In the intricate healthcare ecosystem, the supply chain plays a pivotal role in ensuring that hospitals and medical facilities are equipped to provide the highest standards of care. At the heart of this system is the undeniable reliance of hospitals on wholesalers for surgical instruments. This complex and multifaceted relationship underscores the importance of efficient, reliable healthcare supply chains in saving lives and promoting health. By exploring hospital equipment acquisition, the role of surgical instrument suppliers, and the healthcare supply chain's intricacies, we uncover healthcare institutions' critical dependency on wholesale surgical instruments.
lookmed contains other products and information you need, so please check it out.
Understanding the Healthcare Supply Chain
The healthcare supply chain is a dynamic and vital network that connects medical product manufacturers to healthcare providers. It encompasses a wide range of activities, from the production of goods to their distribution and delivery to healthcare facilities. Surgical instruments, indispensable tools in medical procedures, form a crucial part of this chain. The efficient management of these supplies directly impacts the quality of patient care and the ability of hospitals to respond to patient needs.
The Role of Hospital Equipment and Surgical Instrument Suppliers
Hospital equipment and surgical instrument suppliers are vital links in the healthcare supply chain, bridging the gap between manufacturers and healthcare providers. These suppliers source various instruments, from basic tools like scalpels and forceps to advanced devices for minimally invasive surgeries. Their expertise in navigating the market for medical supplies and their ability to procure high-quality instruments at competitive prices make them indispensable partners for hospitals.
The Dependency of Hospitals on Wholesale Surgical Instruments
Hospitals depend on surgical instruments for several reasons:
Cost-Effectiveness: Purchasing surgical instruments in bulk from wholesalers allows hospitals to benefit from economies of scale, significantly reducing the cost per unit of each instrument. This cost-saving is crucial in managing the financial health of healthcare institutions.
Wide Selection: Wholesalers typically offer a broader range of products than manufacturers, giving hospitals a comprehensive selection of surgical instruments. This variety ensures that healthcare providers can handle a broad spectrum of medical procedures.
Supply Chain Efficiency: By consolidating purchases through a single wholesaler, hospitals can streamline their procurement processes, reduce administrative burdens, and ensure timely delivery of essential equipment. This efficiency is vital in maintaining the readiness of healthcare facilities to provide care.
Quality Assurance: Reputable surgical instrument suppliers have stringent quality control processes in place, ensuring that the products they distribute meet the highest safety and efficacy standards. Hospitals rely on this quality assurance to mitigate the risks associated with surgical procedures.
Challenges in the Healthcare Supply Chain
Despite its critical importance, the healthcare supply chain faces several challenges, including fluctuations in demand, supply disruptions, and logistical complexities. The COVID-19 pandemic has brought these issues to the forefront, highlighting the need for resilience and flexibility in procuring surgical instruments. Hospitals must actively engage with their suppliers, leveraging partnerships to mitigate risks and ensure the continuous availability of essential medical equipment.
Future Directions
The future of the healthcare supply chain lies in the adoption of technology and innovation. Digital platforms, blockchain technology, and data analytics promise to transform the procurement and distribution of surgical instruments. These technologies can enhance transparency, improve demand forecasting, and streamline logistics, ultimately strengthening the healthcare supply chain's resilience against disruptions.
Dependency of hospitals
Hospitals' dependency on wholesalers for surgical instruments is a testament to the critical role of the healthcare supply chain in ensuring the delivery of quality care. As hospitals continue to navigate the challenges of supply chain management, partnerships with surgical instrument suppliers will remain indispensable. By fostering solid relationships with these suppliers and investing in supply chain innovation, healthcare providers can enhance operational efficiency, reduce costs, and, most importantly, save lives.
Call us at ' 213 670 for quality, proper surgical instruments. You can send your queries to ' or visit our website to learn more.
In this section, the publications that applied optimisation methods for management of surgical supplies and management of sterile instruments are discussed.
