A combined financial and physical flows evaluation for logistic process and tactical production planning: Application in a company supply chain

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Int. J. Production Economics 112 (2008) 77–95 www.elsevier.com/locate/ijpe

A combined financial and physical flows evaluation for logistic process and tactical production planning: Application in a company supply chain Mickael Comellia,b, Pierre Fe´nie`sa,b,, Nikolay Tcherneva,b a

LIMOS UMR CNRS 6158, Campus Scientifique des Ce´zeaux, 63173 Aubie`re, Cedex, France IUP Management et Gestion des Entreprises, Universite´ d’Auvergne, Pole Tertiaire, 26 Avenue Le´on Blum, 63000 Clermont-Ferrand, France

b

Received 17 December 2006; accepted 31 January 2007 Available online 10 April 2007

Abstract This paper proposes an approach to evaluate tactical production planning in supply chains. The production panning evaluation is usually based on physical parameters (stock level, demand satisfaction, etc.). Adding financial evaluation to classical evaluation could be relevant. This paper proposes to implement Activity Based Costing (ABC), cost drivers, and payment terms in order to estimate cash flow created by supply chain tactical production planning. Links between financial and physical flow are done by the evaluation of production planning impact on indirect cost. This evaluation is made using logistic process activities. This kind of cost model could be integrated in supply chain software like advanced planning and scheduling (APS) tools. An application of this type of evaluation is done on a real industrial case study. r 2007 Elsevier B.V. All rights reserved. Keywords: Supply chain planning; Performance evaluation; Activity based costing; Cash flow

1. Introduction



A company’s supply chain is ‘‘comprised of geographically distributed facilities such as plants, distributions centres, and supplier’s warehouse’’ and ‘‘transportation links carrying products between facilities’’ (Lee et al., 1997). This chain (Beamon, 1998) is traditionally characterized by three types of flows:



Corresponding author. IUP Management et Gestion des Entreprises, Universite´ d’Auvergne, Pole Tertiaire, 26 Avenue Le´on Blum, 63000 Clermont-Ferrand, France. Tel.: +33 47340 7772; fax: +33 4731 77701. E-mail addresses: [email protected] (M. Comelli), fenies@ isima.fr, [email protected] (P. Fe´nie`s), [email protected] (N. Tchernev).

0925-5273/$ - see front matter r 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2007.01.012



The forward physical flow (purchase of materials, transformations of the raw materials into products, delivery of the products). The physical flow optimization aims to satisfy the final customers. The backward financial flow that circulates in a discontinuous way. The financial flow optimization is made in a local way, in each supply chain link, but seldom in a global way. The financial flow optimization (Badell et al., 2005) will make possible the shareholders satisfaction and the supply chain working improvement. The backward information flow that allows the coordination of financial and physical flow between each node, and the global supply chain coordination.

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The flows integration is usually made by enterprise information system based on software tools like Enterprise Resource Planning (ERP) and/or Advanced Planning and Scheduling (APS). The information flows are out of the scope of paper. The paper objective is to propose an approach to evaluate logistic process performance in supply chain by discussing connections among the physical and the financial flows across the chain. Judging from the literature, these flows do not always overlap in supply chain management. If there are some works which propose to analyze the impact of physical flow in financial flow in strategic planning (Vidal and Goetschlackx, 2001), very few works show relationships between cash position and planning in tactical or operational dimension. A study of supply chain manager interest for integration of financial impact in operational and tactical planning is done by Vickery et al., (2003). These authors show that managers are really interested by tools, which integrate financial and customer aspects in optimization. Despite their real interest, this kind of tools do not yet exist. Hence, the challenging problem consists in formalizing relationships between physical and financial flow by their integration in tactical planning for an internal supply chain (a company supply chain). Our aim (Fig. 1) is to propose an approach that allows the use of budgeting in production planning with APS tools for company supply chain. Indeed, in actual APS, operational and tactical plans do not integrate financial resources synchronisation. This paper proposes to integrate financial metrics in computer model for APS. Regarding to tactical

level, we assume that budgeting and planning financial and physical flows could be synchronized. This kind of synchronization can be seen as a performance driver for supply chain management. By integrating financial parameters such as payments terms in Activity Based Costing (ABC) models and by coupling these kind of models with planning models, each production plan will be associated with a budget and with financial metrics. The paper is organized as follows. Section 2 discusses previous work about cash management and tactical planning evaluation with ABC. Section 3 presents a modelling framework for supply chain evaluation called PREVA for PRocess EVAluation. Section 4 presents computational results based on PREVA approach on a case study. Finally, some conclusions are given in Section 5. 2. Literature review First paragraph deals with tactical planning in supply chain. Second paragraph of this section studies links between physical flow and financial flows in supply chain management 2.1. Tactical planning generic variables in supply chain management Supply chain tactical planning consists in determining quantity of items manufactured or transported across supply chain on a given horizon. Most of the papers about supply chain planning propose mathematical models in order to achieve this goal. All these models rise from a lot-sizing model called

Fig. 1. Towards a planning selection in APS by applying physical and financial flow evaluation.

