Powering a sustainable, self-thinking manufacturing supply chain with Decision IntelligenceBy Neil Kinnear on March 1, 2022 - 5 Minute Read
For a growing number of organizations operating in the broader manufacturing space, sustainability is fast becoming a central focus.
Businesses are dealing with ever-increasing pressure from investors and the board to take action to ensure they hit the UN’s Sustainable Development Goals (SDGs) by 2030, while still ensuring that they’re maintaining product quality and delivering on their other annual objectives, ranging from hitting their OTIF targets to driving higher ROCE.
With the sector currently gripped by a wide range of challenges, such as a lack of raw material availability and skilled labor shortages, many manufacturing leaders may feel like they’ve got little-to-no time (or budget!) to even begin to consider addressing SDGs.
However, while there is no magical sustainability switch to simply flip, manufacturers can take steps to hit their broader SDGs through a culmination of business-wide initiatives that involve leveraging more value from their number one asset – their data – in different areas of the supply chain. In this article, we take a high-level look at some of the most powerful Decision Intelligence-driven use cases that manufacturers can benefit from, and how a combination of these can enable organizations to power a self-thinking supply chain that supports sustainability whilst delivering against commercial objectives.
What is Decision Intelligence in manufacturing?
Decision Intelligence (DI) is the commercial application of artificial intelligence (AI) to the decision making process. It is outcome focused and must deliver on commercial objectives. As we enter a new era in modern technology – the Intelligence Era – for the first time manufacturers can use the power of DI to make faster, more consistent, data-driven decisions to adapt their businesses in volatile conditions.
Peak believes that Decision Intelligence, labeled as a key strategic trend by Gartner for 2022, will grow to become the biggest B2B software movement for a generation. The likes of Aludium, Speedy and Marshalls are already using it to leverage their data to its full potential, driving outcomes by optimizing aspects of their business like inventory, buying and pricing. Let’s take a closer look at some of these exciting applications of DI and the ways they can benefit manufacturers…
Decision Intelligence, labeled as a key strategic trend by Gartner for 2022, will grow to become the biggest B2B software movement for a generation.
Sales Manager – Manufacturing at Peak
Inventory optimization and distribution resource planning (DRP)
One of the most common pain points across manufacturing’s sub-verticals, from chemicals to automotive to electronics components, will be around their inventory holding; knowing how to prioritize stock and avoiding tying up too much cash. Many organizations find themselves dealing with thousands upon thousands of SKUs, but will sometimes only hold a fraction of this number – basing inventory decisions on the SKUs that will go out of the door the quickest, as opposed to thinking about things more strategically and looking further down the line with an optimized, intelligent forecast.
For many manufacturers exploring Decision Intelligence for the first time, inventory optimization is often the first point of call. It uses AI-powered demand forecasting and bespoke algorithms, tailored to a business’ needs, to better manage inventory levels and allocate stock across the network. It also strongly influences how much manufacturers plan to make, providing an indication of exactly how much they need and where they need it. With a focus on ensuring cost-effective resilience, applying Decision Intelligence to your inventory management leads to increased service and profitability, less waste and a reduced carbon footprint.
Forecasting in any industry is notoriously difficult, and the manufacturing space is no exception. In an ideal world, manufacturers will be able to forecast raw material demand so that, in the event of a shortage or delay, they can be agile enough to pivot marketing activities in a different direction to maintain sales or produce another product entirely. However, this is difficult to achieve while many businesses still rely on small teams who spend their time staring at Excel sheets every day to try and forecast what they should be buying and what’s going to happen, and then feeding this into their buying patterns.
Once a business has utilized inventory optimization to deliver more accurate forecasts, this in turn can influence their buying decisions. With buying optimization, you can connect your buying decisions with an AI-predicted demand and inventory view, receiving always-updated recommendations to help you make the right call every time. Decision Intelligence enables you to ensure cost-effective resilience in supply, taking into account factors like predicted demand whilst also considering any contractual, capacity or supplier constraints.
Pricing optimization is a big talking point in the manufacturing space at the moment. A great example of what’s achievable with Decision Intelligence can be found in our success story with leading UK paving manufacturer and supplier, Marshalls. We are able to increase conversion rates by 6% for their builders’ merchant customers by using DI to automatically adjust the price per SKU based on historical data.
Pricing optimization enables businesses to automate their pricing decisions, connecting with inventory management to gain an understanding of what stock is in the warehouse to clear capacity and reduce waste. Imagine a dashboard providing teams with consistent, instantly-available recommendations providing the perfect pricing sweet spot for every single product or job.
Connecting the dots to drive a sustainable, self-thinking manufacturing supply chain
IBM defines a supply chain control tower as “a connected, personalized dashboard of data, key business metrics and events across the supply chain.” However, achieving this isn’t easy, with 85% of senior supply chain executives struggling with inefficient digital technologies in their supply chains (McKinsey 2020).
For most manufacturers, data is siloed across copious systems and spreadsheets, with a lack of end-to-end visibility causing teams to feel left in the dark from some of the other working aspects of their company’s supply chain. Businesses need a new platform – and their own AI – in order to revolutionize the way certain functions in the supply chain communicate, synchronize and work with each other.
No team has the ability, or the time, to analyze the vast amounts of complex data they need to ensure the wider organization is making the right decisions consistently. A Decision Intelligence platform, though, removes this heavy lifting; feeding insights and data from multiple business systems to surface faster, more accurate, more consistent decisions, all the time.
The examples we’ve highlighted above show how a self-thinking supply chain can work in practice; how, by connecting different elements of your business, you can optimize different aspects of your supply chain to not only improve your day-to-day operations, but also contribute to the bigger picture.
Inventory, buying and pricing optimization can ensure that you hit your objectives for 2022 but, at the same time, are doing your bit to contribute to your SDGs. With better SKU optimization, better demand planning and better DRP you can cut emissions, reduce your stock holding and reduce activity, all while achieving group profitability, maintaining or improving OTIF and ticking off some boxes as we continue to head towards 2030 and beyond.