Uche Okongwu
email: u.okongwu@esc-toulouse.fr

Jean-Christophe Deschamps
email: jean-christophe.deschamps@ims-bordeaux.fr

Matthieu Lauras
email: matthieu.lauras@mines-albi.fr

Julien François
email: julien.francois@ims-bordeaux.fr

Impact of the Key Determinants of a Distributed Planning Process on the Performance of a Supply Chain

Keywords: planning process, supply chain management, performance measurement, efficiency, effectiveness, responsiveness

In today's highly competitive world, firms try to maximize value for their customers and other stakeholders through the effective management of their supply chains. Given that performance measurement considerably affects the actions of firms' decision makers, it is increasingly important to understand the key factors that can significantly affect the performance of the supply chain. Using analytical models and simulations, this article aims to identify and analyse the key determinants (both financial and nonfinancial) of a distributed planning process that affect the performance of the supply chain. The identified determinants are discussed with respect to three performance dimensions: efficiency, effectiveness, and responsiveness. Our results show that different combinations of the determining factors have varying positive or negative effects on these three performance dimensions.

Introduction

Supply chains (SCs) are facing growing pressures due to globalisation, harsh competition, fluctuating energy prices, and volatile financial markets. Their strategic goals aim to reduce costs, improve customer services, increase reliability and efficiency of operations, and perform fast delivery of products to markets. These strategic goals can be achieved by effectively designing, monitoring, and controlling the various processes that constitute the SC. The Supply Chain Operations Reference (SCOR) model enables one to identify the five main processes (plan, source, make, deliver, and return) that constitute a supply chain. The determinants of each of these processes can affect SC performance.

Many researchers have studied the relationship between the determinants of three of these processes (source, make, and deliver) and supply chain performance, but the determinants of the planning and return processes have not been sufficiently explored. Moreover, the few studies that have been done on this topic are generally limited to manufacturing [START_REF] Wacker | Effectiveness of manufacturing planning and control systems on manufacturing competitiveness: Evidence from global manufacturing data[END_REF][START_REF] Olhager | Manufacturing planning and control approaches: Market alignment and performance[END_REF] and to the financial aspect of performance [START_REF] Reiner | Efficiency analysis of supply chain processes[END_REF]. Christopher (1998, p. 15) defines a supply chain as "the network of organisations that are involved, through upstream and downstream linkages, in the different processes and activities that produce value in the form of products and services delivered to the ultimate consumer." De Man and Burns (2006, p. 2) state that "a supply chain links production units, one unit's outputs providing inputs into another unit or multiple units." These production units can be within the same organisation or different organisations within a supply network. This article studies the second case, that is, a supply chain where one organisation's output provides inputs to another organisation until delivery of the end products to the final customer. In order to maximise the overall performance of the SC and collectively satisfy the ultimate customer, the different companies should plan their production and delivery activities in a distributed, coordinated, and collaborative manner.

This article aims to study how the determinants of a distributed planning process affect the financial and nonfinancial elements of supply chain performance. It is organised as follows. First, a review of the literature enables us not only to develop our research framework but also to formulate our research hypotheses. Then we present the characteristics of the supply chain studied in this article as well as the mathematical model used to perform some numerical simulations. Subsequently, we present and discuss the results of the simulation. Finally, we draw conclusions, state some limitations and present suggestions for further research.

Literature Review and Hypotheses

A literature review [START_REF] Arnold | Introduction to material management[END_REF][START_REF] Chase | Operations management for competitive advantage[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[END_REF][START_REF] Slack | Operations management[END_REF][START_REF] Stadtler | Supply chain management and advanced planning 1st Edition[END_REF][START_REF] Stevenson | Operations management[END_REF] enabled us to identify 10 determinants of the planning process (as shown in Table 1): planning horizon, time bucket, frozen time fence, capacity management, lot sizing, inventory management, sequencing, scheduling, forecast accuracy, and cycle time.

These determinants can positively or negatively affect the performance of the SC. [START_REF] Walters | Effectiveness and efficiency: The role of supply chains management[END_REF] and [START_REF] Rainbird | Demand and supply chains: The value catalyst[END_REF] argue that although the upstream part of the SC emphasises efficiency (which consists of minimising operational costs), the downstream part emphasises effectiveness (which entails an effective response to customer expectations). In other words, the upstream SC tends to be lean (efficient) by eliminating wastes whereas the downstream SC tends to be agile (effectively responsive) by providing speedy and accurate response to customer expectations. The expression effectively responsive could be broken down into two components: effectiveness (which measures the completeness of the order) and responsiveness (which measures the speed at which the order is delivered).

It follows that a supply chain can be designed, planned, and controlled so as to maximise efficiency, effectiveness, or responsiveness. Many authors [START_REF] Holweg | An investigation into supplier responsiveness: Empirical evidence from the automotive industry[END_REF][START_REF] Reichhart | Creating the customer-responsive supply chain: A reconciliation of concepts[END_REF][START_REF] Stevenson | Flexibility from the supply chain perspective: Definition and review[END_REF][START_REF] Walters | Effectiveness and efficiency: The role of supply chains management[END_REF][START_REF] Zokaei | Achieving consumer focus in supply chains[END_REF] have underlined the vagueness, the multidimensionality, and the interdependency in the definitions of these three performance criteria. For example, as cited by Kritchanchai and MacCarthy (1999, p. 814), [START_REF] Barclay | Improving competitive responsiveness via the virtual environment[END_REF] defined responsiveness as "the ability to react purposefully and within an appropriate timescale to significant events, opportunities or threats (especially from external environment) to bring about or maintain competitive advantage." This definition implicitly incorporates range and time dimensions just as most definitions of flexibility, such as that given by Slack et al. (2007, p. 39): "the ability to change far enough and fast enough to meet customer requirements." The multidimensionality of and Determining factors of the planning process.

