8Figure 2.2: The D3M framework ...9Figure 3.1: Beta Company logo ...12Figure 3.2: ISO 9001:2015 certification acquired by Beta ...14Figure 3.3: Beta’s original Service Report ...16 Tran
INTRODUCTION
Reason to choose this topic
The origins of quality management and quality assurance in a modern sense began in manufacturing organizations at about the beginning of the twentieth century but quality standards have been established since immemorial times in ancient constructions The ancient Egyptians demonstrated a commitment to quality in the construction of their pyramids The Greeks set high standards in art sandcrafts Roman-built cities, churches, bridges, and roads inspire us even today (Mitra, 2016) When competition is the “weapon” that determines the organization's success in the market, quality is considered an indispensable factor. Understanding quality from the business perspective is fundamental for companies to succeed and become profitability leaders in the new global economy (Antony, 2013) Quality management pays for manufacturing and service organizations and it is the adoption of quality principles that is beneficial (Rửnnbọck & Witell, 2008).
As mentioned in the latest statistics of The US Department of Labor (2022), employment of Management Analysts - employees who are responsible for analyzing an organization's data to make recommendations to improve operational quality, is projected to grow 11 percent from 2021 to 2031, much faster than the average for all occupations This shows the importance of data in business operations Today's businesses often take advantage of information technology platforms that are being developed in a modern and synchronous way to collect data, including internal data (information about business operations) and data external data (information about customers, partners, competitors, etc.) These data are collected and processed by the data analysis department and then presented to superiors in the form of periodic reports From there, managers will make decisions that significantly affect the organization’s operation, such as restructuring These decisions are made based on actual data from reports rather than the personal experience or bias of the managers The trend of using evidence (including data) for decision-making is not new, but the development of technology has pushed it to become a trend for companies to follow.
Today, mechanical products play an essential role in the formation of the FourthIndustrial Revolution because they appear in all production lines in all spheres of life, which has laid the foundation for modern electronics production With the development of technology on a global scale, the quality of mechanical products keeps improving while the renewal cycles become shorter Besides producing new products, proper recovery of
Go to course mechanical products can avoid wasting resources and prevent environmental damage (Chen,
Yi, Jiang, & Zhu, 2016) Mechanical product characteristics often have many details, so in the process of warranty or maintenance, decision-makers need to consider many criteria As before, machine maintenance information is simply recorded and making decisions based mainly on experience, which causes many undesirable results With the long-established industry combined with the development of theories of quality management throughout history, the quality management methods in European manufacturers are constantly being improved to optimize operational processes, quality control and product maintenance Beta is an example of a company that applied the data-driven decision-making framework into their maintenance service delivery process In this essay will analyze how Beta collects the main evidence, which is data (through reports) to improve the quality of maintenance-related decisions of mechanicians The essay will also identify the advanatages and disadvantages of the process, and give out solution recommendations.
Objectives of the essay
This essay will refer to the decision-making theories combined with a real case study of Beta company to illustrate the application of evidence-based decision-making (EBDM) in organizations, toward the following objectives:
- Generalizing EBDM theory and related decision-making frameworks and methods
- Analyzing improvements in the reporting process for decision-making at Beta Company
- Analyzing warranty decision-making process based on accurate data reported at Beta Company
- Assessing the advantages and disadvantages of this process
- Proposing solutions to diminish the drawbacks and strengthen Beta maintenance service quality to create a perfect process
Structure of the essay
The layout of the essay consists of 5 chapters:
Chapter 2: Literature review ôn tập quản trị chất lượng
100% (4) 23 ÔN TẬP QUẢN TRỊ CHẤT LƯỢNG
Speaking-lop 4 - ôn tập Tiếng Anh 4
Quan tri kinh doanh 60% (10) 2 ÔN TẬP QTRI CHẤT LƯỢNG - abvv
English - huhuLed hiển thị 100% (3)10
Chapter 3: The implementation of data-driven decision-making in maintenance service delivery of the beta company
LITERATURE REVIEW
Evidence-based decision making
2.1.1 Definition of Evidence-based management
In order to address the definition of evidence-based management and evidence-based decision-making, it is important to understand the meaning of the term “evidence” first. Evidence is stated as a fact, organized body of information, or observation, which is presented to support or justify beliefs or inferences (Goodman & Royall, 1988) Evidence is also required about ethical issues of educational or health care practice, such as whether it is right or warrantable to undertake a particular educational activity or health care intervention. (Davies, 1999).
