what are some of the advantages and disadvantages of each?

ERP Versus Best of Breed
Historically, supply chain managers have faced a dilemma in choosing computer software between comprehensive, integrated, full-function ERP systems and narrowly focused, best-of-breed packages that require integration. Based on this unit’s readings, what are some of the advantages and disadvantages of each? Give two examples, one where best of breed is preferred and the other where a comprehensive package makes sense.
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TRANSPORTATION: ROUTING SYSTEMS
An array of affordable solutions offers fleet managers better visibility and higher profitability
Transporting food and beverage products can pose various challenges within the supply chain, including on-time delivery, traceability, and managing fuel costs. Companies that don’t fully address these challenges risk losing out on higher profits and increased customer satisfaction.
Fortunately, with the use of a robust transportation management system (TMS), these obstacles can be handled with ease and give companies greater control over their fleets at the same time.
A maturing TMS solution
TMS systems are constantly improving, including the software platform options. Currently, TMS systems are available on multiple software platforms, with the two most common being on-premise installations and hosted SaaS (Software-as-a-Service) models. According to LeanLogistics, based in Holland, Michigan, the current inclination is toward SaaS, which compared to on-premise installations, offers more benefits.
“Supply chain visibility, cost structure, flexibility, and scalability, as well as business intelligence are key benefits of a true SaaS platform,” states the LeanLogistics team. A SaaS-based TMS system is also deployed much faster than a hosted installation.
Rik Schrader, senior vice president for global sales and supply chain at Plano, Texas-based Retalix, explains that one of the more recent trends in the industry is the ability for clients to completely manage their business on one TMS system.
“We see more of a focus being put on optimizing inbound and outbound operations together from a transport standpoint,” he says. Combining inbound and outbound operations is an important optimization strategy.
What food/bev professionals want
Simply put, there are many advantages to implementing a TMS solution. For starters, having the ability to view your assets while your fleet is en route is highly important, especially when transporting perishable food or beverage items.
“Food and beverage companies have to monitor logistics costs closely,” says Bill Pritz, vice president of transportation solutions for Atlanta, Georgia-based Logility. “Every dollar in savings goes straight to increasing bottom line profitability.”
With the 2011 Food Safety Modernization Act (FSMA) in place, the ability to easily locate where your cargo is can be important from a safety and regulatory perspective.
“A lot of distributors within this market are really looking to get better at honoring traceability initiatives across the board,” says Retalix’s Schrader. “It seems to be a very hot topic with a lot of clients.” Tracking your cargo is also beneficial in order to guarantee the products are going to the correct destination.
“Proof of delivery is (also) a hot topic,” adds Schrader. If something goes awry during a fleet’s trip, the ability to immediately contact a customer is essential to maintain good customer relations and possibly remedy the situation.
Logility’s Pritz states that the “top priority and the leading benefit of TMS” is cost reduction. “Many companies are increasingly concerned with rising fuel prices,” he says. “Advanced solutions have the ability to leverage real-time fuel prices.”
What service providers offer
In order to help perfect a driver’s route, Cary, North Carolina-based MercuryGate offers an optimization tool called Mojo. The solution can help a company look at all of the “what if’s” in route vehicles by creating routing scenarios with specific preferences.
“You can take your historical data, (and) your external data and you could pull all of this in together and look at your operational requirements,” explains Jane Sandifeer, senior solutions manager for MercuryGate. “You could layer all of that information together and look at your strategy more holistically.”
Retalix Transportation Optimization (RTO) is another product designed to improve a driver’s route. RTO uses sophisticated algorithms in order to produce the best route planning possible. The solution is also designed to collaborate with existing management applications, which can make it easier to use. According to Schrader, this not only helps with the ease of doing business, but also drives up “the overall profitability of the operation.”
TMW Systems of Cleveland, Ohio offers Appian DirectRoute to meet the needs of route management.
“It definitely has improved our ability to plan the route, delivery time and the delivery cost,” says Mike Peterson, information technology director at Kohl’s Wholesale, Quincy, Illinois. “We can also get a better handle on customer satisfaction and the number of trucks in our fleet.”
With the use of TMW Systems’ software, Kohl’s Wholesale was able to merge more deliveries on their trucks, thereby making their fleet more efficient.
Logility’s real-time software, Logility Voyager Transportation Planning & Management, is also a useful tool for fleet managers. The software is constantly monitoring activity and issues alerts for any exceptions that arise.
“Logility Voyager Transportation Planning & Management is designed to improve efficiency, reduce costs, and help increase service levels,” says Pritz. “This increased visibility frees the team to focus on improving relationships with customers and carriers, creating a more productive end-to-end supply chain.”
LeanLogistics’ On-Demand TMS software aids in planning routes and also gives a company visibility into their fleets.
According to one customer, “The scalable environment of On-Demand TMS allows us to be flexible and efficient based on changing business needs.”
Meanwhile, the most recent offering from LeanLogistics is their Web-based application, LeanFleet.
“With LeanFleet, companies are able to maximize asset utilization while minimizing costs,” says Matt Ahearn, president of LeanLogistics. According to LeanLogistics, this technology helps shippers handle all aspects of their fleet by providing visibility into street-level routing.
Recently, Ruan Transportation Management Systems transitioned to a new, wholly integrated TMS called RTMS2.0. The product is a customized transportation management solution that combines software from one of the leading TMS providers, other best-of-breed logistics tools, including several load planning tools and a warehouse management application, and Ruan’s custom intellectual property.