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‘‘Multi-Level Capacitated Lot-Sizing Problem’’ (MLCLSP). This one, developed by Bellington et al., (1983), links together different production planning with the assistance of a matrix called ‘‘Gozinto’’. From this basic model, specific ‘‘multisite’’ models have been developed for two decades. Rizk and Martel (2001) propose a review of the lotsizing literature dedicated to supply chain. For instance, Gnoni et al., (2003), Thierry, (2004), and Spitter et al., (2005) proposed mathematical approach dedicated to multi-site planning. Whereas these models differ on hypothesis, the considered variables and activities are similar, like the quantity Qi,j,t of item i manufactured in factory j during the period t, as well as the quantity Qi,j,k,t of item i transported to an entity j from to an entity k during the period t. These variables can be considered like generic variables of supply chain tactical planning. 2.2. Financial flows and supply chain management Most part of the authors who propose to study financial flows in supply chain management focuses on cost models such as ABC which was introduced by Cooper and Kaplan (1991). If there are theoretical works on ABC modelling and supply chain management (Se´ne´chal and Tahon, 1998; Hombourg, 2004; Boons, 1998; Bih Ru and Fredendal, 2002), very few works deals with supply chain production planning evaluation using ABC. Connections of tactical production planning and

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ABC are presented by Schneeweiss (1998) and Ozbayrak et al. (2004). The aim of their work is to evaluate production strategies in flexible manufacturing system with ABC tools, not to evaluate supply chain tactical planning. ABC evaluation is not a financial flows evaluation because payment delay and depreciations are not taking into account: therefore, this evaluation only deals with costs and not cash management problem, which is a very important parameter in enterprise financial evaluation in tactical level. The main objective of cash manager is not only to have enough cash to cover day-to-day operating expenses but also to have the fewest excess cash because it is not a productive asset. By having excess cash in account, a company loses the potential interest (an opportunity cost) generated by investing money in securities. This implies that firm has to maintain a balance between amount of cash sitting in account and cash invested in securities. Cash management problem was simply formulated by Baumol (1952) as an inventory problem assuming uncertainty (Miller and Orr, 1966). Two types of metrics are generally used to optimize financial flow: cash position that reveals the cash which is available in the end of a period and cash flow which reveals cash generation during a period. In a recent paper, Badell et al., (2005) optimizes financial flow and cash position in the end of each period. To our knowledge, as shown in Table 1, there are very few scientific works, which integrate financial aspect and physical aspect in tactical planning for supply chain.

Table 1 Cash management formulation and supply chain management: a state of the art Authors

Badell et al. (2005) Baumol (1952) Brown and Haegler (2004) Cattani and Souza (2001) Girlich (2002) Graham and Harvey (2001) Gul (2001) Hendricks and Singhal (2003) Inderfurth and Schefer (1996) Miller and Orr (1966) Orgler (1969) Premachandra (2003) Rink et al. (1999) Salameh et al. (2003) Vidal and Goetschlackx, (2001) Wang (2002)

Horizon level

Studied flow

Operational

Tactical

   

     

 

Strategical

Physical flow

Financial flow



               

 

 

   

    

 



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To conclude, many scientific papers (Se´ne´chal and Tahon, 1998; Gunasekaran and Sarhadi, 1998; Shapiro 1999; Gupta and Galloway, 2004), present that ABC is the best type of cost model for complex manufacturing system because of its connections with supply chain management. Other papers, such as (Chan and Spedding, 2003; Ozbayrak et al., 2004; Satoglu et al., 2006) propose to evaluate ABC cost drivers quantities with discrete event simulation. However, no papers propose to integrate payments terms in ABC approach in order to evaluate financial flows. This idea should be relevant because of causal links between products and indirect cost. These links are revealed in ABC modelling, and integrating payment term in this kind of model could give a way to evaluate cash flow. In next section, an approach that proposes the integration of financial and cash management objectives with physical constraints and ABC modelling in order to evaluate tactical production planning is described.

