Rollon Gmbh optimizes stock and picking systems.

Technology advancements have facilitated quick and simple communication between individuals over short and large distances. The increase in the international movement of ideas, culture, and money is a result of the high level of human interaction. These changes are referred to as globalization by Hay and Marsh. Economic transformations are felt all around the world when individuals, businesses, and governments work together. Businesses have begun to separate the operations involved in creating completed goods from raw materials and in getting those things from suppliers to customers. The cost of telecommunications and information technology has been declining, which has fueled the expansion of specialization in global markets. The global reach has made it possible for companies to carry out their respective processes at the most favourable prices and quality by getting the skills and materials needed wherever they are available (Hay & Marsh). With the current high growth being experienced in the global market, competition has increased tenfold. The changes brought about by globalization have influenced a shift of strategies and objectives by organizations, with an aim of achieving customer satisfaction, which is realized through the implementation of customer centric practices. For global organizations to remain relevant in the market, they must be able to integrate several practices into their operations. These practices include timely delivery of products, maintaining credibility in their respective fields, and the introduction of new services and products in the market before the competitors do (Rodriguez et al.).

The rapid growth of e-commerce over the past few years has fuelled competition levels, which has forced several businesses to consider optimizing their processes for more efficiency and higher profit margins (Le-Duc). In e-commerce, most of the incurred expenses are related to warehousing running costs. Warehouse optimization enables the organization to have smooth and efficient processes, and also helps the organization to earn customers’ trust in terms of delivery. In general, the supply chains have been totally restructured for more efficiency, which has enhanced industrial alliances. According to Chang et al., the changes can be attributed to the advancements made in technology, which have also shrunk geographical distances and enabled the timely exchange of information among purchasers and their suppliers. With the expanding market and increasing operations, organizations have to employ cutting edge strategies to maintain a balance. These technologies need to be integrated with efficient logistics and manufacturing policies for more efficiency (Rodriguez et al.). These policies can be leveraged for efficient electronic and physical flow of information, which can reduce system costs. Fulfilment centres for e-commerce should be processing small orders associated with high picking costs at higher volumes. According to Van den Berg, most modern companies are engaging in the practice of using minimal inventories in the logistic chain to achieve high-volume production and reduce response time. These companies are also replacing their several initial small distribution centres with fewer larger ones that are capable of serving larger geographical regions. These warehouses and distribution centres integrate order picking processes that are driven by orders paced by customers, to pick orders from storage locations. The efficiency and effectiveness of these systems facilitate to the overall profitability of the company.

Inventory Optimization has also been an area of focus for most organizations lately. The high interest can be related to the fact that inventory is one of the critical factors that influence the profitability of the business (Daniel & Rajendran ). Monitoring the inventory has become a core process in today’s business world. Many organizations are focusing their investments on technology aimed at addressing inventory fulfilment. Some of the problems related to the lack of inventory optimization include rise in operating costs, which is associated with the increase in inventory, fulfilment, wage rates, excess inventory, and real estate costs. Lack of proper inventory optimization also leads to loss of sales, which is facilitated by poor customer service, and high out-of-stock levels. Out-of-stock levels may lead to the loss of loyal customers. Without inventory optimization, the organization also lacks customer behaviour insights, which facilitate customer satisfaction. The organization may also be faced by high labour turnover, among other problems.

According to Adams, out-of-stock situations are the main problem linked to lack of inventory optimization in a business. Lack of proper inventory management tactics affects the ability of the organization to retain customers. Caggiano et al., argues that retaining customers is by far more profitable for any business than getting new customers. However, many businesses tend to focus on attracting new customers, and ends up losing existing customers due to a low quality of service. When a company such as Rollon GmbH brings in a new customer, those in charge of sales should aim at enticing the customer to buy more products. With low stock levels, completing sales, up-sells and cross-sells becomes more difficult for the sales team. Customers may also lack their desired products, which would mean loss of sale to the company. Effective and efficient inventory optimization can be used to prevent these problems. Organizations should have systems in place for sales associates to have real-time inventory, where they can also place orders to avoid out-of-stock situations. A good inventory optimization strategy would help a business through improved customer service and higher sales levels (Adams). Apart from preventing the loss of a sale, having sufficient inventory can help boost up-sell and cross-sells.

