Supermodeling allows a company to take an end-to-end view of its supply chain and make adjustments in production, distribution, and inventory practices to meet changing market demands.
The outlook for global supply and demand is constantly changing, particularly under the current economic circumstances. The situation is fluid: Fundamental market dynamics shift, the balances and trade-offs in cost equations change, and solutions such as outsourcing and localized production may lose their value. In response, global companies reassess and reshape their supply chain networks and operations, taking future developments into account. No one can accurately predict the future, of course, but companies can plan ahead by defining potential scenarios, risks, and options, and then assessing the likely outcomes of each.
Manufacturers and logistics service providers often struggle with this complex task. They typically apply ad-hoc spreadsheets and inconsistent data-collection methods rather than tailored analytical tools and standardized procedures for gathering relevant data. The tools most companies currently use to help them analyze changing situations in operations and supply chains are not perfectly suited for the job. Most are either very detailed, making this exercise cumbersome and time-consuming, or they are limited and unable to analyze mid- and long-term scenarios. Moreover, many existing statistical models are based on a regression of historical data. But historical models are inadequate when fundamental supply chain parameters such as demand, markets, products, and cost drivers are facing significant and unprecedented changes.
[Figure 2] Transitional planning of production and distribution alternativesEnlarge this image
Supermodeling offers a solution. This modeling method takes an integrated view of the end-to-end supply chain, from market-demand scenarios through order management and planning processes, and on to manufacturing and physical distribution (see Figure 1). It studies historic and "as is" market and order data as well as "to be" market scenarios and demand forecasts. Such scenario-based simulation leads to better strategic supply and demand balancing because new products, expected price changes, and options for physical network changes are dynamically incorporated into the model. Finally, supermodeling —conceptualized and tailored to companies' specific business conditions —provides a fact-based approach for making difficult but necessary decisions that may encounter political and emotional resistance within the company.
How is supermodeling different?
In general terms, supermodeling provides a computer replica of a real or planned supply chain system — what one might call a "model world." The scope and content of the model —entire value chain, highly detailed breakdown of data, full transparency of feedback loops, and high reliability of options —is more comprehensive yet no more complex than the typical supply chain optimization tools. Supermodeling's broader focus can address a wider range of questions and issues, such as volatile commodity pricing and availability, shifting perceptions of market players, and conflicting trading or purchasing activities, that are not covered by traditional supply chain models.
Other types of supply chain optimization tools improve physical networks by looking at transportation, distribution, and labor costs in isolation —an approach that may produce unexpectedly costly results. Supermodeling, on the other hand, not only examines physical production and distribution costs but also takes into account operations planning aspects such as supply management, manufacturing planning, and delivery management. In other words, it assesses the impact of various cost and value drivers, such as labor, transportation, technology, and productivity, on the entire network. Since supermodeling can alter those parameters to develop different scenarios, it can demonstrate how those changes might affect customer behavior or supply chain performance.
Supermodeling's output is more visual than that of traditional supply chain optimization tools. The level of detail it produces is based on a careful assessment of what is required to respond to both strategic and tactical questions. Outputs typically represent key measures in finance, performance of physical process flows and virtual information flows, capacity utilization, stock levels, and customer service, all in the context of various rules and constraints imposed over time.
Accordingly, the model is able to demonstrate the expected benefits of reducing lead times by streamlining business processes, managing or reducing variance, and improving responsiveness and flexibility. This allows users to compare end-to-end supply chain scenarios —from quote to delivery to cash — with each other and with the current, as-is situation. A "supermodeled" replica of a supply chain thus provides the scope needed to determine the appropriate course of action based on future demand scenarios and trends.
The objectivity of this approach makes it a helpful tool for achieving consensus among stakeholders. Ultimately, supermodeling enables "boardroom experimentation." It allows companies' top management to test hypotheses and see a visualization of the answer right away. They can use the simulations to identify how best to balance demand and supply, examining such options as whether —and when —to open or close factories, move production to a different location, or shift inventories between distribution centers.