“Our goal was to replace our previous TMS with a leading transportation management system that will augment Ruan’s competitive advantage in our core business of dedicated contract carriage and third-party logistics,” noted vice president and CIO, Ben McLean. “This change in technology will allow us to continue to focus on providing customized solutions for our customers, while providing us with industry-wide functionality updates from a preferred software vendor. We evaluated many offerings from some of the largest software companies in the world, and the software we chose ultimately provided the functionality that was best suited to our dedicated and 3PL operations.”
On the horizon
Industry experts are indicating that access to cloud-based services will continue to be a hot trend in the future in order to provide further connectivity for customers.
“The requirement of having visibility and reliability with managing your transportation cycle is critical,” emphasizes MercuryGate’s Sandifeer.
Along with an increase in cloud-based services, experts believe there will be a move toward the use of social media to facilitate easier communications and increase reliable service.
“I imagine that through the use of mobility and then some of the social media components, it will continue to have a play not only in the personal space, but also in the business space,” concludes Retalix’s Schrader.
A Conversation with Kohl Wholesale About TMS
Kohl Wholesale, based in Quincy, Illinois, is a service provider for various food companies. The company uses TMW Systems’ Appian DirectRoute management software, which is designed to optimize routing and reduce costs.
Mike Peterson, information technology director of Kohl Wholesale, spoke recently to Food Logistics about the company’s experience with Appian DirectRoute:
FL: What prompted the deployment to the Appian DirectRoute Software?
Peterson: The major reason was high gas prices; diesel is really going high and we were getting more customers, and more deliveries. Instead of buying more trucks, we decided to look into efficiency routes and see if we could make better use of what we had. We also wanted to better track the expenses on the truck to see how much a route would cost us.
FL: How has the Appian DirectRoute Software improved business?
Peterson: It definitely has improved our ability to plan the route, delivery time and the delivery cost. We can also get a better handle on customer satisfaction and the number of trucks in our fleet. We were able to consolidate deliveries onto trucks to get more out of what we have. The optimization in the direct route program has helped and it has really optimized the fleet and made it more efficient as far as it’s seeing things that even our guys didn’t think would possibly work.
FL: Do you have any tips for other companies looking to implement a similar system?
Peterson: Be prepared for a bit of upfront work. That work that you put on the front end is going to pay off for you in the long term. When you’re setting the system up, have people working on it who know the route [such as] dispatcher-it’s basically going to be a kind of brain-dump as far as what they know of, which can include customer preferences, customer delivery locations and also the unload rates of deliveries. Also, realize that things change with your customer master file when you’re making these deliveries.
Once you have the system, have a couple of people from the company go to one of the user conferences. That was really valuable when myself and another dispatcher went to one of the user conferences about TMW. It was really helpful to see other peoples’ trials and tribulations.
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6 Ways WMS Can Improve Operations
Getting better results with the latest warehouse management systems
In the fast moving world of food and beverage warehouses, reducing spoilage and improving order and shipment accuracy is essential for business. Utilizing the latest warehouse management systems (WMS) can assist warehouse managers towards that goal.
Industry drivers
Recent government regulations coupled with the need to manage and increase warehouse velocity and throughput is driving food and beverage companies to implement and upgrade warehouse management systems.
Earlier this year, the FDA rolled out the Preventative Controls rule, part of the larger Food Safety Modernization Act (FSMA) legislation. The proposed regulation will affect food facilities that manufacture, process, pack and/or hold food products. Additionally, a food facility will be required to have a written food safety plan that monitors and prevents food contamination issues. Product traceability and responsiveness to product recalls are also big drivers to using WMS.
Growing companies are also attracted to the benefits of WMS. According to Chad Collins, chief marketing officer and SVP of Colorado Springs, Colorado-based Accellos, emerging niche food companies that are experiencing rapid growth are often hindered by their manual or paper-based processes.
WMS not only alleviates this challenge, but also provides many other advantages for food and beverage companies.
The top 6 benefits
Keeping the various driving factors in mind, along with the rising demands of the fast paced food and beverage industry, there are various gains associated with implementing software solutions such as warehouse management systems.
Below are the top six ways WMS can improve operations:
Traceability: Food and beverage customers are looking for ways to keep track of the freshness of their products from a food safety perspective and an efficiency perspective. Warehouse management systems give food and beverage managers a closer look into their products and help them determine what shipments need to ship first due to time constraints.
“Traceability has been a big initiative for a lot of food distributors because of the obvious perishability of the products and the management of expiration dates,” says Rik Schrader, EVP of sales for Piano, Texas-based Retalix. “The ability to be very proactive in terms of making decisions as well as making sure that mistakes are not repeatable, it’s really important to these type of operations.”
Additionally, in the event of a recall, a company can effectively locate and assess what products are potentially contaminated and where they were sent.
2. Accuracy and Visibility: Warehouse management systems offer better visibility into the warehouse along with a boost in order and shipment accuracy. Through a centralized software system, managers are able to view the movement of their inventory and ascertain the accuracy of the shipments.
3. Increased Throughput With Integration: Integration with other software solutions and automation systems helps boost throughput in the warehouse.
“What we’re seen in both food and beverage is they’re very focused on speed,” says Eric Lamphier, senior director of product management, for Atlanta, Georgia-based Manhattan Associates. “You’ll go into a very high-end fresh operation and they’re moving fresh fruits and vegetables and other fresh products through that facility in just a couple of hours.” WMS provides optimized functionality to handle high levels of complexity and volume throughput.
4. Improved Labor Productivity: Warehouse management systems can also leverage the power of labor management solutions. These solutions can track warehouse workers’ day-to-day activities, including the work they complete.