interdependence between these performance criteria can be seen explicitly in the following sentences extracted from Sanchez and Pérez (2005, p. 683):

The two most agreed upon dimensions for measuring flexibility of any type are range and response [START_REF] Gerwin | Manufacturing flexibility: A strategic perspective[END_REF][START_REF] Upton | The management of manufacturing flexibility[END_REF]. The range dimension measures the variety of available alternatives for the system adaptation, so that it may continue to operate. This dimension is associated with the system effectiveness and is typically measured by counting the number of options or by a normalised index. The response dimension measures the easiness with which the adaptation can be carried out in terms of the reaction time (or cost) needed to respond to the change that occurred. This dimension may thus be associated with the system efficiency.

Here, responsiveness, efficiency, and effectiveness are all incorporated in flexibility. In this article, we do not intend to review the literature of these terms but simply to adopt a strict and clear definition of each of them, such as to be able to define the various strategies that will be used in our model. Therefore, we will adopt the following restrictive and onedimensional (or single factor) definitions:

• Efficiency is doing things right [START_REF] Zokaei | Achieving consumer focus in supply chains[END_REF] and can be defined as the cost of fulfilling customer orders. Although it is generally measured with respect to the best possible way of doing something, some authors define it in relative terms as the best of all possible ways of doing something [START_REF] Bescos | Contrôle de gestion et management[END_REF][START_REF] Halley | Logistics behaviour of small enterprises: Performance, strategy and definition[END_REF][START_REF] Mas-Colell | Microeconomic theory[END_REF]. • Effectiveness is doing the right thing [START_REF] Zokaei | Achieving consumer focus in supply chains[END_REF] and can be defined as fulfilling orders exactly as they are requested by customers (that is, the completeness of customer orders).

• Responsiveness is doing things quickly and can be defined as the speed at which customer orders are fulfilled.

If leanness is linked to efficiency, agility is linked to effectiveness and responsiveness. When a system aims to achieve a balance between efficiency and effectiveness/ responsiveness, it is said be leagile. Naylor et al. (1999, 108) defined leagility as "the combination of the lean and agile paradigms within a total supply chain strategy by positioning the customer order decoupling point so as to best suit the need for responding to a volatile demand downstream yet providing level scheduling upstream from the decoupling point." Partial leagility can be achieved by searching for a tradeoff between efficiency and effectiveness or between efficiency and responsibility.

Each or a combination of the 10 determinants of the planning process listed in Table 1 2007) concluded that, contrary to common sense in operations management, the longest processing time sequencing rule is able to maximise the total production volume per day. [START_REF] Barut | Revenue management in order-driven production systems[END_REF] investigated the effectiveness of a tactical demandcapacity 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-firstserved policy. Looking at the demand side of a supply network in a configure-to-order environment, [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 variability, and configuration capacity) and observed that all the three variables individually and interactively influence customer service performance. From this review of extant literature, we can formulate two hypotheses: H1: When combined with other determinants, a given determinant of a planning process can positively or negatively affect the performance of a supply chain. H2: Given that efficiency is often traded off against responsiveness, a given combination of determinants that positively affects the former will negatively affect the latter. This article aims not only to validate these two hypotheses, but also to find the various combinations of determinants #that have a positive or negative effect on one, two, or all three performance dimensions (efficiency, effectiveness, and responsiveness).

Problem Formulation

In this section, we will present the characteristics of the supply chain studied in this article; the selected determinants that we tested; the three performance metrics that correspond to efficiency, effectiveness, and responsiveness; and the mathematical model used in our simulation.

The supply chain

As shown in Figure 2 The supply chain deliver wooden parts (table legs, table trays, and shelf boards) to the assembly plant that constitutes the second and final stage for the shelves. After assembly, tables are shipped to a third stage for the painting operation. The sawmills, the assembly plant, and the painting unit are assumed to collaborate in a dyadic suppliercustomer relationship. Each plant is considered to be a single resource with a limited capacity. We assume an ideal situationconstant and regular, without shortage -for the supply of raw materials (tree trunks) to the sawmills and the paint plant.

Transport is required between any two plants. Activities and resources needed for transportation are lumped together such that each transport operation is characterized by a given capacity (limiting transfer throughput) and a lead time. Each plant is solely responsible for managing its processes -procurement, production, and delivery. Planning of operations is jointly done by all the partners. Therefore, the lead time for the transmission of information is zero.

Performance metrics

In section 2, we stated that the performance of the supply chain can be measured using three distinct parameters -efficiency, effectiveness, and responsiveness. We will now define the metrics that will be used to express these parameters.

Efficiency can be measured either in terms of cost or profit margin. An efficient system aims to minimise cost, thereby maximising profit. Because the ultimate goal of the supply chain partners is to make a profit, we will use the profit margin as a metric to measure efficiency. In this study, we will normalise it by taking the highest profit as 100% and then calculating the others with respect to it.

Effectiveness can be measured in terms of the percentage of ordered quantities that are delivered within a given time frame. Its value can go from 0% to 100%. Zero percent means that nothing is delivered on time and 100% means that all the ordered quantities are delivered on time. This performance metric will be referred to as completeness.

Responsiveness can be measured as the normalised average delivery time (NADT) of the total quantity that is delivered. For example, let us consider an order of 100 units of a product to be delivered in week 1 (due date), with a maximum acceptable lateness of 4 weeks beyond the due date.