3. An approach for tactical supply chain production planning evaluation The conceptual principle of the approach is presented in a first paragraph. The second one

proposes a mathematical formalization for planning selection. 3.1. Conceptual principle of the approach In a supply chain company, many activities are carried out during the process of getting raw materials, transforming them into finished products and delivering them to customers. To model each of these activities using ABC for each entity of the chain is technically very difficult. It is also quite difficult to evaluate different plans given by an APS in ‘‘What if’’ scenarios. Therefore, each supply chain physical entity (plant, warehouse, etc.) is modelled with SCOR processes and activities (Supply Chain Council, 2002) in order to be able to specify logistic process and to implement ABC. In the proposed approach, the term ‘‘transfer pricing’’ is used to underline the nature of the supply chain company as one profit generating entity. Each autonomous entity (plant, warehouse, etc.) crossed by logistic process is seen as a business unit of the supply chain. Fig. 2 presents the architecture of the proposed computer models, called PREVA for PRocess EVAluation. This approach is based on a coupling of models that allows supply chain managers to associate to each plan a budget. This approach gives

Fig. 2. PRocess EVAluation approach (PREVA).

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the possibility to evaluate planning using financial and physical metrics. PREVA recommends the following steps: Step 1—Physical process evaluation: The generic variables of supply chain tactical planning described above can be used. A specification of the supply chain which has to be evaluated is done using a modelling methodology called ASDI (Chabrol et al., 2006). This methodology recommends to specify supply chain processes with Architecture of Integrated Information Systems (ARIS (Scheer, 1999)). This specification, called knowledge model, is translated into action models (mathematical and/ or simulation models), which gives physical flow planning. Step 2—ABC modelling and financial flow evaluation: Physical variables (planning) given by physical flows action models are used as input data by the proposed action model for financial flows. This one, a mathematical model, determines indirect costs consumption thanks to logistic process cost evaluation on each entity and for the global supply chain. In this context, the cost of an item (a product or a service) is the summation of the costs of activities and processes in each entity with the costs of direct resources. Furthermore, stock value can be evaluated in the chain. The potential of value creation is also evaluated by combining the difference between demand and quantities sold by an entity or the global chain with contribution margin. Between each supply chain entity, physical flows transactions are evaluated with transfer price (or with market price if the business unit is in contact with final customer). Using transfer prices in the same enterprise assumes to model each supply chain entity as a business unit. So, differences between ABC costs and transfer prices or market prices give managers the possibility to evaluate ABC value creation in every business unit of the chain, as well as for the whole supply chain. Fig. 3 explains the ABC part which is implemented in the proposed mathematical model. By applying ABC evaluation, direct and indirect resources consumptions and net sales are determined. Because of the nature of the cost (depreciation and real cost) and because of the payment term which is different between each type of resources and each type of customers, in medium term there is a significant difference between profit level and cash flow in the same period. Therefore, preceding periods have an impact on actual period in cash flow evaluation. The proposed

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mathematical model integrates cash flows evaluation thanks to resources consumption evaluation. Therefore, cash position and cash flow are evaluated in each business unit of the company supply chain and as well as for the company supply chain. Fig. 4 explains the translation from ABC modelling to cash flow evaluation. Cash flow for the whole company supply chain is modelled as the summation of the difference between cash collected and cash paid in each business unit. Next paragraph details a mathematical formalization for production planning selection in company supply chain using financial metrics. 3.2. Formalization for production planning selection in a company supply chain The investigated model is mainly defined by a set I of items (product, service, etc.), a set J of supply chain entities, called business unit (warehouse, plan, etc.), and a set T of periods. The specification of the system is done with ARIS modelling methodology, by using event-driven process chain (See Fig. 5). The specification gives for each activity of the logistic process the cost driver for ABC modelling. Furthermore, the evaluation will associate the set I of items, the set J of supply chain entities, and the set T of periods with parameters given by production plans and supply chain parameters given by data warehouse or company ERP. The major step in extending ABC with cash flow is to describe the impact of physical flow on financial flow. Fig. 6 shows an event-driven process chain that explains the proposed formalization. Let us define the following notations that will be used to describe the model built to calculate the supply chain financial function: Sets and indices I set of item, i an item, iAI; J set of business unit, j a business unit, jAJ; T set of period, t a period, tAT; Q set of planning, q a planning, qAQ; Z set of resource, z a resource, zAZ; B set of logistic activity, b an activity, bAB. Parameters Defined variables of planning q in period t QFi;j;t;q Quantity of item i manufactured by business unit j, QVi;j;t;q Quantity of item i sold by business unit j,



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Fig. 3. ABC value creation evaluation for an item, a business unit and the whole company supply chain in PREVA approach.