Stock optimization entails getting the perfect balance between supply and demand, with an aim of increasing efficiency and cutting down costs. Stock optimization enables the managers to carry out the complex activities related to stock management more effectively and efficiently. Stock optimization goes hand-in-hand with order picking optimization. Order picking entails high costs, which can amount to over 50% of the total costs incurred in operating the warehouse. The reason for the high costs can be linked to the involvement of human order pickers, or large investments required in integrating automated order pickers. The high costs involved at this stage have made the optimization of order be one of the high interest areas for optimization among supply chain professionals. This paper is centred on the optimization of stock and picking systems by Rollon GmbH.

Rollon GmbH

Rollon GmbH is a company that was founded in 1975 as a manufacturer of high precision linear roller bearings, which were used in the machine tool industry. Later on, more services were added, such as the Compact Rail Linear Bearings, Easy Rail Linear Bearings, and the Telescopic Rail Industrial Drawer Slides. These services enabled the company to establish itself in the industry, and it is on this foundation, the company has continued to build upon. More product innovations and development of new products has enabled the company to emerge as a market leader. Currently, Rollon operates through its main office which is located in Vimercate (MB), Italy. The company has other operation branches in France, Germany, China, USA, the Netherlands, India, and Russia. The company also has a wide network of distributors that enable the smooth running of operations. Also, since 2008, Rollon has extended and increased its operations in emerging markets.

The acquisition of El.More in 2011 enabled Rollon to expand its operations to include the manufacturing of linear actuators and tables, normally used in industrial automation (ROLLON). Experience and skills gained by the company over years of successful and profitable operations have enabled the company to develop several strengths in the market. One of the main strengths that give Rollon a competitive advantage is its capability to offer standardized and customized products to meet individual customer needs. According to the company website, the products manufactured are suitable for application in aerospace, railways, logistics, industrial machines, medical equipment, specialty vehicles, robotics, and packaging (ROLLON). Rollon’s mission is to meet the linear motion requirements of its customers by supplying mechanical components. The company aims at becoming a global leader in the production of linear motion equipment and a key player in the manufacture of applications for various industrial sectors that use linear motion.

Advancements in technology have also impacted the way in which Rollon carries out its operations, from the manufacturing process to the delivery of finished products to the respective clients. For effectiveness and efficiency in its operations, Rollon leverages the use of cutting edge systems. Considering that most of the company’s customers are international, the organization needs to optimize its systems and operations to cut down costs and increase profit margins. Some of the areas that can be optimized effectively include stock and order picking systems.

Warehousing

It is normal for businesses to have warehouses, as they are one of the most important parts of every logistic system. The main purpose of warehouses is to establish a link between the customers and the producers, which enables the availability and timely delivery of products. Another reason is the provision of storage space for products, which can also be used as buffer. Products that need storage may include raw materials, parts, or finished goods. Warehouses entail management systems, several integrated operational systems, and a committed workforce who contribute to their effectiveness and efficiency. Some of the most common systems include storage systems such as pallet racking, cantilever racking, mezzanine, horizontal and vertical carousels, among other systems. Order picking systems are also integrated to help in order selection processes. Systems such as the “piece pick” are the most common order picking systems employed, especially by internet retailers and catalogue companies. The wide usage of these systems can be associated with customer ordering behaviour, as case quantity orders are rare. A customer order with several different products at few quantities requires more time and costs to fulfil, as several factors have to be considered. For example, the piece-pick system entails factors such as the order, picker, picking module, pick location, picking device, capacity of picking device, pick strategy, and information technology employed in the process. Most warehouses integrate warehouse management systems (WMS), to improve efficiency and maintain accurate real-time inventory.

