R Guillaume
email: guillaum@irit.fr

C Thierry
email: thierry@univ-tlse2.fr

B Grabot

A decision support tool for procurement planning process under uncertainty

Keywords: supply chain, uncertainty, collaboration, theory of possibility

This communication presents a method to support the customer in the choice of a procurement plan when the gross requirements are ill-known, in a context of collaboration with the supplier. A general model of imperfect parameter representation is suggested, imperfection gathering uncertainty (through various scenarios) and imprecision (through quantities and dates expressed by possibility distribution). A method to compute the possible quantities required to satisfy the gross requirements under the supplier delivering constraints is proposed. From this value, a set of possible supplied quantities is computed to support the decision making of the customer. The decision maker then evaluates the procurement plan with the possible evolution of the inventory.

INTRODUCTION

Enterprises are nowadays more and more tightly integrated in supply chains whereas the globalization of the market and the reduction of the product life cycles increases the uncertainty on the customer's demand. Thus, taking into account uncertainty in supply chains is becoming a major issue.

If no historical data allowing to build a stochastic model is available, uncertainty is often taken into account in the literature by possibility theory and fuzzy set theory [START_REF] Dubois | Possibility Theory[END_REF] (see for instance (Peidro et al., 2009), (Lan et al., 2009), [START_REF] Aliev | Fuzzy-genetic approach to aggregate productiondistribution planning in supply chain management[END_REF]). These methods have for instance been used to model the imprecision on the coefficients of a cost function (Demirli and Yimer, 2006) or the weights of constraints (Mula, 2007).

Moreover, in an uncertain environment, a collaborative process between actors of the supply chain (customer, supplier) is a powerful tool to reduce the risk of the bullwhip effect (Galasso et al., 2006). One of the references of collaborative processes in the industry is CPFR (Collaborative Planning forecasting replenishment [START_REF] References Ireland | Supply chain Collaboration: how to implement CPFR® and other best collaborative practices, Integrated Business Management series and APICS[END_REF]). Within this framework, data such as inventory levels, forecast, etc. are exchanged between the supplier and the customer.

In order to take into account more explicitly the uncertainty related to the demand, a method is suggested in this communication aiming at supporting the decision maker (the customer) in building a procurement plan for the supplier. This decision is made under uncertainty on the gross requirements, in collaborative context (the supplier sends to the customer its delivering capacity constraints). To support the decision maker in finding a feasible procurement plan, we build the set of all the possible procurement plans. This method is composed of three steps:

1) the computation of the required inventory for each period, 2) the computation of the possible procured quantity under the delivering constraints.

3) the evaluation of the proposed procurement plan according to the possible evaluations of the inventory.

This paper is organized as follows: the first section presents a model of imperfect gross requirement. In the second one, a method to compute the required inventory is suggested while in the third section, a method to compute the possible procured quantity for a period is described, together with the framework of a decision support process aiming at building a procurement plan. In the last section, this decision support process is illustrated on an example.

IMPRECISE AND UNCERTAIN GROSS REQUIREMENTS

Our main hypothesis is that the customer has knowledge on gross requirements including imperfections (uncertainty and imprecision), often empirically modelled through expertise. So, the gross requirements can be given by the customer as a set of scenarios, with an imprecision on the value (e.g. "Not more than twenty parts, but certainly between 15 and 20").

Possibility theory is then used to model the uncertainty and imprecision on the demand in a more analytical way. with v denoting an illknown value in S, and L the scale of plausibility ([0,1] for the theory of possibility) (Dubois and Prade, 2006).

w v E , such as i V v & 1 i V w
and the weights of arc are defined by a matrix

E w v w v ) , ( , M with 1 & , max 1 , i i w v V w V v
(consequence of possibility theory).

Fig. 2. Example of graph for a horizon of 2 period and 3 scenarios

To represent a fuzzy number, an interval is used where the extreme values are gradual numbers. This representation enables to apply the interval analysis without using -cuts (Fortin et al., 2008). We also use a set of two linear gradual numbers to represent the trapezoidal possibility distribution.

Figure 2 illustrates the model for a fuzzy number A ~. : set of possible quantity of deliveries l t on period t

COMPUTATION OF THE REQUIRED INVENTORY

The first step, which consists in computing the required inventory (definition 4), is presented in this section.

Definition 4: The required inventory is the quantity required in the inventory to fulfil the gross quantities required under supplier delivering constraints.

This method uses an iterative algorithm, from period T to 1. When the gross requirements are imperfect (uncertain and imprecise), the required inventory is imperfect too. To deal with imprecision, each required quantity becomes a possibility distribution. To deal with uncertainty, only the two extremes cases are computed: the required inventory which satisfies all the possible gross requirements and the required inventory which satisfies one of the possible gross requirements.

The operator used to back-propagate the required inventory depends on the type of required inventory: minimum: used for the inventory which satisfies one of the possible gross requirements, maximum: used for the inventory which satisfies all the possible gross requirements

It is considered that the required inventory for period T+1 is equal to 0. To compute a required inventory (for example: which satisfy all possible gross requirements * , ~t c t r I

), the optimization problem (equations 1 to 3) is solved for each node (t,c t ) (from (T, C T ) to (1,1)) and for the four values of the required inventory (for example:

) 0 ( ~* , t c t r I ; ) 1 ( ~* , t c t r I ; ) 1 ( ~* , t c t r I ; ) 0 ( ~* , t c t r I ) . ) ( minimize , * t c t r I (1) s.t. 1 * , , * ~ t c t c t r I l r G r I t t t (2) 0 ~, * t c t r I (3)
The same method is applied for the inventory which satisfies one of the possible gross requirements (with I* becoming I * ). The required inventory is the minimum inventory needed to satisfy a gross requirement, so the minimum required inventory respecting the delivery constrains (whatever the quantity delivered by the supplier is) corresponds to the maximum possible quantity procured: t l .

To back-propagate the maximum (minimum) required inventory level and keep the representation of the possibility distribution by 4 values (figure 4), an approximate method is used. This method maximises (minimises) two gradual numbers by a linear function which guarantees that all the values of the exact solution are lower (upper) than the approximate solution (for the minimum: equations 4 to 6 and figure 5). In fact, the maximum (minimum) between two values has in the worst case one intersection point (figure 4). The second and the third steps of the method, respectively the computation of the possible procured quantity and the evaluation of the possible inventory level for a given procured quantity, are described in this section. These two steps are performed consecutively from the period 1 to T. The dependant variables are denoted: fuzzy possible inventory at the end of period t in the case when one of the possible gross requirements is satisfied,

Fig. 4. Exact/Approximation of minimum ) 0 ( max ) 0 ( ~* , 0 * , 1 , t t t c t c c t w t r I r I (4) 1 0 ) 0 ( ) 1 ( 1 ) 1 ( ) 1 ( ~, , , , * * , , , , , * , *
x x A ~ B ~Exact result Approximate result A ~ B ~x 1 0.6 A ~ B ~x 1 Equations (7 & 8)
x t : value proposed by the decision maker for the period t : difference between the required inventory of period t and the possible inventory for the period t.

Computation of fuzzy possible procured quantities

This method is applied for the two extreme cases concerning all the gross requirements or one of the possible gross requirements. The decision maker receives so a fuzzy set for each case. In order to compute the two fuzzy procured quantities, the following optimization model is solved (equations 7 to 9) for the four values ) ( minimize [START_REF] References Ireland | Supply chain Collaboration: how to implement CPFR® and other best collaborative practices, Integrated Business Management series and APICS[END_REF]

s.t. ) 0 ( ) 0 ( ) 0 ( ) 0 ( ~, * 1 * , t c t t c t r I r G I P t t (8) t c t t l P l t ) 0 ( ~* , ( 9
)
The required inventory for period 0 is different from the real inventory at the beginning of period 0, which is known. So we are looking for the procured quantity which satisfies the required inventory for the next period (period 1). We associate a possibility level to the possible delivering quantity, corresponding to the possibility level that the quantity will be required.