In terms of Evidence-based management (EBMgt), it is the systematic use of the best available evidence to improve management practice (Reay, Berta, & Kohn, 2017) Evidence- based management is about making decisions through the conscientious, explicit, and judicious use of four sources of information: practitioner expertise and judgment, evidence from the local context, a critical evaluation of the best available research evidence, and the perspectives of those people who might be affected by the decision Moreover, EBMgt fundamentally is something performed by practitioners, not scholars (Briner, Denyer, & Rousseau, 2009) It focuses on the effective oversight of staff and resources within organizations The management literature outlines eight specific actions that should be taken when implementing an evidence-based (EB) framework:
- Use data to identify needs
- Make the best choice according to the information
In addition, EBMgt has been defined as “integrating managerial expertise with the deliberate and prudent use of best evidence in making decisions while taking into account the perspective of those who may be affected by them” (Smith-Colin, Fischer, Akofio-Sowah, & Amekudzi-Kennedy, 2014).
2.1.2 Definition of Evidence-based decision making
Evidence-based decision-making is one of the 7 principles of ISO 9001:2015, which is known as the seven quality management principles International Organization for Standardization (2015) states that evidence-based decision-making (EBDM) is decisions based on the analysis and evaluation of data and information that are more likely to produce desired results EBDM includes a wide variety of applications, such as evidence-based (EB) design, EB planning, EB policy, and EB management have been successful applications in other fields such as health care, social policy, education, and organizational management (Smith-Colin, Fischer, Akofio-Sowah, & Amekudzi-Kennedy, 2014).
Evidence-based decision-making is viewed as a dynamic process through which evidence is obtained, interpreted, and used as a basis for decision-making The process of evidence-based decision-making is influenced by managers’ preferences and values as well as stakeholders’ preferences within institutional, organizational, and individual contexts Finally, managers may also face ethical constraints at both organizational and individual levels in making the final decision from the generated decision options (Baba & Hakemzadeh, 2012). Therefore, in order to have the best and most specific evidence, it is necessary to have the following typical elements:
- Determine, measure and monitor key indicators to demonstrate the organization’s performance.
- Make all data needed available to the relevant people.
- Ensure that data and information are sufficiently accurate, reliable and secure.
- Analyse and evaluate data and information using suitable methods.
- Ensure people are competent to analyse and evaluate data as needed.
- Make decisions and take actions based on evidence, balanced with experience and intuition (International Organization for Standardization, 2015).
2.1.3 Roles of evidence-based decision making
The EBDM principle can be used as a foundation to guide an organization’s performance improvement (International Organization for Standardization, 2015).
Some key benefits of evidence-based decision making:
- Improved assessment of process performance and ability to achieve objectives
- Improved operational effectiveness and efficiency
- Increased ability to review, challenge and change opinions and decisions
- Increased ability to demonstrate the effectiveness of past decisions
Decisions based on analysis and evaluation of data and information are more likely to lead to desired outcomes Decision making can be a complex process and always involves uncertainty This process sometimes involves input of different sources, and the interpretation of that input, which can be subjective Causality and potential adverse effects need to be understood Facts, evidence, and data analysis increase objectivity and confidence in decision- making (Noviantoro, et al., 2020) In addition, according to a discussion session on “the role of expert opinion and judgment in statistical inference” at the ASA Statistical Inference Symposium October 2017, when making any meaningful conclusions science that goes through the process of measuring, observing, and analyzing real evidence for the purpose of informing the pathways or mechanisms by which results are obtained or to support predictions or estimates interested agents (Brownstein, Louis, O'hagan, & Pendergast, 2018). 2.1.4 Types of evidence in evidence-based decision making
Rousseau (2007) identified two types of evidence in EBDM: ‘Little e’ evidence and
‘Big e’ evidence (cited from Jennifer, 2011) ‘Little e’ evidence is also referred to as ‘internal’ evidence, which is evidence that is specific to one person’s experience or observations; it is typically non-scientific, individual and based on local interpretation ‘Big e’ evidence is referred to as ‘external’ evidence, this evidence is gathered through research, is more systematic, and has more general relevance and application; it is acquired from outside sources, including market research, benchmarking, management consultancy, books, the internet and academic research.
Data-driven decision making
2.2.1 Definition of data-driven decision making
Data-driven decision making (DDDM) pertains to the systematic collection, analysis, examination, and interpretation of data to inform practice and policy in educational settings It is a generic process that can be applied in classrooms to improve instruction as well as in administrative and policy settings It can be applied by teachers, principals, superintendents, other administrators, data entry clerks, chief state school officers, and federal education officials DDDM crosses all levels of the educational system and uses a variety of data from which decisions can be made These include instructional, administrative, financial, personnel, transportation, welfare, health, demographic, perceptual, behavioral, process, and other kinds of data (Mandinach, 2012).