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Fig. 4. From ABC modelling to cash flow evaluation in supply chain business unit.

Fig. 5. Event driven process chain and PREVA specification.

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Fig. 6. Supply chain value creation evaluations according to planning q at the end of the period t.

Quantity of item i in opening stock in business unit j, QSFi;j;t;q Quantity of item i in closing stock in business unit j, ri;j;t;z;q Quantity of resources z consummated by item i in business unit j, aj;t;z;b;q Quantity of resources z consummated by activity b in business unit j, hi;j;t;b;q Quantity of activity b cost driver consummated by item i in business unit j, Di;j;t;q Demand for item i in business unit j.  Supply chain ERP parameters given by data warehouse during a period t for a planning q Pi;j;t;q Market price or transfer price of item i sold by business unit j, cj;t;z;q Unit cost of resource z in business unit j, cdj;t;b;q Unit cost of activity b cost driver in business unit j, dej;t;z;q Term of payment for resource z in business unit j, dpi;j;t;q Term of cash collect for item i sold by business unit j.

CRPVi;j;t;q Cost of sold item i in business unit j, SIi;j;t;q ; Value of item i in opening stock in business unit j, SFi;j;t;q Value of item in closing stock in business unit j, E j;t;q Overall resources paid by business unit j, Rpj;t;z;q Resource z paid by business unit j, PPj;t;q Net sales collected by business unit j, Ppi;j;t;q Net sales collected of item i by business unit j, CPj;t;q Business unit j cash position.

Variables Decision variables for a planning q during a period t M i;j;t;q Value creation done by item i in business unit j, CFj;t;q Cash flow in business unit j, PCVi;j;t;q Potential of value creation for item i in business unit j.  Auxiliaries variables for a planning q during a period t Rj;t;z;q Resource z cost consummated in business unit j, DCi;j;t;q Global item i direct cost in business unit j, dci;j;t;q Unit item i direct cost in business unit j, Cabci;j;t;q Logistic process cost for item i in business unit j, CAi;j;t;q Net sales for item i in business unit j, CRPFi;j;t;q Cost of manufactured item i in business unit j,

Using the evaluation function, the planning which has the highest level will be chosen. Details of the proposed modelling are given in Appendix A. Eqs. (A.2)–(A.9) from Appendix A specify value creation from a planning thanks to ABC models. Note that in this evaluation, inventory enters in the evaluation thanks o (A.4) and (A.8). From Eqs. (A.2)–(A.9), the value creation evaluation is obtained and given by the following formalization (Fig. 6). Eqs. (A.10)–(A.15) from Appendix A define cash flow level in supply chain. Eq. (A.13) and (A.14) show the link between ABC model and cash flow evaluation thanks to consumption resources and payment term. From Eqs. (A.2)–(A.15), cash flow evaluation is given by the following formalization (Fig. 7). Detailed evaluation of potential of value creation (Fig. 8) is done using Eqs. (A.6), (A.7), and (A.16) from Appendix A.

QSIi;j;t;q



The above parameters, variables, and auxiliaries variables are used to construct the following supply chain evaluation function F(q) where a 2 Rþ ; b 2 Rþ ; g 2 Rþ : 8q 2 Q : F ðqÞ ¼

XX t2T j2J

" b  CFj;t;q þ

 þg  PCVi;j;t;q Þ .

X

ða  M i;j;t;q

i2I

ð1Þ

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In supply chain function evaluation (1), a, b, g give coefficient to each chosen performance metric. Table 2 explains various possibilities. Theses coefficients give supply chain manager the possibility to select planning from many financials aspects. An operating statement for each business unit in a company supply chain performance evaluation is easy to generate using the system of Eq. (A.2)–(A.16) of Appendix A. This mathematical model implements SCOR activities (set B of logistic activities) in order to give supply chain manager the possibility to take into account all the supply chain activities. Therefore, supply chain manager has to define activities which must be evaluated thanks to SCOR activities. Two computer models will evaluate logistic process of these activities: a computer model (an optimization or a simulation) will evaluate physical flows processes and a second model, which implements the proposed mathematical model, will evaluate financial flows of logistic process. How if the proposed model takes into account fixed and variable costs, the coupling of mathematical model with a computer model for physical flow evaluates mainly variable direct and indirect cost. To conclude this section, Fig. 9 shows a synthesis of our approach which allows to evaluate value creation, cash flow, cash position and potential of value creation induced by a tactical plan in the whole company supply chain as well as in each entity. Based on resources consumption in ABC and resources payment, a bridge is done between physical flows impact on financial flows. In Fig. 9, elements given thanks to physical flows computer models, supply chain ERP and financial flows computer models are detailed.

