to describ e the model are summarised in Tab le 4.

The model plans production, inventory levels, replenishment and delivery according to customers' orders. The ob jective function is a cost function that aims to minimise costs related to production, inventory, purchasing, shortages (orders that are considered not to have b een delivered at the end of the customer's acceptab le delivery lead time) and b ackorders (late deliveries).

min C f = t r ′ r p (q r,r ′ p,t • CA r p ) + r p ′ ((i+) r p ′ ,t + 5 • (i-) r p ′ ,t ) • CS r p ′ + p (f r p,t • CP r p + b r p,t • CB r p + X r p • CR r p + l r p,t • CT r p ) + y r t • AC r t (3)
The ab ove ob jective function is solved sub ject to 7 constraint functions as follows.

Eqs. (4a) and (4b ) represent the evolution of inventory levels. Eq. (4a) concerns the finished products: the quantity resulting from the production at a period t corresponds to a production order released a few periods b efore (depending on the production lead time DP). Eq. (4b ) evaluates the inventory levels of each component according to the quantities received b y suppliers and quantities consumed b y production, b ased on coefficients in the b ill of materials. Given that in a supply chain, the end product of a production unit b ecomes a component for the downstream production unit, we use the same index p for b oth components and finished products. Therefore, in Eq. (4a), i r p,t means inventory level of finished product p in production unit r at the end of period t, whereas in Eq. (4b ), i r p,t stands for the inventory level of component (product) p in the production unit r at the end of period t.

i r p,t = i r p,t-1 + f r p,t-DP r p - r ′ l r,r ′ p,t , ∀p, t, r (4a)
i r p,t = i r p,t-1 + r ′ q r ′ ,r p,t - p ′ (K r p,p ′ × f r p ′ ,t ), ∀p, t, r (4b )
Eq. ( 5) expresses b ackorders, that is, the difference b etween the quantity ordered b y customers and the quantity actually delivered.

b r,r ′ p,t = b r,r ′ p,t-1 + d r ′ ,r p,t -l r,r ′ p,t -x r,r ′ p,t , ∀p, t, r, r ′

Eqs. (6a), (6b ) and (6c) represent respectively capacity restrictions for production, inventory and transportation. The use of an additional capacity for production is allowed b ut requires higher operational costs.

p ⎛ ⎝ ˛p • DP r p =1 f r p,t-+1 ⎞ ⎠ ≤ CapR r t + y r t , ∀t, r, r ′ (6a) p ∈ P r ı p • i r p,t ≤ CapS r , ∀t, r, r ′ (6b ) p ∈ Pr ˇp • l r,r ′ p ≤ CapT r,r ′ , ∀t, r, r ′ (6c)
Eq. ( 7) represents the upper b ound of the additional capacity.

Y r t ≤ CAP SUPP , ∀p, t, r (7)
Eqs. (8a) and (8b ) represent the lower and upper b oundaries of the safety stock threshold.

(i+)

r p,t -(i-) r p,t = i r p,t -SS r p , ∀p, t, r (8a)
(i-) r p,t ≤ SS r p , ∀p, t, r (8b )
Eq. ( 9) is activated only if the state of the capacity policy determinant corresponds to "constant output rate" (entire COR = 1). In this case, the material flow is fixed as the mean value of the production capacity.

(f r p,t = F r p ) ∀p, t, r (9)
Finally, Eq. ( 10) corresponds to the non-negativity constraints for all the variab les.

q r,r ′ p,t , i r p,t , (i+) r p,t , (i-) r p,t , b r,r ′ p,t , l r,r ′ p,t , y r t ≥ 0, ∀p, t, r

The data used for the simulation are presented in Tab le 5.

Concerning the suppliers, their production lead time is included in the transportation lead time and they have no b ackorders. For all the supply chain partners, the additional production capacity (additional capacity) is infinite and its unit cost is 50% higher than that of the normal capacity. For each of the companies, the inventory capacity is equal to 5000 units, the delivery transportation capacity is 3000 units per period and the transportation lead time is 1 or 2 periods depending on the cycle time (short or long).

