Abstract
Supply chain maturity models are an important part of the development of any firm. They enable practitioners to understand where they stand about the ideal supply chain regime. Measuring a firm’s supply chain operations against a maturity model enables it to take the necessary actions to move to the next level.
According to the conventional maturity model, an ideal position is characterized by achieving synergy and cross-relationships with other chain members. This enables the sharing of data that informs an array of decisions that confer value for the firm. Software firms do not conform to the conventional model due to their inherent characteristics. Their maturity model is markedly different from the conventional, with supply chain maturity defined by continuous optimization of standardized models.
Introduction
Supply chain management is a significant aspect of many firms’ quantitative success. A maturity model enables supply chain practitioners to put their actions toward implementing an efficient supply chain regime in perspective (Poirier et al., 2009). The five-step maturity model proposed by Poirier et al. (2009) is a typical model that can guide most businesses wanting to evolve their supply chain management from an internal process into a comprehensive process incorporating external partners.
The supply chain maturity model may have different implications for firms in different industries. In particular, the maturity model for a firm in the retail sector may not be applicable in the software industry. This research paper will interrogate existing literature to detail the different connotations of the supply chain maturity model in the software industry.
Literature Review
The Maturity Model Steps
Enterprise Integration
The maturity model that applies to most conventional firms typically has five steps. The first step is enterprise integration, whereby the firm seeks to organize its operations in the most financially productive way possible (Poirier et al., 2009). Thus, the firm focuses on getting the best suppliers, maximizing its holding facilities, and strengthening its distribution networks.
Corporate Excellence
Once internal enterprise integration is attained, the firm embarks on the second level, corporate excellence (Poirier et al., 2009). At this level, the firm starts developing the most effective and economic stock management policies. It calculates and emphasizes the ideal figures for inventory levels and the best timing for its operations to ensure it meets the demand levels over a forecasted period.
Customer Orientation
In step three, the firm must stop looking inward and move its supply chain management to the macroenvironment. In particular, the firm must engage with its external partners and share information regarding its operations. Such partnerships facilitate synchronized operations between contributors to a supply chain and lay the foundation for incorporating technology and automation.
However, level three is heavily reliant on the cohesion of the firm’s internal departments, which must collaborate by aggregating their data and synchronizing their processes. Internal teamwork between departments paves the way for level four, whereby the firm can integrate with external partners (Shin et al., 2019).
Product/Service Differentiation
In the fourth level, the firm shares its data and obtains data from other firms. The data is acted upon by algorithms that produce the best supply chain decisions that provide value for all the firms making up the chain. These decisions have an end-to-end impact in that they connect the needs of the producer at one end of the chain to those of the consumer at the other.
Sustainability and Agility
Once this is achieved, the firm can evolve to the last level of the conventional maturity model, whereby technology and automation drive the supply chain. A series of automated events backed by extensive macroenvironmental data ensure that value is maintained throughout the supply chain (Chang et al., 2019). Wastage of time, space, and resources is kept to a minimum and all the decisions are informed by computerized analysis of the available data as opposed to human intelligence.
The Need for a Specific Maturity Model for IT Firms
Industry 4.0 firms do not have conventional supply chain networks whereby the supplier firm mobilizes its resources to acquire raw inventory, which is then converted to finished goods and delivered progressively to the consumer. Instead, software firms are tasked with developing software projects that meet a desired end user need (Frederico et al., 2019). Thus, they must mobilize time, human resources, and information technology repositories to ensure the product they come up with is satisfactory and competitive. The adapted form of the conventional maturity model used in the software industry is the Capability Maturity Model Integration (CMMI) (Facchini et al., 2019). It guides companies in the software industry by providing five steps or milestones against which these companies can assess their attainment and progress.
The Capability Maturity Stages
Initial Level
In the first stage of CMMI, the supply chain management regime is haphazard and disorderly. In this case, the software firm charged with delivering projects such as applications is not well organized and primarily relies on individual brilliance to ensure that its products are supplied promptly and in the form desired by the end users (Facchini et al., 2019). There are no rules within the organization, and projects proceed as directed by select individuals tasked with ensuring their delivery. Owing to the lack of organization, the firm is unable to take advantage of the opportunities in its supply chain and may constantly find its projects exceeding their budgetary allocation. Moving on from this stage guarantees several improvements, including increased profits.
