If there’s anything we’ve learned in the last year, it’s that prediction is not only much more difficult than it looks, but also that the volatility and uncertainty in the global supply chain operating environment is making life a whole lot LESS predictable.
Prediction generally involves developing some sort of forecast, and in this case, we aren’t going to predict more volatility and complexity….that is just too easy! We know uncertainty and volatility is going to be a reality in our world. For instance, we know that e-commerce will continue to drive smaller package size, and will drive up volumes even further. (UPS and FedEx both saw package volumes soar by 17% or more during the Christmas season over last year). As people conduct more transactions electronically, whether it’s consumers on Amazon or organizational buyers on Ariba, Coupa, or other P2P solutions, the relational component of supply chains may be seen as less important. We are also seeing massive technology shifts, such as the rise of 3D printing, embedded digital chips, and other digitization technologies that will not only produce larger and larger pools of data, without necessarily greater insights. Security issues such as counterfeit, theft, fraud, and other activity in the global supply chain will continue to be a challenge. Insights will follow for those who invest in increasing their capability to monitor and manage large volumes of data. We will also continue to increased customer expectations, lower tolerance for poor performance, and less brand loyalty, making this a truly treacherous environment for companies of all sorts. This creates a “double whammy” effect, in that digitization is driving up the need for analytics but to truly find collaborative solutions that rely on new technology solutions, companies will need to find ways to partner. My observations over the past year havae produced the following predictions about what the leading companies will be pursuing in terms of competitive supply chain strategy in 2016.
Prediction 1 – Supply Chain Analytics Will Become More Predictive (Not Backward Looking)
My recent interviews with a number of executives leads me to believe that, like the old Wayne Gretzky hockey adage, players need to skate to where the puck is going, not where it is at the moment. In other words, many supply chain and purchasing solutions looks at historical transaction activity (what happened in the past and is captured in our ERP systems). This can be interesting as a “post mortem” after action review, but doesn’t tell you where the puck is going! As one executive I met with said:
“It is important to have meaningful data – but what you do with it is the issue. Can you form insights that are actionable? That is the real question!”
What this individual was emphasizing is that asking the right questions about our data will become important. We need to be able to impute and project data using statistical approaches such as multiple regression, cluster analysis, and other tools that provide us meaningful insights that isn’t just correlational in nature. Historical transaction analysis can certainly provide one set of insights – but only a partial view. Organizations need to be able to tap into multiple sources of information from both structured and unstructured, internal and external sources, to provide greater insights into key research insights into business strategy questions. Such insights, fed by data, can result in the right types of dialogues that will support business decisions and drive competitive actions that are meaningful and impactful on both cost and revenue components of the P&L. To understand what data you need requires first building the right research question.
In one company I interviewed, the analytical team explored the relationship between economic activity, consumer demand, and demand for their products. They discovered that there were several leading indicators of demand, including coal (a 10 month leading indicator). Other indicators included data pulled from the Federal Reserve economic database, the Producer Price Index, the Purchasing Managers Index, as well as internal metrics such as the size of the company’s own sales force. Using multiple regression models, a predictive forecasting model was developed which allows users to input data into the model, and develop specific forecasts for different categories of products purchased from overseas suppliers. The model is also being used to adjust company revenue and budget growth estimates – and can effectively be used to either curb or expand budgeted growth estimates based on these economic forecasting models.
Prediction 2 – Supply chain strategy will become increasingly segmented by customer and technology
Organizations are constantly going through re-organization events, trying to develop the right governance mechanisms that will enable agile decision-making and responsiveness. But are supply chain organizations configured according to the right market and commercial taxonomy that are relevant? Mudambi (2008) notes that “value-added is becoming increasingly concentrated at the upstream and downstream ends of the value chain” and that “activities at both ends of the value chain are intensive in their application of knowledge and creativity”. Value-added along the value chain is, thus, represented by a “smiling curve”, with increasing levels of value added on one end through R&D, Design, and Commercialization activities, as well as the other end where Marketing, Specialized Logistics, and Brand Management, and After-Sales Services that are customer-facing are critical. What this suggests is that supply chain organizations may need to be segmented and organized according to customer requirements, on one hand, and technology maturity on the other. This is a radical representation, as it requires thinking that the key performance metric in this case is agility and responsiveness to customer and technological change.
Supply chain organizations will also need to be organized around incident response, and positioning human and material assets in locations that are able to respond to rapid volatility and change. Incident prediction involves understanding what issues are on the horizon, not just what are current risks. Potential sources of risk need to be identified, to be able to narrow the field and understand where the data needs to be collected and monitored, the form of that data, and how it needs to be consolidated and measured for consumption by decision-makers. For example, if risk is believed to be occuring at the factory level, companies need to design systems that ensure monitoring of current workflow systems, as well as real-time systems will be needed to collect machine data and worker feedback, and to do so in a manner that ensures it is closest to the activity taking place. Social media holds a lot of promise here. In addition, prediction will require simple, holistic metrics that capture all areas of business risk for contract factories, and which encompass both current and future risks. We may also see forums for industry collaboration that identifies best practices, shared insights, and an opportunity to drive a standard approach to what are emerging as very large and complex risks in the global supply chain. On-going research is needed to capture social media, “big data”, qualitative reporting insights, and other non-traditional data to enable predictive insight, and build a shared source of truth for factory and supply chain risk. New insights are needed into risk mitigation practices that go beyond simply “avoiding” risky production locations when a decision is required, but instead drive better business decisions and improved community impacts for sustainable supply.
