How Prescriptive Supply Chain Analytics Can Help You Gain a Competitive Edge
Perhaps, you are already receiving solid insights from traditional BI. Nonetheless, as your chain grows more complex and more data becomes available, traditional tools will no longer bring sufficient insights. Hence, it’s time to explore newer options.
What is Predictive and Prescriptive Analytics?
With the increased presence of IoT devices, supply chain managers can now attain all sorts of new data and insights about the operations. However, this influx of data also requires better managing and operationalizing.
For a long time, to identify the right course of action, managers have relied on traditional and self-service BI along with historical perspective. For instance, based on historical data, they could determine that some part usually took X days to arrive. Such approach even allowed estimating some standard deviations to adjust the procurement plan. Historical demand data could also provide insights about the future demand used to forecast production requirements, adjust logistics requirements, and so on.
Today, supply chain managers can no longer settle for estimations. They need to know exactly how the events will unroll, and what results they can expect.
Predictive analytics in supply chain management has emerged as a new solution, enabling extra visibility into the movement of goods and advanced optimization of distribution. Machine learning algorithms can be effectively deployed to churn your numbers using a number of sophisticated models to determine the most accurate forecast.
The best part is that these new technologies offer a near-zero delay in interpreting the inflow of customer orders, current inventory levels or any other type of internal or external data. In this way, supply chains forecasts will incorporate near-instantaneous updates, helping businesses make proactive changes in real-time. The following supply chain planning and management processes can majorly benefit from the usage of predictive analytics:
- Demand forecasting
- Replenishment planning
- Inventory management
- Fleet and asset management
- Equipment maintenance
- Inbound and outbound logistics.
Emerging prescriptive analytics solutions go even further, helping managers optimize the entire supply chain at once, instead of focusing on improving the efficiency of specific value chain process (e.g., inventory management of spare parts).
While prescriptive analytics is also predictive in its nature, it allows businesses to move from “what will happen if…” to “what action should we take to meet goal X”. For example, predictive analytics can help you estimate the percent of shipping delays your company will likely experience in Quarter 2, while prescriptive analytics can suggest what you can do (e.g., expedite product A via another route) to avoid costly delays.
The end-goal of integrating prescriptive analytics into your digital supply chain is to gain straightforward advice on how the supply chain should operate to achieve the outcome a company desires.
The Benefit of Prescriptive Supply Chain Analytics in Supply Chain Management
According to Ventana Research, 53% of businesses have limited or no ability to understand the trade-offs of their decisions in supply chain management. As a result, just 47% of surveyed companies rank their supply chain plans as “accurate”.
The reasons for low accuracy in planning are manifold. Some organizations do not have the ability to approach planning as a dynamic process. They cannot fully gauge and estimate the impact of a multitude of internal/external data points on the supply chain, e.g., fluctuating currency exchange rates or changing market conditions. As a result, organizations make plans using their “best judgment” instead of hard numbers.
Prescriptive analytics solutions can conduct analysis on a much wider scale, and test multiple scenarios to calculate the outcomes. In order to suggest the next optimal steps worth pursuing, such systems can assess both external data, such as economic factors, as well as all the data stored in the integrated supply chain system.
The self-learning algorithm can largely automate the decision-making process and show how different “what if” scenarios can play out.
For instance, your company can get data-backed answers to questions such as:
- In what location should we build a factory to speed up the product delivery time if the costs were not an issue?
- How adding free shipping and unlimited returns to maximize customer service levels will affect our revenues?
- What is the most cost-efficient shipment strategy for each retail location?
When supplied with enough data, a prescriptive analytics solution can strategize different scenarios for attaining any goal at a minimum cost. In fact, every scenario can be adjusted depending on other factors you choose to program – business risks, external weather factors, etc.
To further illustrate the benefits of prescriptive supply chain analytics, let’s take a closer look at several successful case studies.
Promising Use Cases of Prescriptive Supply Analytics
In 2016, Gartner predicted that prescriptive analytics usage would increase threefold within the next four years (from 10% to 30%). Indeed, today we are already seeing how this tech solution is effectively changing supply chain management in different industries.
FleetPride, North America’s largest distributor of truck and trailer parts, has implemented predictive and prescriptive analytics to determine the optimal warehouse locations, minimize delivery time and costs across its supply chain. Post roll-out the company reported an increased rate in accuracy – 99.5% of their warehouse packing tasks are now error-free; resulting in significantly reduced labor costs and higher revenues. Beyond prescriptive analytics, warehouse management can also greatly benefit from IoT and robotics.
UPS has deployed an advanced on-road integrated optimization and navigation system named ORION that is now in charge of the company’s transportation planning. Post migration, the logistics giant attained the following benefits:
- Save the company around 100Mln miles and 10MLn gallons of gas annually.
- Automatically update ETA and devise new routes instructions to minimize delays and improve service levels.
- Improve customer experience. Using UPS My Choice, customers receive real-time delivery updates.
A Fortune 1000 manufacturer of consumer products in the US recently implemented five different prescriptive analytics models to gain extra clarity into their operations and ramp up their decision making. They were seeking to answer very specific questions, such as what manufacturing plans should be dedicated to making which products; or what inventory strategy will generate the highest cash flow while ensuring acceptable levels of inventory turns.
To obtain data-backed answers to these business problems, the company adopted:
- An advanced long-range planning model to evaluate strategic issues such as product portfolio, capital expenditures, etc.
- A mid-range planning model to analyze inventory strategy and solve tactical issues, e.g., selecting an optimal place for the goods production.
- An operational planning model to devise better production sequencing and shift scheduling.
- A distribution model for optimizing the distribution strategy.
- A truck loading and handling model to boost efficiency and reduce costs.
The implementation of the first two prescriptive models alone has resulted in an estimated ROI of 1,000% to 2,000% for the business. Combined, these models have helped the decision makers to get the insights they needed and overhaul the company-wide decision-making process.
Per Gartner, only 4% of businesses, who implemented advanced supply chain analytics, saw no increase in ROI. Indeed, supply chain analytics generates major cost savings and supports business growth as the previously mentioned case studies illustrate.
Prescriptive analytics, incorporated into integrated business planning can help you make better decisions in functions ranging from production to finance and logistics. The importance of having such solutions will only increase with further digitization of the supply chains. After all, with more data, comes more responsibility of properly operationalizing it and translating into positive business outcomes.