Citation
Manatkar, R.P., Saha, S. and Sarkar, B.D. (2024), "Guest editorial: Resilience in the cold chain: a critical analysis of coordination mechanisms", Journal of Global Operations and Strategic Sourcing, Vol. 17 No. 3, pp. 449-473. https://doi.org/10.1108/JGOSS-08-2024-125
Publisher
:Emerald Publishing Limited
Copyright © 2024, Emerald Publishing Limited
1. Introduction
The concept of a supply chain coordination scheme in cold supply chain management for the cold chain logistic industry is new and relatively more complex than the supply chain of other industries (Das et al., 2023; Habibur Rahman et al., 2023). However, the process of cold chain management begins with the flow of goods and services from the point of provenience, that is, production to storage, transportation, distribution and consumption, involving the process of planning, controlling and efficient implementation to meet general need and customer satisfaction on a worldwide scale (Bogataj et al., 2005; Lotfi et al., 2023). Moreover, cold chain management includes cold products which having a short shelf life, cold transport, value addition and preservation, and infrastructure to provide an integrated cold chain to keep the product fresh in a chain of distribution from producer to consumer, Such as Bananas, from the harvest country of Philippines to end consumers in mainland China (Government Support and Initiative to Build a Robust Cold Chain Ministry of Food Processing Industries, Government of India NCCD Conclave with Nodal Officers For Cold-Chain Development, 2014). The case has been taken from the Indian cold chain logistics industry, which plays a critical function in setting up equipment and procedures or set of accesses that are intended to keep perishable goods like vegetables, fruits, flowers, meat, dairy products and so on (Chaudhari et al., 2023; Monteiro et al., 2023) and required distribution of these perishable goods under controlled environment on a priority basis (Li et al., 2023; Noble et al., 2023). Still, it involves many accomplishment processes like other supply chain management operations such as perishable green groceries processing, perishable goods production and other market drove the downstream process like finishing, perishable goods greengroceries and distributing cum retailing (Sarkar et al., 2023). Further, there are many intermediate processing steps in the cold supply chain, like other cold chain products (apparel products), such as auxiliary materials, production and service sectors involved in this supply chain. However, managing cold chains is more significant and complex than ordinary retail supply chains (Burgos and Ivanov, 2021). As a result, the cold supply chain has a relatively long lead time and relatively high uncertainty (essentially buyer-controlled business) in the whole supply chain. Cold products have short life cycles, high volatility, high market needs predictability, and many purchases (Cheng et al., 2020). Thus, one of the major business organizations in this cold supply chain management is the diminution of the lead time by combining various processes in the supply chain. Cold products in the cold supply chain reflect the downstream portion of an extended supply chain; its members include end consumers, goods owners, retailers, jobbers, distributors, perishable goods producers, and cold product suppliers. Cold chain logistics is an independent entity and may also take on the role of two different cognitive operations. For instance, a brand owner, Amul, may also be handled as a distributor (through Banas Diary) and a retail outlet (Shimokado, 2021). With the quick tempo of engineering innovation and changing consumers’ preferences for cold products, the life cycle of the products is getting shortened over time in the cold product manufacturing industry; however, in a global market, it is important to find good materials to be delivered in the right quantity, at the proper time and the proper place (Tirkolaee et al., 2020). Because of this, the cold chain industry's procurement behavior is seen as a relevant coordination process between Manufacturers and retailers in cold supply chains and acts as the contrast of procurement in the newsvendor type problem.
Apart from cold supply chains, many research papers discuss the impact of contracts. The article by Koussis and Silaghi (2023) explores the effectiveness of revenue-sharing contracts in coordinating a supply chain while considering capacity choices and utilization alongside traditional demand factors. It analyzes a model where a manufacturer sells products through a retailer under a revenue-sharing agreement and investigates how optimal revenue-sharing ratios are influenced by various factors. The paper by Qiu et al. (2022) investigates how contracts can be used to coordinate a supply chain in an Online-to-Offline (O2O) environment where product quality dynamically influences consumer purchasing decisions. The key concept here is the “reference quality effect,” which means consumers judge a product's quality relative to their past experiences with similar products. The study focuses on a two-tier supply chain: a manufacturer and a retailer selling online and offline. The manufacturer controls product quality (freshness), while the retailer sets the retail price. Consumers' demand depends on both the price and the perceived quality, influenced by past product experiences and a decay factor representing memory over time. Jauhari et al.’s (2023) paper proposes a coordination model for closed-loop supply chains with a single manufacturer and retailer. The model considers forward and reverse channels, where products are sold in the primary market, used products are collected and potentially remanufactured or recycled, and then re-introduced into the market.
Moreover, Zhao et al.’s (2022) paper investigates the optimal inventory and pricing policies for a dual-channel retailer (selling online and offline) under a “quantity-sales mode” contract with a manufacturer. In this unique contract, the manufacturer offers discounts based on the total quantity purchased by the retailer across both channels over multiple periods. Xie et al.’s (2021) paper investigates the challenge of coordinating a two-stage supply chain when both yield (production success rate) and demand are uncertain. The authors analyze how different contract types, specifically wholesale price contracts and buy-back contracts, impact the optimality and coordination of the supply chain under these uncertainties. Ye et al. (2020) paper investigates how to achieve coordination in a contract farming supply chain with uncertainties in both yield and demand. The authors focus on minimizing the Conditional Value at Risk (CVaR), a measure that considers potential losses' magnitude and probability. An article by Govindan and Malomfalean (2019) argues that online-to-offline (O2O) integration is a crucial adaptation for businesses in the technology-driven era. It investigates the impact of O2O on profit under different demand scenarios and coordination mechanisms – revenue-sharing, buy-back and quantity flexibility contracts. Research by Taleizadeh et al. (2018) explore efficient ways to coordinate a two-tiered eco-friendly supply chain, where manufacturers strive to reduce carbon emissions while retailers aim for profit maximization. The key insight is that traditional contracts (wholesale price contracts) often fail to achieve true coordination, leading to suboptimal environmental and economic outcomes. The article (2016) contradicts that the current supply chain contracts under uncertainty do not optimize production and procurement. The study proposes two new contracts that achieve perfect coordination and benefit both the supplier and retailer. First, Consignment VMI with production cost subsidy, and second, advance-purchase discount with revenue sharing. Interestingly, both contracts offer similar outcomes in terms of negotiation power. In relation to the literature classification proposed, a large volume of work treats individual contracts or a combination of different contracts. However, limited research work is available for comparison of contracts (Arshinder et al., 2009), in the sense that comparing different types of contracts brings about the possibility of studying the implications on overall supply chain performance.