A very basic inventory control approach is ABC classification, where the classification is based on the cost of the supply. More attention should be paid to the A class that absorbs a high portion of the budget (70%) but accounts for a low percentage of the total items (10%). Around 20% of items fall in group B, which consumes 20% of the budget. The remaining 70% of items are Group C and they absorb 10% of the budget (Gupta, Gupta, Jain, & Garg, ). In conjunction with the ABC analysis, Gupta et al. () propose VED analysis, which relies on the criticality of the items. 'V' stands for the vital items that the function of a hospital highly depends on. 'E' stands for essential items; the quality of the service depends on this group. 'D' indicates desirable items that do not inhibit a hospital's operation if they are not available. The item classification is then extended by Al-Qatawneh and Hafeez () such that in addition to the cost and criticality, usage frequency is taken into account.
Given the most important items, extracted using the above-discussed methods, inventory models need to be established to decide on the inventory control parameters for these items. We have classified the optimisation problems of inventory management in hospitals in three major categories: (1) the global inventory comprises the papers that only address inventory management in the CS; (2) the local inventory consists of research that investigates methods applicable to department storerooms or POUs; and (3) the papers that consider both local and global inventories.
For more information, please visit Surgical Instruments Wholesale.
Two relevant studies have been found in the category of global inventory (see Table 6)
Global inventory models for a healthcare setting.
Study Stochastic/deterministic Method Objective (cost component) Decision Stationary/non-stationary Constraint Type of good Fineman and Kapadia () Deterministic EOQ Purchase cost, order cost, holding cost Replenishment amount Stationary - Sterile supplies Dellaert and van de Poel () Stochastic Simple rules Holding cost, ordering cost s (reorder point), c (can-order), S (up-to-level) Stationary - Not specified Open in a new tabFineman and Kapadia () are the first to study a closed-loop chain known as the 'sterilisation processing cycle'. This process involves receiving contaminated medical devices used to perform a surgical procedure, then cleaning, inspecting, packaging, and storing the grouped items. The authors divide sterile stock into two categories: the processing stock and the replacement stock. The first one is required to support the processing cycle described above and the second one is required to replace items that are lost, damaged, or worn out. In analysing the two categories, demand is assumed to be constant, which simplifies the problem significantly. Thus, they use an EOQ model to determine the inventory requirements for replacement stock.
Dellaert and van de Poel () address the global inventories in a hospital which follow the (R, s, c, S) inventory control policy with stochastic demand. In the (R, s, c, S) model, if the inventory level for an item of a supplier (in R review cycle) goes below the reorder point (s), all other items of this supplier that are below the can-order (c) level are also ordered to increase the inventory to the up-to-level (S). They propose a simple rule for using a given R to calculate s, c, and S in an intuitive way with the aim of minimising total cost, including holding cost and ordering cost. In their evaluation, demands follow the Poisson distribution with normally distributed transaction size.
These early studies examine very classical inventory control models, which rely on assumptions that highly simplify the problem. Therefore, the provided solutions are far from practical and can only be considered as a general rule of thumb.
As can be seen in Table 7, seven research papers are dedicated to inventory models for the POU locations.
Local inventory models for a healthcare setting.
Study Stochastic/deterministic Method Objective (cost component) Decision Stationary/non-stationary Constraint Type of good Burns, Cote, and Tucker () Deterministic EOQ Order cost, holding cost Order quantity Stationary - Injectable supplies Little and Coughlan () Stochastic Constraint programing Service level Delivery cycle, service level Stationary Space Sterile items van de Klundert, Muls, and Schadd () Deterministic Integer programming Delivery cost, storage cost Delivery time Stationary Space Sterile instrument Bijvank and Vis () Stochastic Markov chain Minimising the capacity, maximising service level Reorder point and order quantity Stationary Service level, capacity Disposable products Rosales et al. () Stochastic Simulation Order cost Reorder point, order quantity, order up-to level Stationary - Not specified Rosales, Magazine, and Rao () Stochastic Semi-Markov process Stock-out cost, replenishment cost Review cycle, number of empty bins to trigger replenishment Stationary Inventory balance Not specified Diamant, Milner, Quereshy, and Xu () Stochastic Markov chain Stock-out cost Inventory level for each instrument kit Stationary Service level Sterile instrument Open in a new tabAn application of ABC inventory analysis to the injectable supplies in a care centre along with a classical EOQ model is demonstrated by D. M. Burns et al. (). However, such a model accounts only for the cost without considering other important elements in healthcare such as storage space, demand variability, service level, etc. Because the main goal of any healthcare organisation is to provide high-quality patient care, any effort for inventory cost reduction should not compromise the quality of care. In the context of healthcare inventory management, not having the supplies in stock when needed indeed has serious impact on the quality of care (Moons, Waeyenbergh, & Pintelon, ), which might lead to loss of life (Guerrero, Yeung, & Guéret, ). Measuring impact of such an inventory shortage on patients is difficult, if not impossible. Therefore, the occurrence of a shortage can be preventable by introducing service level as a constraint (Bijvank & Vis, ; Diamant et al., ; Guerrero et al., ; Nicholson et al., ) or an objective function (Little & Coughlan, ). Service level is usually defined as the fraction of the demand that is satisfied by on-hand inventory, without substitution or emergency delivery (Bijvank & Vis, ).
