Note: This is the 4th article in the 5-part “Supply Chain Data Strategy” series, an initiative to aware supply chain leaders about the power of data-driven decision making.
Recap: In our previous article (3rd part) we discussed how to use predictive data analytics to strengthen the supply chain and when it’s appropriate to put your supply chain data strategy into action.
Precap: This article will discuss various approaches to supply chain data management as well as six practical recommendations for creating the best supply chain data strategy.
Check out other parts of the series:
- What is supply chain data strategy (Part 1)
- Why you need supply chain data strategy (Part 2)
- When to implement a supply chain data strategy (Part 3)
- How to implement your supply chain data strategy (Part 5)
Table of Contents
Why Do Supply Chains Fail?
In 2013, Target launched 133 stores all over Canada. Two years later, it was forced to shut shop, suffering around $2 Bn in losses & resulting in more than 17,000 Canadians losing their jobs.
Why?
Target’s supply chain failed to deliver in Canada. With distribution centers needing to be more knowledgeable about the inventory levels at individual stores, shelf-stockers were left guessing what the daily deliveries would bring.
As a result, this led to a loss of customer reputation, culminating in irreversible damage to the business.
You can have the data and the latest industry technologies to power your supply chain, but you need a supply chain data management strategy to avoid problems.
Therefore, the data in supply chain management needs to be handled with a well-planned and multi-tiered approach to deliver business benefits. Unless there is cross-functional participation within the organization, it is difficult to capture the following tactical and operational facets of a supply chain data management strategy:
- A short-term and long-term roadmap
- Organizational priorities and goals
- Recalibration of work processes
- Choice of technology, tools, and techniques
- Data governance framework
- Upskill training to foster digital literacy
- Financing digital transformation
- Change management, particularly leadership mindset and work culture
Defensive and Offensive Approach to Data Management in Supply Chain
Organizations are using more data than ever in their supply chains. In 2017 alone, a typical supply chain accessed 50 times more data than it did five years earlier.
Adopting a data-backed supply chain strategy should involve the right mix of defensive and offensive elements. In addition to data governance and compliance, key objectives of a defensive approach include data security, privacy, integrity, and quality.
Strengthening the competitive position and profitability of an enterprise would be the primary intent of an offensive strategy, meaning it would include pliable policies.
Therefore, with a single source of truth as the enabling architecture, a defensive data management in supply chain strategy would focus on optimizing data extraction, standardization, storage, and access.
On the other hand, an offensive data strategy would accommodate multiple versions of the truth. It would entail data analytics, modeling, visualization, transformation, and enrichment optimization. This will empower each department to use the original raw data imbued with relevant insights to attain another identity.
In recent times, especially in the aftermath of the COVID-19 pandemic, organizations have realized that more is needed to use data for descriptive (what is happening -dashboards) and diagnostic (why did it happen -root cause analyses) purposes alone. It is also important to choose supply chain data management analytics with the following attributes:
- Predictive and preventive – What will happen? Examples include demand forecast and fraud reduction
- Precise and responsive – What do consumers want? This includes using data for collaboration and alignment purposes
- Prescriptive and agile – What to do? – This includes specific recommendations, faster decision-making, etc.
- Cognitive and contextual – What a human may do? – For instance, AI, robotic process automation
6 Tips to Model the Best Supply Chain Data Strategy
Based on these principles, you can model your supply chain data strategy in six ways:
Make it Efficient
Based on pricing, this strategy will succeed if the order fulfillment is exemplary. Businesses that schedule production based on the expected sales for the length of the production cycle adopts it. Using data should ensure high forecast accuracy and guarantee product availability. Optimal asset utilization and maximized equipment efficiency lead to cost reduction, thus making the supply chain truly efficient.
Examples: Cement, steel, etc.
It Should be Fast
Dependent on a frequently updated portfolio of new and affordable products, this strategy will work if the product lifecycle is short. Therefore, with correct predictions on market trends, a “make to forecast” approach, and a short time from idea to market, the supply chain remains fast, and the price for consumers remains inexpensive. Data analytics that maximizes forecast accuracy is mandatory to limit market mediation costs and develop a highly synchronized sales and operations planning process.
Examples: Trendy apparel, catalog shopping, etc.
Have a Continuous Flow of Products
Serving a mature supply chain with steady customer demand, this strategy will suit a constant flow of products with a short shelf-life. So with low inventory and high service levels at the customers’ facilities, costs can be optimized through collaboration with customers with demand variability. Moreover, data management should enable the processes to be scheduled in such a way as to replenish pre-defined stock levels triggered by a pre-defined reorder point for inventory in the production cycle.
Examples: Dairy and bread products, etc.
Bring in Agility
Offering on-demand products with an ability to meet unpredictable demand, this strategy helps manufacture products that satisfy different specifications by customers. Furthermore, the design of materials and components used by many products and processes that can produce small batches will help to maintain the extra capacity needed for agility. Also, users should employ data-enriched insights to ensure short lead times and to maintain inventory.
Examples: Packaging, specialty chemicals
Keep it Flexible
Meeting sudden and unexpected demands, often in emergencies, this strategy is best for problem-solving with products tailored to suit the customer’s specific requirements. The adaptability of internal processes and superior technical strengths will come in handy, along with a high degree of flexibility to join with suppliers and sometimes even competitors. Moreover, data-backed industry intelligence should be tapped to keep critical sources ready and develop rapid response capabilities.
Examples: Industrial spare parts, metal machinery, etc.
Use Analytics for Custom-configuration
A blend of efficient, continuous flow and agile models, this supply chain strategy provides products by mounting or assembling parts in a limited choice of combinations. An accurate order entry system and assured availability of configurable features are central to satisfying end-users’ needs. Furthermore, predictive analytics can reduce complexity by determining maximum configurations, minimum materials, or components required and maintaining a finished goods inventory of the most popular configurations.
Examples: Cars, computers, etc.
In conclusion, it is best to employ a data strategy that maximizes the value of the supply chain and generates profits. Make it efficient enough to fetch more revenue from customers to make up for the costs of the supply chain. If you need expert help, do not hesitate to contact Gramener today.