Each simulation runs in a rolling horizon of 40 periods, with specific parameter values for the tested determinants -two possib le states for each determinant. Each comb ination corresponds to one simulation scenario. By changing the value of one determinant at a time we performed 64 simulations. These simulations enab led us to assess the impact of the six determinants (frozen time fence, cycle time, capacity policy, sequencing, materials availab ility, and forecast accuracy) on the three performance metrics (profit margin, completeness, and NADT). The profit margin, NADT and completeness were determined for each of the supply chain partners. The total values for each of these three performance metrics were computed for the whole supply chain. For the sake of simplicity, we will not present the details for each plant, b ut only the total values: total profit, total NADT for shelves, total completeness for shelves, total NADT for tab les and total completeness for tab les. Also, given the high numb er of simulations performed, we consider that a sensitivity analysis in not necessary since different values of the determinants are tested in the various comb inations. clusters. Then we compared all the scenario characteristics of a cluster and associated them to the overall performance of the considered cluster. Consequently, b y analysing the reasons that justify the discriminations b etween clusters no. 1, 2, 3 and 4, we clearly ob served that the determinants that have the most important effect on performance are manufacturing capacity and sequencing. Manufacturing capacity has a strong impact on profit margin. The constant output rate strategy gives the b est profit margin whereas the chase demand strategy is associated with the worst scenarios in terms of profit margin. It means that the former enab les an optimal use of production resources. Also, inventory costs are generally lower than the production costs associated with the additional capacity. Considering the six determinants that we studied, these ob servations provide a first answer to our research question. We can say that manufacturing capacity is the predominant determinant that impacts on delivery efficiency.

The sequencing determinant has a strong impact on completeness and NADT. In the case of the FIFO rule, completeness increases whereas in the case of the LIFO rule, NADT increases. This can b e explained b y the fact that with the FIFO rule, the supply chain gives priority to b acklogs and consequently minimises late deliveries (shortages), thereb y maximising the delivery quantity accuracy of the supply chain. With the LIFO rule, the supply chain gives priority to the most recent customer orders and therefore maximises the on-time delivery performance measure. We note that with the FIFO rule, the service level (expressed as completeness and NADT) is moderate for the whole population of customers while with the LIFO rule, the service level is maximised only for a sub set of customers. These ob servations constitute a second answer to our research question. In essence, among the six determinants that we studied, we can say that sequencing is the predominant determinant as regards to delivery quantity accuracy and on-time-delivery.

All the other determinants have a limited impact on performance. However b y analysing the PCA results, we noticed several positive and negative impacts of planning determinants on different performance metrics. For example, we ob served that a low confidence on forecast leads to poor profit margins. But, when comb ined with the constant output strategy, this determinant does not have any impact on performance (see cluster no. 4 in Fig. 4). Also, cycle time appears to b e a very significant variab le that impacts on the performance of the whole supply chain. In fact, reducing the cycle time generally induces additional costs. But in our set of simulations, the PCA analysis (see cluster no. 3 in Fig. 4) shows clearly that these additional costs could offset the penalties associated with a poor service level. This is the reason why practitioners should consider the cycle time as a very interesting and effective variab le for improving the performance of their supply chain. Furthermore, the safety stock parameter improves (more or less) the situation of the most critical cases (see Clusters no. 3 and 4 in Fig. 4). But, if the performance results are glob ally satisfactory, then safety stock does not limit performance. In other words, safety stocks would not jeopardise b enefits if there are no significant disruptions in the supply chain. Finally, the PCA analysis does not permit to reach any significant conclusion regarding the impact of planning horizon on performance. This prob ab ly indicates that this determinant is not preponderant compared to the others.

Though the simulations in this paper are performed on only one supply chain configuration, the preliminary results enab le to validate our research hypothesis, which states that different comb inations of tactical planning determinants from two or more supply chain functions (plan, source, make or deliver) would impact differently on a given set of external performance measures. It follows that in tactical supply chain planning, the optimal comb ination of determinants would b e decided b ased on the desired performance of the supply chain. In the case of the supply chain that we studied in this paper, the decision would b e to lay emphasis on manufacturing capacity, sequencing and cycle time as the preponderant and performance-sensitive determinants.