Data science involves principles, processes, and techniques for understanding phenomena via the (automated) analysis of data Data-driven decision-making refers to the practice of basing decisions on the analysis of data rather than purely on intuition (Provost & Fawcett, 2013) Data-driven decision-making enables you to move faster and make fewer mistakes, inevitably leading to higher profitability Collecting and analyzing data gives you the ability to deduce when your content does well and understand why it resonates with customers—or doesn’t You can then use that information to predict how similar content will perform in the future On a strategic level, a company must decide to start making business decisions based on data to grow and enhance the customer experience It becomes a matter of shifting a company’s culture from one reliant on gut feelings to one that revolves around making data-based decisions
There is no question that data-driven decision making is a complex undertaking, even for the trained educator who understands statistical concepts As (Secada, 2001) notes, data should be used to inform educators’ decisions, not to replace them, and this process requires time and effort Educators must have specific uses in mind when examining data, and the decisions they make must be both strategic and timely (Sala, 2021)
Data-driven decision making is seen as an iterative process with data leading to a decision, implementation of that decision, determination of the impact, and perhaps the need to work through some or all of the six processes (Mandinach, Honey, & Light, 2006).
Figure 2.1: Framework for Data-Driven Decision Making in eduction sector
Source: Sala (2021) 2.2.2 The relationship between data-driven and evidence-based
Data driven decision-making is a subset of evidence-based decision making Use of data involves a carefully organized set of evidence – ideally a combination of direct and indirect evidence that provides a rich display of information (Southwest Minnesota State University,
2012) In government sector, the four characteristics of evidence-based decision-making can help the government to consider the decisions based on the objective data, rigorous sources of data, the use of appropriate analytical techniques, and implement the methodology to predict the post-decisionsmade (Luthfi & Janssen, 2019) Luthfi & Janssen (2019) study concluded that whereas evidence-based policy refers to a knowledge-based approach based on impartial evidence to inform the decision-making process, the data is often collected for a particular purpose and using different analytics outcomes can be created
2.2.3 The D3M (Dual-perspective, Data-based, Decision-making process for Maintenance service delivery) Framework
(Sala, 2021) came up with a study that focuses on the topic of maintenance service delivery and proposes a framework that, exploiting the data from the field, allows for improving the operative decision-making process and information flow The proposed framework, having a double perspective on the asset and the service, uses historical and real- time data from these sources to improve the operational maintenance delivery decision- making process The framework introduces a continuous improvement approach that, using aggregated maintenance activity information, allows introduction enhancements not only at the operative level but also at the tactical and strategic ones The proposed framework is A Dual-perspective, Data-based, Decision-making approach to managing the Maintenance service delivery process: the D3M framework.
Source: Sala et al (2022) The industrial asset stream
The first version of this stream saw the presence of the four phases depicted in the final version:
- The definition of the strategy for the asset data collection and analysis,
- The asset data collection and,
The four phases identified were defined to detail the content of the Industrial asset monitoring and analysis phase identified in Figure 2.2 While defining these phases, some methods and tools were identified as support to execute them.
The first version of the critical components identification phase suggested only the Dynamic FMECA as a method to support the identification of the critical components. Instead, the asset data analysis phase proposed the use of machine learning, under the form of a Machine Learning Algorithm Selection Model, for data analysis At this phase of the research, the idea was to propose specific methods or tools for the phases which necessitates them This approach led to the proposal of a single method or tool aimed at supporting specific phases On the one side, this should have led to the adoption of methods and tools guaranteeing structured data collection, management and execution, totally aligned with the scope of the D3M framework Thus, their adoption should be evaluated, considering the necessity and capabilities of the company.
Coherently with the final version of the D3M framework, this stream is composed of three phases, namely:
- The definition of the strategy for service data collection and analysis
For this stream, two supporting instruments were proposed The first instrument, the BPMN2.0, should be used for process mapping It should have been applied to all the maintenance services available in the company to identify all the components of the maintenance service delivery processes The other instrument was statistical analysis, whose aim was to analyse the data extracted from the Service Report and provide information on trends and data significantly deviating from the expected values.
In this case, the phases composing this stream, namely:
- The service delivery decision, and
- The collection of service and asset data during maintenance
The methods and tools proposed as a support for this phase, which are the Optimisation Model (for the cross analysis phase), and the Service Report (for the collection of service and asset data during maintenance phase) evolved during the research, presenting characteristics that were not included in their first version (Sala, 2021)
When aggregating data, the D3M framework can support strategic (e.g., acquisition of skills and technologies for maintenance improvement) and tactical (e.g., maintenance policies definition) decisions Moreover, the D3M framework proposes a set of methods to support decision-making that could be integrated with others depending on the necessities of the user.
THE IMPLEMENTATION OF DATA-DRIVEN DECISION-MAKING
The background of Beta company
Beta is an Italian company, traditionally characterized by a product-centric portfolio, offers its customers corrective and preventive maintenance, warranty, help desk, and spare parts distribution services They produce tools for professional mechanics, from industrial maintenance to car repairs, and for all those whose hobbies are much more than a simple pastime.
