Beatriz Andres
email: bandres@cigip.upv.es

Raul Poler
email: rpoler@cigip.upv.es

Josefa Mula
email: fmula@cigip.upv.es

Manuel Díaz-Madroñero

Raquel Sanchis
email: rsanchis@cigip.upv.es

Decentralised Replenishment-Production Planning Optimisation using Negotiation Rules in a Collaborative Network

Keywords: Decentralised Planning Optimisation, Mathematical Programming, Negotiation Mechanisms, Collaborative Networks, Automotive Industry

This paper proposes a decentralised planning optimisation approach by using mathematical programming and negotiation mechanisms in a collaborative network. Two partners addressing the replenishment and production stages are considered. Concretely, production scheduling plans and material requirement plans, modelled by two mixed-integer programming models, are modified according to the established negotiation rules between the two collaborative partners. The main contribution of this paper is the improvement of the replenishment and production plans upstream of the network; proposing a decentralised collaborative planning process. The validation of the proposal is done using data based on an automotive industry

Introduction

Increasing competitive changes in the global market, combined with rapid advances in information technology have made that collaborative network planning is at the edge of the business practices of most of collaborative partners. Given the complexity of operations and the often-conflicting objectives [START_REF] Andres | Dealing with the Alignment of Strategies within the Collaborative Networked Partners[END_REF] of the enterprises belonging to a collaborative network (CN), such as, purchasing, , manufacturing, distribution and sales, it is essential to develop a unified and rigorous approaches to increase commitments and reduce discrepancies among the network partners. The effective integration of these different features is the main objective of the collaborative network planning [START_REF] Gupta | Managing demand uncertainty in supply chain planning[END_REF]. Planning refers to the coordination and integration of key business activities undertaken by the network partners, from procurement of raw materials to distribution of finished products to the customer [START_REF] Gupta | A hierarchical Lagrangean relaxation procedure for solving midterm planning problems[END_REF]. Nowadays, globalised markets are constantly changing, thus maintaining flexible, efficient and sustainable CN is essential to deal with the volatility prevailing in the business environment, which is constantly transforming, and with the variability of customer expectations.

In response to the challenge outlined, this paper proposes a decentralised collaborative planning approach that focuses on the negotiation of replenishmentproduction plans. The proposed approach centres its attention on two levels of the CN: a manufacturer and its supplier supporting the decentralised collaborative planning between (i) the integrated scheduling and replenishment plan computed by the manufacturer, identifying the quantities of finished products to produce, the sequence of production, the quantity of components required to produce the finished products and the period in which the components are demanded; and (ii) the scheduling plan computed by the supplier, identifying the quantity of components to produce with the main aim of supplying them to the manufacturer, minimising the products shortages. In order to support such collaboration in the planning process, negotiation mechanisms are proposed, in which each collaborative partner defines decision-making rules that will allow exchanging results on plans until an agreement between the scheduling plans of both, the manufacturer and the supplier, converge; obtaining a beneficial solution for both.

In this regard, the paper is organised as follows. Section 1 introduces the problem to be solved, raises the research question, and describes the research approach carried out. Section 2 presents the literature review. Section 3 proposes a decentralised collaborative approach that consists of two mixed-integer programming models, for both the manufacturer and the supplier. Moreover, collaborative negotiation mechanisms are presented. A use case is proposed in Section 4. Concluding remarks and future research lines are drawn in Section 5.

Problem Description and Research Question

The operational planning process, such as replenishment, production, inventory management and distribution processes, is relevant for the establishment of collaborative processes among networked partners [START_REF] Andres | An Operational Planning Solution for SMEs in Collaborative and Non-Hierarchical Networks[END_REF]. Collaboration can be seen as the mutual participants' commitment to jointly solve problems. The collaborative network management has associated a higher degree of complexity; due to heterogeneous and autonomous partners take part in [START_REF] Camarinha-Matos | Collaborative networks: a new scientific discipline[END_REF]. This involves that each collaborative partner defines different goal-based objectives [START_REF] Andres | Dealing with the Alignment of Strategies within the Collaborative Networked Partners[END_REF], making attractive the proposal of approaches devoted to integrate all the nodes through the network planning. Considering the importance of collaboratively perform the planning process, among the enterprises of the same network, in terms of improving the CN relationships, this paper proposes a decentralised approach to support enterprises on the operational planning, from a collaborative perspective. The collaborative planning is focused on the replenishment and production planning process upstream of the CN.

The following research question is raised to support the decentralised collaborative planning process, as the objective of this research: What would be a decentralised and collaborative approach to adequately support enterprises on the modelling, assessment and resolution of the replenishment-production planning from a collaborative perspective?

The constructive research approach (CRA) [START_REF] Kasanen | The constructive approach in management accounting research[END_REF] is used to answer the research question defined. Five are the phases determined in CRA: (i) identify a relevant problem subject to study, the problem in this research is defined by the discrepancies among the network partners, given the complexity of operations and the conflicting objectives of the CN enterprises; (ii) consider the background to solve the problem, this research is based on the operations research, production planning and CN management fields; (iii) propose a solution to the problem in an innovative way. Accordingly, decentralised scheduling-replenishment plans, modelled by two mixedinteger programming formulations, are modified according to the established negotiation rules between the two collaborative partners; (iv) identify the theoretical relevance of the solution and the theoretical contribution, the proposed research focuses on improving the CN long term operation; and finally (v) validate the solution, in this research a use case is provided. In the current phase of this research, the main aim is to provide models and negotiation mechanisms to achieve coherence between the replenishment and production plans of the manufacturers and suppliers that take part in the CN, in order to promote sustainable collaboration.

State of the Art

Collaborative Planning. Different solutions have been proposed to jointly manage production, inventory and distribution planning activities [START_REF] Gupta | Managing demand uncertainty in supply chain planning[END_REF][7] [START_REF] Jung | Decentralized supply chain planning framework for third party logistics partnership[END_REF]. The collaborative planning process can be considered as centralised (CDM) or decentralized (DDM) [START_REF] Schneeweiss | Distributed decision making in supply chain management[END_REF] decision models. Models that address the planning process according to the type of decision-making can be centralised [START_REF] Alemany | An application to support the temporal and spatial distributed decision-making process in supply chain collaborative planning[END_REF] partially centralised [START_REF] Pibernik | An approach to inter-domain master planning in supply chains[END_REF] or decentralised [START_REF] Jung | Decentralized supply chain planning framework for third party logistics partnership[END_REF]. To deal with uncertainty (in demand, materials, etc.) fuzzy approaches are proposed [START_REF] Mula | Optimization models for supply chain production planning under fuzziness[END_REF]. DDM allow the decision-making process to be more flexible [START_REF] Schneeweiss | Distributed decision making in supply chain management[END_REF]. To address the collaborative and decentralised planning process, the literature proposes multiobjective planning models [START_REF] Gupta | Managing demand uncertainty in supply chain planning[END_REF].

Most of the models proposed in the literature focus on the centralised deterministic perspective, when dealing with collaborative planning. While decentralised models are focused on the downstream stages of the network, related with productioninventory management-distribution (third party logistics) planning processes. The main aim of this paper is to fill the gaps encountered with regards the decentralised collaborative planning processes, by focusing on the replenishment-production stages (upstream) of the network. In this context, they are pioneers the works by [START_REF] Herna | Supporting the collaborative decision-making process in an automotive supply chain with a multi-agent system[END_REF] , who demonstrate the benefits of collaborative planning for improving material requirement plans by proposing a reference architecture, negotiation mechanisms and a collaborative multi-agent system as modelling approach. Related to these works, the main novelty of our proposal is the collaborative consideration between scheduling and replenishment plans. Additionally, it is important to highlight the use of mathematical programming formulations, traditionally used for centralised planning, as a modelling approach for collaborative and decentralised planning.

Negotiation. The establishment of DDM has associated the need for establishing negotiation mechanisms, defining pre-agreed business rules, and assessing and comparing planning alternatives using performance measurement techniques [START_REF] Pibernik | An approach to inter-domain master planning in supply chains[END_REF]. The literature review has allowed identifying different approaches dealing with nonhierarchical negotiations based on compensation schemes [START_REF] Herna | Supporting the collaborative decision-making process in an automotive supply chain with a multi-agent system[END_REF]. In this regard, it is worth to mention the work proposed by [START_REF] Jung | Decentralized supply chain planning framework for third party logistics partnership[END_REF], which applies a non-hierarchical negotiation "a decentralised supply chain planning methodology (ADSCP)", allowing partners to create network plans by the simple exchange of information on the supply quantities. Here, the ADSCP methodology is adopted as the basis for the development of negotiation mechanisms.

