By Simon Hayward, Vice President EMEA at Domo.
Supply chain processes are critically important in today’s complex and highly competitive markets. Organisations need to quickly evolve past traditional linear planning and forecasting, which is slow and unfit for modern customers.
The future is digitisation; creating ecosystems where wholesale suppliers, retailers, customers and organisational units are connected in real-time, through data. And the good news? That doesn’t necessarily mean an overhaul of existing systems. As McKinsey observes: “most of the disparity between potential and actual gains from supply-chain digitisation can be explained by technology gaps and management choices.”
Here are seven operational changes that can help your company take full advantage of digital technologies to optimise your sales and operations planning (S&OP).
#1 Start small: Identify one area for improvement
You can’t create a fully digitised supply chain all at once. To start, map out your current visibility and reporting over each unit, identifying weak spots or areas of greatest friction. Depending on your environment, it could be in one of many different needs such as: streamlined communication between the enterprise and suppliers through real-time data; earlier customer insights for demand planning; or better coordination between sales and manufacturing. Tackle each issue in order of potential impact on your business.
#2 Integrate your systems
Research by McKinsey found that the average supply chain has a digitisation level of 43 percent, the lowest of five business areas examined. And just two percent of the surveyed executives said the supply chain is the focus of their digital strategies.
Why is that such a concern? Because in the supply chain alone, most organisations have tens of thousands of data connections. Bringing them together to create a single, unified source of truth is an all-important step.
The right tool can connect departments, systems, suppliers and the wider organisation to offer a holistic platform for decision making. The key is to ensure the output is in real-time, as the impact on the business will be huge, and consider solutions that sort and auto-tune aggregated data. It will save precious time usually used to crunch the numbers, and transform the way teams operate.
#3 Expand your view with the data
There’s no logic in collating your data to then keep it private, as things will quickly silo out of control. With a single source of data in place, the question becomes how that information is shared across the entire organisation.
Enable widespread access, while maintaining governance, so the right people are consuming the right data. Then go one step further and ensure the interaction is tailored to the person’s preferences and data literacy to help both them and your organisation gain the most value. It becomes intuitive and encourages individual exploration, which then only further improves business performance.
#4 Manage by exception
Research shows that the majority of organisations find teams are restricted by the 80/20 rule of Big Data; 80 percent of time is spent preparing data for analysis, and just 20 percent remains to review and extract value.
This imbalance can lead to more time spent working in the data, rather than working on the business. And in organisations where there is a time sensitive nature to decision making – understanding when perishable goods need to be redeployed or a fast moving good is going to stock out – it is critical to remove that friction of decision making and move to a manage-by-exception approach. Here are three key functions to start with:
- Set tiered alerts with varying thresholds across the reporting lines of the organisation (dependent on level of tolerance and seniority. This creates an early warning as when the more senior team has X% as threshold, but the direct report sets their threshold as X-Y%, the data alerts them before their boss does.
- Identify time or context sensitive processes that require constant monitoring and build a predictive and/or proactive set of metrics to identify when an action should be taken. Distribute that to all staff
- Review frontline staff who naturally do not look at data analysis and equip them with apps or alerts (via text or email). Give them the right actions and insights to deliver great customer service or maximise sales/reduce waste
#5 Let AI and machine learning lighten the load
Machine learning and Artificial Intelligence thrive on big data, so don’t keep them apart. Steps 1-4 will create a huge pool of trusted data which, once in place, you can really start using to your advantage.
There may already be a specialist data science team in your organisation, but with the right tools, data can be shared more widely, and workflows automated. That allows for the easier identification of trends, and will accelerate forecasting. With AI you can create alerts that allow for a more agile way of working – you don’t need people to tell you the brand has been mentioned X% more on social media in the last 24 hours, or that stock is running low.
Predictive analytics are also invaluable. Using both past and real-time data, AI can provide a forward view to help shape your S&OP. It could be as simple as an alert that you are on course to miss a production target, right up to a virtual factory, running multiple models on your systems weeks in advance, allowing you to identify issues before they arise.
#6 Make partners an extension of your team
Get closer to producers, partners and customers to create transparency right across the value chain. Empower everyone to interpret through their own lens, creating two-way data flows and a shared infrastructure between suppliers. That data should feed into your one, central data source, and then through the power of the AI, send automated alerts to keep the customer updated on progress.
Implemented correctly, it will enable the S&OP team to add real value to the customer experience. But it’s also a cultural shift. Providing democratised data creates a new layer of transparency and collaboration, the benefits of which are clear.
#7 Create a self-service culture
A data-driven supply chain needs to be sustainable in the long-term. That often means having a dedicated team to oversee the management of data and systems, but it’s vital you don’t allow that hub to become a bottleneck.
As EY reports: “Today, the sheer volume of data produced by supply chains and their newly formed digital ecosystems is not only overwhelming — it has the potential to harm by adding a counterproductive level of complexity that leads to chaos.”
That applies to even the specialists in your organisation. So instead, create a structure where the team responsible for analytics only produces clearly defined data sets that are real-time, focused, and self-service across the business. Don’t let them create bespoke interfaces for everyone and everything just because it is possible, as that will only add complexity and slow processes back down.