Notations

Being normalised, the value of responsiveness must be between 0 and 100%, the latter being the best. A value of 100 means that 100% of the ordered quantity was delivered on or before the due date; a value close to 100 signifies that a high percentage of the ordered quantity was delivered on or before the due date and that most of the delivery was done (quickly) within the first period after the due date, whereas a value close to 0 signifies that most of the delivery was done (late) within the last period of the acceptable time frame after the due date.

Mathematical model

The simulation of the planning decision-making process performed by each partner (also called production unit [PU]) in the supply chain is obtained by solving a generic linear programming model. The notations used to describe the model are summarized in Table 2.

The model plans production, inventory levels, replenishment, and delivery according to customers' demand. The cost function or criterion (1) concerns the financial aspect and ensures that the decision-making process is efficient by minimizing costs related to production, inventory, purchased materials, shortages (considered as never delivered at the end of the delivery lead time acceptable to the customer), and backorders (late deliveries).

Constraints (2) represent the evolution of inventory levels. The first constraint concerns the finished products: the quantity resulting from the production at a period t corresponds to a production order released a few periods before (depending on the production lead time DP). The second constraint evaluates the levels of inventory of each component according to the quantities received by suppliers and quantities consumed by production according to coefficients in the bill of materials.

Constraint

(3) expresses backorders, that is, the difference between the quantity of products requested by customers and the quantity actually delivered. The definition of this expression guarantees the existence of a solution to the planning problem and allows penalising the producer in case of non satisfaction of the customer's order. Constraints (4) represent capacity restrictions for production, stocks, and transportation. The use of an extra capacity for production is allowed but requires higher operational costs. Constraints (5) represent the upper bound of the extra capacity and expressions (7) are nonnegativity constraints for all the variables.

Numerical Experiments and Simulation

Table 3 summarises the value of parameters required to test the model.

In section 2, we identified 10 determinants of a planning process. This study being at the exploratory stage, we have chosen to simulate only 5 out of the 10 determinants: sequencing, frozen time fence, capacity management, cycle time, and demand forecast accuracy.

Sequencing is studied through the definition of two rules that characterise how the model intends to satisfy customers when backorders are observed. The firstin-first-out (FIFO) rule corresponds to serving backlogs first before serving recent or incoming customer orders. The last-in-firstout (LIFO) is the opposite rule, which gives higher priorities to current orders rather than serving the backlogs.

The real principle of frozen time fence is not quite simulated. Nevertheless, the decision-making process is dynamically depicted according to the principle of rolling horizon planning: the period of replenishment is defined by a number of elementary periods. By considering different values (T = 5 or 10 periods) for the period of replenishment T, we impose the application of planning results on more or fewer periods. This principle is assimilated to the definition of a frozen period, which can affect the supply chain performance.

Two different strategies are considered in the management of the capacity of each partner. The constant output rate strategy consists of calculating the mean demand along the horizon and in defining the production rate as equal to this value. The chase demand strategy entails adapting manufacturing capacity to the variation of demand, even if extra capacity is required. We note that the first strategy leads to reducing initial production costs (80% of the nominal costs given in Table 3), whereas the second is concerned with cost increases (150% of the nominal costs).

The impact of cycle time is also studied through the following principle: the transportation lead time stated in Table 3 represents values corresponding to a long cycle time. In this case, transport is assumed to be cheap (transport cost = 0.03). In certain cases, customers need to be delivered with short lead times. Short cycle time then leads to reducing by half all the transport lead times. The consequence of reducing cycle time is a higher cost for the service (transport cost = 0.1).

Main parameters characterizing the SC study case

The last determinant studied is the demand forecast accuracy and stability. Two situations are simulated. The first one considers that demand of finished products is not quite stable but is subjected to weak variations. The demand forecast is modified for 10 periods with a maximum amplitude that is equal to 10% (high confidence). However, a low confidence implies that the demand forecast varies over 20 periods with a maximum amplitude of 20%. We note that all variations are not simultaneously observed and are progressively discovered with the rolling horizon principle. The demand variations are shown in Figure 3.

Variations of the studied supply chain are observed to assess the effect of the five previously mentioned determinants on the three performance metrics (profit margin, normalised average delivery time [referred to as NADT and represented by equation 7], and completeness). The profit of each partner is measured in order to assess the financial consequences of these determinants.

The customer service level (responsiveness) is measured in terms of NADT. Completeness is measured in terms of the percentage of satisfied orders along the planning horizon (equation ( 8)).

(7) (8)

Simulation Results and Discussion

By changing the parameters of the determinants each time, we performed 32 simulations. The profit margin, NADT, and completeness were determined for each of the supply chain partnersthe three sawmills, the assembly plant, and the painting unit. The total values for each of these three performance metrics were computed for the whole supply chain. In order to keep simple and clear the discussion of our results, we will not present the details for each plant, but only the total values: total profit, total NADT for shelves, total completeness for shelves, total NADT for tables, and total completeness for tables. In order to understand the impact of the various determinants, we sorted the results in a descending order with respect to each of the performance metrics. To clearly emphasise the highly positive and highly negative effects, we report only 8 of the 32 simulations: the first four (highly positive impact) and the last four (highly negative impact). Table 4 shows the ranking by the normalised profit margin.

We observe from this table that the combination of constant output rate and FIFO clearly affects profit positively but NADT negatively. In Determinant-performance relationship ranked by profit margin other words, this combination of determinants enables the achievement of an efficiency strategy at the expense of responsiveness. This observation confirms our second hypothesis, which states that a given combination of determinants could positively affect one performance criteria while negatively affecting another. It could be deduced that the constant output rate enables the supply chain partners to minimise cost (thereby maximising profit) by not investing in more expensive additional capacity, whereas the FIFO rule penalises NADT because serving backlogs first will increase the average delivery delay.