Decentralised Planning in Collaborative Networks

The approach proposed to deal with the collaborative decentralised planning consists of the negotiation of two plans, each one pertaining to a different CN decision-maker, (i) the manufacturer that computes its scheduling plan (SP_M) and the material requirement planning (MRP_M); and (ii) the supplier that calculates its master scheduling plan (SP_S). In order to support the negotiation process, a collaboration mechanism is developed to link the two models from a decentralised and collaborative perspective [START_REF] Jung | Decentralized supply chain planning framework for third party logistics partnership[END_REF].

Manufacturer Scheduling and Materials Requirement Planning Model

In this section, a mixed integer programming formulation for replenishmentscheduling planning based on [START_REF] Andres | Capacitated lot-sizing and scheduling problem for second-tier suppliers in the automotive sector[END_REF] is described (see Table 1 for nomenclature). The first decision maker, the manufacturer, assumes the optimum scheduling planning and according to the quantity of products to produce, the manufacturer compute the MRP. Scheduling and MRP are integrated in the same model.

Vit Hit Bit

Amount of i to produce during time period t 1 if product i is produced during time period t, 0 otherwise 1 if a setup occurs from product i to product j during time period t, 0 otherwise Inventory level of product i at the end of time period t Amount of product i to order to suppliers Overtime hours during time period t 1 if product i is produced first during time period t, 0 otherwise 1 if product i is produced last during time period t, 0 otherwise 1 if machine is setup for product i at the end of time period t, 0 otherwise Strictly positive when at least one product is produced in time period t, 0 otherwise 0 if exactly one product is produced during time period t, an unrestricted non-negative number otherwise auxiliary continuous variable to eliminate disconnected subtours auxiliary variable to generate soft inventory constraint for product i during time period t Backorders of the product i in time period t Objective function

∑ ∑ + ∑ ∑ ∑ ⋅ + ⋅ + ∑ ∑ ∑ ∑ ∑ ⋅ + ⋅ = i t it B i cb t i t it H i pcv t TOV t co i j t i t it INV it ci ijt S ij cs z Min • (1) Constraints it it i it it i d B b INV X INV = + - - + 0 0 1 , = ∈ ∀ t J i (2) it it t i it it t i d B B INV X INV = + - - + - - 1 , 1 , 1 , > ∈ ∀ t J i (3) ∑ ⋅ = - + + - j it ji it it it t i X b INV Q SR INV 1 , { } I i ∈ ∀ \{ } t J , ( 4
) i t i INVMAX INV ≤ , t i, ∀ (5)
t i it it cv H INV , ≥ + t J i , ∈ ∀ (6) it it Y M X ⋅ ≤ t J i , ∈ ∀ (7) t t ijt J i j ij it J i i c TOV S ts X tp ≤ - ⋅ + ⋅ ∑∑ ∑ ∈ ∈ t ∀ (8) t J i it i c Q tp sup • ≤ ∑ ∉ t J i , ∈ ∀ (9) it it Y ω ≤ t J i , ∈ ∀ (10) ( ) ∑ ∈ - ≤ - j i t it N Y δ 1 1 t ∀ (11) ∑ ∈ ≤ ≤ j i it t 1 α ω t ∀ (12) ∑ ∈ ≤ ≤ j i it t 1 β ω t ∀ (13) it it Y ≤ α t J i , ∈ ∀ (14) it it Y ≤ β t J i , ∈ ∀ (15) t it it δ β α - ≤ + 2 t J i , ∈ ∀ (16) ∑ ∈ = j i it 1 γ t ∀ (17) ∑ - ≥ j it it jit Y S α t J i , ∈ ∀ (18) ∑ - ≥ j it it ijt Y S β t J i , ∈ ∀ (19) 1 1 , - - + ≥ - t jt t i ijt S ω γ γ t j i J i , , ≠ ∈ ∀ (20) 1 1 , - + ≥ - t j it jit S γ α t j i J i , , ≠ ∈ ∀ (21) 1 - + ≥ jt it ijt S γ β t j i J i , , ≠ ∈ ∀ (22) ( ) 1 , 1 - ⋅ - - - ⋅ + ≥ t i ijt it jt N N S N V V γ t j i J i , , ≠ ∈ ∀ (23) 1 0 ≤ ≤ t ω t ∀ (24) Xit, INVit, t δ , TOVt, Bit, Hit ≥0 t i, ∀ (25) { } 1 , 0 , , , , ∈ it it it it ijt Y S γ β α t j i , , ∀ (26)
Objective function ( 1) minimizes all the performed setups, inventory holding, overtime costs, the penalty of finished goods inventory coverage violations, the backorders. Constraints (2) and ( 3) are typical inventory balance equations for finished goods allowing backorders and (4) for raw materials. Constraint (5) limits the inventory level for each product according to the available inventory capacity. Constraint (6) corresponds to inventory coverage extension for finished goods. In this case, and in order to avoid infeasibilities, it is set as a soft constraint with a penalty cost included in the objective function. Constraint [START_REF] Dudek | Negotiation-based collaborative planning between supply chains partners[END_REF] ensures that whenever Xit ≥ 0, decision variable Yit is set at 1. Constraint (8) establishes the production capacity limits by taking into account possible overtime decisions. Constraint (9) establishes the production capacity of the supplier. Constraints (10) to (17) determine which product is produced first and last during a given time period. They also find the product for which the machine is setup at the end of each time period. Constraints (18) and (19) apply whenever more than one product is produced during a single period. They force at least one Sijt's to be 1 per product i, except when this product is either the first or the last product in the sequence. Constraint (20) forces a setup during production-free periods when the machine's setup state at the end of the period is not the same as the setup status at the end of the following period. Constraints ( 21) and ( 22) are needed to properly count the setups between the periods during which the machine is not idle, while Constraint (23) ensures the subtours elimination. Finally, Constraints (24) to (26) correspond to the binary and non-negativity properties of the decision variables.

Supplier Master Production Scheduling Planning Model

In this section, a mixed integer programming formulation for scheduling planning based on [START_REF] Andres | Capacitated lot-sizing and scheduling problem for second-tier suppliers in the automotive sector[END_REF], is described for the supplier master production scheduling planning model (see Table 2 for nomenclature).

∑ ∑ + ∑ ∑ ∑ ⋅ + ⋅ + ∑ ∑ = i t it SH i csh t i t it H i pcv t TOV t co i t it INV it ci z Min • • (27) Constraints it it it t i Q INV X INV = - + -1 , t i, ∀ (28)
i t i INVMAX INV ≤ , t i, ∀ (29)
t i it it cv H INV , ≥ + t i, ∀ (30) t t it i i c TOV X tp sup ≤ - ⋅ ∑ t ∀ (31) it it it d SH X = + t i, ∀ (32) it it Q X = t i, ∀ (33) Xit, INVit,TOVt, Hit t i, ∀ (34)
Objective function (27) minimizes all the inventory holding, overtime costs, the penalty of finished goods inventory coverage violations and the supply shortage. Constraint (28) is typical inventory balance equation. Constraint (29) limits the inventory level for each product according to the available inventory capacity. Constraint (30) corresponds to inventory coverage extension for finished goods. In this case, and in order to avoid infeasibilities, it is set as a soft constraint with a penalty cost included in the objective function. Constraint (31) establishes the production capacity limits of the supplier by taking into account possible overtime decisions. Constraint (32) establishes that the quantities demanded by the manufacturer are the same as the products produced by the supplier plus the shortage quantity of products. Constraint (33) indicates what the suppliers produces is the same as what is supplied to the manufacturer. Finally, Constraints (34) correspond to the non-negativity properties of the decision variables.

Negotiation Mechanism for the collaborative planning process

In this section a negotiation mechanism is proposed to support the network partners in the collaborative planning process. The negotiation mechanism is characterised by the decision-making rules, defined by the different partners involved in the collaborative planning .These decision-making rules allow to: (i) support the decision-making process and (ii) characterise the collaborative planning process by solving the differences among the planning activities of the parties involved. The decisionmaking rules should aim for a Win-Win outcome in order to meet the interests of all the sides involved. An example of decision-making rules is provided next with the main aim of guiding the enterprises, involved in the decentralised replenishmentproduction planning, in the collaboration process:

• Stockout Rule: This rule guarantees that at the end of the planning horizon the actual stock is higher than the minimum stock defined. • Backorders Rule: It ensures that no demand delays are allowed at each period.