In this context, a general multi-product, multi-period optimisation model is developed by Little and Coughlan (), in which the CS requires delivery of a variety of items to different departments such as the operating theatre or laboratory. Constraint programming is utilised to determine the number of units of each item that needs to be stocked in the POUs, the frequency of delivery, and the best service level subject to the limited space. In their model, a range of desired service levels and delivery frequencies for each item is specified by the user and the model is validated by sterile and bulk items in an intensive care unit within a hospital in Ireland. This model is further developed by Bijvank and Vis () with the consideration of both service requirements and the capacity limitation. The authors provide a capacity model with the objective of maximising the service-level subject to the limited capacity. They also examine a service model with the objective of minimising the required capacity by considering the service level as a constraint.
van de Klundert et al. () address managing of reusable instrument kits to improve their flow between the central sterilisation locations and ORs. They determine the optimum delivery time with the objective of minimising the delivery cost and the storage cost. The storage capacity at the ORs and the capacity of the transportation vehicle are restricted. Since they consider deterministic demand, no stock-out cost is taken into account. However, they suggest keeping safety stock and proposed four replenishment policies to cover the shortage caused by the variation in demand. Diamant et al. () further address the problem of managing reusable instrument kits by considering the stochastic daily demand for instruments. They focus on determining the number of instrument kits that need to be stocked to maintain high service levels. Their model does not deal with the problem of kit configuration (ie, the required instruments to be included in each kit). Instead, the optimal inventory level for each instrument kit, given the predetermined composition of kits is provided.
Emerging advanced identification technologies such as automated dispensing machines (ADM), barcode, and RFID have encouraged researchers to investigate hybrid replenishment policies. Rosales et al. () describe a hybrid model for a single item where inventories in the POUs were replenished periodically according to the (s, S) policy at the beginning of the shift. However, between two consecutive periods, whenever the inventory level reaches a threshold R, an out-of-cycle replenishment would be triggered with the size of Q (ie, a continuous (R, Q) policy). Their results show that the hybrid policy is better than pure periodic review or continuous review policy in terms of the cost, inventory and reduction in the number of replenishments. The single item model is then extended by Rosales et al. () to a multi-item one. In addition they propose a methodology to compare two inventory systems in POU locations: a two-bin system, which is a periodic review policy and is widely used in POU locations, and a bin-level RFID-enabled tag, which is a continuous review policy in the bin level. In the two-bin system, they try to find the optimal value of the reviewing cycle, called parameter optimisation, and the bin-level RFID system aims to find the optimal number of empty bins to trigger a replenishment. They compare the performance of the two policies, called policy optimisation, in terms of the cost per unit time. The objective function minimises the stock-out cost and replenishment cost with the assumption of a fixed size for the bins. Unlike the previously discussed studies, Rosales et al. () directly measure the stock-out cost by estimating the time spent by nurses to request and receive the required items. The implication of such a stock-out on quality of care, however, has not been taken into account.
In hospitals, the inventory decisions at downstream locations of the internal supply chain (ie, point-of-use locations) are connected to the inventory decisions at upstream locations (ie, central storage) and vice versa. Therefore, an integrated approach of local and global inventory optimisation models is necessary to reach a more practical model. A summary of the publications containing the integrated approach along with their specifications is presented in Table 8.
