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Supply Chain Analytics
Supply Chain Analytics
Executive Sponsorship
Leadership must visibly support the effort.
Dedicated Resources
Companies must dedicate appropriate project leadership and full-time staff.
Standardization and Simplification
Processes (such as onboarding or day-to-day usage) must be standardized.
Collaboration
Transformation must involve the entire organization and be cross-function.
Innovation Ecosystem
Sustainable adoption of the business intelligence and analytics platforms.
Pilot Implementation
Piloting the program to focus on a subset of problems provides lessons.
What is a Supply Chain?
A supply chain is a network between a company and its
suppliers to produce and distribute a specific product to
the final buyer. This network includes different activities, people, entities, information, and resources.
The supply chain also represents the steps it takes to get the product or service from its original state to the customer.
Value Chain vs Supply Chain: An Overview
The term value chain refers to the process in which businesses receive raw materials, add value to them through production, manufacturing, and other processes to create a finished product, and then sell the finished product to consumers. A supply chain represents the steps it takes to get the product or service to the customer, often dealing with OEM and aftermarket parts.
While a supply chain involves all parties in fulfilling a customer request and leading to customer satisfaction, a value chain is a set of interrelated activities a company uses to create a competitive advantage.
What Is Supply Chain Management (SCM)?
Supply chain management is the management of the flow of goods and services and includes all processes that transform raw materials into final products. It involves the active streamlining of a business’s supply-side activities to maximize customer value and gain a competitive advantage in the marketplace.
SCM represents an effort by suppliers to develop and implement supply chains that are as efficient and economical as possible. Supply chains cover everything from production to product development to the information systems needed to direct these undertakings.
Your supply chain is made up of more than just the sum of its parts. The people behind it, including fellow employees, suppliers, contractors, partners, and customers, are all part of your success. But it takes everyone being on the same page, working toward the same corporate goals, to get there. Silos, conflicting metrics, lack of visibility, and poor communication are all hurdles today’s supply chains have to leap over. Improving collaboration, having a central corporate vision, and implementing change management strategies are key.
The supply chain industry’s beginning of operations research, as well as industrial engineering, started with logistics. Fredrick Taylor, the founder of industrial engineering who wrote The Principles of Scientific Management in 1911, targeted the process improvement of manual loading in his work. Operations Research of analytics’ value started during WWII for logistical military operational solutions in the 1940s. Industrial Engineering and Operations Research often attempted to function as separate identities and by using integrated frameworks to address supply chain and logistics issues they have had successes. The industry has begun titling this integration “Supply Chain Engineering.”
Supply Chain Management’s Beginnings
II. Historical Evolution of the Supply Chain
Why is Supply Chain Analytics Important?
Supply chain analytics can help an organization make smarter, quicker, and more efficient decisions.
- Gain a significant return on investment. A recent Gartner survey revealed that 29 percent of surveyed organizations said they have achieved high levels of ROI by using analytics, compared with only four percent that achieved no ROI.
- Better understand risks. Supply chain analytics can identify known risks and help to predict future risks by spotting patterns and trends throughout the supply chain.
- Increase accuracy in planning. By analyzing customer data, supply chain analytics can help a business better predict future demand. It helps an organization decide what products can be minimized when they become less profitable or understand what customer needs will be after the initial order.
- Achieve the lean supply chain. Companies can use supply chain analytics to monitor warehouse, partner responses, and customer needs for better-informed decisions.
- Prepare for the future. Companies are now offering advanced analytics for supply chain management. Advanced analytics can process both structured and unstructured data to give organizations an edge to get alerts on time to make optimal decisions. It can build correlation and patterns among different sources to provide alerts that minimize risks at little cost and less sustainability impact.
As technologies such as AI become more commonplace in supply chain analytics, companies may see an explosion of further benefits. Information not previously processed because of the limitations of analyzing natural language data can now be analyzed in real-time. The challenge today is how companies can best use the huge amounts of data generated in their supply chain networks. As recently as 2019, a typical supply chain accessed 60 times more data than just five years earlier.
1. However, less than a quarter of this data was being analyzed. Further, while approximately 30 percent of all supply chain data is structured and can be easily analyzed, 70 percent of supply chain data is unstructured or dark data. 2. Today’s organizations are looking for ways to best analyze this dark data.
1. However, less than a quarter of this data was being analyzed. Further, while approximately 30 percent of all supply chain data is structured and can be easily analyzed, 70 percent of supply chain data is unstructured or dark data. 2. Today’s organizations are looking for ways to best analyze this dark data.
Key Features Of Effective Supply Chain Analytics
IDC’s Simon Ellis in The Thinking Supply Chain identifies the five “Cs” of the effective supply chain analytics of the future.
- Connected. Being able to access unstructured data from social media, structured data from the Internet of Things (IoT) and more traditional data sets available through traditional ERP and B2B integration tools.
- Collaborative. Improving collaboration with suppliers increasingly means the use of cloud-based commerce networks to enable multi-enterprise collaboration and engagement.
- Cyber-aware. The supply chain must harden its systems from cyber-intrusions and hacks, which should be an enterprise-wide concern.