Managed Level
The second level of the model adapted to the software industry results in processes becoming better defined and reproducible. For instance, the process of developing a program becomes well fleshed out, with each segment of the company knowing when and how to take up its role for the benefit of the whole (Keshta, 2019). The firm has started utilizing measures that control time, costs, and personnel in a concerted effort to better manage its resources. Keshta (2019) adds that a project management or supply chain management function may be established in the company to further guide the definition of its processes and operations. However, there are still some operational setbacks that may cost the firm profit and lead to a waste of resources.
Defined Level
In the third level, the firm becomes even more thorough in terms of its process definition. Significantly, the firm comes up with standardized procedures for its product development (Keshta, 2019). These standardized procedures are informed by experience in the former two stages and result in the best performance of the company as far as software project development, maintenance, and delivery. Moreover, the firm’s processes and results become reproducible, resulting in better supply chain management outcomes. At this point of maturity, the software firm reaps the benefits of its supply chain management evolution in the form of improved profits.
Additionally, the firm may start documenting its processes of supply chain management as a matter of policy (Facchini et al., 2019). One of the operational aspects that become well defined in this stage in a company, for instance, is when to start testing the software under development for bugs. Some companies may go for a continuous, random testing model, whereas other firms may decide to debug their software at specific points of its development.
Quantitatively Managed Level
In level four, the firm starts taking feedback from its processes to use it to further improve its supply chain management processes. Facchini et al. (2019) note that CMMI emphasizes continuous data-backed improvement of the supply chain regime. In particular, the firm uses specific quantitative data to make decisions regarding its supply chain management regime.
First, the firm must develop a data-heavy regime that relies on algorithms and analytics to assist it in studying and controlling its processes. Keshta (2019) notes that the company must stay ahead of uncertainty by leaning heavily on the predictions made by its algorithms based on historical data. Notably, software industries are some of the most data-driven industries compared to other industries with global-scale supply chains. Moreover, their unique placement means they can implement data-heavy regimes relatively more easily than other sectors.
Optimizing Level
In the final stage, the firm uses the data collected in stage four to optimize its operations. Continuous optimization is the ultimate goal of any firm, adhering to the guidelines set out by CMMI (Keshta, 2019). For instance, the firm may collect data to show that projects completed three months before their delivery date result in the least number of end-user complaints and increased customer satisfaction. At this point, such inferences from data can be incorporated into the firm’s processes promptly, which is a testament to its flexibility (Keshta, 2019).
Additionally, the firm can innovate within its supply chain management to create algorithms that further improve its supply chain management. Its ability to make small but significant data-driven changes to its supply chain management is symbolic of its maturity within the CMMI model.
Peculiarities of Adoption of Maturity Models
If the firm is engaged in a typical supply chain where it requires other firms to supply it with parts of its software, then the firm must adopt level three of the conventional model. The firm must share its data with the other firms along the supply chain to ensure that all the firms along that chain obtain the maximum possible value from the product (Wagire et al., 2020). For instance, collaborating with other firms in a software product development chain can result in time savings by reducing the replication of tasks such as test runs.
Moreover, collaborating with other firms can lead to the firm learning small improvements it can make to its processes from other firms. Thus, while the two models appear divergent, they are complementary. As software firms and many other firms move towards a more sustainable model, there has been a growing emphasis on green technology. CMMI has been responsive in this regard by incorporating a CMMI green model that will enable software firms to move into an environmentally sustainable supply chain regime (Patón-Romero, 2019). Again, this shows the responsiveness, flexibility, and suitability of the CMMI model for software firms.
Conclusion
In conclusion, the conventional model envisions the firm’s starting point as its internal operations, which it must get in order before venturing outwards towards a comprehensive and integrated supply chain management regime. On the other hand, the software firm’s maturity model is said to be reached when the firm can make never-ending improvements to its supply chain by making objective inferences from its data, according to CMMI. Overall, each industry has its peculiarities, but the goal of achieving full maturity with regard to supply chain management remains.
References
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