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In next section, the proposed modelling is applied on a company supply chain. 4. PREVA application on a company supply chain To illustrate the approach presented in previous section, an industrial case study, which is adapted from a real industrial application done in Clermont Ferrand Computer Laboratory, is firstly described. An instance of PREVA approach for this case study is given in paragraph 2. Score cards and results are given in paragraph 3. 4.1. Case study presentation The company supply chain, called M (a tyre manufacturer) is comprised of six business units, called B1, B2, B3, B4, B5, and B6. Six families of product, called P1, P2, P3, P4, P5, and P6 are manufactured in the system. Fig. 10 presents the supply chain infrastructure. This company supply Table 2 Supply chain manager strategy in using model a, b, g

Signification

aXbXg

Supply chain manager wants to select plans using ABC value creation. In case of planning equivalence, cash flow levels are used to decide. Supply chain manager has not financial difficulties and wants to improve value creation Company supply chain has big financials difficulties and has to focalize on cash position. Supply chain manager wants to select plans mainly from financial point of view Other order for a, b, g is not relevant.

a ¼ 1, b ¼ 0, g¼0 a ¼ 0, b ¼ 1, g¼0 bXaXg Other value

Fig. 7. Supply chain cash flow evaluation according to planning q at the end of the period t.

Fig. 8. Supply chain potential of value creation according to planning q at the end of the period t.

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Fig. 9. ABC and cash flow evaluation: a bridge between physical and financial flow.

chain is shared in four steps. In first step, one business unit called B1 is considered. In step 2, B2, B3, B4 are working both together and have exactly the same structure. In step 3, product from B2, B3, B4 are manufactured in B5 on a special plateform. This kind of plateform gives the product its name and its quality. In step 4, B6 products are prepared for European, Asian, American markets. M logistic processes are as follows: (i) B1 is made up of a factory and a warehouse in which products are stocked; (ii) B2 is made up of one factory; (iii) B3 is made up of one factory; (iv) B4 is made up of one factory; (v) B5 is made up of one warehouse and one factory. Products from B2, B3, B4 are stocked in B5 logistic platform before being transformed in B5 factory; (vi) B6 is made up of two warehouses and one factory. The first B6 warehouse, which is

implanted before B6 factory stocks products from B5 and from external suppliers. The second B6 warehouse, which is implanted after B6 factory stocks final products;  Distribution Requirement Planning (DRP) is done by M supply chain manager; production planning of each supply chain. business unit is done by collaborative planning. This internal collaborative planning is elaborated during meetings and by exchange between M supply chain manager and each business unit supply chain managers;  logistic processes in business unit are modelled with SCOR processes (source, make, deliver) and specified with ARIS (Figs. 11 and 12). A modelling study was done in collaboration with business unit costing managers and supply chain managers, cost drivers are determined for each supply chain process in each business unit. Table 3

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Fig. 10. Supply chain structure.

details for each process the associated cost driver and the budgetary responsable. Note that this kind of study is done for each granularity level. The major objective of this study is to compare two supply chain management strategies in order to elaborate a good collaborative supply chain planning:

 

first strategy is called Pull strategy and consists in managing supply chain with a pull approach and second strategy is called Financial Pull strategy and consists in integrating financial constraint in product selection during supply chain planning elaboration.

The planning horizon level is 12 months, and time periods are the week. In order to compare these two supply chain strategies, PREVA approach is applied. Next paragraph presents implementation of action models, and score cards and metrics are presented in last paragraph. 4.2. PREVA instance on m company supply chain As seen in Section 3.2, a specification of the modelled system is done with ARIS. This specification (Figs. 11 and 12) details supply chain logistic processes, the associated cost drivers, and decisional rules. Each process is detailed in others ARIS models (not presented in the paper). Considering the complexity of the case study, discrete event simulation was preferred to mathematical models for many reasons such as modelling constraints and