Source: Beta-tools (2022) Today the Beta Tools catalogue comprises over 15,000 items divided into 30 product families Some of Beta major products are motorbike repair tools, car repair tools, power tools, workshop equipment, electrotechnics, etc Beta also provides more than 9 types of services for maintaining equipment with certified quality These services include, on request, providing a certification service for digital and measuring instruments such as torque spanners and digital multimeters; providing warranty extension; customizing tools based on customer's preferences and designing maintenance workshops for customers
Beta company has been the Italian and European leader in the tool sector since 1923.They have more than 9 manufacturing factories globally, self-owned logistic facilities and over 250 distributors, to be active in the international market, thereby reaching professional users worldwide every day Beta Tools with its 22.000 m2 surface area, allows quick, prompt and complete deliveries of the 15,000 coded items managed with over 27,000 loading units.They rely on a distribution network boasting no less than 9 commercial branches.
Beta company defined the principle that guides their daily work around the world, driven by four values:
- Audacity to invest and constantly relaunch an enterprise created by an Italian craftsman in 1923, which has asserted itself worldwide and continues to grow after four generations.
- Commitment in taking care of the quality of our products because every retailer knows he can count on an efficient service, and every user knows he can rely on the precision, reliability and resistance of Beta products.
- Harmony of the lines, for a design of excellence, because when something is well done, it expresses a perfect balance between function and design.
- Talent IT is our most precious resource, the one that distinguishes all our people and every professional who chooses Beta to leave nothing up to chance, in the engaging and passionate fulfilment of their ideas and dreams.
- Precision, rigor, bravery, style, consistency, innovation, in order to shape an idea, nurse it until it is finally fulfilled, and then further improve the result This is our mission and commitment to each user, at every stage - design, production and distribution.
At Beta, quality is not only synonymous with an attentive industrial and sales policy,but it also permeates each company division, with a view to achieving unparalleled customer satisfaction The quality of Beta products is also certified by the UNI EN ISO 9001:2015 standard, which defines the requirements necessary to improve the effectiveness and efficiency in the realization of its products, optimizing the management system.
Figure 3.2: ISO 9001:2015 certification acquired by Beta
Applying the D3M framework in Beta Company
This case study describes how Beta transformed some of the activities that characterize the process of providing maintenance services by leveraging the D3M framework and shows how adopting a data-driven approach requires an in-depth discussion of the data to be collected and used Its purpose is to create the conditions for improved resource management and to provide the basis for the accumulation of maintenance engineering knowledge, thereby enhancing the provision of preventive maintenance at the expense of corrective maintenance interventions based on 3 main streams of the D3M framework applied by Beta such as the service stream, the industrial asset stream, and the maintenance service delivery stream
The service stream identifies customer service requirements through BPMN2.0 notation implemented by Beta, with specific error correction and maintenance main tasks such as: identifying all activities, decisions, participants, data, and tools specific to each service from some useful information and criticalities highlighted from the flowchart analysis There were various problems with the original process of providing maintenance services For example, at the beginning of a maintenance intervention, an unstructured trial-and-error activity based on the technician's experience to identify issues with the asset, and even as Beta fills out the service report after each maintenance intervention, recognizing that most of this data is unstructured are unstructured and some important information is missing (e.g processed components, used spare parts).
Definition of the Strategy for Service Data Collection and Analysis
Beta collects data in the maintenance service, but the data lacks information, making data analysis more time-consuming Instead of opting for a new service reporting tool, Beta decided to update the content with new features that fix identified vulnerabilities and enable more valuable data analysis This allows technicians to confidently perform their tasks without special training, as filling in information is simplified (e.g., introducing drop-down menus, showing warnings in case of empty fields)
Beta applies natural language processing (NLP) to analyze free-text cases in reports to find the relationship between errors and main causes (e.g., the bad behaviour of component X may cause the failure of component Y), and then lists the most important suggested solutions.
As a result, Beta can have a better overview of the service performance, understand the most effective resolution approaches (e.g., in terms of resolution time), the technicians most skilled for an intervention.
Figure 3.3: Beta’s original Service Report
Figure 3.4: Beta’s new Service Report Changes are highlighted in green rectangles
Issues related to unstructured trial-and-error work will result in insufficient information until the beta implementation service reports improvements Therefore, when a new method is introduced, Beta allows the technician to directly identify the list of problems and provide possible solutions After the maintenance is completed, customers often negotiate the price.
On the one hand, Beta resolves, by the way, introducing the checklist and activities performed by the company, and on the other hand, Beta presents the maintenance contract case by case, so customers recognize the price before using the service.
Beta analyzes previously defined data and applies to extract new knowledge, monitor service performance for better service improvement.
In the field of industrial asset stream, it is important to identify the most critical components and take the countermeasures that need to be taken to avoid failures The analysis was carried out applying the FMECA methodology, which allowed one to decompose the asset into smaller components and associate an RPN to define the criticality of each one Beta uses FMECA to determine criticalities in its assets, but only in the design phase. However, this form does not address the potential for misuse of property by customers, which can lead to unintentional mistakes Therefore, the company also use the Dynamic FMECA (a part of the D3M framework) to adapt maintenance policies (advised or sold to the customer) to the actual use of the asset, increasing their effectiveness.