Conclusion

The contrib ution of this paper is threefold. Firstly, b y comb ining the SCOR model and the CODP concept, it develops an integrated framework that would enab le researchers to study the relationship b etween tactical supply chain planning determinants and performance measures, for different manufacturing configurations (make-to-stock, assemb le-to-order and make-to-order). Secondly, it proposes a five-step decision-making approach that would enab le managers (and computer software programmers) to choose the b est comb ination of tactical planning determinants that would lead to a good (optimal) b alance b etween conflicting performance measures. Thirdly, using a mathematical model and computer simulations, it successfully shows a numerical application of the proposed methodology.

Though Ref. [START_REF] Bronzo | Planning, capab ilities, and performance: an integrated value approach[END_REF] used the SCOR model to study the impact of planning on performance b y evaluating each of the process areas -source, make and deliver, the authors did not detail the determinants of the planning process. Using the SCOR model, Ref. [START_REF] Li | Ensuring supply chain quality performance through applying the SCOR model[END_REF] also studied and confirmed a positive relationship b etween all the process areas and supply chain performance, b ut did not investigate the impact of the determinants of these process areas. The contrib ution of this paper is therefore unique in two aspects: [START_REF] Lamb Ert | Supply chain management: implementation issues and research opportunities[END_REF] To the b est of our knowledge, this is the first paper that presents an exhaustive list of tactical planning determinants within an integrated framework; (2) It proposes and applies a methodology that enab les to investigate the relationship b etween supply chain performance and various comb inations of determinants from all the four SCOR process areas (plan, source, make and deliver). Our results show how different comb inations of tactical planning determinants impact on three different supply chain performance metrics (delivery efficiency, delivery quantity accuracy, and on-time delivery).

From a theoretical perspective, this framework can b e used to study and develop theories that could explain the trade-offs and mediating roles b etween planning determinants. For example, Ref. [START_REF] Ho | Mitigating forecast errors b y lot-sizing rules in ERP-controlled manufacturing systems[END_REF] studied how one planning determinant (forecast errors) can b e mitigated b y another determinant (lot sizing rules). With our proposed framework and methodology, similar studies can now b e carried out with many determinants at the same time. Also, the practical implication is that managers can more frequently review their planning parameters according to changes in the level of uncertainty linked to the characteristics of the supply chain, as well as to its environment. Though these results are fairly significant, more work is ob viously needed since we were limited b y the capacity of the software that we used for the simulations. More refined results will certainly require very rob ust models that might b e difficult to simulate, especially if more determinants are simulated and less assumptions are made regarding material flows and supply chain variab ility. Therefore, challenging areas for future research will entail simulating more than six planning determinants from the four SCOR process areas and for different levels of uncertainty. Furthermore, our framework and methodology can b e used to study and compare different supply chain configurations -maketo-stock, assemb le-to-order and make-to-order. The models could also b e tested for different industrial sectors (and different kinds of products) and market environments. Finally, it could also b e challenging to use this framework to study the impact of tactical supply chain planning determinants on b oth internal and external performance measures.

Fig. 1 .

 1 Fig. 1. Performance pyramid. Source: Ref. [51].

3

 3 Delivery lead time -Short (some days) -Medium (some weeks) -Long (some months) 4.4 Forecast accuracy -Low confidence -Medium confidence -High confidence used in this paper b elong to the last category since order fulfilment is considered a component of the delivery and logistics function.

Fig. 2 .

 2 Fig. 2. Integrated tactical supply chain planning determinants b ased on the SCOR model.

Fig. 3 .