• Supplied quantity Rule: The quantity of components to be supplied (by the S) should be within a target range defined by the M. • Inventory/Overtime/Setup costs Rule: The inventory/overtime/setup costs are defined in a bounded range. Having identified the decision-making rules, the enterprises involved in the collaborative planning process start with the negotiation mechanism. The proposed negotiation mechanism, based on [START_REF] Jung | Decentralized supply chain planning framework for third party logistics partnership[END_REF], is described next (Fig. 1): Step 1. The manufacturer (M) starts to compute the integrated replenishmentscheduling plan (SP 0 _M, MRP_M), given a planning horizon, depending on what to serve to the end customer (end-customer demand). According to the results obtained in the integrated replenishment-scheduling plan, M obtains the replenishment needs of the raw material or semi-elaborated components (MRP 0 _M). M releases the replenishment needs to the supplier (S) (manufacturer demand).

Step 2. S computes its scheduling plan (SP n _S), according to demand given by M. In case the S cannot satisfy the demand given by M, S communicates to M the quantities that can serve (Qit: Offered supply quantity of product i during period t)

Step 3. M generates a scheduling plan for the given offered supply quantity and considering the end-customer demand, so as to minimize M total costs, and meet the demand; therefore no backorders are allowed. In this step, the obtained replenishment needs are limited by the amounts of components offered by supplier in the previous step.

Step 4.1. On the one hand, if the decision-making rules are achieved (end-customer demand reached, no backorders for the manufacturer, no shortages for the supplier, etc.) and the requested supply quantity of M dit corresponds to the Qit given by S, the negotiation procedure is completed.

Step 4.2. On the other hand, if the decision-making rules are not achieved, the negotiation process continues. Each iteration in the negotiation process is numbered as n. In this regard, M generates a new replenishment-scheduling plan (SP n _M, MRP n _M) considering a new input data as regards the sequence and the quantity of products to produce in each period that allows accomplishing the decision makingrules and satisfying the demand with no backorders. M reports to S the requested supply quantity in order to fulfil the given demand of end customer (manufacturer demand).

Step 5. S generates a new scheduling plan (SP n _S), considering its internal production capacity and the manufacturer demand. S reports the new available supply quantity to M. Then the network nodes proceeds again with Step 3.

Numerical Example

This section presents the application of the proposed decentralized planning approach with a numerical example inspired in a real world automotive supply chain consisting of a supplier and a manufacturer. Both members by applying the proposed coordination mechanism achieve a compromise solution based on the coincidence of the amounts of components offered by supplier and the amounts of components requested by the manufacturer in order to meet the final customer demand following the stockout and backorders rules presented in the previous section, as well as a warehousing limitation rule for final products and components. Among them, the warehousing limitation and backorders rule in the last period are considered hard constraints while the stockout rule is considered as a soft constraint with a corresponding penalty. The mathematical programming models related to the supplier and manufacturer and the corresponding coordination mechanism have been in the MPL language V4.2. The resolution has been carried out with Gurobi 5.6.3 solver in a desktop computer with 4 Gb RAM. The input data and the model solution values were processed with the Microsoft Access database (2010) which was useful for the exchange of information between supplier and manufacturer models.

This study considers one finished good produced at manufacturer by processing one component obtained from the supplier in the same proportion over the four time periods considered. The demand levels at manufacturer are 3, 5, 11 and 0, respectively. The rest of manufacturer parameters are presented in Table 3. The initial backorders and scheduled receptions for each period are considered null and initial inventories are set according to stockout decision-making rule. The values for decision-making rules at manufacturer are presented in Table 4. The supplier parameters are presented in Table 5. The initial inventories are also set according to the stockout decision-making rule. The values for decision-making rules at supplier are presented in Table 6.

Using this data set a total of three iterations of the proposed coordination mechanism are needed to obtain a satisfactory replenishment-production planning according to the hard (backorders and warehousing limitations) and soft (stockout) decision rules defined for each member of the considered supply chain. The results obtained after the third iteration are presented in Table 7.

Figure 1 .

 1 Figure 1. Overview of the negotiation steps

Table 1 .

 1 Nomenclature model manufacturer Demand of product i during time period t Setup time from product i to product j Production capacity during time period t Available inventory capacity for product i Coverage inventory: Minimum inventory required for product i Initial inventory of product i Scheduled receptions of product i during time period t Quantity of i to produce a unit of finished good j Required time to produce a unit of product i Setup cost from product i to product j Inventory cost of product i during time period t Overtime cost during time period t Penalty for soft inventory constraint violation for product i

	Index
	I	Set of products (finished goods and raw materials)
	J	Set of finished goods
	T	Set of periods
	Parameters
	dit
	tsij
	ct
	INVMAXi
	cvit
	INVi0
	SRit
	bji
	tpi
	csij
	ciit
	cot
	pcvi
	bi0	Backorders of product i during time period t=0 (initial)
	cbi	Backorders cost of product i
	csupt	Production capacity of the supplier during time period t
	M	Big number
	N	Number of finished goods

Table 2 .

 2 Nomenclature model manufacturer Demand of product i during time period t Available inventory capacity for product i Coverage inventory: Minimum inventory required for product i Initial inventory of product i Required time to produce a unit of product i Inventory cost of product i during time period t Overtime cost during time period t Penalty for soft inventory constraint violation for product i Production capacity of the supplier during time period t Shortage cost per unit of product i Amount of i to produce during time period t Inventory level of product i at the end of time period t Offered supply quantity of product i during time period t Overtime hours during time period t Supply shortage of product i during time period t Auxiliary variable to generate soft inventory constraint for product i during time period t

	Index
	I	Set of products (finished goods and raw materials)
	T	Set of periods
	Parameters
	dit
	INVMAXi
	cvit
	INVi0
	tpi
	ciit
	cot
	pcvi
	csupt
	cshi
	M	Big number
	Decision variables
	Xit
	INVit
	Qit
	TOVt
	SHit
	Hit
	Objective function

Table 3 .

 3 Manufacturer parameters

	Parameter	Value
	ct	24 h
	tpi	1h/unit
	ci(i=1)t	10€
	ci(i=2)t	7€
	cot	100€
	cbi	1000€

Table 4 .

 4 Values for decision making rules at manufacturer

	Decision-making rule Indexes	Values
	Stockout	i=1, t	3 units
	Stockout	i=2, t	3 units
	Backorders	i=1, t=4 0 units
	Warehousing space	i=1, t	7 units
	Warehousing space	i=2, t	7 units

Table 5 .

 5 Supplier parameters

	Parameter	Value
	capsupt	10 h
	tpi	1h/unit
	ciit	7€
	cot	100€
	cshi	1000€

Table 6 .

 6 Values for decision making rules at supplier

	Decision-making rule Indexes Values
	Stockout	i=2, t	3 units
	Warehousing space	i=2, t	7 units

Acknowledgments. "The research leading to these results is in the frame of the "Cloud Collaborative Manufacturing Networks" (C2NET) project which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 636909"

As shown in Table 7, the coordination mechanism obtains a replenishment-production plan according to the established decision-making rules at each node of the considered supply chain with a minimum deviation. The possibility of introducing the stockout decision rule as a soft constraint allows to focus on strict decision areas in the automotive industry such as maximum warehousing levels and backorders although minimum desired inventory levels are not obtained for only two periods.

Conclusions

In this paper, two decentralised scheduling models dealing with the replenishment and production stages of a CN have been used through a mathematical programming approach. The decentralised collaborative planning approach has been supported by a negotiation mechanism that allows modifying the quantities of components requested by the manufacturer and the quantities of supply offered by the supplier, with the main aim of minimising the shortages in the supply and the backorders to the final customer. The proposed approach allows promoting collaboration between the replenishment and production stages of the CN. In future research lines, negotiation could be performed using the multi-agents paradigm by programming them based on automatic decision-making rules agreed by both network nodes. Another future work could be led to apply the proposed approach in a real size CN.


