Performance Improvement in Manufacturing Shop Floor Operations of Developing Countries Based on Three Characteristics of Information Flow

The management of information flow for production improvement has always been a target in the research. In this paper, the focus is on the analysis model of the characteristics of information flow in shop floor operations based on the influence that dimension (support or medium), direction and the quality information flow have on the value of information flow using machine learning classification algorithms. The obtained results of classification algorithms used to analyze the value of information flow are Decision Trees (DT) and Random Forest (RF) with a score of 0.99% and the mean absolute error of 0.005. The results also show that the management of information flow using DT or RF shows that, the dimension of information such as digital information has the greatest value of information flow in shop floor operations when the shop floor is totally digitalized. Direction of information flow does not have any great influence on shop floor operations processes when the operations processes are digitalized or done by operators as machines.


Introduction
Quality and timely product delivery have always facilitated the improvement of Journal of Computer and Communications performances in Small and Medium Size Enterprises (SMEs) in the performance improvement long term strategy. Performance amelioration has always been at the center of scientific research and many researchers demonstrated that performance improvement is a function of information sharing and decision making. The management of information flow (MIF) moves towards digitalize information known as information of things and it is a key for performance improvement [1] [2], but in some developing countries the concept of internet of things applied to the MIF is still not yet a mere event due to the lack of technology transfer and the random economic situation [1] [2] [3]. It is then an opportunity to work over a progressive transition from the traditional MIF in shop floor operations to the digital MIF. A proper MIF renders manufacturing companies continuously efficient when stochastics and none stochastics event related to machines and operators' behaviors occur [3] [4] [5]. This paper focus on shop floor of manufacturing companies in developing countries which are moving towards a digitalize MIF but which still have some lacks that result in poor decision making when facing operations productions and later cause a decrease of performance of the company. A good MIF is also based on the analysis of information flow characteristics, according to Mbakop et al. [6] and presented by Figure 1 and Figure 2, the MIF consists of giving to information flow a value in order to facilitate decision-making in shop floor operations for performance improvement of companies [6]. Decision-making in company is also subject to  is visible and has to be considered, so a none-timely information won't enable the good performance of the production chain. An information flow in the production chain can then be attributed a value which will have an influence on productions operations. Thus, the integration of information flow in shop floor operations processes. From what the literature research presented, till now research works were done in the determination of the value of information flow (VIF) by integrating one of the characteristics of information flow (CIF) namely quality, later dimension because the companies were almost digitalized, but they haven't considered that they are some companies trying to move from the traditional MIF to the digitalization MIF and also in their works they haven't inte-grate the influence of at list two CIF in their analysis of the VIF to facilitate decision making in manufacturing companies. Some tried to determine the VIF with the quality of information flow by using methods of technical audit, process information integration and recently, dimension of information by using the Value-Added Heat Map (VAHM) method which leads to the degree of digitalization of processes in shop floor, but never dimension and quality at the same time. It is therefore important to consider in this paper a new model that will include information quality, direction, and direction on the value of information flow to analyze the integration of information flow in shop floor operations processes and later using the machine learning algorithms.
This paper aims at integrate the CIF in the determination of the VIF to facilitate decision making by operators in the shop floor process using the approach of machine learning, by taken in consideration the hypothesis, that some of the CIF can be scale according to Tomanek and Schroder [7]. The accomplishment of this gold lead us to related works on the determination of the VIF and Ma-

Related Work on the Determination of the Value of Information
In industrial and job processes, materials and information flow are always on motion except that their motions can be described by contrary direction. An information can then stop the flow of materials in process, cause the materials not to be in process, from this, it is important to have a look on the value of information that are in the process or that may trigger the process. According to the literature, there are many ways to define or to consider the value of information: in the job operations sequences, the value of information can be linked with the benefit that and information adds to a process or service, [8] [9] [10]. The value of information flow can be considered as a measure for the avoidance or minimization of the bullwhip effect [11]. Considering the integration of information flow in a process be it shop floor or not, the value of information is based on the quality of information that is characterized by accessibility, transparency, timeliness and granularity [12] [13]. The value of information can be created by information, which is transmitted correctly. Complete and in a timely manner [14] [15], by avoiding disturbances and media disruptions. Considering the works of Tomanek and Schröder 2016 [7], the value of information flow is function of scale of the dimension of information flow as presented in Table 1. Considering the presence of materials on a production line in shop floor, the value of information can be deducted from the impact that materials undergo on shop floor, and also the value of information flow can be determined by knowing the Verbal or visual exchange of information 3 Electronical exchange of information not real-time (e.g. by spreadsheet application) 4 Electronical exchange of information real-time (e.g. by system-application)