Demand variations

However, the combination of the chase demand strategy and low demand forecast accuracy affects profit negatively but NADT positively.

This combination favours the responsiveness strategy. This is also in line with the second hypothesis. The negative impact on profit is probably due to the additional cost of varying the capacity. We presume that the adjustment of capacity to follow demand enabled on-time delivery, hence a positive effect on NADT. This probably neutralises the possible negative effect of low forecast accuracy. We note, however, that contrary to what one would expect, there is no clear correlation between the LIFO rule and NADT.

Table 5 shows the ranking by the NADT of the shelves. Here, the combination of chase demand and short planning horizon has a very high positive effect (100%) on NADT and completeness. This achievement of total agility (see supply chain strategy number 4 in section 2) can be explained as a result of the cumulative effect of two factors (chase demand and short planning horizon) that reduce the lapse of time between the receipt of an order and its fulfilment. We note that Table 5 confirms what we have already observed in Table 4, where the combination of constant output rate and FIFO affects NADT negatively but profit positively.

Table 6 shows the ranking by the completeness of the shelves. Just as in Table 5, the combination of chase demand and short planning horizon has a very high positive impact (100%) on NADT and completeness. To a large extent, we can say that low cycle time, which is also a responsiveness driver, has a positive effect on performance criteria. On the contrary and again in line with our second hypothesis, these three determinants negatively affect profit. Quite unlike in the results of the ranking by profit and by NADT, the ranking by completeness does not show a clear negative link between any combination of determinant attributes and the performance criteria. However, looking at the last two worst cases (simulations 50 and 54), we can say that the combination of a long planning Determinant-performance relationship ranked by completeness of the shelves Figures 4, 5, and 6 show the graphical representations of the three configurations that we have analysed for the shelves. Looking at the three figures we see clearly that the effect of a given determinant attribute changes when it is combined with different other determinant attributes. For example, when chase demand is combined with short planning horizon, it has a positive effect NADT, whereas when it is combined with high cycle time, it has a negative effect on completeness. Also, when it is combined with low demand forecast accuracy, it has a negative effect on profit. This observation confirms our first hypothesis.

A similar analysis of the ranking by the NADT of the table product group shows that NADT is positively affected by the combination of LIFO and high demand forecast accuracy. From a theoretical perspective, the downstream part of a supply chain is less vulnerable to the bullwhip effect than the upstream part. Because the painting unit is the last stage of the manufacturing process of the tables, it is therefore not surprising that a high forecast accuracy positively affects NADT, thereby enabling the achievement of the responsiveness strategy. Conversely, FIFO and high cycle time negatively affect NADT. The ranking by completeness of the tables does not show any significant correlation between the determinant attributes and the performance metrics. Almost all the simulations show a completeness of 100%. This may be explained by our argument of the bullwhip effect.

Conclusion and Further Research

The contribution of this article is twofold. First, it develops a framework that would enable the study of the relationship between the determinants of a distributive planning process and the performance (financial and nonfinancial) of a supply chain. Second, using a mathematical model and computer simulations, it shows successfully that different combinations of determinant attributes have either a positive or negative effect on the performance of the supply chain.

The analysis of our simulation results also shows that when there is a trade-off between two parameters (for example, efficiency and responsiveness), a given combination of determinant attributes has opposite effects (positive and negative) on the two performance dimensions.

Though still at the exploratory stage, the results in this article could guide managers in choosing the appropriate combination of determinant attributes that would Determinant-performance relationship resulting from ranking by completeness enable them to achieve their desired supply chain performance objectives. This is to say that at different times and in different market and competitive conditions, managers can pursue different supply chain goals by varying the determinants of the planning process. Our results show how different combinations of determinants enabled the achievement of three different supply chain strategies (efficiency, responsiveness, and agility) out of the seven presented in section 2.

Though these results are quite significant and convincing, more research needs to be done using different data and for different supply chain configurations.

  Figure 1Link between determinants of the planning process and supply chain performance

  Figure 2

  If 40 units are delivered in week 1, 20 in week 2, 10 in week 3, 20 in week 4, and 10 in week 5, responsiveness is equal to 0.65. This is obtained by calculating the NADT as follows: NADT = [(40 x 1) + (20 x 0.75) + (10 x 0.5) + (20 x 0.25) + (10 x 0)] x 100%/100 = 65% Table 2

Figure 3

 3 Figure 3

  Figure 4Determinant-performance relationship resulting from ranking by profit

Table 1

 1

	1. Planning horizon -Small (e.g., monthly) -Medium (e.g., quarterly) -Large (e.g., yearly)	2. Time bucket -Small (e.g., daily) -Medium (e.g., weekly) -Large (e.g., monthly)	3. Frozen time fence -Small (e.g., 1 week) -Medium (e.g., 1 month) -Large (e.g., 1 quarter)
	4. Capacity management -Constant output rate -Chase demand -Mixed strategy	5. Lot sizing -Lot-for-lot -Fixed lot size -Fixed-period quantity	6. Inventory management -Low safety stock -Medium safety stock -High safety stock
	7. Sequencing -Earliest due date -First in, first out -Last in, first out -Longest processing time -Shortest processing time	8. Scheduling -Forward -Backward -Fast -Slow 10. Cycle time	9. Forecast accuracy -Low confidence -Medium confidence -High confidence