Definition of the Strategy for Asset Data Collection and Analysis
Considering the outcome of the FMECA, Beta decided to conduct an analysis of the moving belt, which is one of the asset axes, by collecting data on the power absorption and current consumption It would be possible to establish a connection between the operational behavior of an asset and its health status.
Then, following the recommendations of the D3M framework, it was decided to analyze the collected data using four ML algorithms selected by the Machine LearningAlgorithm Selection Model (MLASM) – which will propose a set of algorithms considering the scope of the analysis and the characteristics of the dataset and proposes a set of algorithms able to deal with these drivers.
Once the data collection and analysis strategy is defined, Beta starts collecting data from the asset to monitor its health, compares the data analysis results to defined thresholds, and makes maintenance based on the results of the data processed using the developed neural network algorithm decision making.
Algorithms and thresholds will change in the future depending on what is being analyzed Thus, Beta increases the certainty of the decision.
3.2.3 The Maintenance Service Delivery Stream
Beta divides technicians into two categories: those who can manage both mechanical and electronic errors, and those who can only manage electronic errors Administrative and technical staff need to be involved in the planning process so that technical staff can help with technical issues, as administrative staff are not well knowledge in this regard The optimization model was created on the premise that having knowledgeable experts intervene leads to improved resource management and faster, better implementation of interventions. After analyzing the past service report, two databases were created: one with the skills required to perform interventions by type (e.g., remote, on-premises) and another summarizing the types of interventions that Beta technicians typically perform with an average lead time
The databases used to house optimization models and match them with interventions performed by test engineers and optimization techniques (e.g., skills required, resolution approach, the technician assigned) must be regularly updated with new data The application of the optimization model is established to reduce the time required to perform intervention allocation, save the time for other tasks and establish skill-based improvement plans for the technicians The model also helps allocating multiple interventions in the same window (i.e., the time periods where a technician is free and, thus, available to execute it also considering the traveling time) Eventually, Beta can significantly save the cost of technician travel and penalty for not meeting deadlines.
In the new set of service-type decision-making, the planner must examine the schedule when applying the optimization model, change it according to newly discovered information based on the results of the previous period and facilitate the determination of the final service schedule It is important to emphasize that the planner still plays a fundamental role at this stage and is not required to accept the model proposal The planner must change one or more interventions in the action list scheduled as a to-do list when a clear priority of intervention solutions could not be established, resulting in new problem in the schedule.
Collection of Service and Asset Data during Maintenance
This stage saw the adoption of the new service report developed following the analysis represented in “Definition of the Strategy for Asset Data Collection and Analysis”.
Advantages and drawbacks of the Beta’s D3M model
The aggregated information from the Beta D3M framework could be used to make decisions at the strategic (e.g., dimensioning the service resources) and tactical (e.g., update the maintenance policies) levels, thus shifting the perspective from short-term term towards medium- and long-term impact In detail, the D3M framework aims at favoring dialogue among the departments involved in the PSS delivery (e.g., service and asset design) to create new knowledge that Beta used to improve the asset and the service, change the maintenance policy previously associated with them, deciding to acquire new internal skills (e.g., both in terms of maintenance execution and data management for decision-making), defining new spare parts refilling policies, and proposing new services tailored to the customer needs (e.g., training).
In terms of assets, the new knowledge allowed Beta to improve the management of the asset stream (e.g., to design or redesign components, update maintenance policies through the dynamic FMECA), strengthen the preventive maintenance offering, and reduce the corrective interventions.
Through the D3M framework, companies will be able to identify their weaknesses and define improvement plans based on increased knowledge of the assets and the maintenance service delivery process By analyzing the maintenance execution procedures and timing, skill-based improvement plans for the technicians were defined, allowing them to cover their gaps and making them more efficient.
Increasing the maintenance outcomes quality
Thanks to the D3M model, Beta has the ability to manage and respond promptly to maintenance requests, which was used to define the details of the maintenance contracts with the customers Beta gained knowledge on the number, length, content, and frequency of the interventions, and this, in combination with the adoption of the optimization model, led to an improved schedule of maintenance activities It also helps define the most efficient and effective countermeasures for each failure in terms of execution time and procedure The analysis of the intervention executed also contributed to better defining the instruments required for the execution of each intervention typology Which can result in a reduction in the time required for schedule learning by 10-15% (Sala et al., 2022) Making the time for the intervention process decrease by 10-15% (Sala et al., 2022) So Beta can prompt the allocation of the request and select skilled technicians to execute it
Such improvements would also be connected to reducing the number of corrective interventions (also due to the update of the maintenance policies) and reducing penalties paid for contractual clauses not respected (e.g., interventions executed after the contractual due date) by around 5–10% (Sala, Pirola, Pezzotta, & Cavalieri, 2022).