“We can understand what their expected time is for what they’re asked to do [versus] the actual time,” says Lamphier. “Then we can compare them and rate them based on their work for the day and have a really nice understanding of how the workforce is performing. You have associates that will hustle and really make some incremental pay based on working harder against the standards that have been outlined.”
5. Reduced Paperwork: As mentioned earlier, food and beverage companies can be hindered by their manual or paper-based processes. Reports that were prepared manually can now be managed electronically.
According to Dan Radunz, senior vice president of product development for Minneapolis, Minnesota-based HighJump, a WMS can “significantly reduce the paperwork traditionally associated with warehouse operations, as well as ensure timely and accurate flow of inventory and information.”
Additionally, creating electronic reports will boost a company’s sustainability efforts, by creating a greener footprint and lessening the need for paper.
6. Better Space Utilization: Food and beverage warehouses can improve space utilization due to a speedier fulfillment process with the use of WMS. With less inventory in the warehouse, holding costs will decrease drastically.
“Space utilization can help with ensuring that we optimally use the space in the facility, which can eliminate their need to buy more facility space,” says Tom Kozenski, VP industry strategy of Scottsdale, Arizona-based JDA Software (formerly RedPrairie).
Customer success stories
Republic National Distributing Company (RNDC), the second largest alcohol beverage distributor in the U.S., uses Manhattan Associates’ warehouse management solutions in their operations. According to Stefan Kirshenbaum, VP and director of distribution and logistics at RNDC, there were some challenges during the initial implementation that took place in Denver, CO.
“The learning curve was a challenge,” says Kirshenbaum. However, “Manhattan provided complete assistance from the beginning to the end of that initial implementation of WMS.”
One of the biggest advantages Manhattan Associates’ WMS offers to RNDC is absolute inventory control along with greater space utilization. RNDC is able to efficiently replenish products, which results in improved productivity for the warehouse crew.
“The ability to do cycle counting rather than wall-to-wall inventory” is also essential for inventory management, Kirshenbaum adds.
RNDC will be starting their ninth installation of Manhattan’s WMS, going live in February in Louisville, KY. After their ninth installation, the company has future plans to install the WMS in another eight locations.
“Our company has a reputation of being at the forefront of technology and we’re proud of our operations throughout our chain and Manhattan has been a great partner in providing a continued effort on our part to be the best in class distribution network within our industry,” says Kirshenbaum.
Cloverleaf Cold Storage, a Sioux City, Iowa-based, refrigerated warehouse firm, has relied on AccellosOne Enterprise 3PL third-party logistics management software since 2004 to maintain high levels of productivity and increase profits.
“Accellos’ concentration on the 3PL cold storage market has given them an extraordinary, in-depth awareness of the field and its challenges, and that is one of the primary reasons we selected them as our 3PL management solution,” said Curtis Mastbergen, vice president of administration and finance for Cloverleaf Cold Storage in a case study. “We wanted an expert in our field, not a vendor to whom cold storage was a sideline.”
Since implementing the Accellos software in many Cloverleaf Cold Storage facilities over the course of almost 10 years, Mastbergen noted the software has helped them to serve their clients both accurately and efficiently.
“We win a lot of customers with needs that our competitors can’t handle,” Mastbergen mentioned. “AccellosOne Enterprise 3PL is a big part of that.”
Philadelphia, Pennsylvania-based Frankford Candy, a privately owned candy manufacturer in the U.S., also utilizes Accellos’ management software. The candy company was looking to advance their electronic data interchange (EDI) capabilities to seamlessly work with trading partners.
“We can bring up a new trading partner in minutes, where before, it could involve weeks of frustrating back-and-forth among us, our EDI vendor and our trading partners,” said executive vice president of Frankford Candy, Nathan Hoffman in a press release. “Major retailers have very specific ways of doing things, and Accellos’ EDI for Dynamics AX accommodates them all very nicely.”
Since Frankford Candy has implemented Accellos One Pinpoint EDI for AX, Hoffman prefers to rely solely on the resources provided by the ERP solution.
“The most painful time in any new technology relationship is at go-live,” said Hoffman. “With the Accellos EDI solution, it was totally pain-free. There were no lost orders and no lost invoices–and so no lost revenue.”
Brooklyn and Astoria, New York-based Empire Merchants, LLC, a distributor of fine wines and spirits, is the largest wine distributor in metropolitan New York. The company ships approximately 10,000,000 units per year. With a high order volume along with storage capacity concerns, Empire Merchants turned to CIBER, an integration company, to better identify what warehouse management system would suit their needs.
Empire’s Brooklyn facility had installed HighJumps Warehouse Advantage system, which handles receiving, returns, inventory and replenishment.
“We chose HighJump Warehouse Advantage as our warehouse management system and CIBER as our integration partner based on their previous successful implementations with other wine and spirits distributors,” said Tony Magliocco, COO, of Empire Merchants, in a press release.
This system replaced a paper-based replenishment method while improving inventory accuracy.
“We couldn’t have picked a better software package and implementation partner than HighJump warehouse advantage and CIBER,” said Magliocco. “The software is working as the project team designed it. As a result, our fill rates have increased and our distribution costs have decreased dramatically.”
What lies ahead?
Moving forward, industry experts are expecting to see a shifting demand for cloud-based WMS along with other types of application software. One of the pros of using a cloud-based WMS is the ability to free up food and beverage managers from worrying about servers.
“The market is more open to cloud-based solutions now more than ever,” says HighJump’s Radunz. “This is primarily driven by the desire to focus more IT resources on core competencies related to serving customers better, rather than managing an IT infrastructure.”
Further enhancements with supply chain tracking and tracing will continue to be a future trend in the industry. With mote stringent food safety standards on the way, the ability to seamlessly identify and track all food and beverage items will assist with staying compliant.