Table 3

 3

Table 4

 4

Table 5 Determinant-performance relationship ranked by NADT of the shelves

 5

	Nature of impact Positive Negative	Serial no. 19 23 24 20 35 3 50 54	Period 5 10 x x x x x x x x	Capacity management Constant output Chase demand rate x x x x x x x x	Sequencing Cycle time LIFO FIFO High Low High Low Forecast accuracy x x x x x x x x x x x x x x x x x x x x x x x x	Total supply chain performance Shelves Tables NADT Complete NADT Complete 82,25 Profit margin 100.00 100.00 99.61 100.00 81,54 100.00 100.00 98.51 100.00 81,43 100.00 100.00 97.43 100.00 81,36 100.00 100.00 98.64 100.00 93,47 94.79 97.60 98.70 100.00 93,47 94.79 97.60 98.70 100.00 61,47 97.39 97.50 94.35 94.74 68,00 96.64 97.50 87.16 94.20

Table 6

 6

About the authors

Uche OKONGWU is a professor of supply chain management at Toulouse Business School in France. He holds an MSc degree in mechanical engineering and a PhD in industrial management. At Toulouse Business School, he has held many faculty positions such as director of an MBA program in aerospace management and director of a graduate program in SCM. He has published articles in international journals and his current research interest focuses on the development and performance measurement of SCM systems. He is also an independent consultant on SCM issues and industrial management.

Jean-Christophe DESCHAMPS is an associate professor at the University of Bordeaux. His scientific interest concerns supply chain management and recently the transport control that becomes a major factor for gaining performances, especially in the domain of sustainable development. His current research involves integrating decision-making processes that support an efficient planning of logistics and transport activities through the use of ICT. He has been involved in European and French projects since the mid-1990s.

Matthieu LAURAS is an associate professor of supply chain management (SCM) at the Industrial Engineering Department of the University of Toulouse -Mines Albi, France. He is also an affiliate professor at Toulouse Business School in France, and co-director of the Agilea consulting and training company in France. His research interests are focused on performance, integration, and management of supply chain processes. His research activities involve not only traditional industrial sectors, but also the humanitarian sector. He has published several papers in journals and international conferences in the field of supply chain management and performance assessment.

Julien FRANCOIS became an associate professor in 2008. He teaches production management, project management, automatism, and so on. He continues his PhD topic on the coordination of activities planning along the supply chain. In this work, Julien had to develop and program an analytical model and to simulate and compare distributed and centralised planning control. He participates in the PRODIGE project, which traces products during transportation. He has also started to work on reverse logistics in the supply chain.


Virginie Govaere
email: virginie.govaere@inrs.fr

Liên Wioland
email: lien.wioland@inrs.fr

Julien Cegarra
email: julien.cegarra@univ-jfc.fr

Didier Gourc
email: didier.gourc@mines-albi.fr

Antoine Clément
email: antoine.clement@mines-albi.fr

Smart Planning -Approaching the Characteristics of a Valid, Balanced Transport Round

Keywords:

Road Freight Transport (RFT) companies represent 37,200 companies and roughly 420,000 employees. This sector is confronted by strong competition and growing pressure from customers and suppliers, tight delivery times, exacerbated flexibility, etc. In parallel, they are required to fulfill performance duties in terms of preventing risks of occupational accidents and diseases. In 2016, CNAM statistics reports 70 deaths per year, 3,000,000 work days lost, an average 6 work days lost per employee, an index of frequency (73‰). The planner builds the transport rounds by integrating at best all dimensions (regulation, economic, environmental and prevention of health and safety of their employees). In this context, the Smart Planning project aims to develop a computer system to help create more balanced planning. The purpose of this paper is to present the first results. It proposes, with an ergonomic analysis, to identify the prescribed and tacit constraints manipulated by the planners in two companies. A questionnaire is drawn up to validate and enrich the data on the health and safety dimension.

This study is not a business case; it is ergonomic analysis to validate different determinants identified (health and safety) and investigates the assessment of these determinants and their possible consideration during planning.

Introduction 1.Business Sector Background

Road Freight Transport (RFT) companies represent an annual turnover of 44 billion euros, which is generated by more than 37,200 companies. This sector is confronted by strong competition and growing pressure from customers and suppliers, tight delivery times, exacerbated flexibility, and many other challenges [START_REF] Camman | Supply chain management and business performance: the VASC model, 1st edn[END_REF]. Financial issues impose additional constraints upon the already complex problem of planning transport operations. Procurement, production and distribution are indeed all organized based on stockless production involving "just-in-time" (JIT) deliveries. In transport terms, this is reflected by smaller freight consignments to be moved at higher frequency for recipients requiring delivery as quickly as possible or even by appointment. Ensuring RFT company survival implies taking these constraints into account by optimizing delivery rounds. RFT companies are also subjected to specific national and community standards and regulations (regulatory and environmental issues). Alternative and combined modes are emerging in rail, sea, river transportation on environmental grounds. However, whatever the energy combination, certain delivery modes are rapidly beset by limits in terms of flexibility, delivery times, customer proximity, etc., and the last kilometers of a journey are necessarily traveled by road. Optimization of the number of kilometers traveled and optimum vehicle selection, based on transport round constraints, are levers for responding to part of the financial and environmental issues. At the same time, companies are committed to fulfilling performance duties in relation to preventing risks of occupational accidents and diseases under Article L4121-1 of the French Labor Code governing occupational health-safety issues. CNAM national health insurance fund statistics and other institute show that the claims ratio in this branch in France remains very much higher than the national average for all branches in that employees are twice as often victims of accidents and of more serious accidents [2,3]. The commonly identified risks for employees in this sector involve both the physical aspects (musculoskeletal disorders related to handling, falls on the level, exposure to vibration, etc.) and mental aspects (high mental load, activity segmentation, higher pace of work, dealing with unforeseen events, etc.) of the work activity. Multiple research studies and actions have mobilized stakeholders from different areas and have contributed to improving driver safety. Nevertheless, this problem remains an issue. Furthermore, RFT planner activity is performed within a specific context involving segmentation of various operations, performance of dual tasks [4]. Interruptions, segmentations and performance of several tasks in parallel constitute risk factors for the planner and factors that impact on work quality and the compromises to be adopted during planning. These compromises affect driver working conditions: managing an urgent situation in a tense, stressful environment leads operators to protect themselves less and is often associated with increases in accident risk. We defend a dual viewpoint in this paper in order to act upon the sector's claims ratio:

• Integration of the health-safety issues right from delivery round planning stage.