Some organizations choose to fully automate their warehouses, and employ few operators to handle the necessary tasks. In automated warehouses, products and pallets are controlled by automated storage and retrieval systems and cranes, and are moved on automated conveyor systems. Systems are coordinated by logistic automation software running on computers, and Programmable Logic Controllers (Dallari et al.). There are several factors that may influence an organization to integrate full automation in the warehouses. One of the factors is the nature of the product. For instance, in refrigerated warehouses where products are kept at very low temperatures, an organization may opt to automate the warehouse. In other cases, high-land rates may force an organization to construct high warehouses with automated systems to leverage vertical space. Warehouses with high storage spaces and automated systems can stretch up to 40 metres high.

Optimization is necessary for any warehouse to function effectively and efficiently. All the systems including the storage, and order picking systems should be optimized for more efficiency. Optimization enables the organization to fulfil customer orders in a timely manner with minimal costs. Integrating inventory management systems facilitates the flow of products in and out of the warehouse more efficiently. An optimized inventory management system would enable the organization to avoid out-of-stock situations, as buffer will always be available at the warehouse. Optimization enables the organization to maximize on sales, achieve high profitability, and reduce operation costs (Arbidane & Volkova ). Recent trends in warehousing show that companies are currently more focused on optimizing their inventory and order picking processes. Poor inventory management has been identified as one of the leading causes of business closures. On the other hand, order picking is associated with ever-increasing costs, especially with the decrease of pallet orders and increase in low-volume orders. Successful optimization of order picking and inventory can enable a warehouse to achieve maximum efficiency levels, which translates to higher organizational profits.

Order Picking

Order picking is among the most significant activities in a warehouse. Customer orders are placed with details of the order such as type and quantity. According to Khalil, order picking involves individuals known as pickers, who pick products from their storage locations in the warehouses, for the purpose of fulfilling customer orders. The whole activity of order picking has been associated with over 50% of all warehouse operating costs. There are several approaches to order picking, developed to ensure the process is fully optimized. These approaches are commonly known as Order Picking Systems (OPS). These systems are stuctered depending on several elements of warehousing, including the layout of the warehouse, products, the types of customer orders, among others. According to Dallari et al., order picking systems are classified according to decisions made in the designing phases of order picking systems. These decisions include:

How will the products be picked from their respective storage locations? Will the organization employ humans or machines?

Between the pickers and products, who will move to the other in the picking area?

What picking strategy will be used?

Will there be use of conveyors to transport the picked products?

Figure 1: The figure shows how Order Picking Systems are classified.

Order Picking Systems

The following are four Order Picking Systems according to Dallari et al.

Picker-to-Parts

The system involves human pickers, who move around the warehouse and picking products. The pickers usually move on foot and use carts to collect the products, or ride on specialized vehicles that move around the warehouse. The system is further divided into two subtypes: The first subtype is the low-level picker-to-parts system, which entails products that can be accessed easily from the warehouse floor. The second subtype is the high-level picker-to-parts system, which entails the usage of trucks and cranes to reach products stored higher in the racks.

Pick-to-box

The system also involves human picker picking products along the aisles of the warehouse. However, the picking area in this case is segmented into many different areas, and each area is assigned to a specific picker. In this system, an order may be picked sequentially by zones, or simultaneously. In some warehouses, the picking zones are usually connected by a conveyor belt, which is used to move complete and partially complete orders. Conveyor belts help in the reduction of time used by pickers to move around the different zones.

Pick-and-sort

The pick-and-sort system is a more efficient order picking system than the above. This system entails a process where products are picked independently and moved to a sorting area through a conveyor. In the sorting area, an automated system sorts and assembles the products according to individual customer orders. The system reduces visits to picking locations, which reduces travel time and increases productivity. Implementing such kind of a system is associated with high investment costs.