Maximum
Added Value 5 Digital exchange of information real-time (e.g. by Internet of Things and Services) digitalization degree in a context of industry 4.0 using the method of Value-Added Heat Map (VAHM) [6].
Many methods were used to determine the value of information.

Process Integration Method
According to the process integration method, Aubert et al. [16] integrated the quality of information flow in the and their results show that, the more the information quality cost is low, the more of information is high and more the information process integration is high also according to Equation (1): where VA: Value added by the process, j a : accessibility for activity j, ti: timeliness for activity j, ( ) j C x : cost of providing property x for activity j, tr: transparency for activity j, gi: granularity for activity j, QI: value of the quality of information flow.
Berente and Vandenbosch [17], proposed another form of computation of the value of the process integration depending of the quality of information, instead of considering the cost of information quality, they have tried to attribute to every quality of information the factor of time. They carried out an audit and they determine the different time referring to information quality characteristics.
The obtained formula of the process integration value is given by Equation (3) and the value of information that can be considered from it is given by Equation where TT: total time taken by the process, j a : accessibility for activity j ti: timeliness for activity j ( ) j T x : time of providing property x for activity j tr: transparency for activity j gi: granularity for activity j.
QI: value of the quality of information flow. The works of Aubert et al. [16], Berente and Vandenbosch [17], were focused on the quality of information flow to influence the value of information in the process integration.
They have justified it by the fact that, it is not easy neither to quantify the cost and the time related to accessibility, timeliness, transparency and granularity. To quantify the time of the accessibility of an information is a difficult task and doing that takes much more time. That is why, it is very important to look for another way round to compute the value of information flow.

Value-Added Heat Map (VAHM) Method
According to the Value-Added Heat Map proposed by Tomanek and Schroder [18], which is an innovative visualization tool that indicates the level value creation concerning production relevant factors. The VAHM enables to have a view on the added value level of production relevant factors by using colors scaling and it by developing key performance indicators it finds also its application in determine relevant factors like the internal circulation of information flow. To analyze the VIF, they have done an audit in the company in with they have mapped the flow of information and the results of the audit lead them to scale the information that were given a more added value to the process. Table 1 and Table 2 Table 2. Value Stream Analysis-symbols for the visualization of an information flow [7].

Symbols for the information flow Meaning
Manual information flow Electronic information flow

Electronic information flow
Levelled production planning Route of a kanban card

Digitalization Degree Method
The value of information was then the estimate function of the digitalization degree, the layout-specific digitalization degree indicates, which percentage the de-

Information Quality Value Stream Mapping (IQVSM)
Busert and Fray (2020) [19], based on value stream mapping (VSM), the authors used this method to visualize the information flow that will serve for production planning and control to improve the performance of the shop floor. They considered the following sub-characteristics of information quality: Granularity, timelines (frequency), and accessibility of information to improve the value that an showed that parameters should not be included in information flow to have a good management. For it is difficult to assume or determine the value of the quality of information in the unit interval but you can at least know if the quality of information is accurate or not.
Therefore, this paper does not consider that the characteristics of information flow are data defined in the unit interval, but rather consider that that they are binary data and to analyze them classification algorithm of machine learning will be used to observe the influence that information characteristics such as quality, cision-making. This paper will present two modeling approaches, the process integration modified approach and the machine learning approach.

Machine Learning Algorithms
The analysis of the integration of information flow (IIF) based of the value of information flow (VIF) in shop floor of developing using a machine learning algorithm approach has not yet been a study focus according to article that was read in the literature. This is justified, because the development of information characteristics has been updated by [6].