computation time. Hence, simulation is used to reproduce supply chain working during 12 months. The ARIS model specification of supply chain running is translated in discrete event simulation computer model in Arena 7.0. Both plans were obtained from two dedicated heuristics which are integrated in the computer simulation model. Indeed, the difference between both plans results from the choice of manufactured products after each end of production lot on each factory. Production plans given by heuristics and simulations are then evaluated thanks to an instance of the financial mathematical model. For each plan, a financial budget is then associated. Fig. 13 presents the PREVA instance which is done for this case study. Both heuristics reproduce supply chain managers behaviours in business unit. The first one reproduces supply chain managers behaviours in business units in order to build a planning with a pull strategy. This heuristic is a greedy algorithm which chooses the type of products which gets the least autonomy. The calculation of a parameter called ‘‘Autonomy’’ corresponds to the number of periods where customer demand can be satisfied with the actual stock level of finished products. This heuristic is used as a benchmark to evaluate other physical flows strategies. This one is called pull strategy. Its description is given in Algorithm 1. Algorithm 1. Heuristic for Pull Strategy The second heuristic is similar to pull strategy but integrates financial constraints. Product selection

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Fig. 11. M supply chain specification.

process integrates financial preferences, and product family which has the smallest payment term is firstly chosen. This strategy is called financial pull. Its description is given in Algorithm 2. Algorithm 2. Heuristic for financial pull strategy Regards to these data, these strategies consist in prioritizing in each factory the family of products, which get the least autonomy. State of system is used as input data by two heuristics. Each heuristic takes into account finished products stock levels since these data allow estimating supply chain autonomy for each factory (autonomy i). A full

strategy is thus reproduced along the supply chain. The outline of autonomy calculation procedure is given in Algorithm 3. Algorithm 3. Autonomy calculation procedure These heuristics are implemented in a Discrete Event Simulation Model done with ARENA. Using this simulation model, supply chain running is reproduced, and plans can be generated. Plans of physical flows gives:



quantity of products in each BU and in the whole supply chain,

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Fig. 12. M supply chain specification.

 

quantity of cost drivers used by each process in each BU and in the whole supply chain, and quantity of products sold and stored in each BU and by the whole supply chain.

In a second time, these plans are evaluated by a second model. This second model combines information from physical flows planning with data given by supply chain ERP. This financial computer model is done and linked to Discrete Event Simulation Model with Visual Basic. Both models reproduce financial flows running under supply chains management strategies and give physical and financial metrics.

4.3. Results model evaluation The results for physical flows evaluation by PREVA approach after 12 periods of 1 month are given in Tables 4 and 5. As mentioned above, the chosen model for this step is the simulation one. This model was developed with Arena 7.0 and the simulation run takes 3 min on a PC with 1.8 GHz processor and 256 Mo of RAM. The global model (which links physical and financial flows) takes 10 min after 12 periods of 1 month. Customer satisfaction is a metric, which translates the level of satisfied demand by comparing customers orders with satisfied orders.

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Table 3 Cost drivers and logistic processes in supply chain business unit Business unit

Process

Associated cost driver

Process responsable

BU1

Source Make Storage/deliver

Supplier number Production setup Delivered product quantity

BU 1 supply chain manager BU 1 supply chain manager M supply chain Manager

BU2

Source Make Deliver

Product type number Production setup Delivered product quantity

M supply chain manager BU 2 supply chain manager BU2 supply chain manager

BU3

Source Make Deliver

Product type number Production setup Delivered product quantity

M supply chain manager BU 3 supply chain manager BU 3 supply chain manager

BU4

Source Make Deliver

Product type number Production setup Delivered product quantity

M supply chain manager BU 4 supply chain manager BU 4 supply chain manager

BU5

Source/storage Make Deliver

Product type number Production setup Delivered product quantity

M supply chain manager BU5 supply chain manager BU5 supply chain manager

BU6

Source /storage Make Storage/deliver

Product type number Production setup Sold product quantity

M supply chain manager BU6 supply chain manager M supply chain manager

M supply chain

Plan DRP Production planning

Product type number Production setup

M supply chain manager M supply chain manager

Fig. 13. PREVA instance for M supply chain.