Parts-to-picker

The parts-to-picker system entails the use of automated devices to perform various tasks in the order picking process. For example, an automated system can be used to retrieve pallets from storage and move them to pick locations. After the pickers select the products they require, the rest is moved back to storage by the automated system. Some common types of automated systems and devices include the modular vertical lift modules, Automated Storage and Retrieval Systems (AS/RS), and carousals. The use of systems makes the order picking process more efficient and reduces time spent in order picking.

Order Picking Optimization

Many organizations adopt the low-level, picker-to-parts systems. According to Moeller, the low-level picker-to-parts systems account for more than 80% of all the total systems adopted by all the organizations for order picking in Western Europe. Other organizations have incorporated high-level picker-to-parts, and AS/RS systems. Order picking optimization enables the organization to save in terms of labour costs. The organization’s brand also strengthens as a result of improved customer service, especially shortening delivery time. The organization is also able to minimize cases of overtime and temporary employment of staff in the short-run.

The optimization of order picking aims at reducing the total time spent in the process. The time also entails travel time between pick locations in a warehouse, and pick locations and the order fulfilment centre; the total time spent searching for the products at the pick location; time spent picking orders, in most cases picking devices are used and this may consume time; and the set-up time, which mostly entails updating inventory, and emptying picking devices. Of all sub-activities included in order picking, traveling consumes the most time. The time spent in the other sub-tasks tends to remain constant with minimal changes associated with optimization efforts. This makes travel time the focus of optimization. According to Moeller, some interdependent policies should be taken into account for efficient optimization of OPS. These policies include: layout design, storage, order consolidation, and routine policies. When an organization is constructing a new warehouse, optimizing the layout may contribute to order picking optimization. However, in the existing warehouses, layout optimization is not easily applicable, unless the company is willing to spend on restructuring the entire warehouse. Rollon already has existing warehouses located in different places around the world. The figure below shows the order picking optimization approaches and associated policies.

Figure 2: Order picking optimization approaches and the associated policies

Storage Policy

Storage policy deals with activities that take place when new products are brought to the warehouse.

Dedicated storage. The policy maintains that every product to be assigned a single storage location, where it will be stored over time. This policy is advantageous to the pickers as they get to become more familiar with the locations of inventory in the warehouse, which can lead to a reduction of time spent in order picking. The negative side of this policy is that it leads to underutilization of storage space. The reason for this is because the storage location is left empty when the respective product is out of stock.

Randomized storage. The policy maintains that a random empty location to be assigned to any incoming product. This policy leads to the storage of the same type of product across different locations in the warehouse. The storage system helps in efficient utilization of storage space. According to Ferrari et al., randomized storage is more efficient and effective when implemented in a warehouse with computer-guided systems.

Closest open location storage. The policy is similar to the randomized storage policy, but requires the order pickers to identify the closest empty locations themselves.

Order Consolidation Policy

This policy is concerned with the generation of picking lists used by pickers in the warehouse. Customer orders are transformed to different kinds of picking lists depending on the unique order picking strategies employed by different warehouses. For instance, in the single order picking strategy, a picking list is generated directly from the customer’s order. Order batching entails picking multiple orders at the same time. According to Ferrari et al., order batching is applied best in cases where the size of the order can be easily handled by the picking device or the picker. Order batching is an effective strategy, but it also leads to the development of the order batching problem. The batching problem arises when considering how to handle the order with minimal picking distance. According to Ferrari et al., there are three strategies that can solve the order batching problem. These strategies include:

Priority Rule-Based algorithms. There are two phases of priority rule-based algorithms. Every customer order is awarded priority in the first phase. In the second phase, a process of assigning customer orders to batches takes place, after which priority is assigned to the batches. In most cases, the Best-Fit, First-Fit, and Next-Fit rules are used to identify batches to assign customer orders. Several approaches such as the First-Come-First-Served rule are also considered when determining the priority of a particular customer order.