For example, a global manufacturer of health care equipment used supermodeling to optimize its order-to-cash process, including the physical network, from sourcing through manufacturing to the customer, as well as the information flows. For this company, supermodeling was particularly powerful at speeding up and simplifying the decision-making process because multiple scenarios could be run with the participation of key stakeholders. They could instantaneously see the impact of proposed changes in the supply chain network and consequently make better, consensus-driven decisions, even when opinions regarding a particular situation had been divided prior to using the supermodel.
To obtain optimal results, a supermodeling exercise should follow a four-step process, as illustrated in Figure 2:
Establish a baseline, simulating the as-is scenario to validate and calibrate the model. Run a base-case simulation, applying projected demand over time by product and by region. Identify areas that will require new supply decisions.
Define and evaluate the major external trends that are likely to have a long-term impact on supply and demand.
Identify potential changes to investigate, with the aim of minimizing costs while maintaining the best balance between supply and demand. Set up scenarios that reflect those options in the model, and then simulate market and supply performance.
Evaluate results by comparing output in terms of key performance indicators. Continue iterations, with multiple runs and "what if" sensitivity testing, until the most effective solution becomes evident.
For a brief look at how one company applied this process in a distribution-network analysis, see the sidebar "Four steps to supermodeling success."
Future scenarios, year by year
Companies that have employed the supermodeling approach have been able to reduce costs and free up working capital. They also generally do a better job of matching supply to demand as it evolves from month to month and from year to year. This modeling approach allowed the health care equipment manufacturer mentioned above to avoid a costly physical supply chain setup in Asia and substitute a more costefficient solution based on insight provided by the supermodel. A change in sourcing and better timing of order fulfillment deadlines allowed it to adopt a leaner physical distribution network, cheaper transportation, and fewer stockholding locations. The model not only delivered tens of millions of euros in potential savings but also developed a solution that the whole business could support.
A more detailed example is the case of VELUX, a manufacturer based in Denmark. A major player in the global building materials sector, its products include roof windows and skylights, many types of decoration and sun screening, roller shutters, installation products, and remote controls and thermal solar panels for roof installation. VELUX has manufacturing suppliers in 11 countries and sales companies in nearly 40 countries.
In 2007 and 2008, a project team consisting of VELUX's supply chain and manufacturing strategy specialists and supply chain consultants (including the authors) developed a dynamic supply chain model to study alternative ways of managing information and physical material flow between production, stockholding points, and markets. The primary objective was to build a supply chain model for strategic planning and evaluation of options that would be specific to VELUX. Through evaluation of different scenarios, the model would support strategic manufacturing initiatives and ongoing sales and operations planning for the six-month to five-year time frame.
Today the VELUX Supply Chain Model (VSCM) is used in the company's windows and flashings group for long-term planning. The model is predicated on a baseline setup; its "basis year" employs actual production and sales data and incorporates future expectations for sales, productivity, and projected costs for raw materials, labor, and transportation. The model provides VELUX with the ability to evaluate different scenarios by showing year-by-year development in capacity utilization, product and/or component flows, and even investment costs.
VSCM has enabled the company to increase both the number of alternative scenarios to study and compare as well as the scope, relevance, and quality of the output. VELUX has used the model to examine several European manufacturing and supply chain scenarios. On the basis of that analysis, the company made important decisions that significantly impact manufacturing, logistics, and financial value drivers, such as optimizing production's environmental footprint and designing a longer-term sales and operations planning process, to name just two.
Capitalize on change
Many companies are content to establish supply chain processes and structures, and then allow them to continue as is until they become obsolete and problems arise. Or they may find that business is developing in such a way that their existing supply chain operations and processes are unfit to exploit market opportunities or meet market challenges. In today's competitive environment, that is no longer a viable way of conducting business.
If a supply chain modeling effort is to provide truly valuable decision support, then it must be based on a deep understanding of a company's specific situation, requirements, and key issues. For this reason, it's necessary to have a collaborative effort among model developers, analysts, the company's own business experts, and other stakeholders throughout the supply chain.