The results presented above are given for the whole planning horizon. Note that it is possible to detail them for each period. Results show that the closing stock level is better (in quantity) in strategy

pull than in strategy financial pull. However, it is quite difficult for supply chain manager to choose the strategy since the demand satisfaction level is nearly the same as well as the number of production

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setups. Therefore, an evaluation of cash flow level and ABC margin will help supply chain manager to have more information and by this way to take the right decision. So the next step of the approach consists in combining simulation results with steps 2 of PREVA approach. The strategies are evaluated thanks to the financial decisional model. Same income statements are created for each business unit and for the global supply chain. The computations

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use the mathematical model developed in Section 2. All results are given in Euros. Budgets could be given for each product in each business unit for each period. Coefficients in financial supply chain evaluation function are equivalent. It is very interesting to evaluate (as shown in Section 3) value creation (Fig. 14), cash flow level and cash position (Fig. 15), potential of value creation (Table 6). Value creation evaluation gives

Table 4 Customer satisfaction for each supply chain strategy Products

P1

P2

P3

P4

P5

P6

Global SC

(a) Physical flow evaluation in financial pull strategy (on the whole period) Quantity manufactured  1000 565 521 470 Customer satisfaction 100 100 50 Number of production setups 26 21 25

316 50 19

212 100 17

192 100 17

2276 83 125

(b) Physical flow evaluation in pull strategy (on the whole planning period) Quantity manufactured  1000 577 532 570 Customer satisfaction 100 100 80 Number of production setups 26 21 25

578 100 19

13 40 17

16 40 17

2286 83 125

Table 5 Stocks level (in quantity) in business unit at the end of the planning horizon Products in financial pull Business unit

P1

P2

P3

BU1 BU2 BU3 BU4 BU5 BU6 Global

1200 10000 9700 No storage in BU2 No storage in BU3 No storage in BU4 0 0 0 5600 19900 5480 6800 29900 15180

Pull S

Products in pull P4

P5

P6

Total

P1

P2

P3

P4

P5

P6

Total

10000

800

800

32500

0

1600

0

1600

21000

18600

42800

0 4900 14900

0 3100 3900

0 2900 3700

0 41880 74380

0 2000 2000

0 17200 18800

0 3580 3580

0 2200 3800

0 1300 22300

0 600 19200

0 26880 69680

Pull

FinancialPull

50000000

50000000

40000000

40000000

30000000

30000000

20000000

20000000

10000000

10000000

0

Financial Pull

0 BU1

BU2

BU3

BU4

BU5

-10000000

BU6

Supply Chain

P1

P2

P3

P4

P5

-10000000

Value Creation per business Unit

Value Creation per product

Fig. 14. Value creation.

P6

Supply Chain

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92 Pull

Financial Pull

Pull

Financial Pull

25000000 60000000 20000000

50000000

15000000

40000000

10000000

30000000

5000000

20000000

0

10000000 1

2

3

4

5

6

7

8

9

10

11

12

-5000000

0 1

Cash Flows

2

3

4

5

6

7

8

9

10

11

12

Cash Position

Fig. 15. Physical flow impact on financial flow: cash position and cash flow generated by different planning.

Table 6 Potential of value creation in strategy financial pull and in strategy pull

Potential of value creation in financial pull Potential of value creation in pull

P1

P2

P3

P4

P5

P6

Global SC

0 0

0 0

0 0

3859751 0

0 10347757

0 9740813

3859751 20088570

manager the possibility to know which products create value (ABC margin) and where (in which business unit) value is created and where value is destroyed because of planning. To conclude this section, Table 7 gives final results and planning selection. In Figs. 14 and 15, graph is very similar in period 1, 2, 3. The impact of planning strategy on financial flow is observable only three months after its beginning because of payment term, which is 3 months for main products P1 and P2 in this industrial case study. 5. Conclusion and further researches This paper proposes a global evaluation approach for flows (physical and financials) in company supply chain. This approach, called PREVA for PRocess EVAluation gives the possibility to associate planning and budgeting processes in company supply chain. In case study, the financial model is coupled with simulation model. This choice, explained in Section 4.2, will allow integrating stochastic phenomenon (such as demand variability, uncertain breakdown, etc.) in order to study strategies robustness. Nevertheless, the proposed approach is generic and leaves the possibility to be

Table 7 Final result evaluation and planning selection Financial pull strategy

Pull strategy

Customer satisfaction E E Stock level in quantity E +E Classical physical flows metrics gives very few differences: its very difficult for supply chain manager to select planning a ¼ 1; value creation +  b ¼ 1; cash flow +  Cash position +  g ¼ 1; potential of value +  creation Planning selection + Using PREVA approach, supply chain manager is able to select planning with physical and financials parameters

used with mathematic models implemented in classical APS software. This approach was tested in an internal supply chain (a multinational company) and gives the possibility to select plans. The proposed framework provides more than an ABC approach. Of course, it improves the visibility of cost but the most important point in this approach is to show how physical flow impact is passed down to financial

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flow by taking into account payment terms. PREVA gives managers the possibility to select planning. The choice is based on:

8ði; j; t; qÞ 2 I  J  T  Q : CAi;j;t;q



8ði; j; t; qÞ 2 I  J  T  Q : CRPVi;j;t;q ! SIi;j;t;q þ CRPFi;j;t;q ¼ QVi;j;t;q  QSIi;j;t;q þ QFi;j;t;q



physical metrics (given by physical flows computers models) and financial metrics (given by the generic financial flow model).