Planning stage consideration of the frequency of handling operations to be performed by the driver, of available aids (pallet trucks, bay operators, etc.) and of infrastructures (stairs, lifts, etc.) at the customer, alternation of more and less demanding activity phases or balanced allocation of "difficult" delivery points among drivers may represent a solution to alleviating driver levels of stress. • Provision of assistance suited to planner activity. This could entail planning support in a context, in which the mental aspects of these employees' work are strongly present.

Ultimately, it is up to the planner to integrate all the relevant and often antagonistic financial, environmental and social issues, when designing transport rounds. Under these complex conditions full of uncertainty, the planner must adopt a compromise between these constraints. The quality of the compromise depends on the time the planner can give to this operation, on his/her representation of each constraint and on the margins for maneuver available to him/her.

Research Context

Aids to managing transport or Transport Management Systems (TMS) are available to the planner, but these solutions focus only on the financial issues. The Smart Planning project aims to overcome this limitation by integrating financial, environmental and health and safety aspects for both drivers and planners into robust planning aids for transport operations. The project is financed by the ANR and features an interdisciplinary consortium. The primary scientific aim of this project is to model the constraints integrated by the planner in order to propose valid delivery rounds from financial, environmental and social standpoints, while integrating operator (planner and driver) health and safety aspects [START_REF] Apostolopoulos | Active living in the trucking sector: environmental barriers and health promotion strategies[END_REF]. The objective is also to model the dynamics of the compromise adopted by the planner which respond differently to both the challenges and the transport company strategy. These models should be subjected to break-down into operational criteria, which can be rated and are: (1) financial, (2) environmental and (3) health and safety-related.

To achieve this primary project aim, a number of stages are unavoidable, including listing and categorizing the constraints to be managed by the planner, defining transport round performance indicators from health and safety, financial and environmental standpoints, identifying constraint prioritization strategies to be adopted by planners, etc. Ergonomic analysis is conducted to identify the planners' constraints (formulated, released or satisfied constraints). The purpose of this paper is to present these results, which contribute to defining the initial components of a valid, balanced transport round in financial, ecological and health and safety terms.

The second project aim is to introduce the approach to validate the list of determinants identified in relation to the health and safety dimension and to investigate rating of these determinants by planners during their work. Preliminary results illustrate the significance of this approach.

Methodology

The ergonomic analysis involved the two industrial partners of the Smart Planning project. These partners are freight transporters. These SMEs employ 195 and 60 people. Identical methodology was deployed at both companies.

Stage 1: Description of Planning Activity

Analysis of the planning activity was conducted using instrumented observations (video and audio recordings). The video recordings were made using cameras to observe two planners/company performing a usual operation during their work shifts. Observation-based data were collected from the start to the end of the work shift on full working days during a single week. This operation provided 24 h of recordings (2 planners/company on morning and afternoon shifts, excluding breaks, on 3 full working days). The observations were formalized in the form of an activity code, which subsequently enabled us to determine the time and frequency of the operations performed, the various tools used, the communications, the purpose of these communications and the contact persons involved. This analysis was complemented by interviews with participants both directly (planners) and indirectly (operations managers, drivers and general managers) involved in the planning activity. The latter management personnel establishes the company strategy and hence guidelines in terms of planning criterion selection and prioritization). An interview questionnaire was drawn up and systematized. This included 5 main categories of information: constraints related to the client and its requirement, constraints related to human and equipment resources, constraints related to infrastructures and regulations, company guidelines and strategic decisions and the different types of unforeseen events encountered.

Stage 2: Constraint Identification and Prioritization

Following the interviews and observations, we identified the concepts used by the planners for building transport rounds. For the stakeholders, this involved prioritizing characteristics for each concept based on their importance, when planning transport rounds. We collected the verbal accounts concomitant with this prioritization task. A card game was developed for this prioritization exercise; it included the concepts identified during Stage 1 and their characteristics. For example, there was the "vehicle" concept along with its typical attributes: "tailgate present", "bed length" or "side loading possible", etc. At each company, seven employees took part in interviews and prioritization. Data acquisition required a week-long visit to each company.

Stage 3: Collection of Sociotechnical System (STS) Determinant Ratings by Sector Professionals

A questionnaire was edited based on the Stage 1 interview findings and earlier work in the sector [ref lien]. This questionnaire was validated by the project steering committee, tested by the 2 industrial partners (7 planners), then circulated online via professional federations within the RFT sector. The purpose of circulation was to generalize the results obtained with the project industrial partners. The questionnaire included the following sections:

• A section identifying the respondent's job and the company profile • A section describing the planning activity • A section focusing on factors causing difficulty or arduousness experienced by drivers and their consideration during planning. For each factor, this involved determining its presence or absence in the activity, rating its degree of difficulty or arduousness on a scale of 1 to 10 (1 very low and 10 very high) and stating whether this factor is considered during planning.