Filipe Ferreira

José Faria

Américo Azevedo

Ana Luísa Marques

Industry 4.0 as Enabler for Effective Manufacturing Virtual Enterprises

Keywords: Virtual Enterprise, Industry 4.0, Internet of Things, Cyber-Physical Systems, Intelligent Product Ecosystems

Today, the variety of complex products, low volume and decreasing life cycles require a combination of multiple skills that, often, do not exist in a single organization. This raises the need to extend the traditional organization towards the extended virtual enterprise. During the last decade several research projects developed concepts, methods and tools to support the design and operation of the virtual enterprises. However, the impact in industry remains low mainly due to the lack of vertical and horizontal integration, both at business and technical level. Industry 4.0 may be the missing enabler for effective virtual enterprises, once it integrates both business entities and technical entities into a single concept -the Industry 4.0 component -thus enabling enhanced interoperability. This paper presents innovative Industry 4.0 approaches, concepts, methods and tools applied to real manufacturing environments, showing how they enable the creation of cyber-physical production systems leading to a flexible, efficient and seamlessly virtual enterprise.

Introduction

During the last decades, increasing competitiveness pressure led companies to look at efficiency improvement opportunities obtained from a higher integration level between the design of the products, the design of production processes and the design of the value chains [START_REF] Fixson | Product architecture assessment: a tool to link product, process, and supply chain design decisions[END_REF]. Mass customization and servitization are perhaps the most prevalent examples of responding to such competitive pressure [START_REF] Azevedo | Factory Templates for Digital Factories Framework[END_REF]. Nowadays, companies seek to explore new organizational models such as virtual enterprises, in order to ensure a faster response to new business opportunities, and to offer differentiated products at competitive prices [START_REF] Jovane | The Manufuture Road: Towards Competitive and Sustainable High-Adding-Value Manufacturing[END_REF]. Sustainability, high variety, low volume and shorter product life cycles require a combination of multiple skills that is not easy to find in a single company. This raises the need to extend the organization, including skilled business partners, thus forming virtual enterprises [START_REF] Rojas | Business model development for virtual enterprises[END_REF] [START_REF] Rojas | Pillars and Elements to Develop an Open Business Model for Innovation Networks[END_REF].

Collaboration is moving from the classical supply chain, with stable relationships between stakeholders with fully defined roles, to unprecedented dynamic structures. Some of these organisational forms are goal-oriented, that is, focused on a single project or business opportunity [START_REF] Camarinha-Matos | Collaborative networked organizations -Concepts and practice in manufacturing enterprises[END_REF]. In this business context, virtual enterprises have been widely discussed over the past decades [START_REF] Cheng | Manufacturing network evolution: a manufacturing plant perspective[END_REF] [START_REF] Martinez | Virtual enterprise -organisation, evolution and control[END_REF]. In this research, the definition of virtual enterprise considered is based on the definition proposed by Camarinha-Matos, which states that " a virtual enterprise represents a temporary alliance of enterprises that come together to share skills or core competences and resources in order to better respond to business opportunities, and whose cooperation is supported by computer networks" [START_REF] Camarinha-Matos | Collaborative networked organizations -Concepts and practice in manufacturing enterprises[END_REF].

A virtual enterprise is one form of Collaborative Networked Organisations that allows partners to exploit emerging business opportunities in a flexible way [START_REF] Camarinha-Matos | Collaborative networks -Reference modeling[END_REF]. The effective implementation of a virtual enterprise raises several challenges: the integration of human and technical resources to enhance workforce performance and satisfaction; the need to instantaneously process information gathered from several sources into useful knowledge for an effective decision making and the ability to rapidly reconfigure manufacturing enterprises in response to changing needs and opportunities [START_REF] Ferreira | Virtual Enterprise Process Management: An Application to Industrial Maintenance[END_REF] [START_REF] Almeida | Process Performance Assessment in Collaborative Manufacturing Environments: A Role Oriented Approach[END_REF]. Those challenges raise two main integration issues. The first is a business-oriented and addresses the integration of several business entities within a logistic value chain network. According to Reference Architectural Model for Industry 4.0 (RAMI 4.0), business entities can be represented as a component, exposing services, functions and implementing standard communication channels. The way how such business entities interoperate is typically described and analysed using a business process modelling notation such as BPMN. The second issue is mostly a technical one and addresses the interoperability of new and legacy resources (e.g., equipment, software, people) of the partners participating in the virtual enterprise so that cooperation can take place quickly, seamlessly and in a flexible way. Typically, a single task at the business process model is decomposed in set of tasks at the technical level (e.g. web services invocation). At the technical level, the interoperating entities are no longer companies, but equipment, software and people. However, in Industry 4.0, such resources are also considered entities that expose services and implements standard communication channels, as depicted in Fig. 1.

The Industry 4.0 paradigm seeks the complete digitalization and integration of the industrial value chain in order to produce goods using the most advanced information and communication technologies. The motivation behind this new paradigm stays on the increased digitization of the economy and society, which is changing the way the production will be held in the future. After the steam engine, the introduction of assembly lines and the use of electronic and computer systems in the production, the so-called smart factories with cyber-physical systems (CPS) come now to determine the fourth industrial revolution. The adoption of Industry 4.0 concepts, their related service platforms and innovation business models will help European SMEs to grow and boost European gross domestic product. Nevertheless the ability to adopt Industry 4.0 technologies by SMEs is still very scarce. Benefits of the adoption of these technologies include increased productivity and efficiency improvement, but obviously it is necessary to guarantee a return of investment [START_REF] Barba-Sánchez | Drivers, benefits and challenges of ICT adoption by small and medium sized enterprises (SMEs): a literature review[END_REF]. Hence, there is the need for cloud based, scalable, secure and modular tools so that they can be introduced gradually and seamlessly in all manufacturing levels, from the shop floor, to the management level, without the need of a huge initial effort.

The essential issue tackled in this paper and the underlying research question refer to the Industry 4.0 concepts and technologies applied to virtual enterprise. In this research, it is of interest to face the following questions: (i) Which are the existing key enabling technologies to manage Industry 4.0 virtual enterprises? (ii) How to implement the Industry 4.0 technology in the European SMEs successfully?

This paper proposes an innovative cloud based framework to address these issues and enable collaboration in future Industry 4.0 compliant virtual enterprises. The rest of the paper is organized as follows: section two presents the main phases considered in the research methodology. In section three, the current state of the art in the field of industry 4.0, including recent European project results are presented. Section four describes the considered business case. Section five present the implementation issues as well as the validation process. Finally, overall conclusions and further developments are mentioned in section six.

Research Methodology

The work presented in this paper has followed a research methodology that encompasses three main phases, as described hereafter:

(i) Industry 4.0 enabling technologies, where existing Industry 4.0 concepts and reference models as well as enabling technologies are presented and analysed. A comprehensive framework, founded on collaborative platforms, Internet of Things (IoT) and CPS is presented, answering the first research question (Which are the existing key enabling technologies to manage Industry 4.0 virtual enterprises?).

(ii) Business case presentation and analysis, where a practical case in the field of Automotive stamping planning is presented. Several workshops were conducted, in order to collect qualitative and quantitative data and relevant information, taking into account the expertise of all team members and experts. After analysing the as-is situation, current industrial challenges and issues and consequent improvement opportunities were identified, the to-be business processes were designed and validated by the industrial partners, leading to the definition of requirements for the design, specification and development of new suitable and accurate supporting methods and tools. Industrialists, technology providers and researchers did participate in this task, bringing their multi-disciplinary expertise to the project.

(iii) Framework implementation and validation, where the framework was implemented and validated. In order to guarantee its sustainability, criteria for the framework evaluation were established and measured, paving the way for a successful exploitation and pointing out the strong points as well as aspects to be improved. This answers the second research question (How to implement the Industry 4.0 technology in the European SMEs successfully?)

The next three sections are directly related to the three stages of the methodology presented.