What makes supermodeling an appropriate tool for achieving that objective is that it gives companies that are looking to capitalize on change the ability to re-examine their production and delivery networks by taking into account cost and value drivers in the endto- end supply chain, all within the context of future growth. This approach can make visible to company management the potential payback for selecting a particular course of action.
Four steps to supermodeling success
A successful supermodeling implementation requires working through four basic steps. The following is a reallife example of how one company applied this modeling technique.
The company used to have a decentralized structure with stockholding delivery and service centers (DCs) in 16 European countries. Initial analysis indicated that a more centralized production and distribution process might significantly reduce costs without compromising service.
The primary challenge was to identify how many and which warehouses to close, and what level of service to offer from the remaining locations. That decision would have to be made in tandem with changes in order management processes and stockholding policies. To address those considerations, the project team developed a dynamic supply chain "supermodel" scoped to simulate and study alternative ways of managing information and physical material flow between production, stockholding points, and end customers. The project proceeded as follows:
1. Development of a baseline case. The model was calibrated to simulate one year as-is operation, and a baseline scenario was created. Confidence in the model was established because it could simulate and reproduce history with numbers for total annual l ogistics costs, inventory levels, and delivery performance that approximated what had actually been the case that year.
2. Development of future trend scenarios. By manipulating inputs to the model, several simulated scenarios were produced, along with the associated effects on costs and service. This allowed the team to understand the issues and draw up a short list of realistic potential solutions.
3. Evaluation of options. With 16 DCs in the "as is" situation, a simple optimization exercise was infeasible. Complicating matters was the fact that all of the country directors were against closing "their" warehouses. It was important, therefore, to help them objectively evaluate and compare alternatives.
The model run proposed closing down various groups of DCs and simulated the outcomes. The model was tested for all kinds of "what ifs"; it proved and visualized how stock and transportation costs could be balanced more effectively, without compromising service to local customers. The solution that proved best —cutting back from 16 DCs to three —would allow the company to cover all of its markets in Europe while enjoying a 20-percent saving in logistics costs.
4. Evaluation of results. The model scenarios and options were developed by the company's wider European logistics organization, and members of the group jointly selected the best solution. The modeling approach helped to establish consensus among the stakeholders, avoiding the dangerous route of making decisions based on political and emotional resistance to change.
Artificial intelligence (AI) tools can help users build “smart and responsive supply chains” by increasing workforce productivity, expanding visibility, accelerating processes, and prioritizing the next best action to drive results, according to business software vendor Oracle.
To help reach that goal, the Texas company last week released software upgrades including user experience (UX) enhancements to its Oracle Fusion Cloud Supply Chain & Manufacturing (SCM) suite.
“Organizations are under pressure to create efficient and resilient supply chains that can quickly adapt to economic conditions, control costs, and protect margins,” Chris Leone, executive vice president, Applications Development, Oracle, said in a release. “The latest enhancements to Oracle Cloud SCM help customers create a smarter, more responsive supply chain by enabling them to optimize planning and execution and improve the speed and accuracy of processes.”
According to Oracle, specific upgrades feature changes to its:
Production Supervisor Workbench, which helps organizations improve manufacturing performance by providing real-time insight into work orders and generative AI-powered shift reporting.
Maintenance Supervisor Workbench, which helps organizations increase productivity and reduce asset downtime by resolving maintenance issues faster.
Order Management Enhancements, which help organizations increase operational performance by enabling users to quickly create and find orders, take actions, and engage customers.
Product Lifecycle Management (PLM) Enhancements, which help organizations accelerate product development and go-to-market by enabling users to quickly find items and configure critical objects and navigation paths to meet business-critical priorities.
Nearly one-third of American consumers have increased their secondhand purchases in the past year, revealing a jump in “recommerce” according to a buyer survey from ShipStation, a provider of web-based shipping and order fulfillment solutions.
The number comes from a survey of 500 U.S. consumers showing that nearly one in four (23%) Americans lack confidence in making purchases over $200 in the next six months. Due to economic uncertainty, savvy shoppers are looking for ways to save money without sacrificing quality or style, the research found.