Seed Algorithms. This policy entails the sequential construction of batches, which is done in two phases. The first phase entails choosing a seed order according to the set selection rules. The second phase involves adding more orders to the batch based on selected selection rules. When adding orders to batches the capacity of the picker or the picking device is always taken into consideration.

Savings Algorithms. The policy is based on the algorithm by Clarke and Wright, which was developed to solve the vehicle routing problem. The policy maintains that picking two orders or batches in the same tour, without exceeding picker capacity constraints. The practice results in savings, as the distance travelled and time that could have been spent for individual tours is minimised.

Routing Policy

The routing policy entails minimizing the picker’s travel distance by coming up with a pick list that has been optimized for the shortest travel distance. Individual routing entails optimizing each pick list individually. The strategy may however, give rise to the travelling salesman problem. Most organizations use standardized routing with different routing strategies. Some of the widely used strategies include the return strategy, the largest gap strategy, and the S-shape strategy. The figure below shows the different types of strategies.

Figure 3: Picking tour with different routing strategies. Pick locations are represented by the black squares.

Optimization of order picking systems depends on the successful implementation of efficient warehouse layout, order picking, routing, and storage policies. The management should ensure that the organization adopts the most appropriate approach, taking into account any conflicts that may arise between the operating policies. To arrive at the best approach for Rollon to implement, it is essential to review past literature detailing Order Picking Systems optimization. The following is a literature review of policies and order picking systems optimization attempts made by different individuals.

Literature Review

Most of the researchers focused on the optimization of order picking in warehouses with the picker-to-parts system. For instance, Le-Duc and De Koster came up with an investigative approach aimed at approximating the average distance travelled by a picker in a single tour. The study assumed a warehouse layout structure that entailed a single-block and the ABC storage assignment policy, which entails dividing products according to how frequently they are picked. The researchers later developed their approach and conducted the same study on a warehouse with a 2-block layout structure and a class-based storage strategy. The approach also entailed the development of an optimization model aimed at determining the ideal distance for a middle cross-aisle to improve efficiency.

In their study, Vaughan and Peterson aimed to determine the effect of additional middle cross aisles to the travel distance of a given tour. The researchers came up with a model aimed at determining the optimal number of cross aisles. The study assumed that products were to be randomly assigned storage locations. The main aim was to minimize the distance covered by the picker in every pick tour. Roodbergen et al. also came up with a model designed to achieve the shortest average distance a picker covers when completing a particular pick list. The model was applied on a warehouse with a multiple-block layout structure. Random storage and the S-shaped policy were taken to account when developing the model. The model was designed to work in warehouses that accommodate several blocks and aisles.

Roodbergen and De Koster carried out a study aimed at developing heuristics for routing warehouse setting with multiple blocks and cross the aisles. Five different routing heuristics were compared to determine performance. The heuristics that were compared include the largest gap, aisle-by-aisle heuristic that was developed by Vaughan and Peterson, the S-shape, combined, and the combined+ heuristics. The study entailed a comparison of 80 different layout structures, and varying number of aisles with up to 15 vertical aisles, and 11 cross aisles. Varying pick list sizes of between 10 and 30 were used for the study. The results of the study revealed that the combined+ heuristic performed better than other heuristics. The results also showed that the largest gap is also effective when applied in layout settings that entail low pick frequencies and two cross aisles.

Order picking optimization strategy for Rollon GmbH

The main reasons why order picking is associated with high costs in the warehouse is the total time involved in the entire process, and the increase in labour costs. A fully optimized order picking system should be able to facilitate smooth and efficient running of operations. To achieve this, a system associated with minimal travel time and minimal operating costs is required. Being a global organization, Rollon has several operational centres located in different continents. The company has also commanded authority in the industry by becoming a market leader in the field. The optimization of its order picking system would help the organization to save millions. The most suitable strategy would be integrating a hybrid of the best practices, to come up with a unique efficient and effective order picking system. For instance, where possible, the organization can revamp the layouts of its existing and future warehouses. Optimizing the layout for utmost efficiency in the warehouse enables the organization to save costs. Aisles can be expanded to accommodate more efficient picking devices, using higher racks can help in leveraging vertical space for more storage, among other strategies.

