- Original Article
- Open Access

# Information Flow Scheduling in Concurrent Multi-Product Development Based on DSM

- Qing-Chao Sun
^{1}Email authorView ORCID ID profile, - Wei-Qiang Huang
^{1}, - Ying-Jie Jiang
^{1}and - Wei Sun
^{1}

**30**:160

https://doi.org/10.1007/s10033-017-0160-y

© The Author(s) 2017

**Received:**4 December 2016**Accepted:**14 June 2017**Published:**14 July 2017

## Abstract

Multi-product collaborative development is adopted widely in manufacturing enterprise, while the present multi-project planning models don’t take technical/data interactions of multiple products into account. To decrease the influence of technical/data interactions on project progresses, the information flow scheduling models based on the extended DSM is presented. Firstly, information dependencies are divided into four types: series, parallel, coupling and similar. Secondly, different types of dependencies are expressed as DSM units, and the extended DSM model is brought forward, described as a block matrix. Furthermore, the information flow scheduling methods is proposed, which involves four types of operations, where partitioning and clustering algorithm are modified from DSM for ensuring progress of high-priority project, merging and converting is the specific computation of the extended DSM. Finally, the information flow scheduling of two machine tools development is analyzed with example, and different project priorities correspond to different task sequences and total coordination cost. The proposed methodology provides a detailed instruction for information flow scheduling in multi-product development, with specially concerning technical/data interactions.

## Keywords

- Concurrent multi-product development
- Information flow planning
- Extended DSM
- Project priority

## 1 Introduction

With market competition increasing, achieving success with new product development(PD) in mechanic products markets is becoming more and more challenging, and the capability of meeting the diversified customer demands is playing more important role [1]. Due to shortening product life cycles, businesses are also proposing a set of critical success factors to reduce product development time and respond quickly to vibrant customer demand [2].

To meet diversified customer needs while shortening product development cycle, the product developers should concentrate on development of multiple products simultaneously and improve process efficiency and quality [3], rather than focusing on one product at-a-time. However, the resource conflict among multiple PD projects often leads to the delay of project schedules, and the number of parts, tools and materials also increases linearly with number of products. Ensuring concurrent multiple PD progress [4, 5], and managing the information difference and similarity of multiple products are the key problem of concurrent multi-product development project management.

The Design Structure Matrix(DSM), also called the dependency structure matrix, has become an used modeling framework of product development management [6]. DSM is a matrix representation of a directed graph, which allows the project or engineering manager to represent important task relationships in order to determine a sensible sequence for the tasks being modeled [7, 8]. As compared to Critical Path Method (CPM) and Program Evaluation and Review Technique(PERT), DSM is more suitable for planning information flow in PD project [9–11]. The rows and columns of DSM correspond to the tasks, and the matrix reveals the inputs and outputs of tasks, by manipulating the matrix, the feedback marks can be eliminated or reduced, this process is called partitioning [12, 13]. According to the input/output, the subsets of DSM elements that are mutually exclusive or minimally interacting should be determined, this process is called clustering [14–16].

In recent years, some multiple project scheduling models and methods based on DSM was brought forward, to ensure multi-project progress. P H Chen et al. proposed a hybrid of genetic algorithm and simulated annealing (GA-SA Hybrid) for generic multi-project scheduling problems with multiple resource constraints [17]. C Ju and T Chen developed an improved aiNet algorithm to solve a multi-mode resource-constrained scheduling problem [18]. T Gaertner et al. presented a generic project scheduling technique for functional integration projects based on DSM, to improve the planning of delivery dates and required resources and capacities, to ensure tighter synchronization between project teams, and prioritize tasks in parallel projects [19]. T R Browning proposed an expandable process architecture framework (PAF), which organizes all the models and diagrams into a single, rich process model with 27+ new and existing views, to synchronize various aspects of process (activity network) information in large, complex projects [20]. The above research aims for decreasing project progress delay by arranging task sequences and allocating project resources reasonably, but the influence of task interactions on material flow or product information flow was not taken into consideration.