Three categories of factor were envisaged in the last section: those causing difficulty or physical arduousness, those causing mental arduousness and those causing emotional arduousness (see Table 1). A free data entry zone for factors complementing this list closed out this section.

Results

Description of Planning Activity

The planners managed a client relationship (11% of operator activity time), available physical and human resources (21% of activity time) and transport schedules (16% of activity time). They established the traceability (36% of activity time) and provided an interface between the different stakeholders (management, clients, drivers, etc.). Comparing activity and operation frequency therefore highlighted the fragmented nature of the planning activity. This activity fragmentation was evidenced by the brevity of the operations as shown by their average times (approximately 1 min. 30 secs. per operation). The planners issued or received oral communications (face-toface or by telephones) for 40% of the activity time (i.e. 28 h of communications). ------------------------------------------------------------------------------ These communications occurred in parallel with the transport round planning activity. Planning represented 21% of the planners' total activity time. Typically, it could be divided into collection of data, allocation of resources and optimization of various criteria. Observation of the planning activity revealed a succession of back-and-forth operations on the planned transport round or order addition or deletion.

Identification, Categorization and Prioritization of Constraints

The interviews and card game were run with planners, operations managers and company managers: a total of 14 study participants. The data exhibited no variations in participant status. The interviews and observations revealed that the concepts used by the different players in planning transport rounds were the delivery sites, the vehicles, the client, the order, the driver and the round. These concepts were characterized with attributes. For example, the Site (warehouse, store, private individual) was characterized with address, loading location, unloading location, delivery time window, flexibility, type of vehicle, accessibility, presence of bay, driver contribution, handling equipment. The planners handled these concepts (site, vehicle, client, order, driver and round), when building their planned transport rounds. The concepts and their attributes created resources and constraints, when performing a planning operation. Planners also integrated constraints involving regulations applicable to work, driving or the environment. However, these depended on concepts (e.g. downtown delivery is regulated in some cases): delivery times, type of vehicle authorized, parking location…. and through the planner's handling of the concepts that these constraints covered. Categorization of these constraints therefore corresponded to the concepts dealt with. Interview analysis allowed us to define 4 levels of constraint:

• Major constraints, for which it was imperative that the planner ensure their integration into his/her planning operation. These constraints arose from "regulation" or minimum characteristics to be implemented to fulfill the transport operation. In the former case, regulations were applicable, when they involve the highway code, the labor code, the environment or Heavy Goods Vehicle (HGV) driving. With regard to minimum characteristics to be implemented, these also represented major constraints because they could not be adapted. Thus, if the delivery site imposed maximum vehicle dimensions (e.g. underground passage access, hence maximum vehicle height and width), one major planning constraint would be the height and width of the vehicle to be used for this site • Moderate constraints were to be integrated into transport round planning and their integration was indeed necessary. Adaptations could nevertheless be implemented, if needed. • Minor constraints were more recommendations than necessities.

• "Absent" constraints are those that were not spontaneously signaled as planning constraints. Thus, health and safety determinants were associated with specific situations or points requiring vigilance and were not related to risks. These situations were to be carefully watched, when planning the transport round, but they did not constitute declared constraints for planners during construction of the schedule.

Except for major constraints, managing levels of constraint remained dependent on the situation. For example, a moderate constraint could be relaxed in order to provide an acceptable planning solution. In a low activity situation, driver inexperience could be ranked at a level of moderate constraint and could lead to calling in a more experienced driver. In a high activity situation, round preparation was more constrained. Planners then had to abandon consideration of this constraint.

The card game allowed us to see that the constraints were prioritized by grouping them together or at least by processing aggregated attributes since constraints and attributes were not independent of each other. Type of vehicle, its capacity, driver's license requirement were aggregated and considered globally in some situations. In other situations, such as the "exceptional" request described in the remainder of this text, each attribute and constraint was considered separately.

The expression most often encountered in the interviews or in the card game with planners was "It depends!"….. it depended not only on the client, the site, the driver but also on the company strategy, the company's financial health, etc. The level of constraints and their prioritization were therefore contingent. Moreover, the level of constraints was variable, depending on the planner's knowledge of the situation; in cases involving a new client, an exceptional order or an unusual type of goods, all the constraints would be prioritized differently by globally considering their levels higher than usual. Under these circumstances, what would be a valid, balanced transport round in financial, ecological and health and safety terms? For the project industrial partners, a valid round was a round that, ideally, combined two components, two types of performance:

• Performance internal to the company: suitable usage, from a financial standpoint, of human and physical resources and of the delivery time. This usage was intended to generate a positive financial margin. • Performance external to the company: adequate response to client requirements.

This response constituted quality of service to the client.

The "valid" transport round therefore depended on proportioning the quality of service to clients located in the round, while controlling costs and profitability. These two components and their "proportioning" had to be weighted according to company strategy. Round validity was assessed independently of the "ecological" and "health and safety" dimensions. The project industrial partners integrated these dimensions into the balanced round. The balanced round depended on mobilizing human and physical resources that were adequate for the company's objectives. Nevertheless, this mobilization had to conserve a margin for maneuver in terms of managing clients and unforeseen events during round performance.

Rating of STS Determinants by Sector Professionals

It should be remembered that the planners described health and safety determinants with respect to situations and points requiring vigilance and not with respect to risks or constraints. Thus, site accessibility problems, complex loading/unloading locations, handling and stripping and unusual or extended working hours were all situations identified for their physical aspects. A new client, heavy traffic, complex infrastructures, an unforeseen wait at the client causing time-related stress were all situations identified as hazardous for mental health. Emotional aspects did not give rise to specific situation identification.

