To achieve true end-to-end supply chain integration, retailers must be able to quickly and effectively make decisions in a rapidly changing environment, leveraging data and information from a number of functional areas, such as merchandisers, store operations, inventory management, suppliers, customer service, and warehouse operations. However, integrating data across the supply chain can take a concerted effort, as retailers need to overcome the data siloes that are often created between supply chain functions.  

In this piece, we will consider how retailers can leverage integrated data analytics to bridge retail supply chain gaps and address data siloes, including how they can realize the benefits of data integration and ultimately achieve greater end-to-end integration.   

Enabling More Integrated Data Analytics 

To better understand the data siloes that often occur in the retail supply chain, we could start by simply considering how each individual supply chain team might already have their own specialized software to help achieve their goals: solutions for Point of Sale, Retail Assortment Management, Warehouse Management, and Customer Relationship Management, to name a few.  These systems can deliver great value but can also create challenges when a retail organization wants to leverage the data across their systems, including the ERP system, to increase the resiliency and responsiveness of their supply chain. 

Implementing an integrated data and analytics landscape is a key enabler for running an integrated, physical supply chain. From strengthening data foundations and integrating the various datasets into a robust data model that enables interconnected data analytics, to creating a more robust supply chain digital twin, today’s retailers must actively work to integrate their end-to-end supply chain data and analytics. Below are a few examples of how this first approach of enabling more integrated data analytics could benefit a retail supply chain: 

1. Inventory responsiveness: Let’s say a product starts flying off the shelves, generating sales data in the Point of Sale (POS) system. An integrated data landscape could allow the inventory team to promptly see and evaluate the impact of this increased rate of sale on their inventory plans.  With this additional time and integrated visibility, the distribution team has a higher likelihood of being able to allocate inventory to that store more promptly, and the inventory planner could review their inventory buys to evaluate if their plans are sufficient for the current and forecasted sales. This efficiency and time saved could directly result in better inventory levels in stores and higher customer satisfaction.
2. Mitigating a production shortfall: If a supplier will not be able to meet their production plan or delivery date, timely visibility of that shortfall by the centralized inventory team would enable quicker prioritization decisions of the available inventory, allowing time to better mitigate the shortfall’s impact to customers, both in stores and online.
3. Merchandising line changes: Another use case is for planning new product launches, where a more integrated supply chain data landscape could facilitate a more prompt, efficient, and consistent response to changes during the merchandise planning process. For example, if the merchandise planning team would like to change the date a new product is intended to be in stores, a more integrated data landscape could facilitate a timely communication of that change to the inventory and store operations teams. The inventory team could then promptly review their forecast and purchase orders in the system, updating them as needed to reflect the new launch date – an update that could then be communicated to store operations, who might need to shift their planned store communications and upcoming floor plan changes.

The sooner that the inventory and store operations teams are aware of the change and can see it in their respective system(s), the more effective and timely their responses could be. Similarly, this change in launch date could also impact the warehouse and logistics teams, and with more efficient and quick updates to their forecasts, these teams are better able to mitigate any potential impacts of this change. 

Simply put, across the plan-source-make-deliver SCOR Model, the interconnectivity of data and analytics would allow retail supply chain teams to see the impact of their and other teams’ decisions more directly and more quickly, increasing their effectiveness, responsiveness, and resiliency. 

Getting Started: Creating an Integrated Data Analytics Landscape 

For retail supply chains looking to build a more integrated data analytics landscape, we recommend a few steps to get started:  

1. Conduct an assessment of your data landscape. Where does the data reside and what are the connection points across the data?  These connection points could be elements of data, likely domain data, that are connected between the datasets, such as product, customer, supplier, etc. 
2. Conduct a gap analysis. Given the data landscape and the necessary connection points, what needs to be true to cohesively connect the data across the teams and systems?  This gap analysis should include technical infrastructure, business processes, data management, and data availability components. 
3. Create a plan to close the gap.  This is where planning, prioritization, and cross-functional alignment between teams – especially the IT team – are critical. As you prioritize, focus on areas that have the highest benefit to your business and are easiest to implement; if no projects fall along those parameters, then maybe first pursue the projects that can allow you to achieve some ‘quick wins’ and momentum. This may enable subsequent approval for some of those bigger, more value-added projects that could deliver notable positive impacts to your supply chain and ultimately to your customers…because that’s what it’s all about in the end! 
4. Gain approval and implement the plan. Now that you have a plan, get the needed alignment from your key stakeholders, across both business and IT, and then proceed with implementation. It’s good to keep in mind that as you implement the projects as a collaborative team, there will need to be some flexibility, too, to allow the plan to shift and evolve as you learn and collect new input from the project.  Receiving and incorporating this feedback is also important for achieving successful results and maintaining positive momentum for the duration of the transformation. 

Looking Ahead 

As you evaluate your supply chain integration opportunities, consider how integrated data analytics could be leveraged to help your supply chain deliver better customer service, both in stores and online directly to customers. This data analytics integration across supply chain functions could be a key enabler to unlock more flexibility and resilience in your supply chain. 

Reach out to us today to learn more about our supply chain analytics consulting services.