5 Steps to Successfully Implement a Supply Chain Data Strategy

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Note: This is the 5th article in the 5-part “Supply Chain Data Strategy” series, a plan for empowering supply chain executives on the benefits of data-driven decision-making.

Recap: In our previous article (4th part), we talked about various supply chain data management strategies as well as six useful suggestions for formulating the best supply chain data strategy with examples.

Precap: We’ll go a step further in this article and talk about the five steps to successfully implement a supply chain data strategy and make data-driven decisions in a go.

Check out other parts of the series:

1. What is supply chain data strategy (Part 1)
2. Why you need supply chain data strategy (Part 2)
3. When to implement a supply chain data strategy (Part 3)
4. 6 unsolicited tips to build the best supply chain data strategy (Part 4)

How to Implement Your Supply Chain Data Strategy

It is easier to draft that ‘perfect on paper’ supply chain data strategy than to implement one. Just as enterprises are known to be rich in data and poor in information (intelligence, ideas, insights), supply chain data strategies often remain on paper, with chaos reigning on the ground. So, here is a step-by-step guide to help you carry out the data strategy for the end-to-end supply chain management.

Step 1: Determine Your Supply Chain Data Goal

If your goal is to “get data,” stop right there. If you aim for a large-scale data platform solution that will magically optimize your planning-be it demand, production, material requirements, inventory management, procurement, and logistics – then you are wrong again.

When setting your supply chain data goal, take a long hard look at the problems encountered by your business due to supply chain inefficiencies. Next, ascertain how you can leverage data to solve these issues. Prioritize the supply chain problems that need urgent attention. Among these, pick one to use as a pilot for what will be your long-term supply chain goal. Remember, the success of this will radiate across the organization.

Step 2: Assess Your Supply Chain Data Maturity Level

No one cannot establish a data-backed digital supply chain overnight. It is good to assess where you are in your data journey:

• If your business units are trapped in silos, and you rely on gut feeling to take decisions, then you are a data laggard.
• If your master data is poorly governed and your data warehouse is ill-maintained, you are a data follower.
• If your enterprise has a data culture with clear KPIs and self-service analytics backing business intelligence, then you are at the middle of the data ladder.
• If you use data as a strategic asset, with unified, trustworthy data powering an intelligent supply chain, you are a data leader or a data-native organization.

No matter what your supply chain maturity level is, there is scope for something more. A data leader can even extend data insights and capabilities to come up with innovative AI & ML products that bolster global supply chains.

Step 3: Roll Out an End-to-end Strategy

While you may have a clear idea of where you want to go with data -vision, mission, roadmap et al., it is important to integrate bottom-up people strategies with top-down plans from leadership. A series of workshops should precede the roll-out that helps you create business use cases and project plans. Before starting the implementation, ensure you are up to date on industry developments, regulatory changes, and technology advancements. While you know, you will only do a test case first, always remember the final objective. While most companies work towards efficiency, resilience, and agility, there is growing stress on transparency-led sustainability in supply chains.

Achieving Supply Chain Transparency and Sustainability with Data & Analytics

With real-time analytics, experts can efficiently manage a supply chain to guard against social, environmental, and governance risks. Equally important is the role of an enterprise supply chain in promoting human rights, labor practices, anti-corruption policies, and environmental sustainability.

Supply chain sustainability is adversely affected by:

• Greenhouse gas emissions
• Toxic, hazardous waste emissions
• Deforestation
• Loss of biodiversity
• Water pollution
• High energy consumption

You can employ metrics in core business processes that measure the above in upstream and downstream supply chains to capture financial, social, and environmental data. Business leaders are also opting for data-based monitoring and validation mechanisms across the value chain to pick and choose sustainability-focused suppliers and partners.

As governments put in place supply chain disclosures and obligations, companies are quickly adopting traceability technology to ensure visibility across the supply chain. Undoubtedly helps to prevent product diversion, black and gray market sales, polluting production processes, and other unsustainable practices.

To ensure supply chain transparency, also make sure your data aids:

• Visibility: Identification and collection of accurate data from each link in the supply chain.
• Disclosure: Internal and external communication of the data obtained at the level of detail mandated. According to IDC, by 2025, to improve long-term supply chain profitability, 60% of manufacturers in global supply chains will invest in software tools to support sustainability and circular economy business models.

So, plan and ensure supply chain sustainability by following:

• Circular model: Move from the linear supply chain model to a circular supply chain model where make is followed by maintain, reuse, remanufacture, recycle, or recover.
• Innovation route: Create new sustainable products that meet evolving consumer and business stakeholder demands.

Step 4: Piloting the First Data Project

You can try and test a supply chain data strategy on a pilot project to reduce risk. The success or failure of the project will help your team understand how to use data. Also, you can replicate this framework for data implementation across a portfolio of projects to enable a strong digital supply chain.

Step 5: Measure Outcome, Improve and Scale

The lessons learned while implementing the pilot will tell you what improvements you need to continue with your strategy rollout. Once you are confident that the implementation is going right and you can achieve your organizational goal, you can begin to scale the data transformation project across your entire supply chain.

If you need expert help with identifying your supply chain data goals and supply chain data maturity level, reach out to Gramener. We help organizations by carrying out a scientific data maturity level assessment, conducting training and workshops to inculcate the data mindset, crafting fool-proof supply chain data strategies with a domain-specific approach. We also run pilots and stay on to provide data analytics and advisory services till you gain confidence as a supply chain data leader.