The Importance of Clear Communication in Supply Chain Management:
Effective communication is essential in all areas of business, but it is particularly critical in supply chain management. Companies with supply chain problems often encounter communication breakdowns between different departments, leading to inefficiencies and delays in operations.Let’s take the example of a phone case manufacturer. The company has multiple departments involved in the supply chain process, such as procurement, production, warehousing, and logistics. However, each department operates independently, with minimal communication between them.
The procurement team sources raw materials without considering the production team’s capacity and schedules, leading to excess inventory and storage costs. In contrast, they may not order enough materials at times, causing production delays and stockouts.
Furthermore, the lack of communication between the production and warehousing departments results in inefficient use of storage space, as the production team is not aware of the warehouse’s capacity constraints. This leads to disorganised inventory management, making it difficult for the logistics department to locate and ship products on time.
The communication breakdown between the logistics department and other departments also hinders the organisation‘s ability to react to changes in customer demand or external factors such as shipping delays or natural disasters. In such cases, the lack of real-time communication prevents the company from making timely adjustments to its supply chain operations, further exacerbating inefficiencies and delays.
In the above example, the challenge is not due to a lack of scalable systems that can streamline the processes; rather, the problem lies in organisations not prioritising a data-first approach. ERP systems can collect, store, and manage data from various aspects of a business, including supply chain operations.
However, without a data-first culture, companies may fail to fully utilise the wealth of information available within their ERP system. Consequently, they miss out on opportunities to make data-driven decisions and optimise their supply chain processes.
A data-first culture encourages open communication and collaboration between departments. In organisations that lack a data-first culture, there may be silos that prevent different departments from sharing information and working together effectively. This can lead to inefficiencies, delays, and other problems within the supply chain.
Additionally, data quality is a critical factor in the success of any data-driven initiative. Companies that lack a data-first culture may not prioritise data accuracy and completeness, resulting in flawed decision-making based on incorrect or outdated information. In the context of supply chain operations, this can have severe consequences, such as incorrect demand forecasting, inventory management issues, and increased costs.
Harnessing the Power of Relevant Data
The journey towards a data culture begins with a commitment from top management. Leaders must understand the value of data-driven decision-making and champion the shift to a data-first approach throughout the organisation.
Companies should establish a clear data strategy that outlines their objectives, priorities, and desired outcomes. This strategy should include defining key performance indicators (KPIs) and setting targets to measure the success of data-driven initiatives.
The next step is to invest in data infrastructure. Companies need a robust data infrastructure that enables data collection, storage, and analysis. This may involve investing in data warehousing solutions, analytics platforms, and other tools that facilitate data management and processing.
Once the data infrastructure is in place, firms should implement data governance policies and procedures. Governance is crucial for ensuring data accuracy, consistency, and security. Organisations should establish data quality standards and implement processes for data validation, cleansing, and enrichment.
The primary purpose of collecting and storing data assets is to bring transparency into the process and generate insights. Organisations should consider hiring data analysts and data scientists to thoroughly understand the current state of their operations and potentially build predictive tools that can forecast the future needs of the organisation.
The insights and predictions generated are of no use if they are not actionable. SCM leaders should foster collaboration and communication. Building a data-driven culture will encourage organisations to use a single source of truth and communicate their findings, position their viewpoints, quantify risks, and more, using data. Data-driven communication provides inherent transparency and generates collaboration across departments.
Achieving organisation-wide data literacy and establishing a data-driven culture takes time, and organisations can realise value even before completing all the above steps. Building a data-driven culture is an iterative process, and organisations can refer to the stages below to identify their current levels and mature to the next stage.
Different maturity levels in the data culture journey can be characterised as follows:
Exploratory Stage:
As organisations grow, they must recognise the importance of data-driven decision-making. At the initial stage, companies are just starting to grasp the potential of data, with limited infrastructure and analytical capabilities. Investing in data infrastructure and skills should be a top priority for firms in this stage. They must be patient, as results will manifest gradually.
Emerging Stage:
The emerging/developing stage is marked by firms investing in data infrastructure and tools and working on improving data governance and quality. While analytical capabilities are being developed, data silos and a lack of collaboration between departments can still pose challenges. Leaders in this stage should make it mandatory to use common systems and databases and preach the importance of blurring data boundaries between different functions.
Optimised Stage:
The optimised stage is characterised by organisations having a well-defined data strategy, robust infrastructure, and strong analytical capabilities. They prioritise data quality and have implemented data governance policies.
Data-driven decision-making is a core part of the company culture, and employees across the organisation actively use data to inform their decisions. Firms in this stage should identify key challenges that cannot be solved with traditional analytics, develop business cases, and secure funding to build advanced analytics and machine learning-based solutions.
Transformative Stage:
Finally, organisations in this advanced stage have fully embraced a data-first culture, leveraging advanced analytics techniques such as machine learning and artificial intelligence to extract valuable insights from data and drive innovation. These organisations are leveraging advanced analytics techniques to predict future outcomes and optimise their operations. These firms have a competitive edge in their respective markets, and their data-driven approach is instrumental in shaping their future success.
To succeed in today’s market, organisations need a flexible and interdependent supply chain planning process that can quickly adapt to changing conditions. Clear communication and the use of relevant data play a vital role in building such a process.
The author is Analytics and AI leader at Bose Corporation.