Younger shoppers are leading the charge in that trend, with 59% of Gen Z and 48% of Millennials buying pre-owned items weekly or monthly. That rate makes Gen Z nearly twice as likely to buy second hand compared to older generations.
The primary reason that shoppers say they have increased their recommerce habits is lower prices (74%), followed by the thrill of finding unique or rare items (38%) and getting higher quality for a lower price (28%). Only 14% of Americans cite environmental concerns as a primary reason they shop second-hand.
Despite the challenge of adjusting to the new pattern, recommerce represents a strategic opportunity for businesses to capture today’s budget-minded shoppers and foster long-term loyalty, Austin, Texas-based ShipStation said.
For example, retailers don’t have to sell used goods to capitalize on the secondhand boom. Instead, they can offer trade-in programs swapping discounts or store credit for shoppers’ old items. And they can improve product discoverability to help customers—particularly older generations—find what they’re looking for.
Other ways for retailers to connect with recommerce shoppers are to improve shipping practices. According to ShipStation:
70% of shoppers won’t return to a brand if shipping is too expensive.
51% of consumers are turned off by late deliveries
40% of shoppers won’t return to a retailer again if the packaging is bad.
The “CMA CGM Startup Awards”—created in collaboration with BFM Business and La Tribune—will identify the best innovations to accelerate its transformation, the French company said.
Specifically, the company will select the best startup among the applicants, with clear industry transformation objectives focused on environmental performance, competitiveness, and quality of life at work in each of the three areas:
Shipping: Enabling safer, more efficient, and sustainable navigation through innovative technological solutions.
Logistics: Reinventing the global supply chain with smart and sustainable logistics solutions.
Media: Transform content creation, and customer engagement with innovative media technologies and strategies.
Three winners will be selected during a final event organized on November 15 at the Orange Vélodrome Stadium in Marseille, during the 2nd Artificial Intelligence Marseille (AIM) forum organized by La Tribune and BFM Business. The selection will be made by a jury chaired by Rodolphe Saadé, Chairman and CEO of the Group, and including members of the executive committee representing the various sectors of CMA CGM.
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Supply chain managers at consumer goods manufacturing companies are tasked with meeting mandates from large retailers to implement item-level RFID.
Supply chain managers at consumer goods manufacturing companies are tasked with meeting mandates from large retailers to implement item-level RFID. Initially these requirements applied primarily to apparel manufacturers and brands. Now, realizing the fruits of this first RFID wave, retailers are turning to suppliers to tag more merchandise.
This is one more priority for supply chain leaders, who suddenly have RFID added to their to-do list. How to integrate tagging into automated production lines? How to ensure each tag functions properly after goods are packed, shipped, and shelved? Where to position the RFID tag on the product? All are important questions to be answered in order to implement item-level RFID. The clock is ticking on retail mandates.
Different products, new RFID considerations
Hangtags, the primary form of apparel product identification, present a relatively easy way to attach an RFID tag. Pressure-sensitive labels likewise can carry an RFID inlay. The inlay, consisting of a microchip and antenna, holds the product’s unique identifying information. This tiny device is activated when the RFID reader passes by it. For nonapparel products, in many cases, there is no way to attach a hangtag. Therefore, a pressure-sensitive RFID label often must be put directly on the product. If the product is packaged in a box, the RFID carrier can be attached to or placed inside the box. Either way involves the use of just the right solutions, including the adhesive, shape, dimension, and placement. Moreover, there must be an efficient way to attach the labels to products. This requires process engineering and sometimes capital investment to integrate RFID labeling into highly automated manufacturing lines.
Metals, liquids, and low-surface-energy (LSE) materials pose hurdles for RFID item tagging. Tag and label inlays cannot be read properly through metals and liquids, and the pressure-sensitive labels do not always stick well to product surfaces containing silicone, vinyl, polyethylene, and polystyrene. Very small items are also difficult to tag. Metal paint cans, caulk or paste tubes, lipsticks, and reusable water bottles are just a few products that present RFID tagging challenges.
