In this section, the publications that applied optimisation methods for management of surgical supplies and management of sterile instruments are discussed.
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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.
Two relevant studies have been found in the category of global inventory (see Table 6)
Fineman 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.
An 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.
The 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. ().
This article will satisfy your query related to following question: how to start a wholesale surgical business in India.In Pharmaceutical industry, patient and/or its care taker is end user who will demand for a medicine and drug to pharmacy against prescription of registered medical practitioner.Pharmacy/Chemist/Retail will demand for medicines to its distributor. A distributor will get medicines from its wholesaler/stockiest. Stockiest will purchase medicine from CnF and CnF get stock from pharmaceutical Company. In this way a distribution channel works in pharmaceutical sector.
When starting a surgical pharmacy business, it is important to choose products of the right companies. You do need to stay in touch, but the companies you need to keep. There are many companies, local as well as global. So which companies&#; products you choose to keep is important. There are many local companies in each area, but I cannot talk about it because they differ according to the area, but the standard companies like Flemingo, Visco, DoctorMorepen. If you keep products of these standard companies, then 100% you will be able to sell it. It also has a good margin. If you keep local companies&#; products, your margin may increase. This is a business about the margin. So, the better the company, the better the product. This is one point.
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The next is availability. The products which you keep, the products which are in demand in your area, should be available with you. Once a hospital orders from you, they will keep ordering from you. But, if you have a shortage, then they will shift to other distributor. So you will not only lose a valuable customer, but also reduce your turnover. So to avoid this, focus on that product right from the start. Make sure that they are available in bulk so that you can provide a good service.
Next point is online services. Many consumers prefer to purchase products directly online, so you need to maintain an online presence, even if the order maybe less or more. Many of the manufacturers, distributors, retailers maintain an online presence, even consumers or patients can directly purchase online. So you can get orders directly, no need of order-boys to go anywhere to take an order. So you should create a platform, an online application. There are many platforms which provide B2B business so you can generate revenue in this way as well.
Next point is service. Whichever your product is like glucometer, thermometer etc., if there&#;s any issue in it or even if it is any other instrument, providing service is your responsibility. If the product is faulty, you should replace the product. Customers should be able to trust you. That&#;s why service is important. So service is a point which you should think of before staring the business and you need to work on it.
Shop Act
Now let us talk about documentation. So which all documents do you need? The first is Shop act. You can get shop act online. If you hire minimum 20 people then you can get shop act directly online or if there are more than 20 people, even that is available online but you need to go to a Shop Act registered office and spend some time on it, but both the procedures are easy. You can check it&#;s fees online.
GST Registration
Next you will need GST number. Why do you need it? You need a GST number to purchase all the companies&#; products. It is not the case that your turnover will always be less. Even in less investment you can earn more profit but it is not that you will continue to invest less, as time goes on, your investment will increase, which will lead to an increase in turnover. Hence it is compulsory for you to have a GST number. So, I will recommend you to make a GST number.
Drug License
Next is the drug license. Just like pharma wholesale, even here, there is a drug licence. Actually it is not applicable to most of the products like surgical instruments, but all those which come in category of medicine needs a drug license. So I will suggest that you have a drug license, because as you will increase the variety of the products, you might need the drug license somewhere.
So this was all about documentation. Now let us talk about which all types of devices, products, can you keep being a surgical distributor. I have discussed at various places, researched and found 25 products which you can keep.
So I have told 25 types of categories. They can be further divided into various types. You can keep all those. In general, if you keep these products, it will be more than sufficient. So this was related to products. Now let us talk about challenges. Now you can keep these products but you must be thinking this is very easy, there is a lot of margin, you can keep a variety of products, so this seems very easy, why can&#;t everyone do it? But there are many challenges in this as well.
Now let us talk about challenges. The first challenge is that of Credit. If you are giving products to retail stores or hospitals, it isn&#;t like allopathic that you give order and you get cash. Many hospitals do billing on credit basis. Minimum 50 days, one month, two months or even four months credit is there. So you will get a big order but you won&#;t get immediate payment, it will take some time. You should have rolling money for this,you should have more money at backhand for this which you can use for the time being. Also you should not give more credit. Minimum 15 days to maximum one month credit should be given. This will help maintain the relationship and also keep more rolling money coming in and you can expand your business.
I can understand that we have discussed a lot in this video, most of the things, you might have understood. If you have any doubts, do ask in the comment box. And friends, all this information is enough to start your business as a pharma surgical distributor.
That&#;s all for this video, my friends. If you liked the video, please click on the like button, don&#;t forget to subscribe to the channel, and share it with other people. Idea will be shared, people&#;s confusion will be cleared and this video will be able to help people become entrepreneurs. So that&#;s all for this video friends, let&#;s meet in the next video. Till then be healthy, be happy.
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