We compute then the fuzzy quantity delivered for the period. In the case of the procurement plan which satisfies all the possible gross requirements, we merge the nodes with the max operator, in the other case with the min operator (equation 10: procurement plan which satisfies all the possible gross requirements)

) ( max ~* , * t t c t c t P P (10)

Computation of fuzzy possible procured quantities

From the possible procured quantities, a crisp value is proposed according to a risk level in terms of inventory. For example, the lower bound of the most possible value for the case which satisfies all gross requirements can be proposed as a first suggestion since it is a prudent solution.

Then, two resulting inventories are computed (one aiming at satisfying all the possible gross requirements; one aiming at satisfying one of the possible gross requirements), using the minimum operator for the first one, and the maximum operator for second one. The minimum and the maximum are computed like the required inventory (equations 4, 5 and 6). Equations 11 and 12 compute the inventory levels which satisfy all the possible gross requirements (with x t standing for the proposed value by the decision maker).

t c t c t t t c I r G I x t t , , * 1 ~(11) ) ( min ~* , * t t c t c t I I (12)
Then, the fuzzy possible procured quantities are computed for the next period.

From the first proposition of procurement plan (x 1 to x T ), the decision maker analyses the evolution of the inventory and modifies the procurement plan until it gives him satisfaction.

EXAMPLE

In this section, we illustrate on an example the method of computation of the procurement plan under uncertainties.

Data

The considered planning horizon is 2 periods and the inventory at period 0 is equal to 0. The imperfect gross requirements are represented in Figure 6 and Table 1, and the delivering constraints in Table 2.

l l

From these data, the required inventory is computed.

Computation of the required inventory

It is considered that the required inventory of period 3 is (0;0;0;0). Then, the required inventory for the node 1 of period 2 is computed as follows.

For the last period, the required inventory which satisfies all the gross requirements is equal to the required inventory which satisfies one of the possible gross requirements

( 1 , 2 * 1 , 2 * ~r I r I & 2 , 2 * 2 , 2 * ~r I r I ).
The lower bound is then computed:

) ( minimize 1 , 2 * r I s.t. 3 * 1 , 2 1 , 2 * 2 r I l r G r I 0 ~1 , 2 *

r I

In this case, the equation ( 5 From these results, the required inventory of the node 1 of the period 1 is computed. The back-propagation of the maximum required by the inventory of the period 2 is used for the case which satisfy all the gross requirements (otherwise, the minimum). We apply the equation ( 4), then the equation ( 5) and the equation (6). Figure 7 In the same way, but using the min operator, we compute the required inventory which satisfies one of the possible gross requirements (Figure 8). The result of the computation of the required inventory is illustrated in Figure 9.

Computation of fuzzy possible procured quantities

First, we compute the fuzzy procurement plan which satisfies all the gross requirements. We apply the equations 11, 12 and 13 at the period 1:

Computation of fuzzy possible procured quantities

On the left of FA prudent solution is proposed: the lower bound of the most possible value for the case, which satisfies all gross requirements (value=9). On the left of Figure 10 a set of fuzzy procured quantity are proposed. On the right, the resulting inventory level for the proposition is represented with the required inventory level for the next period.

Then, the decision maker can modify the first crisp value and the right side to evaluate the new proposition. In the example, he proposes the quantity 11 which leads to a less risked solution . Fig. 10. Dashboard of the decision maker for period 1

CONCLUSIONS

In this paper, we propose a decision support tool for helping a human decision maker in building a procurement plan under uncertainty. The decision maker knows a set of possible quantities evaluated by possibility levels and can see the consequences of his choices through the inventory evolution. This decision support tool allows the decision maker to take decision in knowledge of the facts and also to evaluate the risk of the chosen procurement plan.

As a perspective, we want to solve this problem as an optimization problem using robust optimization (as minmax or minmaxRegret criteria). Those criteria minimize the maximum possible cost over all possible scenarios of the gross requirement. Thus, this method enables to minimize the risk in term of backordering/obsolete inventory in fuzzy uncertain context.

Fig. 1 .

 1 Fig. 1. Trapezoidal distribution of possibility Definition 3: The graph of the gross requirements is a directed arc weighted layer graph E V G , with quantities on node defined as: Node set

Figure 5

 5 Figure 5 illustrates the equations defining the minimum between to gradual numbers A ~and B ~.

Fig. 5 .

 5 Fig. 5. Illustration of the approximation of the minimum

Fig. 7 .

 7 Fig. 7. Maximum Required inventory of period 1

Fig

  Fig. 8. Minimum Required inventory of period 1

  : fuzzy possible inventory at the end of period t in the case satisfy all the possible gross requirements are satisfied,

	; ~* , [ * , * , t t t c t c t c t P P P		]	: fuzzy procurement quantity for
	node (t,c t ) which satisfies all the possible gross requirements,
	; ~, * [ , * , * t t t c t c t P P P	c	t	]	: fuzzy procurement quantity for
	node (t,c t ) which satisfies one of the possible gross
	requirements,
	; ~* * [ * t t t P P P	]	: fuzzy procurement quantity for period t
	which satisfies all the possible gross requirements,
	; ~* * [ * t t P P P	t	]	: fuzzy procurement quantity for period t
	which satisfies one of the possible gross requirement,
								1	3	7	13	1	3	11 13
								Minimum
								1
								1	11	x

* ~t I t I * ~:

Table 2 . Delivery constraints

 2

							1					1
		Start	1		1 , 1 ~r G		0.8	1 , 2 , 2 ~r G 2 G ~r			1		End
	Fig. 6. Imperfect gross requirement
					Table 1. Gross requirement
	t, c t	~, t r G	t c	(	) 0	~, t r G	c	t	) 1 (	~, t r G	c	t	) 1 (	~, t r G	c	t	(	) 0
	2,1	12		17		17		25
	2,2	15		16		20		22
	1,1	6			7		7

  illustrates the calculation.

	); ~* 1 0 ( max ) 0 ( , 2 * 1 , 2 * 2 r I r I r I	(	) 0
			max	7 ; 10		10
	~* 2 , 2 r I	) 1 (	5	1	8 . 0 . 0	8	5	10	75 . 3
	); ~* 1 1 ( max ) 1 ( , 2 * 1 , 2 * 2 r I r I r I	) 1 (
			max	; 2	. 3	75	75 . 3

Acknowledgments. This study has been performed with the support of Région Midi-Pyrénées and of the University of Toulouse.

 Information Sciences, 177 (20), 4241-4255. Guillaume, R., C. Thierry and B. Grabot. (2010)


Florian Hémont
email: florian.hemont@univ-tlse3.fr

Bernard Grabot
email: bernard@enit.fr

Anne Mayère
email: anne.mayere@iut-tlse3.fr

Cooperation in Supply Chains: from practical problems to conceptual models

Keywords: Supply chain, cooperation, trust, partnership

Improving the performance of supply chains is a critical issue in nowadays industry. In that purpose, cooperation between partners motivates a great attention. Technical aspects of current cooperation procedures as promoted by large companies are firstly described, and compared to some real problems identified after interviews in the aeronautical industry. Technical issues are in our opinion insufficient for fully explaining the described situations. Therefore, behavioral concepts suggested in the literature of management and sociology of organizations are explored and conceptual models linking the selected concepts are suggested, allowing to have a deeper understanding of real cooperation situations.

Introduction

Relationships between SMEs and large companies is a critical issue in the aeronautic context. Within different frameworks, the authors have participated to interviews of supply chain's partners of the aeronautic industry in the South-West of France, and to projects involving them. On the base of these interviews, concepts suggested in the areas of management and sociology of organizations have been considered for formalizing cooperation situations in supply chains.