The analysis of the spare part used during the intervention contributed to better defining the stocking policies as well as their consumption rates, which, in the future, will allow one to also achieve benefits in terms of sustainability (both in economic and environmental terms). Especially with a global company like Beta, the amount of equipment is large, the D3M provides a be more precision tracking, as well as decreases the number of inventory staff. Therefore, the company can save cost as there are less training required and avoid unwanted mistakes caused by human factors (now following safety procedures, checking mistakes, petty theft, etc).
Beta model simultaneously affording customers the flexibility to individualise to customer circumstances When the data is appraised and fully understood, providers can decide if and how to incorporate it into practice In addition, using data-driven helps providers determine treatment plans, including in situations in which there are limited data or experience
There are multiple maintenance methods applied to different specifications of products, using data-driven treatments offers providers a starting point to develop complex treatment plans Which will save provider time and resources by choosing a utilized plan, reduce customer waiting time, leading to customer satisfaction.
Clear feedback from market research
The data-driven decision making approach helps Beta to formulate new products, services, workplace initiatives, and even identify trends in the maintenance industry By investigating the historical data, the companies is enabled to know what to expect in the near future and what they should change to better perform and compete
Beta can also analyze client feedback to understand how to maintain a great relationship with their customers and discover smart ways to introduce new products and services to move their brand forward.
Overemphasis on using data-driven could erroneously ignore other maintenance tools, most notably the professional’s own maintenance experience Similarly, when data-driven methods are applied by Beta too rigidly, there is a risk of diminishing their value Such over- reliance on rules may result in maintenance delivery service practice that is management driven, rather than customer-centred
The introduction of the D3M framework in Beta company is not trivial and requires the involvement of personnel with proper skills for data management and decision-making. However, in the service sector, each request made by customers is different If the personnel evaluate the customer request incorrectly, it would lead to a lack of the necessary depth in the analysis Or even after an analysis uncovers insight, there is a risk that the technician would not able to give out a suitable recommendation every time.
The implementations could be costly
In fact, in some cases, the full implementation of the D3M framework might require a profound reorganization involving many processes from various departments In addition, it would require the availability of an infrastructure able to collect and transfer all the necessary data, which is not always available This transformation requires Beta to have a huge budget investing in data-support equipment and training, and it takes a considerable amount of time. Plus, some managers would reluctant to adapt this new form because the benefits from it would not show up immediately at the start.
From the practitioner’s standpoint, dedicated use of data-driven Beta company could impose burden in terms of continuing updating and training Mechanicians using data-driven must maintain up-to-date knowledge of the latest evidence supporting current or new methods, which takes a considerable amount of time Providers must have adequate training, consistently gathering new data to identify and implement the most appropriate method for clients.
The Beta data-driven decision-making is time-dependent to perform a chain of activities such as data collection, quality assessment, analysis, reporting, etc Those make the manager's decision-making time longer than the old method Besides, if just one of the activities in the chain of operations has a problem, it will affect all subsequent activities, including maintenance decision-making One of the common problem is bad data If the data is not reliable or relevant, it would be harder to analyze and result in an error decision.
SOLUTION RECOMMENDATIONS
There may be trade-offs between cost and the application of the D3M framework. Though the framework brings utility for Beta and values for customers, the full implementation takes a considerable amount of budget For Beta, they could test the implementation on a specific stream or phase first If the implementation on that stream or phase works effectively, Beta can respectively apply the framework to another stream and phase By doing this, Beta can assess the suitability of the framework for the company, saving the cost of large-scale training and purchasing new equipment.
Before the implementation, Beta should set up some standards for measuring the outcomes of the D3M framework compared it the traditional method Those standards could be time spent for schedule planning time, intervention process, the interval of mechanicians between each task and customer satisfaction, etc Beta can also directly ask their clients (or use a survey) if they see any difference in the maintenance delivery service.
Providing basic training for mechanicians
Mechanicians who work on a specific stream in Beta company must be provided with training in basic data usage The training is about how mechanicians can recognize the data during their operation, equipping them with knowledge about how to collect data, report data, and use it to improve their decision-making The training would eventually help Beta to quickly adapt to the new model, begin to increase the company’s overall effectiveness and customer satisfaction.
In order to tackle the doubt in the implication of the new model, there must have a training session to inform mechanicians about the value of transformation Showing how the D3M transformation can improve their productivity, open up many job opportunities for them The training should move beyond the technical aspect of the data-driven decision- making framework As Beta should show mechanicians how the framework is fits with the company culture and the company’s direction toward the future.
After a period time of implementation, Beta should pick a few mechanicians who well adapt to the new model and give them extra data training sessions These mechanicians will be aimed to become executives in the data field, who consistently update data and knowledge for the company Then, Beta can apply the training-of-trainers method, which means giving data executives responsibilities to provide new data and training other mechanicians This method would help Beta to avoid lengthy data training over time.
