Within businesses, supply chain professionals are under constant pressure to increase efficiency. Customer expectations are at an all-time high and competition is increasing steadily.
At the same time, the amount of available data is growing. Companies are bringing together order information, customer requests and thousands of different configurations of products, services and bundled deals. Getting this right is not easy, even for firms with well-established procurement and supply management processes.
Any ability to show where improvements can be made – and give organisations a competitive advantage – is attracting serious attention. It should therefore be no surprise that data analytics has become vital to the supply chain. Among its benefits, supply chain analytics can help companies save thousands of pounds, improve their procurement efficiency, and support overall business growth.
However, this potentially valuable data is currently handled in inefficient ways, with companies often using multiple spreadsheets and other analytic tools across many departments. With so many views of the same data, supply chain managers often lack trusted data on which they can make timely decisions.
This can be resolved by automating the refinement of these multiple data sources. By rapidly bringing together data from across the supply chain, it is possible to gain real-time visibility into data such as sales forecasts, bookings, shipments, tracking numbers, backlog, product reliability, and inventory, and make better decisions on what product to ship to which customer at which time and what price. More importantly, each team within the business can better collaborate, in a trusted manner, with other groups working across the supply chain.
Getting to business context
The cloud’s potential to transform operations is leading to a step change in the way things are done. It is opening our eyes to the future of supply chain analytics, as it links up with departments such as sales. This offers the opportunity to spot ‘best customer behaviour,’ as well as improve interaction with customers.
This involves having a joined-up, ‘networked’ approach to data, rather than relying on each department or individual running their own apps and spreadsheets. Instead, data from supply chain and procurement systems can be linked with other business data sources, such as sales and operations data. With this data network in place, the impact of supply chain performance on sales and customer retention can be directly modelled. Similarly, improvements in performance can be linked to increased sales or greater profitability.
When planning ahead, firms can use supply chain analytics to gain business advantage in competitive scenarios. If companies can view data in context, they can make smarter decisions, resulting in improved delivery times and reduced obsolete inventory.
Real-time intelligence into deliveries, orders and returns, as products move through the supply chain, will also see huge efficiency gains. Using dynamic, real-time visualisations of data, organisations can report on items that customers are sensitive to, and this insight can be used to spot bottlenecks and lead to an increase in inventory turns.
This use of data can also increase overall product margins by leveraging predictive models to anticipate which products will be needed, when and where. Better still, a uniform view across departments eliminates conflicts over who has the right numbers.
A networked way of looking at the process enables the supply chain to give more back to the business. Other parts of the organisation can benefit from this increased access to data; for example, the marketing team would probably never think to look at supply chain information for insight into how customers are thinking or reacting, but by networking multiple sources of data, marketing can make use of supply chain data in the context of their own objectives and results. This can then be used to make better decisions around future product, service or bundle offers.
Improving supply chain and procurement processes – in concert with wider business management thinking and other sources of data – leads to huge gains in profitability and decision-making. Using data, firms can make value-driven decisions with real-time visibility into all data across the supply chain and beyond.Con
Linking data and the supply chain – a case study
Sunny Delight Beverages Company managed data externally separately from the company’s ERP system. This led to potential issues around managing the company’s supply chain analytics, particularly when it came to fluctuating product order volumes. As an example, it was more difficult to link marketing promotions to increased order volume; this in turn made it difficult to establish accurate ROI metrics. Everything from marketing to manufacturing data through to inventory and transportation information was held in separate silos and required much manual labour to bring it all together.
The company decided to look at how it could use analytics to network its data. By linking information from multiple systems together, the firm could see how decisions in marketing affected demand as well as where potential costs due to increased supply chain operations could be avoided.
At the same time, the team improved customer profitability for each account. This included providing both marketing and supply chain teams with analytics on promotions campaigns, so that both sides could see the business impact.
By getting more insight into planned promotions, the supply chain team created new business arrangements with wholesale customers in advance. This meant the company could reduce overtime costs for manufacturing and shipping products more effectively around each campaign. Using analytics in this way and making these changes helped the company realise $1 million in annual savings related to production planning and transportation.
The preceding article was originally published in Spend Matters UK/Europe on November 5, 2015.