Improvements of Supply Chains: a Technical View

The challenge of supply chain management is to coordinate companies of different sizes and cultures, which have local strategies and objectives, in a way which satisfies the global objectives of the supply chain. Two main principles may be used for an operational coordination of the partners in a supply chain: centralized or decentralized planning. In practice, centralized planning using APS (Advanced Planning Systems [START_REF] Stadtler | Supply Chain Management and Advanced Planning[END_REF]) is not adapted to the coordination of independent entities, which have to preserve their autonomy. "Push" an "pull" methods may be used for a point-to-point management of Supply chains. MRP (Manufacturing Resource Planning [START_REF] Orlicky | Orlicky's Material Requirement Planning[END_REF]) is the most well known of the push methods. "Pull" methods, like just-in-time and lean manufacturing require a drastic simplification of the material flows, resulting in a simplified management.

The strategy of the focal companies of the aeronautical domain mainly consists in spreading the principles and tools of MRP towards their suppliers, especially the smallest ones. Huge efforts for increasing the flexibility and reactivity of the chain through lean manufacturing have also been recently made recently. Some critical aspects of the point of view of suppliers and customers on these questions, and more generally on cooperation, are summarized in next section.

Summary of Interviews and Projects

The situations described afterwards have mainly been identified through projects and interviews involving around 20 SMEs and 5 large companies (see details in [START_REF] Marcotte | Cooperation models for Supply Chain Management[END_REF][START_REF] Ming | Coordination in Supply Chains: from case studies to reference models[END_REF]).

Customers Points of View

The efforts of the large customers for improving SC performance are mainly directed on the SMEs, for which they have noticed that the service ratio is conversely proportional to the load. In order to address this problem, the customers have a multifacetted strategy: -Some of them try to protect their weaker suppliers, for instance by giving them programs with larger firm periods than their customers do for themselves.

-Most of the customers ask for evidences of proper use of production management tools before agreeing a supplier. For instance, some of them ask for examples of a middle term load planning, for being sure that the supplier is able to create a "control loop" giving him some visibility.

-The customers ask their suppliers to be certified, in order to show their capacity in Quality Management. Suppliers which are not yet "mature" in production management, but are considered as having a "potential" of development are selected for "supplier development" projects. Techniques such as MRP or Lean Manufacturing are mainly promoted in that purpose.

-One of the customers informs his suppliers on his stock level, so that they can anticipate the real effect of a late delivery. The aim is to increase the sense of responsibility of the supplier.

-For large companies, there will be soon a "critical size" for participating in an aeronautical supply chain.

-SMEs are encouraged to join "clusters" for reaching this size. The objective is to simplify the management of the supply chain by decreasing the number of partners. In spite of these efforts, the large companies are globally disappointed by the goodwill of their suppliers, and by the effects of their projects.

Suppliers Points of View

Production management is indeed considered as an "administrative work" by many SMEs. The logic of the MRP method is often poorly understood: having an a-priori estimation of their internal cycle times is for instance considered as impossible for most of the SMEs. On the opposite, they all have developed internal procedures allowing to group orders according to technological constraints in order to decrease set-up times, and so to meet the customer's expectations regarding the costs. These calculations are performed on MS Excel© or on MS Access©, the resulting production plan being not re-introduced in the production management system.

The long supply times of some alloys (around one year) make that firm orders have to be sent to the raw material providers on the base of uncertain programs. For the suppliers, "risk sharing" promoted by customers mainly means "risk sent to the suppliers" since they do not see the protective aspects included in their contracts. Moreover, their contract often includes an article saying that it could be cancelled as soon as better prices were got from another supplier1 . All the suppliers gave us examples of more and more complex parts removed from their production plan for being processed in low cost countries, sometimes without explanation... Specific requirements are always added by the customers to standard certifications (like ISO 9000). A surface treatment company was for instance subject to a certification of 800 product/process couples per year. For all the suppliers, dialog is more and more difficult with the customers, since their contacts are often managed by young engineers having a poor technical competence on the processes, but a hard pressure concerning costs and delays. Lean manufacturing is often considered as an attempt of the customers to decrease their autonomy; a "first in-first out" logic is for instance promoted for surface treatment companies, which internal delays are hardly predictable by the customers. In that case, the SME would not be able to have its own strategy regarding priorities, for example they use to privilege the interests of the "best" customer. The possibility that small companies define their own way for continuous improvement was discussed during a public debate gathering large and small companies. A person from a large company cut the debate arguing that "it was very dangerous to let SMEs think that they can do what they want. The solutions being known, the only problem for SMEs was to implement them...".

We shall show in next section that concepts currently used by academics from management science or sociology of organizations may provide an interesting point of view on these issues.

Concepts Linked to Behavioral Issues in Supply Chains

Risks in supply chains are usually associated either to product demand or to product supply (linked to capacity limitations, supply disruptions) [START_REF] Johnson | Learning from toys: lessons in managing supply chain risk from toy industry[END_REF]. The buyer can try to cope with the risks linked to the necessity to synchronize actions with its suppliers by formalizing communication rules, while keeping a clear distance with them: this strategy is called "arm's length relationship" in the literature, and can be compared to the "formal contracted rules and procedures" considered by Williamson [START_REF] Willamson | The Economic Institutions of Capitalism[END_REF]. Anyway, collaborative arrangements are nowadays preferred to arm's length trading or full integration approaches (see for instance [START_REF] Möllering | A typology of supplier relations: from determinism to pluralism in inter-firm empirical research[END_REF]). Cooperation is sustained by communication and knowledge sharing and is considered both as a way to cope with risk and as a new potential source of risk, since it creates the opportunity that a partner exploits the other [START_REF] Andersen | Emotion, trust and relationship development in business relationships: a conceptual model for buyer-seller dyads[END_REF]. To avoid this, the first solution for the customer is to "control" the supplier: "control mechanisms are necessary to reduce opportunistic behaviors" [START_REF] Liu | Control mechanisms across a buyer-supplier relationship quality matrix[END_REF]. Control requires power; the relative power of an organization over another is the result of the net dependence2 of the one on the other [START_REF] Caniëls | Power and interdependence in buyer supplier relationships: a portfolio approach[END_REF]. Coercive power (punishment and reward), non coercive power (which indirectly affects the partner's behavior by changing its business objectives, etc.), contracts and relational norms are usual control mechanisms. Coercive power decreases the risk linked to cooperation at short term, but leads to conflicts [START_REF] Johnson | Behavioral relations in across-culture distribution systems: influence, control and conflicts in US-Japanese marketing channels[END_REF], reduce willingness to cooperate [START_REF] Skinner | Cooperation in Supplier-dealer relations[END_REF], damage channel's member satisfaction [START_REF] Skinner | Cooperation in Supplier-dealer relations[END_REF], and causes the collapse of mutual trust and commitment [START_REF] Brown | Power and relationship commitment: their impact on marketing channel member performance[END_REF]. Coercive strategies decrease the extent of shared beliefs [START_REF] Gaski | The differential effects of exercised and unexercised power sources in a marketing channel[END_REF]. On the opposite, non coercive power focuses on common beliefs and attitudes.

"Supplier development" summarizes efforts made by a buyer in order to increase the number of viable suppliers and improve supplier's performance or capability [START_REF] Krause | The relationship between supplier development, commitment, social capital accumulation and performance improvement[END_REF]. A problem is that the buyers must determine what knowledge and resource investments are likely to yield benefits if transferred to suppliers [START_REF] Krause | An empirical investigation of supplier development: reactive and strategic processes[END_REF]. If the appropriate mechanisms are not in place, the suppliers may not perceive the benefits associated with these investments and may reject the initiative to modify or improve their processes [START_REF] Krause | An empirical investigation of supplier development: reactive and strategic processes[END_REF].

Verbeek [START_REF] Verbeek | Materialising Morality: Design Ethics and technological Mediation[END_REF] uses the "script" concept [START_REF] Akrich | The description of technological objects[END_REF][START_REF] Latour | Where are the missing masses? The sociology of a few mundane artifacts[END_REF] for suggesting that technological prescriptions may be used in supply chains for influencing human actions. For us, the MRP method, the production management tools or the lean manufacturing principles can be seen as "scripts" aiming at substituting to the informal culture of the small suppliers, based on tacit knowledge, formalized procedures allowing to better predict the supplier's behavior.