Prediction 3 – Corporate responsibility (diversity, environment, labor and human rights) will become an integral part of the supply chain strategy building process.
The recent publicity generated by the Rana Plaza disaster in Bangladesh, and other labor risks, has elevated the importance of integrating these issues into the sourcing decision. As pointed out by my colleague Andreas Wieland in his blog, the United Nations Conference on Climate Change (COP 21) finally reached an agreement this past month. The Paris Agreement is certainly not perfect, but it will provide a hook on which people can build on and a framework on which to drive future supply chain strategic performance goals. There are many different data resources that are potentially available to augment and create a rich set of metrics that can be used to drive insights into improve sustainability performance. Organizations will focus on creating centers of excellence tasked with creating indices that provides a quantitative and visual representation of Life Cycle Analytics that exist in the global supply chain, as well as the related financial cost impacts associated with these issues. Such indices should be proactive in nature, and provide an early warning system as well as an estimate of potential financial impact of diversity, environmental, and human labor rights violations (as well as opportunities) to procurement. These centers should be not tasked not just with mitigating risks, but to provide early warning and a dashboard that can be used to alert management and serve as an early warning mechanism of possible threats to the supply chain, and the relevant financial impacts to the organization.
My friend Andreas believes we will see are totally new business models or as Unruh (2015) puts it in a nutshell: “A rule-of-thumb I give managers is that if your sustainability performance indicators only improvewhen customers use your product less often, it means you’re in trouble.” But if business will not be as usual, we cannot afford to manage supply chains the same way as before. Rather we need to revolutionize our supply chain toolset. I expect that a large part of our future research projects will be about whether supply chains, as the backbones of business value creation, can make CO2-neutral business models and fair labor working settings become a reality.
Prediction 4 – Organizations will build stronger modeling capabilities to plan and manage future supply chain talent requirements.
As we’ve noted in prior posts, talent is the key to building effective supply chain strategies. People are the key to integration, not technology. However, most organizations don’t think of talent as a critical input to value creation or cost savings. (What is the first asset to go in a downturn?) The assumption is that you can find the right people “on demand” and that everyone is easily replaceable. Unfortunately, this is proving to be a big assumption that is not panning out. Organizations are finding a critical shortage of talent for many of the roles they are seeking. As baby boomers retire, a lot of intangible knowledge (an asset) is going out the door. Talent shortages are occurring not just at the management level, but in warehouses, transportation, regulatory, quality, and planning roles. Talent should be part of building a procurement transformation, and not an after-thought.
Some of the research we’ve done has identified predictive models that consider the future requirements and skill sets in different areas of supply management. Given this future state, the model considers the current state, which includes a number of parameters including Staff Pipeline, Candidate Pipeline, Min/Max/Most Likely Inputs for each Recruiting Activity, % Interviewed, % Hirable, and % Accepting. In addition, the model should consider retention rates for employees. This is particularly challenging, given the many different companies that “millenials” will move around with over a period of time. Efforts for building talent should be a partnership between human resources and supply management, to truly think about how to achieve the right long-term outcomes.
Prediction 5 – Real-time supply chain analytics will become the single greatest source for Total Cost Reduction and Cost to Serve effectiveness in the supply chain
Real-time analytics allows people to respond to what is happening now, not last week. Real-time analytics are technologically possible, but the real issue is WHAT to measure. Lost in this discussion is how to identify the right types of key indicators that captures key performance dimensions from suppliers through to customers. Technology co-development will be key to this effort. For example, P&G used their demand sensing algorithm to use customer signals to drive activity backwards throughout the suply chain. Similarly, Flextronic has developed a “Flex Pulse” algorithm that pulls in data from multiple data sources into a data mashup visualization screen that allows people to drill down into any level of the supply chain to pull information as needed. Although there are multiple solution providers who claim to offer this capability, the issue is not HOW, but WHAT to include. What are the key leverage points that determine the bottleneck, the critical incident, the customer point of contact, or the key technical link that leads to the breakthrough market creation? That is where the real genius of supply chain innovation will take place: In the harnessing of analytical, technological, and relational capabilities that lie dormant and undiscovered today, and integrating them in a fashion that is unique, difficult to replicate, and creates insight unlike no other competitor in the market.
One executive at a major oil and gas company stated this very succinctly. Oil and gas companies are in dire straits, and probably will continue to be, as the traditional sources for price reduction have dried up. As many prepare for major reductions in capital spending, all areas of asset management spending are fair game for cost savings opportunities.
“Our existing systems and ways of gathering data and information is adequate for the category management or process – the pre-award work. We can you what is happening in terms of how much we spend in this category, what business unit level spending we have, what types of things we are buying, and derive “good enough” information to do strategy work and enough consumption information to negotiate volume tenders around the world. But were we fall down – is where we believe 80%+ of our opportunity for continuous improvement exists – which is in the brownfield post-award stuff. For example, do we have the information on when we are buying energy – during peak hours or not? How is the service or consumption information being used in real time at the asset to drive savings, and where is the analytics for that?”
Managing assets will have a much greater focus. The opportunity to analytics that drive down working capital to build cash positions for their companies will continue to be critical. Organizations I’ve spoken with recognize that this is the key. One company I met with last week has a focus on improving productivity in its retail brands, by considering not just the price of food, but also the operational characteristics of food preparation that drive a total cost solution for many of the purchases they are making. This level of operational and price-based management will be a key focus for supply chains in the future.
So that’s it for this year…see you in 2016!