Note: This is the 2nd 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 (1st part) we talked about the fundamentals of data science and how a supply chain organization can benefit from it. We discussed what a robust supply chain data strategy is and how to design, plan and execute it.
Precap: In this article, we’ll move a step ahead and talk about why you need a supply chain data strategy and the consequences for not having a strategy in place.
Later, we’ll share an intimate case study where we helped a retail supply chain leader identify bottlenecks and reduce their shipment duration by 50%.
Check out other parts of the series:
- What is supply chain data strategy (Part 1)
- When to implement a supply chain data strategy (Part 3)
- 6 unsolicited tips to build the best supply chain data strategy (Part 4)
- How to implement your supply chain data strategy (Part 5)
For every enterprise today, the opportunity is real – devise a supply chain data strategy and use advanced supply chain analytics to outperform the competition. When business models are being redefined based on Data-Driven Decision-Making (DDDM) and actionable insights, every company needs to identify the right data sources and commit to a data view of the business.
A good strategy will equip you to make the right interpretations of available data and differentiate yourself from the competition in the industry.
Read the 1st part of supply chain data strategy series and find out what it is and how it is mapped to your business RoI!
Table of Contents
Developing Data Capabilities with Supply Chain Data Strategy
It is not enough to have data; you need the right capabilities to put that data to use and improve supply chain efficiency. When you capture, collect and store data needed to improve supply chain efficiency, you also need capabilities in data parsing and drawing data insights. Data value extraction is possible only with the right blend of data, business, and technical knowledge and skills. When done with the intent of maximizing business impact, it helps leadership leverage actionable insights for enhancing supply chain efficiency.
Let us take a look at a fictitious scenario involving a data novice (an enterprise without a data approach to supply chain management).
When a retailer faces operational bottlenecks, supply chain efficiency is compromised. What is its immediate need in this case? Visibility! Or complete information on what is happening while transporting goods at various touchpoints in the supply chain. These touchpoints include manufacturers, suppliers, vendor partners, distribution centers, warehouses, or stores.
Without a mature supply chain data strategy, the company will:
- Lack the systems to capture data
- Not have collected helpful data
- Not have data in a functional form
- Lack the technology to store, access, and analyze data
- Lack the capabilities to run databases and other data technologies and tools
- Lack the capabilities to develop actionable data-based insights
The lack of a data and analytics strategy has been cited as a “significant obstacle to success” in a recent McKinsey survey while creating one is also the foremost challenge.
Supply Chain Data Excellence Amplifies Team Efforts
Without a supply chain data strategy, our fictitious retailer would also lack a full-fledged team of data scientists, business analysts, technical professionals, and leadership. As a result, the company will be unable to:
- Have ready access to in-house expertise in data analytics
- Make statistical correlations, and track quality parameters
- Identify the business relevance of data patterns and anomalies
- Prioritize issues that need urgent attention
- Process data due to lack of hardware and software skills
- Adopt agile data-driven decision-making (DDDM) approach to improve business performance – including changes in processes or models, enhancements to products or services, or building new ones.
Had the retailer adopted a supply chain data strategy, the operational bottlenecks would have been identified in advance, and remedial measures would have been taken.
A supply chain data strategy makes way for a robust supply chain with the following attributes:
- Efficiency: Through data maturity and a data-savvy team
- Visibility: Through track and trace technologies across the logistics trail
- Transparency: Through disclosure of sourcing, labor, and environmental practices
- Sufficiency: Through real-time inventory management that balances demand & supply
- Reliability: Through smart warehouse operations that reduce human error
- Resiliency: Through an optimal mix of people, processes, and technologies
Learn more about the benefits of AI in supply chain and how AI/ML systems can leverage data to produce insightful outcomes.
Hiring Supply Chain Data Experts for Implementing Supply Chain Data Strategy
Coming back to the case of the retailer. Not every enterprise has to set up an internal infrastructure to support its supply chain data strategy. At Gramener, we help companies of all sizes with our data advisory services, production performance improvement, warehouse optimization, and supply chain visibility. Reach out to us if you are data-driven to help you improve your supply chain efficiency.
Gramener Helped a Retail Supply Chain Leader
A retail supply chain leader approached us to identify their supply chain bottlenecks. We quickly stepped up to the situation and produced a complex supply chain simulation, complete with an analysis of transit value, transit time, and product type. Watch this video for details.
It was possible to pinpoint the bottleneck across one strand of the supply chain -where the delay was more than 200% of the shipment duration. Immediate corrective steps were taken that led to a 50% reduction in stock over risk to boost the supply chain efficiency.
In addition to the above,
- Calculation of cost of goods sold helped to determine profitability and efficiency
- Identification of supply chain hot spots helped to prioritize interventions
- Linking the findings to improvements in the logistics scheduling system helped to smoothen deliveries.
Improvement in supply chain efficiency was possible as the retailer supplemented its supply chain data strategy with the right people, processes, and tools. It helped to understand precisely what kind of data value extraction was needed and who to reach out to for actionable insights. The retailer was thus able to improve the data-driven decision-making for supply chain resilience, leveraging the already activated digital information trail along the supply chain.
If you are facing operational bottlenecks or other supply chain management issues, it is time to adopt a data-driven approach. Contact us for custom built low code data and AI solutions for your business challenges and check out supply chain AI solutions built for our clients, including Fortune 500 companies. Book a free demo right now.