In other cases, it is not so much the product itself that hinders readability but rather the shipping method. For example, it is relatively straightforward to apply an RFID tag or label to a bag of fertilizer. But the fertilizer bags might be stacked 60 deep on a pallet. The pressure is too much. It damages the inlay, killing the tag’s readability. So, RFID tags, which were perfectly fine coming off the production line, are now dead from the stacking pressure.
Solutions and testing
RFID tagging and labeling programs take time to get right. While some manufacturers can set up a successful process in a few weeks or months, for others it can take six months, nine months, a year or longer. Variables influencing implementation time include capital equipment investments, the product types (for example, are the materials, shapes, or surfaces potentially problematic?), label supplier capacity and capabilities, and third-party testing rounds.
The good news is that best practices are being refined every day to incorporate RFID on difficult-to-tag products. A case in point is finding answers to RFID-inlay readability issues on metal or liquid products. There are ways to attach an RFID label to the product’s lid or cap.
The University of Auburn RFID Lab is the de facto U.S. authority on all things retail RFID. Through its ARC program, the lab works with end users to make sure RFID tags meet or exceed their required performance and quality levels. Walmart, for example, requires its suppliers to source from Auburn RFID Lab’s ARC program-approved inlay companies. “ARC is a test system and database that stores comprehensive performance data of in-development and market available RFID tags,” according to the lab’s website. “ARC has been working with end users to translate RFID use cases into specific levels of performance in the ARC test environment.”
High-quality RFID tags and labels are at the heart of it all. The following are some considerations to keep in mind when choosing an RFID tag and label provider:
What are their quality control and testing capabilities? Can they confirm that every tag is readable? Do they have software to verify that UPC and RFID information match up? Do they possess familiarity with Auburn’s RFID Lab approval process?
What is their capacity? How many thousands or millions of inlays do they create per day? Are there minimum order quantities?
What are their order management and shipping processes like? What is their delivery speed? How easy are they to order from? Where are their print facilities located?
Do they offer customization? Do they possess specialized equipment? Can they die cut irregular shapes, including very small dimensions? Do they possess adhesive expertise and application equipment? Do they have solutions for metal, liquid, and other difficult-to-tag items? Are they able to configure label rolls to work on automatic label dispensers?
It takes trial and error to implement RFID item tagging for nonapparel products. Effective, compliant programs do not manifest overnight. Collaboration with experienced label providers and the Auburn RFID Lab will help manufacturers overcome even the most complex RFID tagging challenges. There will be a roadmap to success, and the results in the form of better inventory visibility, swifter sell-through, and stronger sales will be well worth it.
Economic activity in the logistics industry expanded in August, though growth slowed slightly from July, according to the most recent Logistics Manager’s Index report (LMI), released this week.
The August LMI registered 56.4, down from July’s reading of 56.6 but consistent with readings over the past four months. The August reading represents nine straight months of growth across the logistics industry.
The LMI is a monthly gauge of economic activity across warehousing, transportation, and logistics markets. An LMI above 50 indicates expansion, and a reading below 50 indicates contraction.
Inventory levels saw a marked change in August, increasing more than six points compared to July and breaking a three-month streak of contraction. The LMI researchers said this suggests that after running inventories down, companies are now building them back up in anticipation of fourth-quarter demand. It also represents a return to more typical growth patterns following the accelerated demand for logistics services during the Covid-19 pandemic and the lows of the recent freight recession.
“This suggests a return to traditional patterns of seasonality that we have not seen since pre-COVID,” the researchers wrote in the monthly LMI report, published Tuesday, adding that the buildup is somewhat tempered by increases in warehousing capacity and transportation capacity.
The LMI report is based on a monthly survey of logistics managers from across the country. It tracks industry growth overall and across eight areas: inventory levels and costs; warehousing capacity, utilization, and prices; and transportation capacity, utilization, and prices. The report is released monthly by researchers from Arizona State University, Colorado State University, Rochester Institute of Technology, Rutgers University, and the University of Nevada, Reno, in conjunction with the Council of Supply Chain Management Professionals (CSCMP).