 3 Fig. 3. The supply chain.

Finalp

  customers' b ackorders of product p in the PUr at the end of period t x r Quantity of product p never delivered to customer r f r p,t Quantity of product p to b e produced in the PUr during period t l r,r ′ p,t

f

  + f ′ ; f = min(D); PC = x; PC ′ = 1= 15; F2-SH2 = 10; F1-SH3 = 20; F3-SH3 = 5; SH1-Ass = SH2-Ass = SH3-Ass = Ass-Ptr = 2; TC = 0.03•Profit Low Short cycle time F1-SH1 = 7; F2-SH2 = 5; F1-SH3 = 10; F3-SH3 = 5; SH1-Ass = SH2-Ass = SH3-Ass = Ass-Ptr = 1; TC = 0.SS = 0 Not null Safety stock SS = K √ D > 0; K = 1.28; = 7.469

Table 1

 1 Definition of tactical supply chain planning determinants.

Table 2

 2 Determining factors of the tactical planning SCOR processes.

	1. Determinants of the plan process
	1.1 Planning horizon	-Short (monthly)
		-Medium (quarterly)
		-Long (yearly)
	1.2 Frozen time fence	-Short (some days)
		-Medium (some weeks)
		-Long (some months)
	1.3 Time b ucket	-Short (1 day)
		-Medium (1 week)
		-Long (1 month)
	1.4 Cycle time	-Short (some days)
		-Medium (some weeks)
		-Long (some months)
	1.5 Non-frozen interval	-Liquid order policy
		-Slushy order policy
	2. Determinants of the source process
	2.1 Lot sizing (replenishment	-Fixed order quantity
	policy)	-Fixed period quantity
		-Period order quantity
		-Lot-for-lot
	2.2 Inventory policy (materials	-Low safety stock
	availab ility)	-Medium safety stock
		-High safety stock
	2.3 Procurement lead time	-Short (some days)
		-Medium (some weeks)
		-Long (some months)

Table 3

 3 Sources of uncertainty.

	Uncertainty group	Factors of uncertainty	Description
	Internal organisational	Product characteristics	Product life cycle, packaging, perishab ility, mix, or specification.
	uncertainty
		Process/manufacturing	Machine b reakdowns, lab our prob lems, process reliab ility, etc.
		Control/chaos/response uncertainty	Uncertainty as a result of control systems in the supply chain.
		Decision complexity	Uncertainty that arises b ecause of multiple dimensions in decision-making
			process, e.g. multiple goals, constraints, long term plan, etc.
		Organisation structure and human	E.g. organisation culture.
		b ehaviour
		IT/IS complexity	The realisation of threats to IT use in the application level, organisational level
			and inter-organisational level, e.g. computer viruses, technical failure,
			unauthorised physical access, misuse, etc.

Table 4

 4 Model notations.Demand of product p during period t from PUr to PU r ′ Inventory level of product p in the PUr at the end of period t upper/lower safety stock Quantity of product p in the PUr delivered late to customer r ′ at the end of period t

	Parameters
	H	Planning horizon
	K r p,p ′ DP r p DL r,r ′	Bill of materials coefficients Production lead time Transportation lead time
	˛p	Quantity of resource required
	ˇp	Unitary weight or volume
	ıp	Space for stocking one unit p
	CapR r t CapS r t SS r p CapT r,r ′ t AC r t CA r p	Production capacity Storage capacity Safety stock Transport capacity Unitary added capacity cost Unitary purchase price
	CS r p	Unitary average inventory cost
	CP r p	Unitary average production cost
	CB r p	Unitary b ackorder cost
	CR r p	Unitary shortage cost
	CT r p	Unitary transportation cost
	F r p d r,r ′ p,t AL r,r ′	Fixed production quantity Acceptab le delivery lateness of customer r ′ for supplier
		r
	Indexes
	r, r ′	Index of PU (Production Unit)
	p, p ′	Index of products
	t	Index of planning period
	Variables
	i r p,t
	r p,t /(i-) r p,t
	b r,r ′ p,t
	b r p,t

Inventory level of product p in the PUr at the end of period t, for b oth semi-finished and finished products (i+)

