“Having a supply chain and having an optimized supply chain are not the same thing,” asserts supply chain and logistics expert Gary Marion (@SpplyChainROSCO).[1] No one, of course, desires to have an unoptimized supply chain because the very name implies there is waste and inefficiency involved. But, as Marion notes, “Your supply chain is only going to be optimized because you optimized it.” The first question your company must ask is: What am I optimizing my supply chain to do? That may sound silly; but, there are many different objectives a company may desire to achieve and each objective could require different actions. To achieve balanced supply chain optimization, there needs to be some prioritization of goals. Marion recommends, “To begin planning for optimization, start with the end result. This is basic demand planning.” As we enter the holiday season, many companies know that their annual revenue figures are highly dependent on vigorous holiday sales. But companies don’t start optimizing the holiday supply chain in November, they begin right after the last holiday season (see my article “Preparing Your Supply Chain for the 2015 Holidays“). Marion explains:
“An optimized supply chain is one that gets your customers what they want, when they want — while you spend as little money as possible making that happen. And you can get there by starting with demand planning, understanding your delivery and production lead times and getting your purchasing policies and production planning aligned.”
Implied in Marion’s discussion of supply chain optimization and demand planning are the availability and analysis of data. Kris Kosmala, Quintiq’s Vice President for Asia, insists that too many companies are trying to conduct this analysis with outdated tools. “Supply chain professionals,” he writes, “must now continually sense and respond to supply chain problems by making highly accurate business decisions ‘right now’, not later. To do this, they need advanced analytical techniques they can apply to datasets whose volume, velocity or variety exceeds computing capability of their legacy IT tools.”[2] Fortunately, the field of cognitive computing is emerging at right time to help with this challenge. Cognitive computing systems (like the Enterra Enterprise Cognitive System™ (ECS), a system that can Sense, Think, Act, and Learn®), don’t replace legacy systems they make legacy systems better. Cognitive computing systems can help transform industrial age organizations into digital enterprises that can take advantage of the oceans of data being collected by most companies. For many companies, a cognitive computing system is what they need to optimize their supply chains while taking into account all of the competing goals that may exist.
Kosmala, for example, divides the supply chain into three segments: the buy side, produce side, and the sell side. Each of those segments will have different objectives and will look at supply chain optimization differently. Optimizing each segment separately might seem like a good course of action; but, the best course of action is to optimize the overall supply chain to achieve overall company objectives. A good cognitive computer system can deal with ambiguous or conflicting situations and help executives make the decisions that contribute to end-to-end optimization. In addition, cognitive computing systems are capable of making routine decisions on their own, only involving decision makers when anomalies occur. Autonomous decision making helps companies keep pace with the velocity of data now confronting businesses. Because cognitive computing systems can handle both structured and unstructured data, data integration is easier to achieve. And data integration is essential for achieving supply chain optimization. Kosmala explains:
“I deliberately made the division between the buy side, produce side, and the sell side to illustrate a broader point. While the data quantities are generally getting bigger, supply chain ‘big data’ is still made up of large data silos distributed among business functions and external sources, capable of being, but largely not, interconnected. … To make insightful, optimal supply chain decisions, the optimization needs to make disparate data accessible by aggregating it into a single value chain optimization process, and disaggregating the optimal decisions to the functional ‘branches’, so that any event at the end of each functional branch can be always considered in context of one, whole-chain optimum.”
Only cognitive computing systems are capable of dealing with the complexity Kosmala describes; and, because cognitive computing systems are just becoming available, writes Renee Boucher Ferguson, up to now, “The transformational potential of big data and predictive analytics … haven’t quite panned out for supply-chain managers.”[3] Ferguson explains, “The biggest obstacles appear to be the cost of hiring skilled employees and the complexity of connecting nodes across an extended supply-chain network.” Those conclusions were taken from a survey conducted by The Economist. Writing about the survey, Joel Schectman (@joel_schectman) observed, “As supply chains become more tangled, with a greater number of far flung suppliers, managers are faced with risks that can crop up in dozens of countries.”[4] He continued:
“Companies have long used complex data sets to plan manufacturing to meet customer demand. But firms are now looking to combine data from external sources to better predict future risks. Integrating multiple, disparate sources of external data poses an obstacle for companies looking to make more use of complex predictive analytics, according to Ken Waldie, author of the report. External sources like company credit ratings, data on a country’s political climate and even weather patterns, could all help executives assess the risk profile of suppliers.”
Cognitive computing systems can help overcome those obstacles. The Enterra® approach empowers the business expert by automating the statistical expert’s and data expert’s knowledge and functions, so the ideation cycle can be dramatically shortened and more insights can be auto-generated. Even some of the business expert’s logic is automated to help tune and re-analyze the data. And web scraping techniques can be used to obtain available data from across the supply chain. Things are improving, Louis Columbus notes, “Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.”[5] That’s because businesses have discovered that they must use more advanced computing systems than those available in the past. Columbus explains, “Choosing to compete on accuracy, speed and quality forces supplier networks to get to a level of contextual intelligence not possible with legacy ERP and SCM systems.” Susan Fourtané (@SusanFourtane) calls this new level of integration Supply Chain Execution Convergence.[6] She explains, “Supply Chain Execution Convergence (SCEC) describes the next generation of supply chains, which create a totally integrated supply chain by bringing together individual silos within the organization. This model offers supply chain managers new ways to create end-to-end visibility and the ability to quickly react to disruptions.” Cognitive computing systems are perfect platforms for synchronizing and orchestrating optimized supply chains.
Footnotes
[1] Gary Marion, “Introduction to Supply Chain Optimization,” About, November 2015.
[2] Kris Kosmala, “Big data and supply chain optimization: Illusion vs. reality,” Quintiq, 4 November 2015.
[3] Renee Boucher Ferguson, “Are Predictive Analytics Transforming Your Supply Chain?” MIT Sloan Management Review, 18 December 2013.
[4] Joel Schectman, “Complexity Keeps Big Data Out of Supply Chain,” The Wall Street Journal, 25 November 2013.
[5] Louis Columbus, “10 ways big data is revolutionising supply chain management,” CloudTech, 7 September 2015.
[6] Susan Fourtané, “Supply Chain Execution Convergence: The Next Generation of Supply Chains,” EBN, 8 September 2015.