Industry 4.0 Enabling Technologies

The notion of enterprise systems architecture is crucial to understand and manage virtual enterprises. From an internal perspective, services are characterized by a modular structure with building blocks that are combined to create a Cyber-Physical Production Systems (CPPS). From an external perspective, they are a part of a larger CPPS (i.e., a virtual enterprise), often found in industries where product design increasingly determines the organization of partners, such as high technology and automotive industry [START_REF] Chandrashekar | Toward the virtual supply chain: the convergence of IT and organization[END_REF]. In this context, concepts such as Smart Objects (SO), Internet of Things (IoT), CPPS and finally Industry 4.0, have emerged over the past decade aiming at address the technological requirements imposed by virtual enterprises [START_REF] Rojas | Business model development for virtual enterprises[END_REF] [6] [START_REF] Wang | Current status and advancement of cyber-physical systems in manufacturing[END_REF]. The concept of horizontal integration and decentralization of production processes push the boundaries of collaboration between organizations in the field of CPPS to an unprecedented dynamic level. According to the RAMI 4.0 [15], the paradigm of service-oriented architecture plays a key role in support to CPPS. SOA allows to describe and use services in any of the automation pyramid levels: from the sensors and actuators, machines, through work centres and production lines, up to enterprise management levels. These services encapsulate well defined functionalities implemented through software artefacts [START_REF] Barba-Sánchez | Drivers, benefits and challenges of ICT adoption by small and medium sized enterprises (SMEs): a literature review[END_REF].

Recent research results from two major European Projects -ADVENTURE [START_REF]D3.4 Conceptual Manufacturing Reference of Adventure Project -ADaptive Virtual ENterprise ManufacTURing Environment FoF[END_REF] and Apps4aME [START_REF] Ferreira | Large Project Management in the Automotive Industry: A Flexible and Knowledge Based Approach[END_REF] -show how to introduce semantics to optimize and automate the modelling and execution of distributed production processes as an orchestration of such services. These research and innovation projects demonstrate that there are different approaches for managing virtual enterprises depending on its nature. For instance, Make-to-Stock (MTS) environments, where the factory and process configuration is inflexible and fixed to produce in large quantities, are suitable for totally automated processes, where typical workflow systems are able to manage and orchestrate the entire process. ADVENTURE project has developed a fully automated process manager [START_REF] Azevedo | A framework to support the lifecycle of virtual manufacturing enterprises[END_REF]. On the other hand, an Engineering-to-Order (ETO) environment imposes, flexible, adaptive and knowledge based processes [START_REF] Faria | Supporting the Operation of Semi-structured Work Systems[END_REF] [START_REF] Faria | An Agile BPM System for Knowledge-Based Service Organizations[END_REF]. Based on these concepts, Apps4aME project developed a hybrid process manager capable of manage knowledge intensive processes with high level of human interactions and decisions [START_REF] Ferreira | Large Project Management in the Automotive Industry: A Flexible and Knowledge Based Approach[END_REF].

Process description languages like business process execution language (BPEL) allow the composition of various services to simple or complex processes and semantic annotations are designed to express the context of a virtual service functionality [2] [4]. This approach has been widely adopted, with the goal of providing companies with the ability to generate, adapt and control production processes in distributed organizational networks, integrating from the so-called smart sensors and machines to production and enterprise level management systems, or other legacy systems. Based on the definition of individual production steps such as services and making use of a production-oriented approach to services, it was possible to model manufacturing processes for assembly services based on the capabilities of real factories of different business partners. These physical services are combined in production processes and take advantage of SOA technologies [START_REF] Rojas | Business model development for virtual enterprises[END_REF]. The results of several projects were combined to develop a framework for integration and interoperability within organizational distributed networks that meets what is now the concept of horizontal integration described by RAMI4.0, reducing market access barriers for Small and Medium Enterprises (SMEs), which do not usually have the ability to control the entire production life cycle, neither the marketing strength to impose their own interfaces and standards. Thus, it becomes easier for SMEs to get involved in Industry 4.0 compliant distributed organizational networks [START_REF] Rojas | Business model development for virtual enterprises[END_REF]. If CPS are not in accordance with a common standard, the integration of systems in the decentralized and virtualized production environment will require multiple peer-to-peer interfaces, exponentially increasing complexity. With a SOA based platform, each entity publishes its services in a central repository in accordance with a common standard. When it is necessary to produce a specific product, a specific process model previously stored in the production support system is applied. This process relies on cyber services on the available physical resources (e.g. machines, work centres, software) [START_REF] Wang | Current status and advancement of cyber-physical systems in manufacturing[END_REF]. Note that in the case of knowledge intensive processes such as engineering, people also play a central role as they are the main decision makers on the orchestration of the process [START_REF]D3.4 Conceptual Manufacturing Reference of Adventure Project -ADaptive Virtual ENterprise ManufacTURing Environment FoF[END_REF].

The RAMI 4.0 introduces the concept of Industry 4.0 component as a model to describe in detail the properties of CPS, i.e. real objects in a production environment linked to virtual objects. The real objects (production systems, machines, systems and modules) necessarily need to contain a set of defined RAMI 4.0 properties in order to be considered compliant with RAMI 4.0 objects. One of the properties is the ability to communicate in real time and to expose its functions and data. This definition is aligned with the concept of SO developed under previous European projects such as ADVENTURE. An important Industry 4.0 requirement is that a component collects all the relevant data throughout its lifecycle, providing these data to the companies involved in the virtual enterprise. The data storage and respective communication are provided by the so-called administration shell [START_REF] Adams | Worklets: A Service-Oriented Implementation of Dynamic Flexibility in Workflows[END_REF]. An Industry 4.0 administration shell contains a large amount of data and information provided by manufacturers, such as CAD data, connection diagrams, datasheets, manuals, etc. Integrators and operators can add important information about the maintenance or connection with other hardware or software. The Industry 4.0 defines data security measures and ensures data availability, confidentiality and integrity. The administration shell also provides some functions such as planning, configuration, operation, maintenance as well as complex functions related to business rules. Data and functions are available within the component itself, inside a corporate network or even in the cloud. The advantage is that information is stored only once and may be provided in a transparent manner through IT services for any user in any use case. The horizontal and vertical integration is facilitated by a combination of Industry 4.0 compliant communication protocols [START_REF]PLM as Enabler for Industry 4.0 A NTT DATA whitepaper As of[END_REF].

In Summary, all information is fully available for the engineering as well as operation and maintenance. The data stored on the component can be expanded as desired. Intelligent systems vendors and integrators can implement services through the creation of new information, information models and technical functions. Thus, information can be provided to stakeholders within an Industry 4.0 network. This enables the intelligent virtual enterprise. In this context, companies should develop their new systems and adapt the existing ones taking into account these concepts in order to be aligned with Industry 4.0 standards [START_REF] Adams | Worklets: A Service-Oriented Implementation of Dynamic Flexibility in Workflows[END_REF]. Fig. 2 shows a cloud based Industry 4.0 virtual enterprise management platform architecture based on Industry 4.0 concepts allowing vertical and horizontal integration that also includes the components described in the following paragraphs. The services repository provides a model and tools for representation, publication and discovery of companies and its services, required for the design, management and operation of virtual enterprises. The process manager orchestrates all the interactions within a virtual enterprise according to the process models defined in the process manager designer. Process manager interacts with the simulation and optimization tools, in order to ensure the highest efficiency. As described above, the process manager must suit the business environment in terms of automation degree. In fact, lessons from industry tell us that many types of industry involve collaborative knowledge intensive processes (e.g ETO equipment manufacturing considered in the next Section) where fully automation is not possible as process management depends on several human decisions taken on the fly. The simulation and optimization module relies on the process manager and uses Industry 4.0 shell services to get temporal and execution events from partners, supporting decision makers against particular partners. The optimization module provides a means to optimize virtual enterprises regarding the assignment of services to the business process steps and tasks. The business intelligence provides data extraction and transformation scripts and loading of real time and historical data relating to finished processes and instances. It also implements data mining techniques to generate useful information from big data sources towards a more proactive decision making in virtual enterprise environments [START_REF] Shamsuzzoha | Business Process Monitoring and Management in Virtual Enterprise through Interactive User Interface Layer[END_REF] [24] [START_REF] Shamsuzzoha | Visualization Functionality of Virtual Factories-An Enhancement to Collaborative Business Process Management[END_REF]. All these software modules are also considered Industry 4.0 components and therefore are able to expose services and communicate using internet standards.

Business Case Presentation and Analysis

The business case presented hereafter was firstly considered in the context the FP7 apps4ame research project research project and you may find additional information about this project at http://apps4ame.eu.

The business case is based on a flexible automotive plant using a mixed production system (several models in one assembly line). During a new car model launch process, a set of stamping dies have to be planned, designed, produced and tested. Planning is a critical stage whose results directly impact the global performance of the virtual enterprise. This stage involves several activities, such as the setup of the team, process simulation, die set feasibility, resources assignment and die set design. Each technician handles a set of dies being allocated to several suppliers. At that point it is important to execute the right re-enactments and achievability tests, with a specific end goal to comprehend if the specialized particulars of every die are right from an item and assembling perspective. After validation of the technical specification for each DS, it is imperative to comprehend if the plant, where the DS will be utilized, has the assets to apply the composed DS.