Table 5

 5 Simulation data.

	(a)
	Company	Product	Weight or	Stocking	Quantity of	Production	Production	Production	Inventory	Backorder	Shortage
			volume per	space per	resource	capacity	lead time	cost per	cost	cost per	cost per
			unit	unit	required			unit		unit	unit
	PU	p	ˇı	˛CapR	DP	CP	CS	CB	CR
	SC1	Wooden feet	0.25	1	1	40	1	1.25	0.0325	0.325	6.5
	SC2	Wooden tray	1	1	1	10	1	2.5	0.35	3.5	70
	SC3	Wooden b oard	0.05	1	1	600	1	0.62	0.014	0.14	2.8
	Assemb ly plant Primary tab le	2	1	2.6	91	1	5	0.625	6.25	125
		Shelf	2	1	2.6			6.25	0.475	4.75	95
	Painting plant	Painted tab le	2	1	1	30	3	10	0.85	8.5	170
	(b )
	Raw material	Customer company		Purchase cost per unit		Inventory cost	Transport lead time
	p	PU			CA			CS		DL
		SC1			50			0.2		15
	Wood	SC2 SC3			50 50			1.2 1		10 20
		SC3			80			1		5
	Screw	Assemb ly		0.1			0.01		5
	Paint	Painting		2			1		5
	(c)
	Company		Component		End product		Safety stock
			Paint						10
	Painting plant		Primary tab le					12
						Painted tab le		15
			Wooden feet					48
			Wooden tray					12
	Assemb ly plant		Wooden b oard Screw					528 1330
						Primary tab le		9
						Shelf			16
	SC1		Wood			Wooden feet		13 34
	SC1		Wood			Wooden tray		41 9
	SC1		Wood			Wooden b oard		56 373
	(d)
	Determinants		Rules		Description		Parameters
	Period (frozen time fence)	Small		Short period of		T = 5
					replenishment
			Large		Long period of		T = 10
					replenishment
	Manufacturing capacity	Constant
			output rate

Table 6

 6 PCA analysis: what the axes imply.

		Axis 1 (+45.99%)		Axis 2 (+21.99%)
	Positive impacts	Profit margin	+41%	Completeness: shelve	+55%
		NADT: tab le	+29%	Completeness: tab le	+4%
		Completeness: tab le	+13%
	Negative impacts	NADT: shelve Completeness: shelve	-11% -4%	NADT: shelve NADT: tab le	-35% -4%

Clusters Goo d Bad Good Bad Goo d Bad Good Bad Goo d Bad

Completess NADT

Tables' performance Shelves' performance Whole SC

No significative discrimination

Profit Margin

Completess NADT

No preponderan t criteria

Fig. 4. PCA factorial plan and cluster analysis.

Moreover, sensitivity analyses would b e more valuab le in a real world b usiness application with real data.

Step 5: Analysis of the simulation results and decision making Given that this paper aims to explain the relationship b etween comb inations of determinants (simulation scenarios) and performance measures (simulation results), we have chosen to use the Principle Component Analysis (PCA) to analyse our results since it enab les to highlight key quantitative variab les that explain the major quantitative differences b etween the simulation scenarios (especially the effect of scale). A glob al analysis was performed in order to define clusters of the performance results of the simulation scenarios. Based on this, we assessed the common characteristics of the scenarios that composed a cluster in order to highlight the key determinants that can explain the differences b etween the clusters. In other words, a PCA was carried out on the whole of the numerical variab les used in our study: 64 simulations and 5 characters (profit, completeness for tab les, NADT for tab les, completeness for shelves, and NADT for shelves). As can b e seen in Tab le 6, two axes represent ab out 70% of the information and can consequently b e interpreted.

The first axis shows that the glob al profit margin character and the performances on the characters of tab les (NADT and in a lesser manner completeness) are closely related. It appears so b ecause tab les are more profitab le than shelves. If the NADT is good on this product then the profit will equally b e good. We also note that no character is really opposed to this trend. The second axis shows for shelves some discrimination b etween the simulations that have a high level of performance in terms of completeness and those that maximise the NADT performance level.

The PCA singled out five clusters as shown in Fig. 4. We note that cluster no. 5 is not really significant. Its central position indicates that no character of the factorial plan allows isolating the results of the simulation from the others. In other words, cluster no. 5 constitutes the "weak underb elly" of our study. We therefore focus our analysis on the first four clusters.

In order to identify the effect of each determinant on the performance of the supply chain, we first analysed the detailed characteristics of each of the simulations that composed the