The contract is also supposed to protect against opportunistic behaviors [START_REF] Willamson | The Economic Institutions of Capitalism[END_REF] by creating formal rules and procedures. Nevertheless, contracts seem unable to completely prevent opportunism and conflicts are most of the time solved without references to the legal aspects [START_REF] Nogatchewsky | L'exercice du contrôle dans la relation client-fournisseur[END_REF].

Trust is often seen as an alternative to power: it allows decreasing the required level of monitoring of the relationship [START_REF] Andersen | Emotion, trust and relationship development in business relationships: a conceptual model for buyer-seller dyads[END_REF] and is an antecedent to cooperative behavior [START_REF] Johnston | Effects of supplier trust on performance of cooperative supplier relationship[END_REF]. The role of trust in cooperation has been a prominent theme in the literature on buyer-supplier relationship [START_REF] Möllering | A typology of supplier relations: from determinism to pluralism in inter-firm empirical research[END_REF]. Trust is defined in SCM as the belief that an exchange partner's work of promise is reliable and that the partner will fulfill his obligations [START_REF] Rotter | A new scale for the measurement of interpersonal trust[END_REF][START_REF] Schurr | Influences on Exchange Processes: Buyer's Perception of a Seller's Trustworthiness and Bargaining Toughness[END_REF]. For [START_REF] Johnston | Effects of supplier trust on performance of cooperative supplier relationship[END_REF], supplier's trust results from supplier's perception of buyer's benevolence 3 , but also from supplier's perception of buyer's dependability. On the opposite, buyer-supplier relationships characterized by asymmetric interdependence are often believed to be deficient because the independent partner holds higher power and might be attempted to exploit it [START_REF] Anderson | Determinants of Continuity in Conventional Industrial Channel Dyads[END_REF][START_REF] Frazier | The use of influence strategies in interfirm relationships in industrial product channels[END_REF][START_REF] Geyskens | The effects of trust and interdependence on relationship commitment: A trans-Atlantic study[END_REF]. A high level of total interdependence is usually considered as an indicator for a strong, cooperative long-term relationship in which both parties have invested; mutual trust and mutual commitment will characterize those relationships [START_REF] Geyskens | The effects of trust and interdependence on relationship commitment: A trans-Atlantic study[END_REF].

In our opinion, these concepts have a direct "echo" with the situations related in section 3. We shall attempt in next section to formalize the links between these concepts and to apply the resulting framework on the situations identified through the interviews.

Modeling the Cooperation Context

Human behaviors can hardly be described by simple models: our goal is not here to describe cooperative situations under a deterministic formalism, but to underline different possible "circuits of interaction" between the attributes of cooperation, based on the literature analyzed in previous section. These interactions may make the cooperation situation evolve either positively or negatively. Within the various concepts listed in previous section, let us distinguish between: -those describing the context of the relationship, e.g. level of dependence of the partners, risks that they are facing (white boxes in the figures) etc., -the attitudes of the partners (willingness to cooperate, trust, opportunism, etc...) (grey boxes in the figures), -the actions (control, influence, information sharing), which influence/are influenced both by the context and attitudes (black boxes in the figures).

In Figures 1 and2, an attempt has been made to suggest qualitative models for describing the type of relation between two entities. As usually in such models, "+" means that an increase of the level of the influencing entity provokes an increase of the level of the influenced one (conversely for "-").

In Figure 1 are summarized as an example the limitations of coercive control in the present context of aeronautical supply chains. In bold lines is suggested a classical reasoning of the customers: the dependence of the supplier (as it is usually the case when suppliers are SMEs) gives power to the customer. Power creates the possibility to mitigate risk (mainly due to uncertainties) by controlling the suppliers through coercive control, in order to prevent opportunistic behaviors. The limitation of these opportunistic behaviors is considered as decreasing the risk. Similarly, coercive control also allows to prescribe values/beliefs/tools which should participate to decrease the level of risk. In the situations described in section 3, the possibility to cancel a contract is an example of threat clearly linked to coercive control. Imposing the use of production management tools can be seen as prescribing values (see the "script" concept). Number "1" on Figure 1 shows the expected decrease of risk as a result from these actions. The interviews clearly show the possibility or other -less favorable -interactions: asymmetrical dependence and coercive control decrease trust, which prevents from adopting the customer's prescribed values, especially if they are far from the culture of the SMEs preferring action to procedures [START_REF] Grabot | SME and Planning in Supply Chains: a Socio-technical View[END_REF]. While limiting opportunism, coercive control also limits commitment, preventing the supplier from taking initiatives and decreasing its flexibility and reactivity. As a result, the possibility exists that risks are increased (circuit 2 on Figure 1) more than decreased by the conditions of control. In Figure 2 are illustrated some problems linked to supplier development. "Maturity" is considered as linked to the use of formalized methods, like MRP or lean manufacturing. As seen in the interviews, some (rare) companies are also open to information sharing (e.g. concerning their stock level) for increasing the consciousness of the suppliers, and to show their own trust. This "virtuous" circuit may be possible in some cases, but it suffers in our opinion from an "original sin": the asymmetrical dependence between partners, which reassures the customers but prevents real trust to be established from the supplier's side. Indeed, the power of the large companies make that SMEs must show an appearance of adoption of these new methods and tools, even if these new values are not fully shared. The formal values/beliefs transmitted to the suppliers, not fully understood nor used, may decrease the use of the traditional "informal" methods insuring reactivity, and may as a consequence participate to increasing risk.

Conclusion

Through some real examples, we have tried to show that the efforts conducted by large companies of the aeronautic sector for improving their cooperation with their small suppliers do not always have the expected effects. Concepts suggested in the domains of sociology of organizations and management have been used in order to provide a better understanding of the observed situations. The first perspectives of this preliminary work concern the production of complementary conceptual models linked to other situations. The validation of these models will then been addressed, including the definition of criteria allowing a quantitative assessment of the identified behaviors. The diagnosis of the reasons of the observed attitudes and beliefs, based both on technical and behavioral aspects, will then be addressed.

Fig. 1 .

 1 Fig. 1. Limitations of coercive control

Fig. 2 .

 2 Fig. 2. Limitations of supplier development

For the customers, this point is only meant for keeping some pressure, since they consider that the suppliers know that approving a new supplier is a costly and long procedure.

Buchanan [29] conceptualized power-dependence imbalances in buyer-supplier relationships as the difference in value that buyers and sellers attach to the relationship.

Benevolence is usually defined as providing proprietary information or providing assistance without compensation[START_REF] Möllering | A typology of supplier relations: from determinism to pluralism in inter-firm empirical research[END_REF].


R Guillaume

C Thierry

B Grabot

Integration of the supplier capacity for choosing the less risked schedule within an uncertain environment

Keywords: Planning, Decision under uncertainty, Possibility theory, Supply chain

This communication addresses the integration of the supplier capacity in the procurement planning process of a customer within a supply chain. Since this supply chain evolves within an uncertain environment, uncertainties may be due to an ill-known demand (prevision) and to the customer production system (scraps, imprecise processing time...). Thus, we propose a collaborative process where the customer aims at taking the less risked decision. The integration of the supplier capacity in the gross requirement enables to assess the risk of back order so that the feasibility of the requirement plan. It then enables the customer to make the decisions, which limit this risk.

Introduction

Nowadays, companies are not competing as independent entities but as a part of collaborative supply chains. Within these supply chains, the collaborative processes are usually characterised by a set of point-to-point (customer / supplier) relationships with partial information sharing [START_REF] Galasso | Decision support for supply chain planning under uncertainty[END_REF]. More precisely, a procurement plan, or several procurement plans, are built and propagated through the supply chain.