Known and accepted the DS, and in addition the essential assets, it is possible to produce a 3D model of every DS, and also to arrange the DS development, as far as spending plan, time and quality. Here it gets to be basic to determine the quality norms to be approved toward the end of the procedure execution. In house try-out is likewise a basic assignment. The machining stage, is where the supplier needs to change the 3D model into a last and usable DS, proficient to create the parts as indicated by the item manager. The last stage must be centred on the assessment and change of the DS model produced, towards the effective creation of the admired parts. Therefore, as first activity, experts must form some model parts keeping in mind the end goal to approve if the DS satisfies the quality norms. After all item needs are approved, the assembling procedure is tried thought a pre-arrangement assessment.

Subsequent to breaking down the as-is procedures, it was possible to declare that the persistent reports on every DS status were expending a lot of time and assets, turning the process behaviour very reactive instead of proactive. It was additionally conceivable to see that the data was spread by various sources. The absence of business and specialized coordination is a major concern since it can be the reason for wrong or ease back choices because of falsehood in a procedure that is exceptionally time subordinate.

Implementation and Validation

After a detailed process analysis it was easy to recognise that many of the faults could be abolished by implementing Industry 4.0 key enabling technologies such as cloud based platforms, standard internet communication protocols and smart objects for manufacturing, in order to cope with the challenges and thus achieve a more pro-active approach in the die-set manufacturing process.

The aim of the proposed solution is to facilitate the flow of information among stakeholders, making it more reliable and actual, allowing a closer control and fast reaction. While collaborating with other departments and external partners, the stamping planning has to manage a package of DS, track the workflow process, schedule operations, state milestones, handle project changes, evaluate risks and define actions, keeping cost and available capacity controlled all along the project development. In order to successfully accomplish this mission, for each project the stamping department needs to collect and manage a huge amount of information and knowledge, coming from several heterogeneous data sources along the virtual factory. Indeed, managing all this information not only is intensively time consuming and slows down the decision-making processes, but also hinders project reliability. Cloud based Industry 4.0 virtual enterprise platform addresses the main challenges that are typical in this kind of environments by implementing horizontal and vertical integration: horizontal integration is addressed through a cloud based collaborative platform whereas vertical integration is accomplished using technologies from the IoT field, such as connected smart objects and internet communication protocols. The total integration along these two axis leads to the development of a global dashboard (Fig. 3), enabling holistic view of project status, dynamic work planning and monitoring, real-time monitoring and control of ongoing work activities, knowledge and fully integrated document management and integrated communication / messages throughout the process. The cloud based hybrid process manager, supports company processes and enables a more reliable product development process. For instance, when a during the lauch of a new model, the chief manager defines some milestones that can be organised automatically. However, the real work tasks are not prone to be automated as they rely on intensive knowledge and decision tasks. The combination of well-defined and illdefined activities is equally important in terms of leading a project to its success. The communication tools that were developed allow linking all the stakeholders and interdisciplinary teams involved in the development of the product, guaranteeing realtime information regarding updated documents, machine breakdowns and quality related issues, thus saving time and improving the decision making processes. The platform was evaluated in real engineering environment, which involves part designers, die-set designers, planners, managers and suppliers in a unique virtual enterprise engineering environment.

Amid this assessment stage, a particular project has been utilized to test and check the functionalities implemented. Every partner has a particular certification to get to, test and approve the functionalities, giving criticism for persistent change [START_REF] Ferreira | Large Project Management in the Automotive Industry: A Flexible and Knowledge Based Approach[END_REF]. This platform has proven to be very effective and efficient when applied in hybrid work systems and in particular in the automotive engineering collaborative processes.

This platform has ended up being exceptionally successful and productive when applied to this work frameworks and specifically in the car designing community oriented procedures.

The management system has also improved the communication, the cooperation and trust the exchanged information, which are key factors for a successful project. The virtual enterprise integration allow a proactive decision making, speeding up the product development process. The reaction time and resilience was also improved, making the process more responsive to unforeseen events.

Conclusions and Further Developments

Several technologies related to Industry 4.0 concepts have emerged during the past decade, however, due to the lack of technological maturity and also lack of capabilities for technology adoption, the supporting tools for virtual enterprises were not implemented successfully in industry, especially in SMEs. Now, with the new Industry 4.0 wave, several technologies from the fields such as IoT, interoperability, SO are being tested and introduced in industry. In this paper we presented several technologies and described the successful introduction of those technologies in a knowledge based manufacturing engineering environment. In summary, Industry 4.0 led us to the full automation world but lessons from industry show that there are several manufacturing environments where full automation is hard to achieve even with advanced technology. However, Industry 4.0 technologies, combined with flexible and suitable managing tools may be the missing enablers for effective virtual enterprise implementation and management.

Despite the success of the pilot business case implemented in the automotive industry, several challenges were identified related to the adoption of the technology and the with the enterprise interoperability, so that the entire CPPS can work seamlessly.

A large portion of today's IT frameworks are not completely integrated. Organizations, suppliers, and clients are once in a while connected. Capacities from the top management to the shop floor level are not completely coordinated. Indeed, even inside divisions, a full integration is as yet lost. Industry 4.0, is seen as the empowering influence to make organizations, divisions, capacities, and abilities more durable, as cross-organization, widespread information reconciliation systems develop and empower genuinely mechanized expanded virtual manufacturing plants.

Fig. 1 .

 1 Fig. 1. Business entities and technical entities as Industry 4.0 Objects

Fig. 2 .

 2 Fig. 2. Industry 4.0 virtual enterprise architecture

Fig. 3 .

 3 Fig. 3. Cloud Based Industry 4.0 Virtual Enterprise Platform Dashboard Fig. 4 depicts the architecture of the system, where the business process tasks are connected to the several resources along the value chain, including IT systems, industrial equipment, transportation as well as the product itself. The hybrid process manager orchestrates the flow, invoking services in the physical resources through the Industry 4.0 administration shell. The old equipment challenge has been overcome by the implementation of the smart object for manufacturing depicted in Fig. 5.

Fig. 4 .

 4 Fig. 4. Business and technical integration through process oriented approach

Fig. 5 .

 5 Fig. 5. Smart Object for Manufacturing

Acknowledgments. This work is co-financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).


Wei Min Wang
email: w.wang@tu-berlin.de

Pascal Lünnemann
email: pascal.luennemann@ipk.fraunhofer.de

Sebastian Neumeyer
email: sebastian.neumeyer@ipk.fraunhofer.de

Haygazun Hayka

Rainer Stark

Product Development in Collaborative Networks -An Expert View on Current Challenges and Future Trends

Keywords: Product Development in Collaborative Networks; Collaborative Engineering, Product Lifecycle Management, Industrial Collaboration Practices, Expert Study, Trends of Collaborative Engineering

Cross-enterprise collaboration in product development has become a common practice in industry. Companies increasingly depend on collaborations with networks of partners from different tiers of value creation, various geographic locations and even from outside of their own sector. Moreover, there is a growing tendency to involve parties from later stages of the product's lifecycle in the product development phase. To face the challenges of the increasing complexity of collaborative product development a holistic approach of Product Lifecycle Management (PLM) has evolved. Although methods, processes and IT-systems of PLM proved to be beneficial in supporting the internal collaboration of industrial companies, they seem to have only little effect in the context of cross-company collaboration. Reasons and solution approaches for that were evaluated in an expert study with 40 experts from industry and academia. Results from that study and practical implications are presented in this paper.