The randomized storage policy is the best policy option for Rollon to implement. Integration of the policy will require the implementation of a randomized storage system integrated with computer systems to help pinpoint the specific locations of the product to the picker. The system will ease congestion in the warehouse as the same type of products will be distributed across the warehouse. The policy also enables the organization to utilize storage space more efficiently. For the order consolidation policy, Rollon should implement the order batching policy, specifically the priority rule-based algorithms. The policy will entail giving every customer order priority, and then sequentially assigning orders to various batches taking the order of priority to account. Considering the industry that Rollon is serving, it is very rare for clients to order multiple types of products in small quantities. This makes the order picking process much easier.

The routing policy is concerned with identifying and implementing the best strategy for a picker to reduce the distance of a pick tour. The best option for Rollon is the standardized routing with the largest gap strategy. The reason for this is because other options such as heuristic and optimal approaches are difficult to implement. For instance, the optimal algorithm implemented by Ratliff and Rosenthal is not easily adaptable to most warehouse layouts. To implement the strategy, the organization has to take the routine policy into account when constructing the warehouse for easier integration. The strategies also fail to take into consideration aisle congestion, which also affects the order picking process. Pickers often find picking strategies too ambiguous and end up switching the picking routes. Standardized routing gives room for several picking strategies, which can be implemented depending on various factors such as warehouse layout, picking device capacity, and products, among others. The largest gap strategy optimizes the process by maximizing the distance not covered by the picker in the warehouse.

Stock (Inventory) Optimization

With advancement in technologies, the business environment has completely changed. Organizations now rely on technology to complete various functions, with more efficiency and effectiveness. This has resulted in a wider market to service and more complex operations to run for organizations. Without effective management, a small misstep may lead the organization to great losses. To cope with these changes and carry out operations smoothly and efficiently, organizations develop strategies and systems. Currently, organizations employ fully integrated systems to increase their profitability and minimize costs. One of the most challenging areas has been managing inventory, which is identified as one of the main cause of high costs and low profitability. Over the past few years, inventory management and optimization has been a top investment priority by most organizations, especially the manufacturers, according to Amir, the main objective of these inventory optimization efforts has been to reduce operational costs, and gain a competitive advantage by improving customer service.

Optimization of inventory is basically known as a method of freeing up working capital or increasing level of service delivery at minimal costs. According to Amir, inventory optimization mostly takes place at the end of the supply chain that deals with finished goods. Arbidane and Volkova, argue that inventory optimization in the warehouse environment occurs in organizations that service multiple customers. Rollon is a multinational organization that runs its business on a complex network of warehouses and stores in different continents. To run operations efficiently, Rollon requires the use of complex systems that may not be fully optimized. The organization’s inventory is spread throughout the supply chain, beginning from the sourcing of raw materials to the delivery of finished products to customers. Optimizing the inventory of such an organization may help the organization by reducing operational costs and increasing its profitability by a large margin. According to Arbidane and Volkova, inventory optimization is associated with several advantages to the organization. For instance, with optimization the organization is able to recognize all the inventory stages, and tell which stock is excess and where it is located in the supply chain. Optimization also reveals to the organization the value of postponing processes. Effective optimization also enables the organization to minimize the inventory of finished goods, which minimizes obsolescence, among other advantages.

Inventory Management Systems vs Warehouse Management Systems

According to Closs et al., a distinction should be made between warehouse management systems and inventory management systems. The main difference is that warehouse management systems are largely concerned and limited to