Proposed Methodology
In this paper the methodology is based on two main approaches. The proposed methodology is presented by

Modeling of the Value of Information Flow in Shop Floor Operations
The IF is a row matrix for an order one only the dimension information value will not be binary data but they will derive from the scale of information dimension as presented in Ta Dimension of information has as component, , , , X x x x x = (9) An information flow shared in a shop floor or organization for services or product manufacturing or delivery has the following Matrix given by Equation The VIF for every information flow arriving in the system is described by Equation (14), is presented by Figure 5.

Questionnaire and Dataset Collection
Questionnaires were built and submitted to industries of some developing coun-

Comparative Analysis of Machine Learning Algorithms: Supervised Learning
The comparative analysis will be based on the score and metrics of the classification algorithms models given by Equations (16)

Heat Map of Information Flow Describe by the Correlation Matrix When Having Binary Data
When the CIF are totally binary or except the dimension which isn't binary, from the data collected in a sharing of an information flow based of its characteristics in shop floor of developing countries, it happens that the correlation matrix is the same and its shows the relationship or the dependency between the CIF as presented by Figure 6.
The correlation Matrix for the two cases indicates the following observations.

Comparison of Machine Learning Models: Classification Algorithms
In this paper, the focus is based on these various classification algorithms: DT, The training scores of the classification ML models are presented in Figure 7 and Figure 8 for dataset 1 and 2 respectively. From Figure 7 and Figure 8, all the ML models have been well trained, but DT has an excellent learning ability in the dependencies that exist between the VIF and the characteristics of information flow, when data are mixed. GBN is the only model which has a least coefficient, because the data obtained aren't completely random but it derives from an audit. To choose the best model, the accuracy of each model will be an important aspect.
From Figure 8, GB, KNN, SVC, LR, DT, RF, and GB are suitable models with a score of 0.99 for the determination of the VIF.
According to Figure 7 and Figure

Interpretation of the Prediction Analysis
An information flow sharing between shop floor operators (humans and or machines) during a operations processes can have a VIF that is to considered or not according to the characteristics of the information flow. Depending on the kind of operators some, information characteristics differs it can be illustrated by this example: an information flow can be complex for man operator but not for an automated machine, a paper information is more for a human operator than for machine or computer, digital information is more suitable for interconnected machine than for humans, so whether an information flow is destined for a machine, human or computer each characteristics of information flow will correspond to each operator for a better interpretation for a better decision making, that is why Figure 3 presents DT/RF model in decision-making.

Analysis Based on the Dimension and the Direction under Good Quality of Information Flow Dataset 1 and 2
With the DT/RF models on dimension of information flow it comes that: DT/RF 1: An information flow shouldn't derive from more than one medium, if not the value of information flow will be 0. Concerning the document, the Audio, Visual, Electronic non-real time (E_N_R_T) information and the electronic real time information (E_R_T), the CIF can give out a good VIF if the information flow has to be transparence (1) and well details or granular (1), the information flow must not come from than one direction. In shop floor where is are still paper information, or audio, transparency of information, accessibility, granularity, timeliness has to be maximized. Journal of Computer and Communications DT/RF 2: Concerning digital information, the model gives out a good value of information flow when there aren't the disruptions that can occurs in the information sharing from the directions, and also from the network problems in the developing countries, this then can't allow machines to operate in their optimum performance level.
DT/RF: In this work the model shows us that, here the direction of information does not have a mere influence of the value of information flow except when the information is coming from more than one decision making level.
It is not in all developing countries that directions of information have the same effect of information as in this work (where it represents a total independence decision making production operators with facilitate it performance for production). This is because of the presence of human personnel at the decision level of operations, the order from the tactical level or a supervisor may have indirectly a great influence on the human operator, consequently on his production action.
The results obtained from this paper based on the dimension and the direction of information flow are comparable with the one of [20]. In the sense that, Which is to say that, dimension of information flow has to be of a good medium as digital for information flow to be well integrated in process operation and also a paper information can give out a good value of information flow in shop floor operations if it is transparent and granular for both dataset 1 and 2 respectively. In production process an information flow shouldn't come from many directions or from many levels of decision for it can cause disruptions in information flow analysis then reduces the value of information flow can consequently the performance of the shop floor. The present work is limited when the information flow arrives randomly for production processes, so it will be for a great scientific help to evaluate the performance of the shop floor based on the arrival of random information.