Introduction

The development of modern products often requires interdisciplinary collaboration of several companies [START_REF] Krause | Efficient product data sharing in collaboration life cycles[END_REF][START_REF] Stöckert | Fehlervermeidung an Schnittstellenprozessen der verteilten Produktentwicklung[END_REF]. This is partly a consequence of the increasing product complexity that is driven by trends such as Industry 4.0 and Product Service Systems (PSS) [START_REF] Stöckert | Fehlervermeidung an Schnittstellenprozessen der verteilten Produktentwicklung[END_REF]. For example, cyber-physical products may be offered with accompanying services or even completely as a PSS. In both cases knowledge from various domains have to be combined. Furthermore, the expanding scope of product functions, their maintenance and repair often requires the knowledge of several companies to be merged to develop the product [START_REF] Krause | Efficient product data sharing in collaboration life cycles[END_REF]. Hence, interdisciplinary and cross-company collaboration in product development has become a common practice in industry, causing companies to increasingly depend on collaborations with networks of partners from different tiers of value creation, various geographic locations and even from outside of their own sector [START_REF] Hayka | Kooperationsplattform fur mechatronische Produktentstehung[END_REF]. Moreover, there is a growing tendency to involve parties from later stages of the product's lifecycle in the product development phase. The transformation of the enterprise-wide product development also requires an adaptation of the working environment in terms of tools, IT systems, technical infrastructure, information and data models, processes and organizational structures.

To meet these demands while achieving short development cycles, cooperation between enterprises will become even more intense. This and the widely dispersed allocation of the necessary expertise are driving the conversion of the former coordinated product development into a collaborative product development. To identify essential aspects of innovations and obstacles regarding planning and implementation of measures to realize this change, an expert study on product development in collaborative networks was conducted by the Fraunhofer Institute for Production Systems and Design Technology (Fraunhofer IPK) in cooperation with the CONTACT Software GmbH and the Association of German Engineers (VDI). In this study 40 experts from industry and academia with relevant experience in the field of Product Data Management (PDM) / PLM and engineering collaboration were asked to share their expertise on current challenges and future trends. The results of these interviews give insights to relevant drivers for cross-company collaboration from both the industrial and the academic perspective and combines empirical knowledge from industry and innovations from PLM system vendors, consultancies, research institutes and universities. Based on these insights, best-and worst-practices from industry and strengths and weaknesses of current collaboration approaches are discussed. Finally, expectations regarding collaboration processes, respective IT-tools and strategic goals are derived, especially with regard to PDM and PLM.

Comparing to other survey in the field of engineering collaboration this study focused on the value creating engineering activities performed by people in a multienterprise environment, their coordination through processes and benefits of used IT tools. While the general motivation for collaboration is comparable to existing studies (e.g. [START_REF]CISCO Collaboration Work Practice Study[END_REF] although this study focuses more on the social aspect of collaboration). While several current studies analyze the collaborative software development (e.g. the controlling and tools [START_REF]Trustworthy Computing Workshop; Workshop on Trustworthy Computing; Workshop on Model-Based Verification and Validation; MVV; Workshop on Safety and Security in Cyber-Physical Systems; SSCPS Workshop; Workshop on Human and Social Aspect of Software Quality; HSASQ; Workshop on Software Engineering and Knowledge Management; SEKM Workshop. A Survey on Knowledge Management in Software Engineering[END_REF][START_REF] Marshall | Tools to support systematic reviews in software engineering[END_REF]) the presented study focuses mechatronic and cyber-physical systems as multi-domain products integrating different areas of product development.

Overall, it can detect that the collaborative product development is facing multifaceted challenges, in particular:

The change in the working situation, the increase of CPS, advancing globalization and related performance in data deployments and integration challenge the collaboration. As well as the coordination of very large and distributed development networks with nowadays worse transparency in the procedures.

And the optimizations of the provided it-support, in particular in their alignment with the development activities and processes, as well as the data security, data availability and user friendly interface design need to be addressed. Especially these challenges are taken into consideration in the context of this paper.

2

Study Design and Implementation

Definition of Collaboration in Context of Product Development

There are numerous definitions of collaboration depending on the context of its application. Based on the definition of Lu et. al [START_REF] Lu | A Scientific Foundation of Collaborative Engineering[END_REF] this study regards collaboration in context of product development as an collective endeavor that "requires a team of individuals to work on tasks that not only have shared resources and shared outcomes, but, most importantly, a shared common goal". He distinguishes collaboration from coordination and cooperation as lower levels of collective endeavors. While coordination only requires sharing of resources to fulfil a collective task, cooperation also includes sharing of outcomes, but in both cases tasks designed to be disjunctive and are carried out independently. Whereas, collaboration explicitly requires combination of individual skills and collective efforts to fulfill tasks in order to reach a common goal. Therefore, organizational boundaries are neglected in favor of continuous exchanges of knowledge, information and resources [START_REF] Lu | A Scientific Foundation of Collaborative Engineering[END_REF]. Steinheider [START_REF] Steinheider | Supporting the co-operation of R&D-teams in the product development process[END_REF] further characterizes collaboration by the dimensions Coordination, Communication and Knowledge integration, each describing characteristics regarding one specific aspect of collaboration (see Fig. 1). As this study also evaluates IT-supported processes, the fourth dimension Information logistics was introduced, to provide a better understanding of information distribution within the product development process and throughout the lifecycle.

Structure of the Study

The interviews were based on a standardized and structured questionnaire comprising closed and open questions, grouped in four sections (see Fig. 2). The first section focused on the evaluation of the current working situation and expected changes in collaborative product development with regard to the four dimensions mentioned before. In the second section, current practice and prospective future forms of cooperation are considered. Especially, the changing engineering environment and the related coordination aspects are considered there. In the third section, actual industrial practices and used tools are analyzed and best-and worst-practices are examined. In the fourth section, requirements regarding collaborative product development in the future are collected. These questions aim at the evaluation of both, current technological trends and organizational forms. Based on this structure, the study captures the current situation of collaboration in product development and shows a potential path for its development in the medium-term (i.e. in the coming five to ten years) from the surveyed experts' perspectives.

As part of the analysis of the responses recorded and protocolled conversations were analyzed on clusters and summarized in qualitative statements. According to the relatively small sample the responses were rated primarily qualitative and not quantitative. Even so in statistical reports for prioritizing statements equivalent weights were made (for example, placement weighting 1 to 5) and were taken in consideration accordingly in the qualitative analysis. In total, 40 experts were interviewed using the structured questionnaire. The interviews were mostly conducted through phone calls and web-conferencing and took about one to two hours. In order to ensure a balanced mixture of practical industrial experience and innovative research, about one third of the interviewees are acquired from research institutes. Hereby, both an image of the current challenges and needs, as well as the currently under consideration innovations in research can be determined.

Two-thirds of the participants comprise representatives of the automotive industry, plant construction, consulting, aerospace and rail transportation (see Fig. 3a). With the aim of a possible heterogeneous and comprehensive picture of the challenges in enterprise-wide development, different roles were interviewed to get into the business. The models range from the experts in process management and information technology, corporate research and development departments (product-related), project managers and managers in departments, division and company boards. That way, both the practical work of community development as well as the strategic perspective of cooperation could be considered. Half of the participants from industry hold executive positions in their companies. With regard to the academic participants, the surveyed professors were counted analogously (see Fig. 3b). About half of the industrial participants work in OEMs, the other half in first-tier supplier companies.

Findings

Changes in Collaboration Practices

The increasing scope of product functions, new business models including product related services (e.g. PSS), the integration of software systems (e.g. user assistant systems) and communications to peripheral infrastructure (e.g. for condition monitoring) often require skills and expertise beyond the capabilities of one single company. In this respect, cross-enterprise and cross-sector collaborations are often seen as a way to acquire the necessary expertise [START_REF] Krause | Efficient product data sharing in collaboration life cycles[END_REF]. This significance of this well-known driver is also confirmed by the answers of the surveyed experts. The acquisition of know-how, which is not or not sufficiently available in the own company, was ranked first by roughly 30% of the respondents. The second main driver is Cost reduction that refers to both, outsourcing in low-wage countries as well as the national subcontracting of specialized engineering services. With regard to the duration of collaboration, the experts expect that the current timeframes of joint development projects will rather remain in a similar scope or even decrease. This reflects the decreasing duration of development projects per se, which in turn results from the ever more rapid innovation-cycles. At the same time, the experts expect that the duration of partnerships between the companies will increase or at least remain stable. This opposing development is explained as consequence of overheads invested. As stated by the respondents, it is anticipated that long-term partnerships allow for "smoother" collaboration processes as mutual trust between involved engineers can be established and organizational structured harmonized.

Concerning the total number of partners within collaboration, two opposing developments are anticipated by the experts. On the one hand, there is a trend of consolidation of small and medium sized suppliers into larger system-suppliers (e.g. through merger and acquisition). On the other hand, a growing number of small and highly specialized component suppliers are entering the market. Which effect will outweigh depends could not be determined generally, but will depend on the tier-level and the industrial sector. However, more than the half of the experts is expecting a general increase in the amount of partners. Only twenty percent are assuming a decrease. Especially, experts from companies of second-tier suppliers / developers expect a general increase in consortia sizes.