Furthermore, the need for radio frequency identification (RFID) systems will continue to rise. An RFID system increases automation and generates accurate visibility in the warehouse while concurrently lessening misplaced food or beverage products.
“As products move through the facility, we’re not going to require the manual intervention of actually scanning barcodes or entering information into a mobile computer,” says Accellos’ Collins. “A lot of that information will be automatically fed into the warehouse management system via sensors and other technologies.
PHOTO (COLOR): Warehouse management systems can improve labor productivity and increase visibility into the warehouse.
PHOTO (COLOR): Alcohol beverage distributor Republic National Distributing Company (RNDC) uses Manhattan Associates’ WMS solutions.
Tony’s Fine Foods: Stepping Up With Retalix’s WMS
Tony’s Fine Foods, located in West Sacramento, California, has been utilizing Retalix’s WMS solutions since 2003. Before deploying Retalix’s WMS solutions, the company used their own in-house WMS. As with most new software installations, Tony’s Fine Foods encountered a few challenges during the switch to Retalix’s WMS.
“The hardest challenge with anything is that you’re dealing with a partner that doesn’t work for you,” says Mark Geery, CIO of Tony’s Fine Foods. “You have to really stand up for yourself and make sure you cover all the areas that you need to cover. There were certain parts of the software that we had customized and made unique for our needs in our prior in-house WMS, [so] we had to take a little bit of a step backwards in those areas, but the gains of having a robust system with a company that can support it offset that.”
Warehouse managers at Tony’s Fine Foods, like Pete Moody, see the upside to working with Retalix’s WMS software. They are able to seamlessly manage their business and identify potential sources of problems in the warehouse. Additionally, the software helps to properly manage warehouse employees by measuring workers’ productivity and accuracy. The WMS becomes “an extension of them,” says Geery.
Another benefit for Tony’s Fine Foods is having a system with real time inventory and traceability. Previously, the company would wait for invoices to post before the inventory would get updated.
“The fact that we’ve been able to double the size of our business in almost the 10 years that we’ve been on this WMS is a testimony that the software’s been a success,” says Geery.
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Purpose – The purpose of this paper is to present an analysis of how different sourcing policies and resource usage affect the operational performance dynamics of warehouse processes. Design/methodology/approach – The system dynamics (SD) methodology is used to model warehouse operations at the distribution centre of a leading fast-fashion vertical retailer. This case study includes a detailed analysis of the relationships between the flow of items through the warehouse, the assignment of staff, the inventory management policy, and the order processing tasks. Findings – Case scenario simulations are provided to define warehouse policies enabling increased efficiency, cost savings, reduced inventory, and shorter lead-times. Practical implications – The case study reaffirms that a flexible usage of human resources, outsourcing of selected warehouse operations, and sourcing from reliable manufacturers may result in important performance improvements for centralised warehousing. Originality/value – It is proved that SD is a valuable tool in the field of operations management, not only to support strategic evaluations but also to execute a detailed analysis of logistical processes and make scenario-based dynamic decisions at the operational level.
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1 Introduction
The increasing need to improve supply chain (SC) performance has been forcing warehouses to focus on integrating the production effort with the market ([17] Frazelle, 2002; [6] Baker, 2007). Receiving, transferring, handling, storage, packing, and expediting operations at the warehouse directly affect the effectiveness of a company as a whole as well as its quality and logistic service level ([33] Rafele, 2004). In this sense, a proper warehouse management process has become critical to gain competitive advantage through better customer service and shorter lead times ([12] De Koster, 1998).
However, warehouse operations are confronted with a rising complexity tied to nonlinear relationships between performance factors ([14] Faber et al. , 2002) and face increasing costs associated with the need for reducing the time-to-market. This has led SC managers to undertake cost-saving sourcing strategies ([13] De Koster and Warffemius, 2005) integrated with efficiency-oriented management policies ([29] Maltz and DeHoratius, 2004).
Increasing complexity and cost are particularly important to the mass apparel retail industry, where extremely short product life-cycles, seasonality, and unpredictable demand require effectual warehouse operations ([7] Bruce and Daly, 2006). In fact, most of leading mass fast apparel retailers are fashion-followers that exploit the market by bringing new products to their stores as frequently as possible; therefore, a large variety of clothes of diverse sizes, shapes, colours, etc. are designed as late as possible to include the ultimate fashion trends and are produced and centrally distributed as quickly as possible to make them readily available to serve on store shelves in sufficient quantities to assure sales and replenishment ([10] Christopher et al. , 2004). This is the industry that has spawned the agile SC and the philosophy of the quick response as a set of production, centralised inventory, and distribution management policies to increase speed and flexibility ([27] Lowson et al. , 1999; [9] Chandra and Kumar, 2000).
As far as centralised distribution is concerned, appropriate sourcing strategies from a variety of suppliers located in low-cost countries and the procurement of a temporary workforce are crucial elements that increase the system complexity but are important drivers to reach SC competitive advantage ([35] Rollins et al. , 2003; [25] Kumar and Samad Arbi, 2008).
Building on previous research, this work is aimed at understanding how different sourcing policies may affect the operational performance of a distribution centre (DC) by using a case study of an Italian apparel retailer.
The operation of the DC is illustrated by using the system dynamics (SD) methodology. SD is considered to be a useful structural theory for operations management, which provides models as content theories of the real world systems that they represent and is known to be an effective approach for problem solving and evaluating the strategic implications of business decisions ([21] Größler et al. , 2008). In addition, this case study reports a novel deployment of the SD methodology to the discipline of operations and production management with regard to some singular issues. The SD methodology in this study is used for a detailed operational analysis of a business system rather than just focusing on the overall understanding of the systems performance behaviour. Second, the methodology is specifically applied to inventory and warehouse management and proves to be a valuable technique for case-based performance improvements of industrial operations.