The product information clustering technologies based on DSM were also presented [21–23], DSM was adopted to cluster product components into modules with minimum interfaces externally and maximum internal integration, but these model aimed for information interaction in the single product, lacked the research about the information difference and similarity among multiple products. Such as, E P Hong and G J Park proposed a new design method to design a modular product based on relationships among products functional requirements, to overcome the difficulty of modular design, with combining axiomatic design, the function-based design method and design structure matrix [21]. A H Tilstra et al. presented the high-definition design structure matrix (HDDSM) to captures a spectrum of interactions between components of a product [22]. T AlGeddawy and H ElMaraghy proposed a hierarchical clustering (cladistics) model to automatically build product hierarchical architecture from DSM [23].

Different from one PD project, information flow planning in concurrent multi-product development should emphasis on two factors: first, partitioning tasks sequences according to tasks relationships and project priorities to decease influences of information feedback on project schedules. Second, clustering the similar tasks [24] of multiple PD projects to reduce number of parts and tools, decrease interaction cost and improve development efficiency.

## 2 Task Relationships in Multiple Projects

### 2.1 Relationships Among Projects and Products

- (1)
Multiple projects for single product development. Each project is responsible for the development of one or more subsystems, and multiple projects cooperate with each other, to complete the development of a complex product. Cars, airplanes, and other complex PD often use this kind of management mode.

- (2)
Multiple project for the interrelated multiple products. Each project is responsible for the development of one or more products, and there exist the technical or information interaction among multiple products, such as the same product structure, the similar design technology, etc. The new product is often developed based on product families, so this relationship is very common, which was the main research object of the paper.

- (3)
One project for one product, and each product is independent. There exists no the same and similar parts or sub-systems among multiple projects, which only occurs in the process of few new product development.

### 2.2 Input/Output Relationships Description

### 2.3 Task Relationships in Multiple Projects

## 3 Extended DSM Model

DSM represents the system, product or process by aggregating individual interactions among components, people, activities, or parameters. DSM is essentially an \( N^{2} \) diagram that is structured in such a way as to facilitate system-level analysis and process improvement. The mark in cell \( i \), \( j \) of DSM indicates that the item in row \( i \) requires information from the item in column \( j \) as an input. Under the diagonal corresponds to the forward information flow, and above diagonal shows the feedback.

## 4 Information Planning Based on EDSM

Information flow planning based on the extended DSM is different from DSM, which involves four types of computation: merging, converting, partitioning and clustering.

### 4.1 Merging Converting and Partitioning Regardless of Project Priorities

### 4.2 Clustering Regardless of Project Priorities

The goal of clustering is to find subsets of the extended DSM (i.e., clusters) so that the tasks within a cluster are maximally interdependent and clusters are minimally interacting. Some researchers have proposed different clustering algorithm based on different principle, such as A Yassine presented a clustering objective function by using the minimal description length principle [26], S E Carlson and N Ter-Minassian proposed a clustering algorithm based on coordination cost [27].

Clustering can be done based on the previous computation, according to Eq. 4, \( p\_k_{1} \), \( p\_k_{2} \), \( p\_k_{3} \) corresponds to different level clusters, \( p\_k_{1} \) indicates the power coefficient of smallest cluster, \( p\_k_{2} \) corresponds to the upper level of cluster, which consists of a series of sub-cluster and tasks, \( p\_k_{3} \) corresponds to the whole extended DSM, adopting \( p\_k_{1} = 1 \), \( p\_k_{2} = 2 \), \( p\_k_{3} = 3 \), the clustered extended DSM can be obtained:

Adopting \( p\_k_{1} = 1 \), \( p\_k_{2} = 1 \), \( p\_k_{3} = 2 \), the different clustered extended DSM can be obtained:

which means that the lower level clusters will contain more tasks as \( p\_k_{2} /p\_k_{1} \) increase, and the upper level will contain more sub-clusters and tasks as \( p\_k_{3} \) increase.

### 4.3 Merging Converting and Partitioning with Regarding Project Priorities

### 4.4 Clustering with Regarding Project Priorities

Different from clustering regardless of project priorities, by introducing \( f_{ij} \), the difference in project priorities was taken into consideration, with \( f_{ij} \) increacing, the tasks with different priorities will be divided into different clusters. Such as, \( pri_{a} \) and \( pri_{b} \) denotes project priority of project \( P_{a} \) and \( P_{b} \) respectively, adopting \( pri_{a} = 3 \), \( pri_{b} = 1 \), \( p\_k_{1} = 1 \), \( p\_k_{2} = 2 \), \( p\_k_{3} = 3 \), after merging, converting and partitioning with regarding project priorities, the tasks sequence can be determined, and the clustered extended DSM with regarding project priorities can be obtained:

## 5 Example

Two machine tool products was developed during the same period, the worktable size of one machine tool is 650×650 mm^{2}, the other is 1250×1250 mm^{2}, two machine tools have the similar product structures.

Regardless of project priorities, the similar tasks tend to be divided into the same clusters, to reduce the influence of information interaction on project schedule. the similar tasks tend to be divided into different clusters, to reduce the influence on schedule of high-priority tasks.

Total coordination cost with different power coefficient

1/1/1 | 1/1/2 | 1/1/3 | 1/2/2 | 2/2/2 | 1/2/3 | |
---|---|---|---|---|---|---|

\( con_{1} \) | 4800 | 87 480 | 3 394 680 | 138 400 | 138 400 | 3 445 600 |

\( con_{2} \) | 3828 | 86 508 | 3 393 672 | 109 254 | 115 000 | 3 416 454 |

\( con_{3} \) | 2822 | 64 532 | 2 162 672 | 77 230 | 78 338 | 2 175 370 |

\( con_{4} \) | 2325 | 54 306 | 1 867 320 | 56 488 | 68 554 | 1 963 882 |

The first two conditions didn’t take relationships between two projects into consideration, and the last two conditions was computed based on the extended DSM, with regarding dependencies among multiple projects. The differences of \( Tcc(M) \) show that optimizing tasks sequence based on the extended DSM can decrease influence of task interactions on project schedules, and values of \( p\_k \) influence computation results obviously, the power coefficient should be determinded according the task relationships and PD teams.

## 6 Discussion

The extended DSM was built to optimizing task-sequences and task-clusters in multiple PD projects, focusing on the technical information interactions and similarities in multiple projects, taking project priorities into consideration, was mainly applied to the mechanical product development projects, such as automobiles, machine tools, construction machines, etc. Although software, construction and other industries also involve multi-project management [26, 28], but the task-relationships are different from mechanical product development projects.

A series of coefficients was introduced in the converting and clustering calculation, such as \( k \) and \( b \) in converting function \( mwr_{ij}^{'} = k \times mwr_{ij} + b \), \( p\_k \) in Eq. (4), \( p\_k \) and \( p\_pri \) in Eq. (5). The calculation results indicated that the value of these coefficients had a great effect on the information planning. It is necessary to determine these coefficients by taking the completed projects as example, changing the values, and judging the rationality of task-sequences and task clusters combined with the expert experience.

Resources sharing and conflicting was the important feature of multi-project management [17, 29], but the extended DSM model didn’t take resource allocation into consideration, therefore the information planning results based on extended DSM should not be the final task sequences, the further adjustment according to resource capabilities was needed.

## 7 Conclusions

- (1)
Describing task dependencies is the basis of information flow planning based on the extended DSM, the task relationships in multi-project is divided into four types: serial, parallel, coupling, and similar. Similar is the specific dependency relationship of the extended DSM.

- (2)
The initial extended DSM can be expressed as a partitioned matrix, describing task dependencies in one project and among multiple projects concurrently.

- (3)
By adopting different power coefficients in converting or clustering function, different task-sequences or task-clusters can be obtained. Determining the appropriate power coefficient is important for information flow scheduling based on the extended DSM.

## Notes

## Declarations

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

## Authors’ Affiliations

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