In parallel, the experts also describe a globalization of their development activities, as local development centres for localized modules are established often near-or onsite of OEMs to compete with other global acting companies. As a consequence, intercultural work has to be increasingly considered in engineering collaboration. From the experts' viewpoint, the current mode of collaboration, where partners are only involved for the detailed engineering, will be extended to other lifecycle phases such as concept design, market analysis or later phases such as production planning and manufacturing as well as supporting services.

People are the Success Factors of Collaborations

As defined by Lu et. al [START_REF] Lu | A Scientific Foundation of Collaborative Engineering[END_REF], collaboration is the highest form of collective endeavour requiring the sharing and alignment of resources and goals, and the integration of knowledge. As it is also fundamentally a social process [START_REF] Patel | Factors of collaborative working: A framework for a collaboration model[END_REF], the success of collaboration often highly depends on the involved individuals and their social relationships [START_REF] Pennington | Cross-Disciplinary Collaboration and Learning[END_REF]. In the context of cross-enterprise collaboration, this aspect is even more important. Firstly, a huge variety of individuals with divers cultural and professional backgrounds will come together when organizations from different industrial sectors and geographic locations engage in collaboration. Secondly, there is an additional level of organizational culture and habits, which may influence the collaboration behaviour of individual persons [START_REF] Patel | Factors of collaborative working: A framework for a collaboration model[END_REF]. Hence, the individuals engaged in collaborative projects are the key success factors. Still, the development of tools and IT-system to support collaboration often tends to focus on technical requirements. This aspect was also stressed by the experts surveyed in this study. Various statements pointed out that the human is getting increasingly "lost" in the process of digitalization. One expert stated: "The IT-support for collaboration have reached a good maturity level, but the human factor within such processes have to be regarded. It is crucial to include direct communication on personal level to facilitate establishing a common base and building of mutual trust. 1 " Another respondent from industry even said that "it has been forgotten, that every IT-system is used by humans". He pointed out, that the development of tools is often heavily dominated by technical requirements, while the human factor is totally neglected. In his opinion, this kind of focus led to "disastrous" user interfaces in all current PDM systems. As confirmation of these statements, the summarized responses showed that personal discussions are the preferred best practice (20% of total answers), followed by the realization if transparency (11% of total answers) and communicative or spontaneous employees. Also the usage of multimedia as well as a frequent communication and a common language are success factors. The usage of email as main communication channel has been identified in a previous study conducted by Müller et. al in 2013 [START_REF] Müller | Kollaborative Produktentwicklung und digitale Werkzeuge: Defizite heute -Potenziale morgen[END_REF], and could be confirmed again in the current study (23% of answers). In addition, video conferences have been mentioned (20%), while the Phone as well as instant messaging tools, screen sharing and PDM-/PLM-Solutions for communication have been mentioned with 10% or less(see Fig. 4).

The coordination of an engineering collaboration is due to different organizations, their different cultures and processes, always a challenge in projects. The previous study [START_REF] Müller | Kollaborative Produktentwicklung und digitale Werkzeuge: Defizite heute -Potenziale morgen[END_REF] revealed in this regard that coordination and communication are covering more than twenty percent of the working time of design engineers. This was also confirmed by the experts in the current study. As best practice for coordination, a shared comprehension of processes (18%) as well as taking responsibility (11%) and scheduled deliverables (10%) have been highlighted by experts. In contrast, coordination is inhibited from the experts' point of view by unsuitable IT-solutions, bad leadership and missing process comprehension. These results again shows that the persons involved in the collaborative activities are of integral meaning to it's success. Preferred IT-solutions in context of coordination are project management tools with nearly one third of mentioned answers (29%). PDM-and PLM-solutions (15%), workflow management (15%) and project management functionalities in PLMsolutions (13%) turned out to be less important as expected (see Fig. 5).

Beside coordination and communication, the information logistics in engineering collaboration need to be considered for a holistic view. To provide the engineer with the right information at the right time is the most important factor from the experts' viewpoint (16% of answers). This aspect is followed by proper access privilege management (11%), availability of the information (9%) and clear specification (7%) as best practice in information logistics (see Fig. 6).

Security concerns as well as unclear interfaces, unreliable IT-systems and missing information within an IT-system are show stoppers in this dimension. These IT-systems are in majority PDM-/PLM-Solutions (33%), while only 6% of the experts confirmed that direct partner integration in PDM-/PLM-solutions is given. Commonly used solutions for data exchange are dedicated Team Data Management (TDM) systems. Cloud-solutions were rarely mentioned in the answers (7%) and are hence, considered exceptions in information logistics (see Fig. 7a).

The knowledge integration as the last building block is understood as methodological and procedural definition of the mode of operation and the securing of gained knowledge of an engineering collaboration. However, the current activities are focussing on documentation and transfer of explicit knowledge. Therefore, handbooks (14%), guidelines (12%) and IT-systems (12%) are used. The use of Wikis for continuous documentation of group knowledge, the capturing of abstracted knowledge as well as lessons learned were only mentioned peripheral (7% each). In this regard, the human factor proves to be a major hindrance, as one essential reason for insufficient knowledge integration is the lack of personal motivation share or provide knowledge. This is mainly due to a low acceptance of that activity per se (17%), resulting partly from concerns about becoming easier replaceable and partly from a lack of defined processes. This in turn results in inadequate documentation and useless efforts. A further obstacle for knowledge integration is that the value of knowledge management is often not or not clearly defined in the working context of engineers (6%). Hence, it is rather perceived as an overhead than as essential value creation activity.

Conclusion

The current situation in engineering collaboration is characterized by a wide variety of tools, interfaces, communication channels and process definitions. Due to security reasons, companies usually do not allow partners to access their central data sources, therefore the internal project members have to spend high efforts to search, extract and transfer data to portals so partners and suppliers can access them.

Partners have to deal with the challenge, that each customer provides different interfaces, individual processes and rules for the execution of collaboration. Information, data and processes need to be persistent though all the layers of developers, which hardly can be achieved by a high amount of interfaces and formats. While today engineers are already overwhelmed by the existing process instructions.

In addition, neither the currently implemented PLM-solutions provide the necessary flexibility and openness, so individual as well as different approaches for partner integration can be achieved. Nor the necessary human-to-human communication is adequately supported.

In consequence, collaboration needs to be planned and executed in a more holistic way and not treated only as a technical solution or a process definition. Due to the finding, that collaboration will extend, the number of partners will increase and the integration of non-sector specific partners is expected, collaboration need to be treated under multidimensional aspects with special emphasis on the human claims. Only by covering processes and organizations, tools and IT-systems as well as information and data models aligned with activities performed by people, the engineering collaboration will be a success.

With regard to the PLM-approach, the holistic consideration of all these aspects has to be covered to achieve a successful collaboration. Last but not least the most important element of collaboration has been mentioned: only with individual humans, who are motivated, communicative, upright and flexible with different situations, collaboration will finally succeed.

Fig. 1 .

 1 Fig. 1. Four dimensions of collaboration regarded in this study

Fig. 2 .

 2 Fig. 2. General structure and core questions of the study

Fig. 3 .

 3 Fig. 3. a) Branches of experts, b) Positions held by the experts

Fig. 4 .

 4 Fig. 4. Tools used for communication in industrial practice

Fig. 5 .

 5 Fig. 5. Tools used for coordination in industrial practice

Fig. 6 .

 6 Fig. 6. Best practices in information logistics

Fig. 7 .

 7 Fig. 7. a) Tools used in information logistics, b) Tools used in knowledge integration c) Worst cases in knowledge integration

Acknowledgments. We thank all participants of the study for their contribution and support. Only by their individual commitment and their willingness to answer our questions patiently, even at early and late hours, this study could gained such in-depth insights about the present and future of collaboration in product development. We also want to thank Ms. Helena Rott (Fraunhofer IPK) and Ms. Barbara Scholvin (CONTACT Software GmbH) for their persistent contribution to the text editing and creation of graphics. Finally, we thank Dr. Heinz Bedenbender (VDI-GPP) and Ms. Sophia Rogge (TU Berlin) for proofreading.

A Appendix

Structure of the questionnaire and content summary: