Never has the issue of supply chain management been so immense
In particular, Covid-19 has made these challenges all the more prominent, with unprecedented pressure on the supply chain after lockdowns and varying restrictions imposed by different countries around the world. Businesses within the supply chain must be resilient and adaptable as the combination of changes that are underway, such as increased globalisation, digitalisation, and driver and other skill shortages, have increased the industry's complexity. While Covid-19 restrictions have eased and many countries are learning to live with the virus, the supply chain crisis isn't going away. Political unrest has hampered the movement of products and services worldwide, notably to and from China and, more recently, Russia.
Artificial intelligence (AI) has been cited as a solution to
some of the problems businesses within the supply chain. Over half (53%) of UK supply chain
decision-makers believe AI advances are crucial to managing
disruption. On the finance side, technologies such as AI are being
used by innovative companies to better understand their capital
through data analytics and performance insights so they can meet
their goals through effective financial management. However, data
and the overarching strategy must be in the right state to
effectively utilise AI, analytics, and data science.
Top three financial management data challenges
- Granular financial management
Calculating important metrics such as cost to serve is vital for any supply chain business. Still, it can be difficult without real-time data visibility across your service, costs, and inventory. Platforms for enterprise resource planning (ERP) and supply chain management (SCM) produce information on point of sale, inventory, manufacturing, warehousing, and transportation. You can optimise your supply chain if you know how to analyse this data, spot patterns, identify trends, and produce insights. By implementing a supply chain data strategy, you can eliminate complex supply chain issues by implementing a plan backed up by accurate financial data.
Supply chain financial planning should include inventory management, asset coordination, supply and demand balance, and monitoring to maximise the distribution of products, services, and information from the point of origin to the customer. However, for many companies, manual processes are still in place and are prone to human error. Without a clear understanding of data that supports business, choices like dynamic pricing, routing, and stock control are impossible to garner. Crucial metrics like net profitability by customer and net profitability by product category cannot be readily ascertained.
- Data integration & data
silos
The use of multiple essential applications is standard practice in logistics businesses, with typical applications including financial planning and analysis (FP&A), delivery planning, warehouse management (WMS), and order management. There are various leadership roles responsible for channels, territories, and products, although traditional monthly management accounts are aggregated at a level above these operational roles at the company P&L level.
Advances in data engineering, data analytics, and data science can enable three additional versions of monthly management accounts based on customers, geography, and products, concurrently with traditional monthly management accounts - or at higher frequencies.
Through this, sales leaders can identify and take immediate action on loss-making customers; territory managers can identify and take action on loss-making locations, and product leaders can also identify and take action on loss-making products. Different leaders need to be able to cut the data so it can be analysed more efficiently.
Siloed applications don't answer cross-cutting questions across different business areas, such as:
- Supply Chain: What costs should be added if large customers demand open-book pricing?
- Order Handling:How do order handling costs affect order net profitability per order?
- Warehouse:How does warehouse activity match planned warehouse activity per pick?
- Delivery:How does delivery activity match
planned delivery activity per location?
- Data sharing across the supply
chain
Within the supply chain industry, it's important to share data with third parties, including partners, suppliers, and customers – quickly, in as near real-time as possible - to make decisions fast.
Data sharing can provide insights and visibility across the chain for all involved in the entire supply and demand journey. It makes planning across the supply chain easier through the use of business intelligence and AI, which can spot anomalies and make predictions of the impact the anomaly might have. This is a hugely valuable opportunity and a challenge, as all parties need to be involved in getting data sharing working across the board. Understanding the logistics journey as it happens has become more vital than ever in the post-Covid climate. A delay in one part of the supply chain can have a significant impact through its knock-on effects. With the right technology in place, it's possible to increase your business intelligence – throughout the supply and demand within the marketplace.
Data and AI in action
AI can be embedded into your data platform – it enables you to use predictive analytics to get better insights into all levels of the supply chain – an improved understanding of demand fluctuations and their effect throughout the supply chain. AI data models can help deliver competitive advantage, improve financials and help businesses gain control across many areas. Implementing a big data platform is critical to get insights in real-time or daily. With so much data at hand, the platform must be scalable to ensure success.
This requires breaking down data silos, joining data across the organisation, and using modern advanced analytics in a performant, scalable, and cost-effective data platform with data governance in place.
With AI and machine learning systems in place, historical data can be used to determine the effects of various occurrences, such as wars, natural disasters, significant price swings, and similar events. Then, based on previous data, they offer predictions about what is likely to occur in the future. These forecasts provide another layer of knowledge, which can help the algorithms find more optimal solutions within the chain management decision-making process.
Logistics companies already have excellent conventional data
warehouses (ERP, WMS, FP&A). But, modern data warehouses have
massively scalable new sources of data and analytic methods, for
example:
- IoT data from sources such as real-time scanning of products, drones for stock-taking (to gain data/ insight from this), and automated warehouses.
- Massive analytic workloads and complex machine learning models.
The effect on energy usage when processes in the supply chain go wrong and are delayed is a new consideration for many businesses operating in the current climate of high energy costs and scarce energy supplies. The embedded carbon in a product can be analysed through environmentally extended input-output (EEIO) analysis, an environmental accounting, production, and consumption structure. As such, it is becoming an essential addition to material flow accounting and, alongside the AI-driven solutions discussed, may help with the challenges faced by businesses within the supply chain.
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.