Right now, everyone is looking for some way to offset the pain of inflation.
At the start of 2023, 71% of all global companies identified raw material costs as their most urgent supply-chain related threat.
For retailers, there’s not much any one business can do to stop their costs going up. As long as costs do keep rising, passing them onto the consumer will be fraught with danger. To avoid sacrificing competitiveness by overpricing, it’s best to balance out any inevitable price rises by making efficiencies elsewhere.
But shrinking your expenses shouldn’t mean compromising on quality. By getting more acquainted with the ins and outs of your logistics, you can create a system that finds and eliminates waste all by itself, without ever affecting your service levels.
That’s the key promise of a data-driven supply chain.
WHAT IS A DATA-DRIVEN SUPPLY CHAIN?
It’s a logistics system set up to collect and analyse data on itself. In its ideal form, it allows retailers to make decisions based on richly textured datasets that encompass each stage of every product’s journey. The data-driven logistician will also be well-furnished with forecasts and automated analytics that can guide future planning.
Let’s break down the essential parts of the data-driven supply chain, and how retailers can make it a reality.
1. WELL-DEFINED OUTCOMES
For everyone apart from the very smallest boutiques, retail supply chains create head-spinningly large and variegated datasets. Collecting, moving and processing all this information is an unavoidably mammoth undertaking.
Before following the rest of the steps we outline below, it’s important to fully define your intended purposes behind each project. Specifically, what business benefits do you hope that data will unlock?
Starting with a set of clearly-defined, discrete outcomes will save you a lot of resources. Instead of taking the operational risks of processing all your data at once, you should segment your activities based on what they will achieve. This might mean repeating workflows for different data fields.
Ownership is vital here, so be sure to divide the work between handpicked teams. The right people with the right knowledge will deliver the best outcomes.
When it comes to data, a trickle is more manageable than a big bang. Remember that basic principle when approaching each of the stages below.
2. A SUPPLY CHAIN MAP
Before you can evolve your supply chain, you need to know its constituent parts. Supply chains can be long and complex, so it helps to break them down into two core components:
- An entity is you and anyone you do business with in the flow of goods. This stretches from raw material producers to your wholesalers and all the way along to your customers.
- These are the processes that glue the entities together and facilitate the onward progression of goods. It might be a truck journey, or just an email.
Identify as many instances as possible of these categories in your own chain, and their relative costs, risks and values. Don’t forget to identify points of contact!
Plotting this all out will reveal aspects of your supply chain that could be working harder. It should also call your attention to any black boxes along the way.
3. INTERNAL DATA AUDIT
There’s almost certainly a wealth of data about your supply chain that you didn’t even know you owned.
Are you familiar with your return rate for products? Or the annual spikes in your website traffic? How about your latest social media marketing campaign’s reach?
All this information might seem at a distance from your logistics proper. Yet it all impacts your order book.
Some de-siloing will clarify your supply chain’s changing requirements. Reach out to your different departments to build up a list of all the data that is currently available to you. This audit will help you identify the strengths and weaknesses of your data collection processes, as well as any gaps in your data.
4. AUGMENTED DATA COLLECTION
Once you’ve wrung out your relevant existing data, it’s time to make sure you’ve got a steady stream coming through to replenish it. Retailers and D2C companies alike should beef up their capacity for acquiring data on each of their business priorities, at every level of operations.
Digital marketing and sales metrics provide the raw material for demand forecasting. IoT-enabled devices like beacons can make shopfloors and warehouses smarter, while intelligent packaging keeps track of products’ conditions as they pass towards the last mile.
When mapping, you may have found that some external entities and functions are more opaque than others. This calls for cultivating closer relationships with partners further down the supply chain. For smaller retailers and D2C brands who outsource their warehousing and logistics, smooth B2B data sharing is particularly crucial.
5. CLOUD-NATIVE DATA PIPELINE
Supply chains produce immensities of information. As goods are assembled, shipped, handled, stored, stocked and sold, the number of data points grows almost exponentially. All this data has to be processed, stored and shared between many stakeholders. That takes a seriously robust data pipeline.
Cloud-based platforms are best placed to handle the quantities of information typical to retail supply chains while still providing scalability. Keeping your data in cloud-hosted warehouses and marts will also make it more shareable.
6. DATA CLEANSING
Making decisions based on corrupted data is arguably worse than acting on intuition. Retailers can safeguard their reputations by data cleansing. This is the process of identifying and correcting inaccuracies in your data.
Typos, formatting issues and duplicated information might sound small fry. But little flaws in the wrong places can render whole datasets unusable.
A range of tools exist to automate this process, from in-built features in CRMs like Salesforce to specialised scrubbing tools.
With your data spick and span, it’s time to find out what patterns, trends and anomalies it contains.
With so much data, you’ll need to approach this strategically. Gnomic as it sounds, the first step is knowing what you need to know. You might not, for instance, need a graph of your warehousing partner’s ESG rating over time. But you will probably want to know their rate of on-time fulfilments.
Data analytics is a broad church, spanning tools from good old Microsoft Excel to the rarefied software used in scientific research. You probably already deploy some of these tools to monitor KPIs like revenue and sales. Let’s look at how you can harness two emerging analytics technologies and become truly data-driven.
Machine Learning (ML) and AI
- AI/ML tools automate the hard work of identifying correlations and causal relationships in your data. But the real beauty comes in their predictive abilities. By analysing past data, Machine Learning can create actionable forecasts for the near and medium terms.
- This new technology takes the potentials of AI/ML up another level by creating a virtual model of your physical supply chain. This allows you to safely stress-test your operational resilience by simulating different scenarios. You can also try out different strategies to optimise your logistics, risk-free.
8. CUSTOMER RELATIONSHIP MANAGEMENT
A data driven supply chain is incomplete without insights into your relationship with your customer base. After all, your value chain should be calibrated to benefit one group of people above all: your customers. Today’s breed of CRM tools allow you to monitor your customers’ expectations, levels of satisfaction, retention rates and complaints.
This data can then be fed back to the start to optimise your supply chain. When all these steps are followed properly, a virtuous cycle is created. Efficiencies provide the headroom for growth. With greater scale comes more data, and, well, you can work out the rest! Calibrating your system to become self-optimising will help you weather the storm of rising prices.
Want to know more about the intelligent systems redefining global supply chains? Download our new whitepaper – Technology Priorities for a More Resilient Supply chain – here.