Within this context, this communication focusses on the integration of imperfection on the customer's gross requirements, taking into account his knowledge on the capacity of the suppliers, with the final goal to cope with an uncertain environment (linked to demand, scraps, processing time...)... The theory of possibility and the theory of fuzzy sets [2][3] are often used to model the uncertainty when historical data can hardly be obtained or are obsolete [START_REF] Peidro | Fuzzy optimization for supply chain planning under supply, demand and process uncertainties[END_REF][5] [START_REF] Aliev | Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management[END_REF]. Different models have been used to represent the imperfection on the requirements. [START_REF] Fargier | The use of Possibilistic Decision Theory in Manufacturing, Planning and Control: Recent Results in Fuzzy Master Production Scheduling[END_REF] represents the imprecision on the quantity by a trapezoidal fuzzy number; [START_REF] Grabot | Integration of uncertain and imprecise orders in the MRP method[END_REF] integrates the uncertainty on the order and the imprecision on the quantity to compute the demand.

In a previous work (see [START_REF] Guillaume | Intégration de besoins en composants mal connus dans un plan[END_REF]), we consider the uncertainty on the order itself, so that the imprecision and the uncertainty on the quantity and on the date. Moreover, the dependencies between the requirements are considered (see [START_REF] Guillaume | Integration of ill-known requirements with dependencies into a gross requirement plan[END_REF]) to compute a gross requirement, modelled by a graph. This graph has possibility levels on arcs and fuzzy quantities on nodes.

In this communication, we propose a method to integrate the supplier capacity before choosing the final procurement plan and the associated production sequence. This method is composed of three steps: 1) design of the graph which represents all possible gross requirements (section 2), 2) computation of the level of feasibility of each node (section 3), 3) choice of the final schedule (section 4).

Gross Requirement

In this section is first introduced the model of requirements and dependencies, followed by the method to design a graph representing these possible requirements.

Model of Requirements

We consider that the customer has a make-to-Order production. In this context, the requirements of components are expressed by a quantity for a date which can be both imperfect (uncertain and/or imprecise). Moreover, the requirement itself can be uncertain (i.e. may be cancelled). Possibility theory is suggested to represent these imperfections. The requirement is thus composed by two sets of fuzzy sets: the first one represents the imperfect quantities and the uncertainty on the requirement, whereas the second one represents the imperfectly known date. For instance, Figure 1 represents an imperfect requirement (this model is developed in [START_REF] Guillaume | Intégration de besoins en composants mal connus dans un plan[END_REF] and [START_REF] Guillaume | Integration of ill-known requirement into a plan[END_REF]). Dependencies often exist between requirements. These dependencies are linked to constraints linked to the bills of materials or to the resources (assembly lines) (see [START_REF] Guillaume | Integration of ill-known requirements with dependencies into a gross requirement plan[END_REF]).

Possibility

The first kind of dependency (denoted "quantity dependencies") exists if the required component appears at different levels oin the bill of material of the same final product (see Figure 2). On the other hand, requirements processed on the same assembly line can be linked by "precedence dependencies" (see Figure 3). Moreover, the customer may consider some feasible sequences evaluated with a level of preference (see Figure 4).

Gross Requirement Model

Since the supplier is supposed to perform a Make-to-Stock production, he has to compute gross requirement expressed as quantities by period from requirements Precedence dependency date expressed as quantities for date (due to make to his order production) in order to build a requirement planning.

From the information on the requirements, the dependencies and the levels of preference between two decisions, a graph which represents the gross requirements can be built. The method to design this graph is explicitly explained in [START_REF] Guillaume | Integration of ill-known requirements with dependencies into a gross requirement plan[END_REF]: the nodes (t,c t ) of the graph represent the possible quantities (represented by a trapezoidal fuzzy number) for each given period) (see Figure 5). The arcs (t, ,c t , c t+1 ) of the graph are valued by a set of characteristics 1 , ,

+ t t c c t
A : a possibility level and the preference linked to a given quantity for period t and a given quantity for period t+1. So, the graph (see Figure 6) takes into account the imperfections on the requirements, the dependencies between requirements and the preferences on the chosen sequences.

µ µ + + = . (1,1) (0,1) (T+1,1) (1,C 1 ) (t,1) (t+1,1) (t,C t ) (t+1,C t+1 ) (T,1) (T,C T )

Computation of Feasibility Level

This section addresses the computation of the feasibility of the graph by considering the capacity constraints of supplier. The capacity constraints are illknown. The capacity of the supplier dedicated to the customer can be transmitted by the supplier himself. The customer may also have his own knowledge on these capacities (in this study we focus on the computation of the level of feasibility and not on the problem of confidentiality of data and degree of collaboration between the customer and the supplier). The capacity constraints are modeled by triangular fuzzy number (see Figure 7). To compute the level of feasibility of the gross requirements, the inventories or backorders are considered. Thus, we evaluate the necessity to satisfy the gross requirements with the possible delivered quantity (production plus inventories minus backorders). Φ : level of feasibility of the gross required quantity (t,c t )

Notations

The fuzzy numbers F ~ are expressed as quadruplets (a F ; b F ; c F ; d F ), as shown in Figure 6. The level of feasibility is computed using algorithm 1. The possible inventory level of the previous nodes is firstly computed, then the feasibility of the nodes is assessed

Possibility 1 0 a F d F b F c F
and the critical general fuzzy inventory is deduced. Finally, the fuzzy inventory and the fuzzy backorder levels are estimated.

Algorithm 1: Computation of feasibility level

Input: Graph > =< A V G , ,
- + = - - - ; Else t t t t c t c t t c c t D B C I , , 1 , , ) ( ) ( ' ~1 1
( ' ~1 1 - - = - - - ; 1 , , ) ( )
End if End for each //computation of the feasibility of the nodes// (

1 t t t t t t c t c t t t t t c c c c t w c c c t c c t w I - - - - - - - × ≥ Ν - = Φ = Φ ≠ //computation ) ( ) ( ); 0 ' 1 min ) ( min , , , 0 , , , 1 1 1 , , 1 1 1

Choice of the manufacturing sequence

From the feasibility level and the knowledge on the manufacturing sequences, the "risk" s k s r , linked to the choice k s for the sequence s (equation ( 1)) is computed. The choice K s is made, which maximizes the preference level in the set of the less "risked" decisions (equation ( 2)).

The preferences between two sequences are considered as less important than the "risk". Therefore, the preferences are used for the choice between two sequences of equivalent risk. After making all the choices on the sequences, the graph is reduced. The characteristics of the arcs (t, ,c t , c t+1 ) . Figure 8 shows an example of graph before and after decision. In the initial graph (before the decision), there are two possible choices for one sequence. After the decision (choice 1), the resulted graph is obtained.

) 1 ( max , , , 1 , , 1 , t t t c t c s k s t c t A c t k s r Φ - = - - ∈ µ . ( 1
) ) ( max , ) ( min , s s k s s k s s,k s k s r r k s s k K µ = = .
(2) Fig. 7. Representation of graph before and after the integration of supplier capacity. The graph has two possible choices for one sequence (left graph). The decision is taken and the resulting graph is on the right side of the figure (choice 1 has been selected).

Conclusion

In this paper, a model of gross requirement plan which takes into account the imperfections on the requirements, the dependencies between requirements and the possible manufacturing sequences has been presented. A method allowing to compute the feasibility of each sequence, then to choose a sequence has been proposed. This method allows the customer to choose the less risked sequence in terms of backorder. Nevertheless, the customer wants to minimize the backorder risk but does not take into account the tardiness of the possible backorder. As a perspective, considering this tardiness can be an interesting issue.

Fig. 1 .

 1 Fig. 1. Uncertain requirement (quantity equal at zero possible) with an imprecision on the quantity and an uncertainty and imprecision on the date

Fig. 2 .

 2 Fig. 2. Quantity dependencies; the requirement is linked with the requirement r o'c by the component B. The possible quantities of r oc are linked with the possible quantities of r o'c . When r oc is equal at 5 then r o'c is equal to 20 (similarly, 10 with 40 and 15 with 60).

Fig. 3 .

 3 Fig. 3. Illustration of a "precedence dependency" between two requirements: the requirement r o (black) and the requirement r o' (white).

Fig. 4 .

 4 Fig. 4. Possible sequences, between two requirements: requirement r o (black) and requirement r o' (white).

Fig. 5 .

 5 Fig. 5. Representation of gross requirements

Fig. 6 .

 6 Fig. 6. Representation of fuzzy number F ~= (a F ; b F ; c F ; d F )

  Representation of gross requirements : preference level of a sequence s for the choice k s

	t D , ~	c	t	: fuzzy gross required quantity on node (t,c t ) combination c t for the period t
	c c t t A , 1 +	,	t	: set of characteristics of the arc (t,c t , c t+1 ) linking the node (t,c t ) with the
	node (t+1,c t+1 )
	c c t t w , 1 +	,	t	: possibility level of the arc linking the node (t,c t ) with the node
	(t+1,c t+1 )
	k The set s, s	c c t t A , 1 +	,	t	is defined by	c A	t	,	c	t	1	,	t	{ w c	t	,	c	t	1	,	t	;	, 1	k 1	;...;	S	,	k	S	}

Notations:

t: period with t ∈[1,T] c t : index combination pas clair of fuzzy gross required quantity with c t ∈[1,C t ] s: index of the sequence with s∈[1,S] k s : index on the choice of sequence s with k s ∈[1,K s ] µ

+

  : possibility level of the arc linking the node (t,c t ) with the node (t+1,c t+1 )

			:
	input data: C ~ : maximum supply capacity of the period t t t c t D , ~: gross required quantity (t,c t )
	c c t t w , 1	,	t
	Dependent parameters: c t I , ' ~ : possible inventory which can be positive or negative, for the node (t,c t ) t c t I , ~ : inventory of the node (t,c t ) t t c t B , ~ : backorders of the node (t,c t )
	Ouput data:
	i t ,

  and fuzzy supplier Capacity t

													C ~;
	Output: level of feasibility of each node	t c Φ ; t ,
	For each t do
	For each note (t,c t ) do
	For each	t c	1 -	c t w	1 -	,	c t	, t	1 -	≠	0	do
	//computation of the possible inventory level of the previous nodes// If 0 ~1 , 1 = --t c t B
	Then	I	t	t c	t c			I	t	t c ,	t C	t D	t c ,

  of the critical fuzzy general inventory level//

	' ' ~1 1 , , , , , , --Φ = Φ = t t t t t t c c t c t c c t c t I I	;
	//computation of the fuzzy inventory and fuzzy backorder levels//
	     ' ) ) ~, ,      = = = = = ; 0 max( ); ; 0 max( ; 0 max( ); ; 0 max( , , , , , , ' ~' ' , t c t t c t t c t t c t t c t t c t t c t t c t t I I I I I I I b b a a I c t d d c c I ;
	     ' ) ) ~, ,      -= -= -= -= = ; 0 max( ); ; 0 max( ; 0 max( ); ; 0 max( , , , , , , ' ~' ' , t c t t c t t c t t c t t c t t c t t c t t c t t I B I B I B I b b a a B c t d d c c B ;
	End for each
	End for each
	Return	t c Φ ; t ,

Acknowledgments. This study has been performed with the support of Région Midi-Pyrénées and of the University of Toulouse.


Olivier Glassey
email: olivier.glassey@idheap.unil.ch

Jean-Henry Morin
email: jean-henry.morin@unige.ch

Design Thinking and Storytelling in eGovernment: The Case of ThinkData.ch

Keywords: Design thinking, data protection, transparency, storytelling, reuse, multi-jurisdiction

ThinkData is an interactive service for raising awareness on data protection and transparency in the organisational context. Originating from a study carried out by an interdisciplinary work group as part of a think tank on services science and innovation (ThinkServices). ThinkData allows its users to become familiar with the concepts of data protection and transparency through short stories, situations involving employees, managers, HR managers and information systems professionals. In this paper we present the design thinking approach used to develop ThinkData.ch and we describe the main features of the service. In the analytical part of the paper we discuss the main challenges and issues brought about by the tentative generalization of our approach, particularly in terms of modelling the multi-jurisdictional aspects of data protection and transparency.

Introduction

Many political processes involve solving wicked problems, such as environmental or socio-economic issues. This paper deals with such problems, where for example public and semi-public organizations have to find a balance between data protection and transparency legal requirements. Oftentimes these legal requirements are very difficult to understand for people who are not legal experts but still have to implement services required to be compliant with legal frameworks, e.g. human resources managers handling employee data or IT specialists dealing with employee emails or mobile devices. We argue, based on a concrete case, that design thinking, co-creation and innovation games can be valid and valuable approaches to address such wicked problems. However, we have found that the use of such techniques is not very frequent (or at least not much documented in the literature).

In this paper we therefore investigate how design thinking and innovation games approaches can be used, based on the concrete case of Thinkdata.ch. Moreover, in this specific case we report on, we also show the value of using storytelling in raising awareness on complex problems. In the first part of the paper we present the design thinking approach that led to the development of ThinkData.ch and we discuss the main features of the service. In the analytical part of the paper we provide an assessment of ThinkData by applying a framework derived from previous work of the authors and initially inspired by the Technology Acceptance Model (TAM) of Davis. Finally we discuss the main challenges and issues brought about by the tentative generalization of our approach, particularly in terms of modeling the multi-jurisdictional aspects of data protection and transparency. We conclude by discussing the hypothesis of the value of Design Thinking and Storytelling as key factors for designing global people-centric awareness services on complex legal and public policy frameworks.

Design Thinking and Gaming Approaches

Governments generally support participation in order to improve the efficiency, acceptance, and legitimacy of political processes [START_REF] Sanford | Characterizing eParticipation[END_REF]. As any political process deals with solving wicked problems, governments have to find innovative ways of encouraging participation. Indeed wicked problems are "those that defy conventional approaches to understanding, planning, design, implementation and execution because: (i) The stakeholder interests are so diverse and divisive; (ii) Interdependencies are so complex and so little understood; (iii) Behaviors are so dynamic and chaotic (unpredictable)" [START_REF] Newman | Gain a Foundation in Design Thinking to Apply Gartner's Hybrid Thinking Research[END_REF]. Hybrid thinking is one approach proposed by [START_REF] Newman | Gain a Foundation in Design Thinking to Apply Gartner's Hybrid Thinking Research[END_REF] to address such issues: it is based amongst others on design thinking and co-creation. One such example is VoiceS research project [START_REF] Holzner | Beyond Web 2.0 -What Serious Games, Semantics and Social Networks can Contribute to eParticipation[END_REF]) that uses serious games to foster eParticipation and make "complex EU co-decision procedure accessible to a large audience (especially among younger citizens), thus providing necessary understanding and enabling them to contribute actively to the platform". [START_REF] Holzner | Beyond Web 2.0 -What Serious Games, Semantics and Social Networks can Contribute to eParticipation[END_REF] define gaming "as a structured or semistructured activity, usually undertaken for enjoyment and sometimes also used as an educational tool. Key components of games are goals, rules, challenge, and interactivity." Furthermore serious game "came into wide use with the emergence of the Serious Games Initiative in 2002" which is "focused on uses for games in exploring management and leadership challenges facing the public sector" [START_REF] Susi | Serious Games -An Overview[END_REF]). Furthermore, gaming techniques have been used very successfully for decades in innovation and market research. Early adopters of such techniques were Silicon Valley companies who needed to find alternative ways to create new products. A notable example of this is the Innovation Games initiative of Luke Hohmann [START_REF] Hohmann | Innovation Games: Creating Breakthrough Products Through Collaborative Play[END_REF]) now turned into an online collaborative service1 .