The paper is organised as follows. Section 2 analyses previous research on warehouse and inventory management issues. The basics of the SD modelling and simulation approach, together with its relevant applications, are presented in Section 3, whereas the main characteristics of the fast-fashion industry as well as an introduction to the case study are detailed in Section 4. The case-study model and simulation are illustrated and the results are discussed in Section 5. Finally, we draw conclusions and give future research directions.
2 Relevant research
The experience reported in this case study builds upon previous research in the fields of inventory management and warehouse operations management. Inventory management is a well-covered stream of operations management explorations providing many models and approaches for different situations ([40] Williams and Tokar, 2008).
Warehouse operations management is also a well-covered topic of research. Some authors discuss the requirements and the benefits of effective warehouse processes ([22] Gunasekaran et al. , 1999; [32] Petersen II, 1999). Other works give suggestions on how to organise stockrooms and replenishment systems ([26] Landers et al. , 2000; [24] Huq et al. , 2006), about the integration of warehouse complexity with tailor-made control structures ([14] Faber et al. , 2002), and regarding the links between resource allocation and warehouse decision-making processes ([41] Zomerdijk and De Vries, 2003).
However, little work is available to show the benefits of integrating inventory control with warehouse management and to propose effective approaches for integrated decision making at both strategic and day-by-day levels. To answer this deficiency and contribute to the state-of-the-art, we propose the use of SD as a structural methodology for operations management and, in particular, to support warehouse decision making based on detailed modelling of the interrelated factors affecting the warehouse operations performance and inventory management of a DC.
3 SD approach
Founded on system thinking ([16] Forrester, 1961), SD is a computer-based modelling and simulation approach that assists in solving complex problems.
SD allows one to diagram a system of causally looped variables, define the mathematical relations between them, and instruct a computer do the discrete-step computational effort of solving the differential set of equations ([37] Sterman, 2000).
The trends of all variables out of computer simulations are plotted over a specified period of time into the future. The validation of the model is based on historical data and sensitivity analyses.
SD provides an understanding of the overall performance behaviour of the system and of the influence of the various factors to the problem to support policy design by making simulations of different scenarios ([19] Greasley, 2005). As a quantitative modelling methodology, SD allows the explanation of performance factors of real-life processes and capturing decision-making problems faced by managers.
In the field of SC management, SD contributes to the task by adding human-bounded rationality, information delays, managerial perceptions, and goal-setting approaches to inventory management traditional rules and control theoretic models ([1] Akkermans and Dellaert, 2005). Yet, the number of practical applications of SD to SC that have appeared in academic literature is still limited.
Some works are concerned with the dynamic behaviour of SCs, both in the manufacturing and service sectors ([2], [3] Anderson and Morris, 1999, 2000; [37] Sterman, 2000; [30] Panov and Shiryaev, 2003; [36] Schieritz and Größler, 2003; [4] Anderson et al. , 2005; [34] Rafele and Cagliano, 2006); others involve demand planning ([5] Ashayeri and Lemmes, 2006), capacity control, and inventory instability ([39] White, 1999; [11] Croson and Donohue, 2005), just-in-time production, data-driven and process-driven performance improvement and control ([23] Gupta and Gupta, 1989).
Our model was developed to provide the case company with a valuable decision support system to implement dynamic management policies enabling performance improvement of inventory and warehouse integrated operations. Through an iterative process of scenario planning and the evaluation of outcomes and the changing of policies, insights into the dynamic nature of the organisation have been acquired ([20] Größler, 2007). In particular, the simulation was mainly directed to ascertain the impacts of different sourcing policies for the tasks of counting incoming items and allocating warehouse personnel.
The model shows that the application of SD to business process improvement is of great practical value. This in turn demonstrates that SD can be used not only for strategic thinking but also for detailed case-based modelling and for supporting performance improvement actions in SC management.
4 The case study
This work analyses warehousing operations at the DC of a mass-fashion vertical player: Miroglio Fast Fashion Division (Miroglio), part of the Miroglio group of companies ([8] Cagliano, 2010).
Typically, mass fast-apparel industries are global buyer-driven value chains with very short time-to-market requirements that design, procure from low-cost manufacturing sources, distribute and sell, in company-owned stores, short life-cycle clothes intended to capture the consumers’ mood and edging fashion of the moment ([18] Ghemawat and Nueno, 2006).
Market volatility, unpredictable demand, and impulse purchasing approaches make the business turbulent and dynamic. These qualities demand short manufacturing and distribution lead times, which can be achieved through a variety of means, such as automated warehousing, fast transportation, and improved manufacturing methods ([15] Fisher and Raman, 1996). This type of business also requires prompt management reactions, timely decision making, and flexible sourcing strategies.
This kind of quick response management approach is currently applied by the most successful international companies, such as Inditex-Zara, Hennes&Mauritz, The Gap, Benetton, Miroglio, and Mango.
Miroglio, headquartered in Alba, Italy, sells women’s garments and accessories at accessible prices through the Motivi, Oltre, and Fiorella Rubino brand chains. By the end of 2008, with a total annual turnover of more than [euro]1 billion, the company had produced approximately 20 million clothing items while operating more than 1,550 mall and town centre brand stores as well as 130 outlets around the world.