Integrated approach of local and global inventory optimisation models for a healthcare setting.
Study Stochastic/deterministic Method Objective (cost component) Decision Stationary/non-stationary Constraint Type of good Nicholson et al. () Stochastic Mathematical programing Holding cost, stock-out cost Par-level (up-to-level) Stationary Service level Not specified Hammami, Ruis, Ladet, and Hadj Alouane () Deterministic Linear and non-linear programming Handling cost, holding cost Period and the quantity of replenishment Stationary Space Surgical supplies Lapierre and Ruiz () Deterministic Linear Programming Holding cost Period and the quantity of replenishment Stationary Space, human resource Not specified Rappold et al. () Stochastic Linear programing Holding and Shortage (backorder) cost Purchase quantity, number of prepared items (kit), transferring unprepared and prepared amount to the OT Non-stationary Availability of stock in CS Surgical supplies, surgical kit Guerrero et al. () Stochastic Markov chain Holding cost in CS and POUs s and S for central warehouse and POUs Stationary Service level, ordered quantity by CS, Storage capacity Infusion solutions Wang, Cheng, Tseng, and Liu () Stochastic System dynamic Inventory cost Replenishment quantity, safety stock Stationary - Not specified Open in a new tabThe procurement department in hospitals has to make scheduling decisions in terms of when and how often each point-of-use should be visited for replenishment. These decisions would indeed affect the staffing decisions (eg, how many workers are required and when they should work). Lapierre and Ruiz () consider a scheduling approach to address a multi-product, multi-period, two-echelon internal supply chain system where the CS purchases supplies from external suppliers and is responsible for delivering the required amount to the POUs. In addition to the primary objective of minimising the total inventory (holding) cost, limited availability of human resources led them to define a secondary objective of balancing the workload over the weekdays. The model decides when the POUs should be visited and how much of each product is delivered to the POUs.
Despite the papers discussed in the previous section that defined the service level as the percentage of demand coverage, Lapierre and Ruiz () describe the service level as the frequency of visits to POUs. They assume that minimisation of the inventory cost would force the model to increase the service level. Guerrero et al. () use a constraint to provide a minimum service level (ie, probability of avoiding stock-out in a given period) in a stochastic, multi-product, two-echelon (s, S) inventory control system. In their model, a central warehouse receives infusion solutions from the external suppliers and distributes them to the POU locations in different hospitals that all belong to the hospital's network. Wang et al. () incorporate a system dynamic approach, in which a set of decisions is changed in response to changing of the input information, to minimise the inventory cost without occurrence of stock-out.
Nicholson et al. () go beyond the internal supply chain for addressing inventory management in hospitals. They consider a healthcare provider network in which a central warehouse, owned by the provider, receives supplies and distributes to the hospitals inside the network. Each hospital has its own central storage and distributes stock to its departments. The authors formulate two models. The first model is a three-echelon system containing a central warehouse, a central storage room in each hospital and POU locations in the departments. The second model contains a central warehouse and POU locations with no central storage room, in which the distribution of the non-critical items are outsourced to a third party. They conclude that outsourcing will reduce the inventory cost without having a negative impact on the quality of services. This finding is consistent with the benefits of outsourcing some logistics activities reported in the literature (Beaulieu, Roy, & Landry, ).
Hammami et al. () consider a classical (R, Q) inventory model, as well as a supply chain approach for surgical supplies in a system where supplies are stocked in ORs, block warehouses (Cores) and CSs. However, they simplify the model by excluding the ORs from investigation because the inventory level in the ORs is highly dependent on surgeons' estimates of need. This is due to the fact that patient condition may unexpectedly change during their stay in the hospital, and consequently induce unplanned requisition for some supplies. Modelling the system in this way (ie, removing ORs from the model) would over-simplify the problem, which leads to formulating an unpractical model. Vila-Parrish, Ivy, and King () describe an inventory model as a Markov decision process to manage perishable drugs by considering the possible changes in the patient condition. Although addressing perishable products goes beyond the scope of our review, incorporating patient condition in the study of Vila-Parrish et al. () is an interesting issue. In their model, patients are classified into N types. Each patient type has an associated profile of prescription drug usage (which resembles a BOM). They assume that patient condition (type) changes stochastically overtime. In the event of a stock-out, demand would be satisfied from another location (eg, other hospitals).