Research Methodology

ThinkServices is a think tank on Services Science and Innovation that was created in 2008 by the Institute of Services Science of the University of Geneva and the Technology Foresight Office of the Canton of Geneva in Switzerland. The ThinkGroup Data, Society and Transparency (TG-DST) was initiated in 2010 by ThinkServices and the Geneva data protection and transparency commissioners. This group brought together around 20 data protection and transparency experts as well as professionals and academics from various disciplines (law, management, information systems, graphic design, and human resources).

The main research objective of TG-DST was to raise awareness on data protection and transparency issues in the organizational field, i.e. to address one of those wicked problems mentioned in the introduction by making the relevant laws and regulation understandable and usable for an audience of professionals who are not legal experts.

In order to reach that research objective we set up monthly half-day meetings that were held throughout 2011 with the goal of developing a self-assessment toolkit on data protection and transparency. A deadline for launching the service was set for January 28th 2012 on the occasion of the Data Protection Day. The focus on organizations quickly appeared based on the lack of understanding of these issues among the various stakeholders (HR specialists, top management, IT management, employee, etc.). Moreover, the service had to be based on realistic data protection and transparency issues, and to provide recommendations, legal references and links to concrete examples. Four subgroups focused on stakeholder profiles defining scenarios on the basis of a generic storytelling canvas similar to the ones used in fairy tales such as the Little Red Riding Hood. The stories were then reworked in order to ensure coherence and were validated by data protection and transparency commissioners both at federal and cantonal levels. The first implementation of the service was done by a web design agency and was launched in French as planned on the Data Protection Day (January 28, 2012). Currently Thinkdata.ch is also available in German, Italian and English.

Description of the solution

Our main objective was to bridge the understanding and awareness gap found between the increasing complexity of legal frameworks and their target audiences. The area of data protection and transparency is a good example of this situation, as these laws impact employees, managers and citizens from both organizational and personal standpoints. Moreover, these requirements are increasingly difficult to meet, with the digitization of our society, cloud computing and the growing use of personal devices in organizations (BYOD, Bring Your Own Device). Indeed connected smartphones along with their many apps, their GPS, etc., raises new issues which were unheard of ten years ago. The EU Data Protection reform [START_REF] Hustinx | The Reform of EU Data Protection: towards more effective and more consistent data protection across the EU[END_REF][START_REF] De Terwangne | La modernisation de la Convention 108 du Conseil de l'Europe[END_REF] is currently underway (see European Data Protection Reform) to adapt the 1995 rules in order to offer more control on personal data, with notions such as "explicit consent" or the "right to be forgotten" (European Commission 2012).

We believe that an appropriate way to bridge these gaps between the complexity of legal requirements and the understanding of the target audience is education and training. Indeed we wish to raise awareness on these issues in a way that is both engaging and useful. In order to achieve this we chose a design thinking approach. Indeed some members of the ThinkServices think tank had a technology and innovation background and since the objective was to design a service (artifact), Design Science appeared to be a suitable methodology. These techniques have been used by ThinkServices since the beginning [START_REF] Glassey | Design Thinking and Participation: Lessons Learned from Three Case Studies In[END_REF]. Globally our approach is adapted from techniques originating from design thinking: for an introduction see [START_REF] Brown | Design Thinking for Social Innovation[END_REF] or [START_REF] Brown | Design Thinking for Social Innovation[END_REF]; for more on design science we recommend [START_REF] Vaishnavi | Design Science Research Methods and Patterns: Innovating Information and Communication Technology[END_REF]; several great innovation games are documented in [START_REF] Hohmann | Innovation Games: Creating Breakthrough Products Through Collaborative Play[END_REF].

Based on the initial "design brief", the first six sessions were used to define the service, its features and requirements through design thinking based workshops. A key moment in this process was when the group identified storytelling and serious games as central elements of the planed service. While serious games as a technique was initially left for later due to the importance of the required work, storytelling immediately appeared as a simple yet powerful approach for our service.

The Use of Storytelling

Stories help explain, engage imagination, spark new ideas, create a shared understanding, and persuade as argued in [START_REF] Quesenbery | Storytelling for user experience: crafting stories for better design[END_REF]. Since one of the key requirements for the ThinkData.ch service was simplicity, both in terms of graphical interface and in terms of content, it was only natural that we relied on a storytelling approach to engage our target audience. Legal jargon is most of the time out of reach for non-specialists, especially when one has to deal with seemingly contradictory requirements in terms of data protection and transparency. We thus put a strong emphasis on writing scenarios that were easy to understand and included practical recommendations. We chose the narrative schema found in most fairy tales, like The Little Red Riding Hood:

• Introduction: the girl is asked to bring food to her grandmother;

• Trigger: the girl meets the wolf and changes her path;

• Incident: the wolf swallows the grandmother and the girl;

• Resolution: a lumberjack kills the wolf and saves them both;

• Conclusion: one should not go off the rails. This narrative schema is also emphasized graphically on the service through lettered bullets with visual floating indications when moving the pointer over them (rollover web design technique). This allowed us to structure all scenarios consistently (Fig. 1).

Service Design and Implementation

Most of the second half of 2011 was spent specifying and designing the service from a features and interaction point of view. We did loose some time thinking of some fancy round radar like approach for tablets and surface computer. But when the time had come to actually implement the service, it became clear this would not be feasible. The implementation was given to a Web agency in late November 2011 in order to be able to do some testing before the launch in January 2012. The implementation cost was minimal. The respective institutions of the members of the project supported it. In 2012 our main achievements were the translation in German, Italian and English, as well as some new features such as a seal to show authoritative validation of the stories and their recommendations by an official data protection and transparency authority (Fig. 2). Other minor enhancements and features were included in this last release such as RSS feeds, news, donation button for fundraising campaigns, illustrations for the scenarios and comments on the stories. This second version was launched on the 2013 Data Protection Day (January 28, 2013).

Description of ThinkData Service

ThinkData is an online interactive service designed as a simple website implemented on a content management system. Upon accessing the service homepage, users see a question, as a "teaser", related to data protection or transparency displayed randomly on a carousel (Fig. 3). When clicked, the user is taken to the relevant scenario. Users can also choose to browse the stories by general themes (e.g., biometry, geolocation, surveillance, etc.), activities (e.g., HR manager, IS manager, employee, etc.) or data types (e.g., banking, personal, medical, etc.) Currently, the service holds around 50 scenarios available online. Users also have the possibility to submit their own stories to be included after being edited for the site and checked by data protection and transparency commissioners.

For each scenario, the service provides three key information blocks (Fig. 3). First a plaintext recommendation that outlines what has to be done in a very straightforward way. To some extent this can be seen as a best practice "how to" recommendation to help users undertake the concrete steps towards doing the things right. The second information block contains references to the relevant legal texts and principles prevailing in the described case. Finally, real life examples or external resources are linked to the scenario. These can include press articles, TV clips, or court rulings. All scenarios can also be displayed on a single page (Fig. 4) where a filtering mechanism allows users to display only relevant scenarios, others being grayed out. In order to facilitate discovery scenarios are also displayed randomly on the page. Another feature is that users can download scenarios with a fixed layout in pdf optimized for print (e.g., for distribution to employees, supervisors or to use as reference material for training). Finally users can submit their own questions, story ideas or issues. Data protection and transparency commissioners then rework relevant ones in order to publish them online as structured scenarios. As mentioned above, in the 2013 version we introduced a "ThinkData seal" (Fig. 2), in order to show that these recommendations were indeed validated by data protection and transparency commissioners, either at the Federal or at the Cantonal levels. Even if submitted questions or issues are not published, each contributor receives a personal answer or recommendation by the commissioners. In six months we published a dozen new scenarios and we even created a new stakeholder category, namely citizens. Indeed we received many requests on data protection and transparency regarding citizens' everyday life. This led to extending our scope, even though it was initially limited to organizations.