Product design is performed centrally. Production takes place partly in company-owned factories located in North Africa and partly through offshore low-wage fashionists. Worldwide distribution is managed centrally through the DC, which has 6,000 square meters of usable floor area, is located close to the headquarters, and is equipped with a huge automated sorting conveyor system. A partnering global freight forwarder executes shipments via air and truck transport.
To ensure it achieves its time-to-market goal of six to eight weeks, Miroglio is committed to perform all of these functions as quickly as possible and, in particular, to make centralised distribution operations more efficient.
4.1 Warehouse operations at Miroglio
At a glance, Miroglio’s warehouse operations are structured as follows. Supplies from owned factories and fashionists are shipped to the DC, either directly or through an intermediate platform located in central Italy. The supplies are then counted, stored, picked, sorted, packed, and shipped to retail stores (Figure 1 [Figure omitted. See Article Image.]).
In more detail, boxes arrive on a truck at the DC, are unloaded by assigned teams of workers and placed in the incoming product floor area. Urgent items are picked from incoming boxes and sent directly to the sorting system, to make them available for quick loading and shipment. The remaining items are received, counted either manually or via the automated sorting system to match the receiving list with the order placed, and stored in barcode-labelled boxes, to allow for periodical inventory update. Each box contains items with uniform combination of item, size, and colour. Then the variety of items ordered by store managers is ordinarily picked and, together with urgent items, automatically sorted into boxes. Finally, assorted boxes are loaded on outgoing trucks and shipped to the allotted retail stores.
4.2 Methodology
Challenged with the issue of increasing the efficiency of warehouse operations at the DC, our research group engaged into the development of a model with the goal of understanding the effects that different supply sourcing policies and warehouse personnel usage may bring to the operational and economic performances and to support the company with a viable decision-making system at an operational and detailed level.
In particular, the company asked whether the task of item count should be outsourced and about evaluating the extent to which personnel flexibility, obtained through seasonal manpower, may benefit operations performance.
To this end, we applied the SD methodology. A few considerations led us to select this simulation methodology rather than other suitable ones, such as discrete-event simulation (DES). First, SD appeared to be suited for representing nonlinear processes, such as warehouse operations. Second, the company needed a model to depict the entire warehouse management structure, and SD is considered to be focused on the analysis of systems as a whole, while DES usually models particular processes. Third, the company’s request for a tool to support decision making required a system that allows for the analysis of policy options. Finally, decision making requires the ability to understand events that might change model variables. This knowledge is assured by the attention SD gives to feedback, whereas in DES, the parameters are often fixed once entered into the model ([38] Sweetser, 1999).
The process of building the SD model was developed according to the directions given by [28] Lyneis (1998) and was accomplished over a period of six months. First, we worked on understanding, structuring, and analysing the flow of clothing items all of the way through the DC, from receiving to shipment. Information and data were collected and analysed with the help of process stream mapping, interviews with logistic managers and employees, and direct observation of operations.
Then, we developed a quantitative SD model of the warehousing system with stocks, flows, causal feedbacks, and mathematical equations ([37] Sterman, 2000). The understanding of the cause and effect relationships among the system variables clarified the main elements of complexity.
The model was then validated through historical data curve fitting, robustness assessments when confronted with extreme exogenous conditions, and sensitivity analyses associated with random values of variables.
After validation, simulations were run under many case scenarios to capture the impact of different management policies on warehouse activities and inventory levels.
5 The SD model
5.1 General structure of the warehousing model
The whole SD model of Miroglio’s warehouse operations may be decomposed into a few interconnected sub-systems associated with each phase of the logistic flow of items shown in Figure 1 [Figure omitted. See Article Image.].
In particular, each process phase is represented by a stock of clothing items flowing out to the successive process step. For example, the stock of unloaded items decreases as clothes flow to either a stock of urgent items or an accumulation of items to be counted, and so forth, throughout the warehouse workflow. Regardless of any sequencing or overlapping, all tasks share the same human resources, and the picking job is mandated by incoming orders.
The model structure also considers the cost of operations, so it is able to represent both time and economic performance metrics.
The model and associated differential equations have been developed using the Vensim® DSS software package by Ventana Systems. The simulations were performed with Euler integration, with one-day time intervals, and a simulation horizon consistent with one season (26 weeks composed of five working days).
Because there is no space here for an extensive presentation of the complete system, the following is an overview of the sub-systems of the SD model and a detailed description of the “Count” section, which is the only section directly connected to the decision-making process discussed in the next sections. The reader may ask the authors for the complete model and mathematical equations.
5.2 Subsystems overview
The structure of the SD model is as follows. The number of items entering the DC in each time interval is the input flow for the “Unloading” section of the model. This calculates the unloading rate based on the number of items to be unloaded, people assigned to this activity by the “Human Resources” sub-model, and their productivity. A portion of the unloading rate feeds the “Urgent Picking” section of the model, whose aim is to determine the urgent picking rate in a similar way as the “Unloading” sub-model. A second portion of the unloading rate enters the “Count” section, while, as far as the “Inventory” is concerned, the storing rate determines the level of inventory. This, together with confirmed orders, quantifies the required picking workload and, in turn, the ordinary picking rate. Ordinary and urgent picking rates join together in the “Sorting” sub-model to feed the stock of clothing items waiting to be sorted by the automated conveyor. In this case, the sorting rate depends not only on staff productivity but also on the productivity of the automated sorter. Finally, the sorting rate is the input flow to the “Loading” sub-model, which is very similar to the “Unloading” section.
Also, two organisational processes are included into the case model, namely human resources and order management. On the one hand, human resources flow among several stocks of staff devoted to execute different operations, such as unloading, count, picking, etc. At the beginning of simulations, all operators are part of the stock of available staff, while the other stocks are empty.
