Companies need to continuously evaluate and improve their value chain strategy and operational performance to best meet the customer requirements while maintaining profitable growth. As business landscape continuously changes, we are asked to make more complex decisions with less time. What are the trends that are increasing this complexity? How are these trends influencing or would influence the supply chain planning processes at your company?
Emerging trends in value chains have created a level of complexity previously unheard of but one thing that remains constant is: “Change”. Companies need to continuously evaluate and improve their value chain strategy and operational performance to best meet the customer requirements while maintaining profitable growth. Most companies have some form of 'Sales & Operations Planning' (S&OP) or supply-demand planning or Integrated Business Planning (IBP) process that helps in this endeavor. As business landscape continuously changes, we are asked to make more complex decisions with less time. What are the trends that are increasing this complexity? How are these trends influencing or would influence the supply chain planning processes at your company?
Value Chain Trends
Value chains are becoming more regional and less global Unrelenting pressure to drive down costs saw manufacturing base move from US to Mexico to China. China became the manufacturing hub for the world. However, a recent McKinsey report says "the decline in trade intensity is especially pronounced in the most complex and highly traded value chains. However, this trend does not signal that globalization is over. Rather, it reflects the development of China and other emerging economies, which are now consuming more of what they produce".
New technologies enable faster product innovation and time to market From 3D printing to IoT to digital logistics and transactional platforms are all enabling faster time to market for product. These technologies are not only lowering product manufacturing costs but also the transactional costs and result in unprecedented speed of business. Also, you don't the excuse of 'Data not available' anymore. There is more data that you can comprehend, analyze and derive insights from. 'Instant Gratification' Economy Thanks to Amazon, we are a part of this new economy when 'instant gratification' trumps brand loyalty. Going from 2-day free shipping, Amazon raised the bar recently by announcing 1-day (or same day in some markets) free delivery. Convenience has become a competitive advantage today but this is customer's convenience. Your company's supply chain is feeling the opposite effect as result of this - capacity constraints, wrong inventory mix, stock-outs, shipment expedites from the factory or warehouse transfers. Knowledge-Intensive and High-Skilled Labor Share of goods trade based on lower-cost arbitrage is on a decline over last decade while the investment in intangible assets like R&D and IP, has doubled since 2000. In the context of supply chain planning, with the complexity increasing, companies struggle to find talent at the intersection of analytics and planning. Spreadsheet capabilities and skills have hit a wall. Even if you could throw bodies at this problem, you struggle to find the right skills.
Growing does not necessarily mean getting better and bigger does not necessarily mean faster
These are just a few key challenges for planning team today but the list goes on. Is your supply chain planning aligned with these business needs?. The most basic requirement is to get smarter answers faster from your planning team. There is no one single solution to address this but here are some proven methods and focus areas to help you get up get there.
Unified Data Model Planning starts with data. The planning logic could be unique or may become unique as business conditions change, however the input data is always more or less the same in supply chain planning. Just because your planning solution or spreadsheet is integrated with disparate data sources doesn't mean the data is unified. A planning solution requires a unified data model, which is a single version of truth in terms of data format, and its interpretation across the organization, assuring data integrity and lineage as it is being used across different functional groups. Using a unified data model not only collects the data and reports it but also helps connect the dots between data sets and categories. Automation The need for consensus-based, multi-criteria planning is greater than ever before and will continue to grow as companies create greater value and gain competitive advantage. Fragmented, spreadsheet-based approaches with fixed planning cycles are incapable of responding to these challenges. We all perform certain repetitive tasks at work. Bi-Weekly or monthly planning cycles have a significant amount of tasks that are repetitive and non-value add. For example, downloading data from transactional systems, copy-paste parts of that data into the master planning spreadsheet model, running pivots, creating charts in spreadsheet to analyze results, uploading the results back to ERP to execute. Map your planning process to identify the value-add vs. non-value-add activities. Automate the non-value add activities and make your planning team efficient. Let the machine perform those tasks while the team focuses on the outputs, running what-if scenarios and analyzing potential opportunities to improve business performance. This is imperative considering talent shortage in this trade.
Before we replace human with a robot/AI, we need to take the robot out of the human
Optimization and Machine Learning It is important to have efficient and effective planning with the resources on hand but extremely important to leverage the historical planning data to make subsequent planning cycles smarter. Planning data holds a wealth of information about your supply chain operations, actions and results. The data size gets big very quickly and, simply put, a human brain cannot analyze this big data with basic tools. In more than 90% of cases, this data is discarded or lost due to constraints of spreadsheet planning. Augment human intelligence with good data and software technology; this is where Machine Learning plays a significant role. It starts with the application of basic optimization algorithms to identify areas of improvements like inventory, capacity, distribution and trade-offs between them. Machine Learning (ML) is used in analyzing historical patterns, inputs and resulting outcomes help you in key areas like forecasting demand/supply, inventory shortages and dynamic replenishment.
Value-Driven Implementation Implementation of planning solutions is unlike others due to the complexity of the problem at hand. This problem is exacerbated because planning is cross-functional, cross-border and cross-organizations so ‘think big, act small’. Take the value-driven implementation approach. Do not try to automate everything from the start or fall into the classic "death by features/functionality' trap. Breakdown the process map to identify key areas that would provide immediate results to make the life of your planning team easy without making this a multi-year long process.
Augmenting human intelligence with smart tools for supply chain planning gives you a competitive advantage in today’s digital supply chain era.