Assessment of ThinkData.ch

The initial goal of ThinkData.ch was to bridge the gap between legal complexity and end-users' understanding of data protection and transparency requirements. In order to do so we used the body of research on user satisfaction, most notably Wixon and Todd's paper (2005). They propose "an integrated research model that distinguishes beliefs and attitudes about the system from beliefs and attitudes about using the system". To survey the behaviour of using technology Wixon and Todd rely on the well-known technology acceptance model (TAM) developed by [START_REF] Davis | Perceived usefulness, perceived ease of use, and user acceptance of information technology[END_REF]. This theory suggests that users confronted to a new technology are influenced in their use by the:

• Perceived usefulness: Davis defines it as "the degree to which a person believes that using a particular system would enhance his or her job performance"; • Perceived ease-of-use: Davis describes this as "the degree to which a person believes that using a particular system would be free from effort".

We have not yet investigated these questions methodically, e.g. with an online survey for ThinkData.ch users or with interviews. However we had feedback on how the service is used:

• As an FAQ screening tool for organizations on data protection and transparency: some people reported using the service in their organizations to direct their employees to get more familiar with data protection and transparency issues and to first check there for any related questions before asking the person in charge in the organization.

• As a training tool in education and awareness training on data protection and transparency:

as part of the public administration training in Geneva, ThinkData is used within a course on data protection and transparency provided to state employees (State of Geneva Continuous Education programs 2012). • As a way to alert or warn on a potential data protection and transparency issue: some people have reported using the print your story takeaway feature of ThinkData to actually give it to their managers or simply leave it on a table somewhere so other people can be alerted. • Personal verification use: many people reported using the service to check on how their personal information are used and as a way to make sure that they are doing the right thing in potential data protection and transparency situations.

This gives us a few elements of response regarding perceived usefulness (service recommended to employees and used in training) and perceived ease-of-use (print a story and show it around). Of course this is only very preliminary and not based on scientific evidence. Future work will be the realization of an online questionnaire that ThinkData.ch visitors will be asked to fill when accessing the website.

Furthermore we wanted to assess the usability of ThinkData.ch, by measuring attributes of electronic services such as accessibility, timeliness, personalization, navigation or online facilities. In order to do so we used an assessment framework we developed in order to measure the "proximity" of eGovernment services [START_REF] Glassey | A Proximity Indicator for eGovernment: The Smallest Number of Clicks[END_REF]). The unit of measure used in this assessment is the Smallest Number of Clicks (SNC) to reach a given element of measure (Table 1).

Navigation for handicapped Translations Use of life events or other topical navigation Transparency

SNC to find elements that help understanding administrative services and to give feedback regarding these services.

Survey Data protection Official publications

Table 1: Measuring Proximity of eGovernment Services

The version 2 of ThinkData.ch has all of these features available in one-click (or at the most two), apart from personalisation. This topic leads us to our next section, as we would need to develop a multi-jurisdictional model for ThinkData.ch in order to be able to give personal recommendations to users according to their localization (Federal laws in Switzerland are completed by Cantonal laws, and in some cases the Cantonal law is more restrictive that the Federal one, e.g. in the case of data protection).

Generalization of the ThinkData approach

In our opinion the generalization of the ThinkData approach could be done in two directions: (i) in the same thematic domain of making complex legal requirements more understandable and usable, by integrating multi-jurisdictional frameworks in our model; (ii) in other eGovernment domains such as eHealth or eTaxation, by reusing and adapting the design thinking and storytelling approach we developed.

Multi-jurisdictional Applications

Based on existing international collaborations among the ThinkData team members, initial contacts were established with several data protection and privacy related agencies and commissioners in other countries in order to share our findings and the service. Among those, South Korea was identified as a likely early adopter of the ThinkData approach given their regular high ranking as number one in the United Nations E-Government surveys. In the latest 2012 survey their rank is respectively #1 for the E-Government Development Index and #1 ex aequo with the Netherlands for the E-Participation Index (United Nations E-Government Survey 2012). In November 2012, ThinkData was presented to the Privacy Protection Policy Division of the Korean National Information Society Agency (NIA). Korea has among the strictest laws on Data Protection and Privacy that entered into force in 2011. The initial approach they followed was a traditional Frequently Asked Questions (FAQ).

The ThinkData approach appeared much more effective and appealing to NIA and they decided to implement a quick pilot of the ThinkData storytelling approach in December 2012. This was done with ten scenarios together with an assessment survey to measure service acceptability. The model used was based on a Task Technology Fit (TTF) and Technology Acceptance Model (TAM). Preliminary results indicate that 93% of the respondents are willing to use such a service on a regular basis. These results still need to be formally documented and reported. We actually plan to conduct the same survey in Switzerland and to combine our results in order to also assess the potential cultural differences between Asia and Europe.

Ultimately, our goal is to allow all interested countries to reuse the ThinkData approach in a noncommercial setting. It is for this reason that ThinkData has been released under a Creative Commons license (CC BY-NC-SA 3.02 ) allowing to share and remix ThinkData under attribution, share alike and noncommercial conditions.

Further, assuming a network of ThinkData services, their interconnection in a linked open data way appeared as a promising direction to help address increasingly complex issues related to the evaluation of necessarily different jurisdictional approaches to data protection. Such interconnections would allow different countries to share stories together with their specific legal basis, thus offering new ways to compare how similar situations are addressed in different legal contexts. This could in turn offer new insights into how countries shape and design public policies and consequently their legal frameworks. In an increasingly globalized and interconnected world, designing tools and services able to help address global scale wicked problems will become more and more necessary.

Several other interesting use cases can also be anticipated. First in the area of awareness, training and education in data protection, we contend there is an opportunity to help people better understand data protection, privacy and transparency in a way that is closer to users rather than lawyers. Therefore, a global awareness service relying on storytelling might be a promising outcome of a multijurisdictional approach of ThinkData. A second interesting use case can be anticipated in connection with how businesses assess and prepare themselves for global data protection. Many companies, including SMEs, need to deploy activities in different countries, thus facing the issue of how it impacts their operations. One value added service we have anticipated in this context is a service allowing to visually represent how different legal frameworks overlap, or not, thus providing a rather effective way to assess the impact and consequently the cost of doing business in that other context. Further, one could imagine using such visualization tools to study and even shape public policy reforms.

In the background of all these opportunities lie two key issues: first, in terms of representing the legal contexts (much work has been done in the area of legal ontologies and we will need to review the state of the art in this area); second, in terms of linking these different legal knowledge contexts together.

Design Thinking and Storytelling in eGovernment

Whereas we have already done some preliminary work in the domain of multi-jurisdictional approaches, we have not yet developed concrete applications of our approach in other eGovernement areas. However we already have ideas for future work on the concrete case of eFiles. Indeed the problem of patient's or citizen's eFile is a very complex problem (what data are stored, who has access, for which uses, etc.) and we believe such an approach based on storytelling and serious gaming could be absolutely relevant to address these kind of issues.

Conclusions

To conclude, we would like to open the discussion on a key question we think is worth tackling to address the challenge of designing people-centric awareness services on general complex legal and public policy frameworks. In other words, we hypothesized the value of Design Thinking and Storytelling are key factors to address this challenge. The ThinkData.ch case is a preliminary step towards establishing this. Our ongoing and future work in addressing the multijurisdictional dimension of the internationalization of ThinkData will provide good insights towards formal validation. The value of Storytelling will also be formally assessed through cross-cultural formal evaluation. Finally, the working group behind ThinkData is also including serious gaming in the evolution of the service in order to enhance the learning experience for users.

Figure 1 .

 1 Figure 1. ThinkData story and narrative schema.

Figure 2 .

 2 Figure 2. ThinkData seal.

Figure 3 .

 3 Figure 3. Resources linked to stories.

Figure 4 .

 4 Figure 4. ThinkData story selection and filtering screen.

Innovation Games, online service, http://innovationgames.com/ (retrieved Jan

2013)

Creative Commons, http://creativecommons.org/licenses/by-nc-sa/3.0/deed.en (retrieved January 2013)