Consistently seeking a new way to develop the D3M framework
In order to achieve continuous innovation in the future, Beta should not over-rely on the D3M framework in the maintenance service delivery Over-reliance makes mechanicians decrease creativity as they always look up to data for intervention methods, making the company lack of new maintenance solutions in the future Instead, Beta should give a greater appreciation of the value of the mechanician specialists Based on their experience in the field, combined with the data support, they can be flexible in their implementation of procedures based on the unique needs and preferences of their individual clients
Beta should frequently trace back to the data-driven decision-making process to look for a way to improve the quality of a decision made with less time taken To attain this, managers should do literature research about the maintenance industry, seeing if researchers' recommendation about the D3M framework is suitable to apply to the company, and find out a method to consistently gather quality data Beta managers should also notice that the development of the D3M framework should be about customer-centric, with the final result is establish a relationship between customers and Beta service delivery.
Alongside data-driven, Beta should concern with other factors affecting the company's maintenance service delivery quality Some major factors are compensation for mechanicians, customer perception, ethical and social image, etc If Beta can also cover up these factors, it would drive the company to greater brand equity, which would put the company in a better position, including partnership agreements with big enterprises, market share increased, etc. Applying technology to manage each stage
Adopting the D3M framework as a model for improving the quality of management decisions brings consistent improvement to the entire operational process However, it also requires improving the old process by changing how some work is done and adding some new steps The newly applied model means that the errors of the new approach can frequently occur at all stages One of the optimal solutions for Beta is to digitize the entire process.Digitize the whole process using specialized software (which can be outsourced or use available software services) All jobs are monitored in real-time by the manager This can be done using a computer system or a mobile device The staff will update the work progress into the software; then, the management will evaluate the overall situation through the reported results Although applying technology to the improvement process will cost more in the initial investment, it helps to minimize errors and reduce execution time at all stages,which contributes to the stable operation of the company when using new approaches.
Applying digitalization also helps Beta to diversify their products and service offers. Beta can deliver virtual maintenance services for customers in other regions This can be done by putting a mechanician on video call so the person can take requests from customers Then the mechanician can analyze data for a possible approach and guide customers for a repair on his own This form of business also helps Beta to broaden its data scope, and collect and analyze various types of data As a result, Beta can have a better understanding of the international market, making its expansion becomes less challenging.
CONCLUSION
This essay has presented theories related to evidence-based decision making The content of the article focuses on analyzing DDDM and D3M models, which are two types of EBDM An Italian mechanical product manufacturer is presented as a case study to analyze the application process of the D3M framework The essay demonstrates how the process of Beta improved in terms of structure, methods, and decision-making, showing how Beta's processes were reshaped for the new approach The company has applied the D3M framework to restructure the maintenance service delivery process through the improvement and addition of a number of activities such as asset data analysis, data collection methods, data types, Data quality assessment and reporting were analyzed in detail Specifically, discussions related to the kind of data to collect, manage, and analyze were carried out the instrument, allowing for evaluation of each improvement step, and selecting the proper data-driven transition for each phase of the process (i.e., knowing the data is initially available, the data that would have been useful to have, and the methods and instruments necessary to collect and analyze the data) In particular, the structure of the process was built in the scope of favoring the information flow between the actors involved in the process, allowing each one to have a complete overview of the operating context and driving decisions based on that A reflection on how the newly introduced methods would act in the process was carried out, also allows one to understand how they would support the user and the information to communicate to drive the users’ decisions
Through the analysis of the case study, the essay also presented the advantages and disadvantages of the model being applied by Beta company Thereby, solutions have been proposed to diminish the drawbacks and strengthen Beta maintenance service quality. Limitation
The essay is based on theories of evidence-based decision-making and relevant studies in various fields However, the scientific data on EBDM found in the area of manufacturing and business is quite limited, mainly appearing in medical and education Therefore, the relevant theories presented may not be correct in all contexts In addition, the team members are all students and have no professional experience, so the proposed solutions may be theoretical and not applicable in practice.