- Cognitively enabled. The AI platform becomes the modern supply chain’s control tower by collating, coordinating, and conducting decisions and actions across the chain. Most of the supply chain is automated and self-learning.
- Comprehensive. Analytics capabilities must be scaled with data in real-time. Insights will be comprehensive and fast. Latency is unacceptable in the supply chain of the future.
Supply Chain Management Tools
- Shipping Status Tools
- Order Processing Tools
- Lean Inventory Tools
- Warehouse Management Tools
- Supplier Management Tools
- Demand Forecasting Tools
- Analytics and Reports Tools
- Security Features Tools
Top 8 Supply Chain Management Software Leaders
- SAP Supply Chain
- Oracle SCM Cloud
- Epicor SCM
- Infor Nexus
- Infor Supply Chain Management
- Sage Business Cloud X3
- Manhattan Active Supply Chain
- Blue Yonder
Covid-19: Managing Supply Chain Risk and Disruption
Could COVID-19 be the black swan event that finally forces many companies, and entire industries, to rethink and transform their global supply chain model?
One fact is beyond doubt: It has already exposed the vulnerabilities of many organizations, especially those who have a high dependence on China to fulfill their need for raw materials or finished products.
China’s dominant role as the “world’s factory” means that any major disruption puts global supply chains at risk. Highlighting this is the fact that more than 200 of the Fortune Global 500 firms have a presence in Wuhan, the highly industrialized province where the outbreak originated, and which has been hardest hit. Companies whose supply chain is reliant on Tier 1 (direct) or Tier 2 (secondary) suppliers in China are likely to experience significant disruption, even if, according to the most optimistic reports, conditions approach normalcy in China by June.
How can organizations respond to immediate change?
As the COVID-19 threat spreads, here are measures companies can take to protect their supply chain operations:
For companies that operate or have business relationships in China and other impacted countries, steps may include:
- Educate employees on COVID-19 symptoms and prevention
- Reinforce screening protocols
- Prepare for increased absenteeism
- Restrict non-essential travel and promote flexible working arrangements
- Align IT systems and support to evolving work requirements
- Prepare succession plans for key executive positions
- Focus on cash flow
For companies that produce, distribute, or source from suppliers in China and other impacted countries, steps may include:
- Enhance focus on workforce/labor planning
- Focus on Tier 1 supplier risk
- Illuminate the extended supply network
- Understand and activate alternate sources of supply
- Update inventory policy and planning parameters
- Enhance inbound materials visibility
- Prepare for plant closures
- Focus on production scheduling agility
- Evaluate alternative outbound logistics options and secure capacity
- Conduct global scenario planning
Covid-19 Is Testing Supply Chains Like No Crisis In Recent History
“We have to make decisions with 50% data and the other 50% directed by our purpose and values.” — Supply Chain 50 Member.
Analytical capability will assist SC planning to mitigate issues by way of:
- Analyzing sales data vis-à-vis SCM planning KPIs and interpretation of trends and patterns
- Analyzing different aspects of SC production efficiency, stocking norms, and distribution efficiency
- Analyzing variations and inefficiencies in logistics costs incurred at various legs of stock movement till it reaches the end consumer
- Utilizing statistical techniques to identify controlling parameters towards SC planning, efficiency KPIs, and quantify the impact of individual parameters. For example, Forecast bias (positive / negative) = fn (base demand, demand drivers, adjustments, etc.)
- Provide future outlook on KPIs such as SC efficiency, forecast bias polarity (under / over-forecasting), etc.
- Statistics-based customized forecasting approach for individual products, categorized into different scenarios like strong/weak seasonality, stability, small packs, etc.
Some critical challenges faced by ERP dependent companies are:
- Inability to provide insights that optimize SC planning decisions. For example, how much should individual demand driven/s be varied to control forecast bias?
- Stretched lead time to understand how interdependency among Key Performance Indicators (KPIs) results in experience / gut-feel-based optimized decision making.
- Standard demand forecasting techniques don’t accommodate ever-changing product behavior during its product lifecycle (sudden growth, seasonality, demand stability, etc.).
- Absence of future outlook/prediction feature.
Critical to harnessing the universe of supply chain information is an analytics platform that can collect and synthesize data so supply chain leaders can easily:
- Access key drivers of business performance
- Access key drivers of business performance Visualize and monitor supply chain performance to identify performance fluctuations and take action to address poor performance Predict events before they occur to prompt pre-emptive action Prioritize and allocate resources to resolve issues and drive improvement
- Better customer service: Use real-time data to better anticipate customer behavior and pre-emptively modify production schedules, tailor deliveries, or deploy inventories where needed
The Hackett Group’s 2017 Supply Chain Trends study revealed companies unanimously agree that advanced analytics platforms are very important. Based on experience with clients across industries, the following benefits of an advanced analytics program have been identified:
- Greater profitability: Use analytics to identify targets for reducing or avoiding costs; improve utilization rates, efficiencies, and material usage
- Better quality: Use advanced analytic models to enable a deeper level of insight that can better associate machine performance data to quality results
- Better customer service: Use real-time data to better anticipate customer behavior and pre-emptively modify production schedules, tailor deliveries, or deploy inventories where needed
- Improved working capital: Use analytics to evaluate the end-to-end supply chain to optimize inventory
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