¼ QVi;j;t;q  Pi;j;t;q ;

supply chain net sales evaluation;

ðA:3Þ

activity based costing evaluation; This approach completes actual approach in tactical planning selection. Since the approach is generic for logistic process in supply chain, it would be interesting to test it on other decisional levels such as operational or strategic level. Moreover, this approach, built for logistic process evaluation in company supply chain could be used in others domains (services, health care systems, etc.), it would be relevant to test it in such domains. In further research, the integration of ABC and cash flow in optimization model will be proposed in order to improve supply chain working. Exchange rates will be taken into account. An extension of this approach in a global supply chain (external) is now on test in order to improve its working and give supply chain manager the possibility to share value creation which is done by collaborative planning between supply chain partners.

Appendix A. Details of the proposed formalization The above parameters, variables, and auxiliaries variables are used to construct the following supply chain evaluation function F(q), where a 2 Rþ ; b 2 Rþ ; g 2 Rþ : 8q 2 Q :

F ðqÞ ¼

XX

"

þ

X

8ði; j; t; qÞ 2 I  J  T  Q : SIi;j;t;q ¼ dci;j;t1;q  QSIi;j;t;q ;

stock evaluation;

ðA:6Þ 8ði; j; t; qÞ 2 I  J  T  Q : DCi;j;t;q X ¼ ðri;j;t;z;q  cj;t;z;q Þ; direct cost item evaluation; z2Z

ðA:7Þ 8ði; j; t; qÞ 2 I  J  T  Q : CRPFi;j;t;q ¼ DCi;j;t;q þ Cabci;j;t;q ;

cost item evaluation; ðA:8Þ

8ði; j; t; qÞ 2 I  J  T  Q : Cabci;j;t;q X ¼ ðhi;j;t;b;q  cdj;t;b;q Þ, b2B

indirect cost in item evaluation;

ðA:9Þ

CFj;t;q ¼ PPj;t;q  E j;t;q ,

cash flow evaluation;

b  CFj;t;q #

ða  M i;j;t;q þ g  PCVi;j;t;q Þ ,

ðA:5Þ

8ði; j; t; qÞ 2 I  J  T  Q : dci;j;t;q DCi;j;t;q ¼ ; unit direct cost item evaluation; QFi;j;t;q

8ðj; t; qÞ 2 J  T  Q :

t2T j2J

ðA:4Þ

8ðj; t; qÞ 2 J  T  Q : PPj;t;q ¼

ðA:10Þ X

Ppi;j;t;q ,

i2I

i2I

supply chain evaluation function;

business unit net sales evaluation;

ðA:11Þ

ðA:1Þ 8ði; j; t; qÞ 2 I  J  T  Q : Ppi;j;t;q ¼ CAi;j;th;q with dpi;j;t;q ,

8ði; j; t; qÞ 2 I  J  T  Q : M i;j;t;q ¼ CAi;j;t;q  CRPVi;j;t;q ;

value creation evaluation;

ðA:2Þ

evaluation of item cash collect in business unit; ðA:12Þ

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X

8ðj; t; qÞ 2 J  T  Q : E j;t;q ¼

Rpj;t;z;q ,

z2Z

evaluation of global payment in business unit; ðA:13Þ 8ðj; t; z; qÞ 2 J  T  Z  Q : Rpj;t;z;q ¼ Rj;tg;z;q with g ¼ dej;t;z;q , evaluation of resource payment in supply chain business unit; ðA:14Þ 8ðj; t; z; qÞ 2 J  T  Z  Q : Rj;t;z;q ¼ cj;t;z;q 

X i2I

ri;j;t;z;q þ

X

!

aj;t;b;z;q ,

b2B

evaluation of resource consumption;

ðA:15Þ

8ði; j; t; qÞ 2 I  J  T  Q : PCVi;j;t;q ¼ ðPi;j;t;q  dci;j;t;q Þ  ðDCi;j;t;q  QVi;j;t;q Þ, potential of value creation:

ðA:16Þ

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