On the other hand, in the “Orders” sub-model, four input flows represent the incoming orders according to their nature and geographical origin. These form stocks of orders to be fulfilled, whose output flows represent order fulfilment rates calculated taking into account the picking rate evaluated in the related SD sub-model.
5.3 The “Count” section of the model
The task of counting the number of unloaded items is one of the most sophisticated tasks at Miroglio’s warehouse. Non-urgent incoming items undergo a specific process according to the supply source.
The complete “Count” section of the SD model is shown in Figure 2 [Figure omitted. See Article Image.]. The following description aims to analyse each specific part of this sub-model.
The representation of the different flows of incoming clothing items is in Figure 3 [Figure omitted. See Article Image.].
Incoming items from the external platform, where items are checked against orders, flow straight to inventory.
The items coming directly from vendors are counted either manually or automatically by way of the same automated sorting conveyor, which is later used for sorting the outgoing boxes. More specifically, items from new or unreliable suppliers are automatically counted, while items from reliable fashionists are manually sample enumerated. If the sample item count is not compliant with the expected quantity, a complete manual count is performed.
The “Rate of items to automated count” is the rate of items counted by the sorting carousel and is defined according to equation (1): Equation 1 [Figure omitted. See Article Image.] where: Equation 2 [Figure omitted. See Article Image.] Equation 3 [Figure omitted. See Article Image.] The automated item count process is shown in Figure 4 [Figure omitted. See Article Image.]. The sorter counts the full delivery. The operators load items onto the machine; a conveyor passes items under a barcode reader, which registers their model, version, and size. Then the sorting conveyor puts items into boxes, one for each combination of item, version, and size, which are stocked in the storing area.”Automated count rate” is a function of the “Work required for automated count”, the actual productivity of staff operating the automated sorter, and the number of staff assigned to automated count. However, because the conveyor also sorts outgoing boxes, the staff must perform both counting and sorting. Thus, the “Automated count rate” is also negatively influenced by the work required for picking items from the carousel, as per equation (4): Equation 4 [Figure omitted. See Article Image.] Finally, the manual sample count process is shown in Figure 5 [Figure omitted. See Article Image.], where two rates flow out of the “Sample count queue”, namely, the “Sample count rate” and “Rate of items to re-count”.
On the one hand, the “Sample count rate” includes those items that can be stored after a successful count. This is a function of the number of operators assigned to manual counting and the productivity of staff assigned to the counting (“Sample count productivity”), which in turn depends on the actual productivity of the staff assigned to manual counting (“Manual count productivity”), the “Sample size”, the maximum productivity of staff taking care of manual count (“Maximum manual count productivity”), and the “Staff usage” (equation (5)): Equation 5 [Figure omitted. See Article Image.] On the other hand, the “Rate of items to re-count” is the input flow for the portion of the SD model that depicts the full manual re-count job when the sample count is not successful. This has a similar structure to the previous sections of the model (Figure 6 [Figure omitted. See Article Image.]).
5.4 Model validation
The model was refined and validated through historical data curve fitting and robustness and sensitivity analyses.
The curve fitting analysis compares simulated results against historical series of data recorded during the spring/summer 2007 season. This analysis allows for refining the model when large discrepancies arise until an acceptable level of curve fitting is available.
For example, Figure 7 [Figure omitted. See Article Image.] shows the actual curve line of the “Rate of incoming items” against the simulated results out of the final refined release of the model.
Here, deviations between the two curve lines arise from week 17 to week 20 because the model does not consider a few Italian holidays, and the model fails to take into account the new orders for the coming fall/winter season at week 26 (which is out of the simulation timeframe).
Despite minor discrepancies, these results, together with other curve fitting analyses, suggest that the final release of the model appropriately replicates the real warehousing system. The model also proved to be robust because most of simulations under extreme values of the most important exogenous variables resulted in an acceptable behaviour of the system.
Finally, we performed univariate and multivariate sensitivity analyses ([37] Sterman, 2000) to evaluate probability distributions of relevant outputs, together with their confidence bounds.
For instance, Figure 8 [Figure omitted. See Article Image.] shows the results of the univariate analyses simulating two stocks associated with counting tasks when the exogenous parameter “Percentage items from reliable suppliers” changes randomly out of a uniform distribution between 0 and 70 per cent.
The diagrams show the confidence bounds within which the output values can be found with a probability of 50, 75, 95, and 100 per cent, and demonstrate that the stocks are highly susceptible to changes in the quantity of items that come from reliable supply sources. This means that changing the supply source is a relevant driver of performance and an important factor to consider in making warehousing process improvements.
5.5 Case scenarios and discussion of results
Table I [Figure omitted. See Article Image.] reports a short list of performance parameters that are output to the model and are used to quantify the behaviour of the warehousing system when organisational changes are introduced.
In particular, with the purpose of supporting the decision-making process, the validated SD model was used to assess the effects of potential policies to increase the efficiency of the item count activity. To stress the relationship between warehouse and inventory management, this discussion will first focus on the impacts of different item count strategies. Then staff and economic considerations are provided.
We present two case scenarios. Scenario No. 1 evaluates the implications of outsourcing the count task by varying the share of items received through the third-party logistic platform from 0 to 100 per cent.
Figure 9 [Figure omitted. See Article Image.] show how the average seasonal values of inventory level (a) and the average number of warehouse staff assigned to manual count (b) vary with the fraction of items coming from the logistic platform.