The Questionnaire

The findings presented are from the questionnaire's experimental stage with the project industrial partners (7 planners); they provide elements of information in terms of STS determinant rating. These elements need to be supported by data from the questionnaire's wide circulation. Two general issues were included prior to presenting the results based on factors causing difficulty or arduousness.

With regard to safety, defined by 4 items (theft and holdup, assault, accidents when stationary or driving), 57% of participants asserted that they took it into account by providing technical solutions such as secure vehicles (holdup and assault) and assignment to rounds based on driver skills and potential hazards. Planners, who did not integrate this aspect into their planning, asserted that this was dealt with by corridoring or watchmen at client sites to prevent assault, by driver training and compliance with regulations preventing accidents, when stationary or driving.

With regard to health, 87% of participants stated that they took this into account during planning by providing technical solutions to alleviate certain constraints (e.g. electrical pallet trucks), driver assignment (attention given to driver age, known health problems…) and order management (high volume orders broken down). One of the planners did not integrate health into planning because he considered health to be managed by applying regulations and by equipment selection by company management. Following these two general issues, more specific issues were raised (Table 1). These are based on physical, mental and emotional aspects.

Physical Aspects

These aspects were based on situations involving site access difficulties and handling situations. 87% of the participants considered that handling and site accessibility were factors causing difficulty and arduousness. However, all participants took them into account in their planning. Site accessibility-related difficulty was rated at between 5 and 7 on a 1-to-10 scale with 1 very low and 10 very high.

Mental Aspects

"Driving" and "Prolonged attention" were envisaged as mental aspects. With regard to these aspects, the planners considered that all their drivers were affected. Hence, these aspects were taken into account during planning and were rated between 4 and 8 with 42% rated 8).

Emotional Aspects

Assaults and relationship with the client were situations identified as causing emotional difficulties. All the planners reckoned that drivers were exposed to such situations. With regard to relationship with the client, emotional difficulty was "often" effective in 28% of cases, "sometimes" in 43% of cases and "rarely" in other situations. 71% of planners integrated this difficulty into their planning. However, this difficulty was rated moderate to low (between 1 and 5).

Other Difficulties

The 7 planners referred to difficulties or situations considered tough:

• Lifestyle (sleep, food, adapting to variable work rhythms, etc.) was quoted 3 times with a rating of 9 • Meteorological conditions were quoted twice with ratings of 8 and 7, Traffic was quoted twice with a rating of 8, Time-related stress (lateness) was rated 8, Bay entry and driving maneuvers were rated 7 • Lack of knowledge of the client was a difficulty rated 5.

Physical, mental and emotional dimensions were all present within these "other difficulties".

Discussion and Conclusion

The findings of this study highlight that transport round planning activity is segmented, that situations involving dual tasks are usual and frequent, and that usage of tools is intensive and brief. Work intensity and information load handled by various tools are constants of the planner's activity. We also recall that planning is spread right across the entire daily activity.

Planners manage multiple constraints, which arise from outside the company or depend on decisions made by the company. The former are constraints related to regulations, infrastructures or clients. Constraints inherent to the company are related to the choice and sizing of the vehicle fleet, the strategy of the company and its human resources (drivers) for managing the activity. Constraints external to the company are considered major constraints to be overcome whatever the situation. Constraints inherent to the company have no status a priori; depending on the situation, they can be major, moderate or minor. Planners consider these constraints to be mutually dependent and they therefore deal with them globally. However, when the situation, context or demand is exceptional, constraints are prioritized differently and processed separately. With the exception of major constraints, prioritization cannot be performed a priori. It must be contingent on both the context and the situation.

It should be noted that employee health and safety aspects during planning were not spontaneously referred to during the study interviews. Furthermore, when the employees were questioned, they spoke mainly about situations requiring special care by the project industrial partners and not about exposures or risks. The physical dimension was referred to within this framework. When the mental and emotional dimensions were raised, they were not dismissed but were considered inherent to the driver's job or to the driver's responsibility. However, with the questionnaire, consideration of the health and safety dimension in planning was effective for a large majority of planners, even though a variability in dimension integration was noted: the emotional dimension being less integrated by planners. It is also the dimension that was rated as having a lower intensity. It is not because the health and safety dimension was not stated as being systematically integrated during planning that it was not necessarily taken into account. The planners considered that certain factors, such as the company's choice of handling equipment, vehicle fleet deployment, corridoring or compliance with regulations on driving time, safety training for drivers, etc., effectively meet the requirements of driver health and safety.

We also noted that the RFT context and the corporate strategy led planners to adapt constantly their management of constraints and consideration of the different dimensions. The next stage of this research will therefore be to focus on designing a compromise development system rather than a constraint prioritization system. Compromise development analysis conducted by planners during their activity would appear to represent an interesting route to preparing a delivery round that is both valid and balanced for industrial stakeholders.

Table 1 .

 1 Example of questioning for each factor category

	Physical Do you reckon han-dling is a factor of ar-duousness in driver activity? • Yes • No Definition Handling: Factor present or absent carrying packages, moving pal-lets without mechanical equipment, filming, sorting, etc. Rating factor) (for each 1-	Mental Do you reckon your drivers encounter driving situations that require a high or prolonged level of attention? • Yes • No This may involve driving at night, driving electrical silent vehicles or maneuvers requir-ing constant vigi-lance	Emotional Can the client rela-tionship be a factor causing difficulty in driver activity? • Very often • Often • Sometimes • Rarely • Never Factor related to wel-coming, assisting or collaborating, possi-ble relational difficul-ties, etc.