Antony, J (2013) What does the future hold for quality professionals in organisations of the twenty-first century? The TQM Journal, 25(6), trang 677-685 doi:http://dx.doi.org/10.1108/TQM-07-2013-0079
Baba, V V., & Hakemzadeh, F (2012) Toward a theory of evidence based decision making Management Decision, 50(5), trang 832 - 867 doi:http://dx.doi.org/10.1108/00251741211227546
Briner, R B., Denyer, D., & Rousseau, D M (2009) Evidence-Based Management: Concept Cleanup Time? Academy of Management Perspectives, 23(4) doi:http://dx.doi.org/10.5465/AMP.2009.45590138
Brownstein, N C., Louis, T A., O'hagan, A., & Pendergast, J (2018) The Role of Expert Judgment in Statistical Inference and Evidence-Based Decision-Making Taylor & Francis Online ĐưVc truy lWc tX https://www.tandfonline.com/doi/full/10.1080/00031305.2018.1529623
Chen, S., Yi, J., Jiang, H., & Zhu, X (2016) Ontology and CBR based automated decision- making method for the disassembly of mechanical products Advanced Engineering Informatics, 30(3), trang 564–584 doi:https://doi.org/10.1016/j.cirp.2010.03.099 Davies, P (1999) What is Evidence-based Education? British journal of educational studies, 47(2), trang 108-121 doi:https://doi.org/10.1111/1467-8527.00106
Goodman, S N., & Royall, R (1988) Evidence and scientific research American Journal of Public Health, 78(12), trang 1568-1574 doi:https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.78.12.1568
International Organization for Standardization (2015) Quality management principles. Geneva: ISO Central Secretariat ĐưVc truy lWc tX https://www.iso.org/files/live/sites/isoorg/files/store/en/PUB100080.pdf
Jennifer, R (2011) Evidence-based marketing: A perspective on the ‘practice–theory divide’. International Journal of Market Research, 54(4) doi:https://doi.org/10.2501/IJMR- 54-4-521-541
Luthfi, A., & Janssen, M (2019) Open Data for Evidence-based Decision-making: Data- driven Government Resulting in Uncertainty and Polarization International Journal on Advanced Science Engineering Information Technology, 9(3) doi:http://dx.doi.org/10.18517/ijaseit.9.3.8846
Mandinach, E B (2012) A Perfect Time for Data Use: Using Data-Driven Decision Making to Inform Practice Educational Psychologist, 42(2) doi:http://dx.doi.org/10.1080/00461520.2012.667064
Mandinach, E B., Honey, M., & Light, D (2006) A Theoretical Framework for Data-Driven Decision Making Research Gate.
Mitra, A (2016) Fundamentals of Quality Control and Improvement Hoboken: John Wiley
Noviantoro, R., Maskuroh, N., Santoso, B., Abdi, M N., Fahlevi, M., & Pramono, R ( 2020). Did Quality Management System ISO 9001 Version 2015 Influence Business Performance? Evidence from Indonesian Hospitals Sys Rev Pharm;11(8), 499-507. Provost, F., & Fawcett, T (2013) Data Science and its Relationship to Big Data and Data- Driven Decision Making Big Data, trang 51-59 doi:https://doi.org/10.1089/big.2013.1508
Reay, T., Berta, W., & Kohn, M K (2017) What's the Evidence on Evidence-Based Management? Academy of Management Perspectives, 23(4) doi:https://doi.org/10.5465/amp.23.4.5
Rửnnbọck, Å., & Witell, L (2008) A review of empirical investigations comparing quality initiatives in manufacturing and service organizations Managing Service Quality: An International Journal, 18(6), trang 577-593 doi:https://doi.org/10.1108/09604520810920077
Rousseau, D M (2017) A sticky, leveraging, and scalable strategy for high-quality connections between organizational practice and science Academy of Management, 50(5) doi:https://doi.org/10.5465/amj.2007.27155539
Sala, R (2021) A Dual-perspective, Data-based, Decision-making approach to manage the Maintenance service delivery process: the D3M framework ĐưVc truy lWc tX https://aisberg.unibg.it/retrieve/e40f7b89-ac66-afca-e053-6605fe0aeaf2/
Sala, R., Pirola, F., Pezzotta, G., & Cavalieri, S (2022) Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study Applied Sciences, 12(15) doi:https://doi.org/10.3390/app12157395
Secada, W (2001) From the director Using data for educational decision making Newsletter of the Comprehensive Center - Region VI,6.
Smith-Colin, J., Fischer, J M., Akofio-Sowah, M.-A., & Amekudzi-Kennedy, A (2014) Evidence-based decision making for transportation asset management: Enhancing the practice with quality evidence and systematic documentation Transportation Research Record, 1 doi:https://doi.org/10.3141/2460-16
Southwest Minnesota State University (2012) Southwest Minnesota State University ĐưVc truy lWc tX https://www.smsu.edu/resources/webspaces/administration/committees/ CIA/Evidence-vs-Data_EL_010915.pdf
The US Department of Labor (2022) Management Analysts : Occupational Outlook Handbook: : U.S ĐưVc truy lWc tX https://www.bls.gov/ooh/business-and-financial/management-analysts.htm#tab-6
Go to course ôn tập quản trị chất lượng
Chất lượng 100% (4) 23 ÔN TẬP QUẢN TRỊ CHẤT LƯỢNG
TÀI LIỆU THAM KHẢO Nlmar
Chất lượng 100% (2) 72 bộ đề thi trắc nghiệm
KẾ HOẠCH KINH Doanh NẾN THƠM…
Lập kế hoạch kinh doanh None 38
Lego xây dựng nhà máy tại Việt Nam
Quản trị Đa quốc gia None
Phân tích tổ chức xuất nhập khẩu
Speaking-lop 4 - ôn tập Tiếng Anh 4
Quan tri kinh doanh 60% (10) 2 ÔN TẬP QTRI CHẤT LƯỢNG - abvv