Figure 9(a) [Figure omitted. See Article Image.] shows that inventory levels increase as more of the counting task is outsourced to the logistic platform because of a reduced time spent at the DC on item count. At the same time, with more outsourcing, fewer staff are required to perform the counting job, as shown in Figure 9(b) [Figure omitted. See Article Image.]. The square marks represent the current average values of inventory (about 370,000 items) and workforce (four persons) associated with the share of items currently coming at Miroglio’s DC from the logistic platform (30 per cent). The two plots suggest that, if the number of staff is not fixed, the required number of operators is between 2 and 0, depending on the fraction of items coming from the platform. This in turn allows for savings on labour cost without affecting the inventory level significantly.
Scenario No. 2 focuses on the consequences of changing the percentage of items sourced from reliable suppliers, and thus subjected to sample counting, while keeping constant the current portion (approximately 30 per cent) of items received from the third-party logistic platform. Therefore, the fraction of items from reliable suppliers ranges from 0 to 70 per cent. Figure 10 [Figure omitted. See Article Image.] shows indications on how the average seasonal values of inventory levels (a) and the average number of warehouse staff assigned to manual count (b) vary with the fraction of total items coming from reliable vendors.
Figure 10(a) [Figure omitted. See Article Image.] shows that the curve line of average inventory levels has its minimum value associated with a 30 per cent fraction of items from reliable suppliers, with small increases away from this point.
Figure 10(b) [Figure omitted. See Article Image.] reports the straight-linear increase of the number of staff required with the fraction of items sourced from reliable manufacturers. This proves that the larger the reliable source base, the more the staff necessary for the manual count. In turn, this reduces the time for the automated conveyor to support the item count duty and increases the time the sorter can be used for the more appropriate task of sorting outgoing boxes.
Here, also the square marks plot the average values of inventory (373,000 items) and workforce (four persons) associated with the share of items currently sourced from reliable suppliers (40 per cent), as per the first scenario.
The two graphs highlight that, if the number of staff is not fixed, the required number of operators is between 2.6 and 0, depending on the fraction of items from reliable sources. Similarly to the first case scenario, this enables manpower cost savings without remarkable consequences on the level of inventory.
So far, we could examine the operational efficiency brought by changing the supply mix. However, these aspects have to be completed with the economic outcomes of the case scenario simulations.
To this end, as far as Scenario No. 1 is concerned, Figure 11 [Figure omitted. See Article Image.] shows the total cost of warehouse operations calculated by the simulation at the end of the season as the fraction of items coming from the logistic platform is varied.
The associated regression line shows that savings up to [euro]164,000 can be obtained per season. For example, a 10 per cent increase in the quantity of items coming from the platform would bring savings of approximately [euro]13,000 per season.
Also, the square mark shows that the cost Miroglio currently faces is the greatest. According to these results, if the company changed the way the staff is assigned to count (from a fixed number approach, to the variable one suggested in this paper), about [euro]75,000 per season would be saved, assuming the portion of total items coming from the platform remaining fixed at 30 per cent.
Considering Scenario No. 2, Figure 12 [Figure omitted. See Article Image.] shows the total cost of warehouse operations calculated by the simulation at the end of the season as the fraction of items coming from reliable suppliers changes. Here, it is shown that an increase of one tenth in the portion of items coming from reliable suppliers would bring savings of [euro]10,000 per season. Also in this case, Miroglio currently faces the greatest cost (square mark).
Moreover, the company would also save a relevant amount of money when using flexible human resources, regardless of the fraction of items sourced from reliable vendors. Simulations show that the total cost of warehouse operations is kept at a minimum when both the staff allocation is based on actual work required and the fraction of items from reliable manufacturers equals its maximum value (i.e. 70 per cent of total items). In such a scenario, the expected cost is [euro]921,000, with approximately [euro]106,000 saved per season.
Finally, the simulations also give insights about the opportunity of outsourcing the count task: when all items are set to arrive from the platform, savings would attain [euro]0.04 per item. This unit cost is the maximum additional expense Miroglio may be willing to pay for having the count job performed by a supplier.
Similarly, if all items currently purchased from unreliable suppliers were purchased from reliable sources, [euro]106,000 per season would be saved, which represents a ceiling additional price for compensating vendors for reliability.
All simulation results confirmed mental models of Miroglio’s management.
6 Conclusion
This paper discusses the application of SD to a case study of the detailed warehouse operations of a vertical manufacturer of mass clothing items. This approach has been used to study the relationships linking warehouse with inventory management and to understand the complex behaviour of a DC. In particular, the proposed case study suggests that a more flexible usage of human resources, outsourcing of selected warehouse operations (such as item count), and sourcing from reliable, yet more expensive, manufacturers may result in cost savings, reduced inventory, and shorter warehouse lead times.
It is also proved that SD is a valuable supporting tool not only for strategic evaluations but also for making decisions and taking managerial actions aimed at improving the performance of detailed warehouse, inventory, and logistics operations. The model can be used as a flight simulator to anticipate any consequences of various policies by leveraging on a few parameters and as a supporting tool for continuous managerial learning resulting from ongoing process feedback.
However, this approach poses limitations in the sense that the application of the proposed model is highly specific to the particular case study on warehouse operations reported ([31] Pegels and Watrous, 2005). Moreover, a SD model gives the current picture of a system, so it must be constantly updated to include the latest organisational changes. Finally, simulations predict behaviours arising from particular case scenarios and assumptions, which require post validation because the best way to assess the responsiveness of a case model is to compare real world performance records.
This study is part of the Miroglio SC reengineering project performed in conjunction with the research group for Engineering Systems and Logistics of Politecnico di Torino. The authors wish to acknowledge the Miroglio Group and, in particular, Mr Piero Abellonio, Logistic Director, for his active collaboration, support, and permission to publish this study.