Some research has incorporated the concept of the Material Requirements Planning (MRP) and Manufacturing Resource Planning (MRP II) to address the material planning problem in hospitals. The backbone of MRP relies on the Master Production Schedule (MPS) and the Bill of Materials (BOM). Stevenson () defines the MPS as 'which end items are to be produced, when these are needed, and in what quantities' (p. 502) and the BOM as 'a listing of all of the raw materials, parts, subassemblies, and assemblies needed to produce one unit of a product' (p. 503). In the healthcare context, the master surgery schedule (MSS) can serve as the MPS (Roth & Van Dierdonck, ). The BOM can be created through a system called diagnostic-related groups (DRGs). DRG classifies patients into clinically similar groups often based on the similarity of the procedure (or a group of procedures) and their ages (Roth & Van Dierdonck, ). Patients in the same group require similar treatment and therefore a similar BOM. Showalter () is the first one who used the MRP concept for material management in hospitals. Roth and Van Dierdonck () discuss that the traditional MRP has shortcomings when applied in hospitals. They develop a control system called Hospital Resource Planning (HRP) based on the MRP II concept in a deterministic condition. Van Merode, Groothuis, and Hasman () suggest using Enterprise Resource Planning (ERP) (ie, the next generation of MRP II), in planning and controlling a hospital's deterministic processes.
The flow of surgical supplies and challenges in the ORs are referenced by Rappold et al. (). They utilise MRP to address the material planning problem in the OR and discuss that the MSS, and consequently the scheduled procedures, are usually known weeks in advance. In this context, they consider two types of uncertainty in the OR. A stochastic number of surgical procedures performed in a day (resulting in a stochastic number of surgical cases) and a stochastic BOM (SBOM) (resulting in a stochastic usage of supplies). The source of the SBOM is the surgeon 'preference card'. Although it is determined by the surgeon, the actual usage amount would be different case by case depending on the condition of the patient during the procedure, even for a given surgeon and specific procedure. The authors take uncertainties into account and formulate a model that provides an optimal purchase quantity from the supplier, as well as transferred unprepared and prepared (kitted) quantity to the Cores subject to the available stock in the CS. Finally, they quantify and evaluate the impact of information sharing between the surgical scheduling department and the material management department (to decrease schedule uncertainty), as well as the consequence of BOM standardisation (to decrease BOM uncertainty) among the physicians by varying their corresponding Variance-to-Mean Ratio (VTMR).
One important aspect of inventory control models, especially in the highly uncertain environment of healthcare, is how the models address the uncertainty involved in the system. In the supply chain context, there are two main sources of uncertainty, which can result in undesirable system performance, eg, shortage of required supplies and shortage of capacity. The first source, which is called disruption risk, is caused by the occurrence of natural disasters such as earthquakes, floods, epidemic diseases, environmental crises, and other sources of loss. The second source, operational risk, is caused by the intrinsic uncertainties of supply chain parameters such as uncertainty in demand, transportation time and cost, and lead time (Farrokh, Azar, Jandaghi, & Ahmadi, ; Tang, ).
Prior research that incorporates stochastic models (discussed in Sections '4.1.1, '4.1.2, and '4.1.3) does not clearly specify which sources of uncertainty were considered. According to the formulations, which often fitted a probability distribution to the historical data to model demand structure, we conclude that these prior studies just dealt with the operational risk. However, Tang () discusses that the impact of the disruption risk on the supply chain is greater than the operational risk. The scope of this article does not review the different methods that are applicable to deal with each type of uncertainty. To read more details about the methods of stochastic programming and the papers that dealt with either operational risk or disruption risk in a general supply chain, one can refer to the review paper by Govindan, Fattahi, and Keyvanshokooh () and for a method to cope with the hybrid uncertainty (both operational and disruption risks), we refer readers to Farrokh et al. ().
Want more information on Disposable Biopsy Forceps? Feel free to contact us.