This paper delves into the resilience and reemergence potential of different contracts within the cold supply chain, aiming to identify the most suitable option for manufacturers and retailers. In an industry where disruptions and volatile demand are commonplace, effective coordination is not just essential, it is the lifeblood of success. This has sparked a growing interest among both industry players and academics in exploring coordination mechanisms that can overcome sub-optimizations and build robust supply chains. An attempt is made in this paper to analyze some coordination schemes that are often applied to the news vendor type problem (Shi et al., 2020; Yan et al., 2020). Five such schemes have been identified, and their performances have been evaluated concerning a no-coordination situation. The comparison is worked on through the mean-variance approach. The ultimate objective of this paper is to “Recommend” the application of “Coordination Schemes in cold supply chain management industries.” For the schematic presentation of the study, we divided this paper into seven sections (e.g. see Figure 1). Section 2 deals with the literature review. Section 3 includes an outline of the coordination scheme. Sections 4 and 5 include the model description for assessing coordination schemes with assumptions evaluation based on profit functions. In Section 6, a comparison of performance of coordination schemes with five different cases is assessed. In Section 7, a discussion and general conclusion is drawn.
2. Literature review
An immense study on supply chain coordination has already been done. Ye et al. (2020) examines the best production and pricing options that can be made along the supply chain in a contract farming system between an agribusiness and various risk-averse farmers. Analysis of optimal production quantity, wholesale price and retail price decisions is conducted while considering the effects of yield and demand uncertainty and the farmer's risk aversion. When the farmer is risking averse, and yield uncertainty is considerable, the loss incurred by the decentralized decisions rises. Another work on contracts shows the green channel coordination issueG in a two-tier supply chain where demand is price- and quality-sensitive. The manufacturer and the retailer have control over the product's eco-friendliness, but the retailer sets the price. A mix of “greening cost-sharing” and “revenue sharing” is designed to commence channel coordination and set up a win-win conclusion for both sides (Heydari et al., 2020). This research adds to the existing body of knowledge by proposing a methodical way to solve the problems of channel coordination and pricing in a green supply chain, taking into account the environmental consciousness of consumers and the fact that producers can improve their products' eco-friendliness by investing more money.
The article by Govindan and Malomfalean (2019) builds a comparison using an O2O method; their model considers two distinct sorts of demand in the context of three distinct sorts of coordination mechanisms (revenue-sharing, buy-back and quantity flexibility contracts). Researchers show that deterministic O2O demand works best under a quantity flexibility agreement, leading to maximum returns. The same outcome is achieved, nevertheless, in the stochastic demand situation. In the future, researchers may use the Stackelberg game theory to compare different coordinating systems, as introduced in this study. Further, the article by Manatkar et al. (2019) presents an integrated inventory distribution optimization model for a multi-product, multi-tier supply chain system. Stock, delivery and placement are considered. The article was to help steel retail supply chain practitioners choose the best distribution center and determine stock at each inventory-keeping point (retailers and distribution centers), minimizing distribution costs. The framework offers a systems approach and optimally calculates inventory for distributors and retailers. Part II, a hybrid Evolutionary method for non-dominated sorting and multi-objective self-learning particle swarm optimization, solves this problem. Steel retail industry data validates the model and solution techniques. Distributor and retailer stocks, order volumes and delivery costs provide inputs. Realistic limits can assist supply chain managers in choosing distribution hubs and determining product inventory. The strategy helps distribute manufactured goods efficiently and cheaply from the optimal place. Another piece of research by Furtado et al. (2022) shows that multiple participants and levels in the supply chain network increase the risk of data manipulation and authenticity, reducing data openness and responsiveness. Several strategies are used to safeguard supply chain networks. Blockchain technology has opened up new business industry advancements, making management easier. Thus, using block-chain technologies with traditional supply chain services can improve supply chain management. This project proposes a “Decentralized Supply Chain Management Smart Contract Using Block-chain” to make the supply chain more trustworthy. To test supply chain process design, we suggested a block-chain framework using smart contracts. The smart contract simplifies management by providing detailed product details to the manufacturer and real-time transaction status to the consumer. Each product's non-fungible token keeps data immutable. Another research paper by Qiu et al. (2022) presents an integrated inventory distribution optimization model for multiple products in a multi-echelon supply chain environment. Inventory, transportation and location decisions are considered. The objective is to offer a practical guideline to steel retail supply chain practitioners to choose the correct distribution center and find inventory levels at individual inventory-keeping points (retailers and distribution centers), helping them reduce overall distribution costs. The framework presented endorses a systems approach and suggests a near-optimal approach to calculating inventory for an individual distributor and his retailers. Two algorithms are used to solve this problem: a novel hybrid multi-objective self-learning particle swarm optimizer and a non-dominated sorting genetic algorithm II. The model and solution methods are tested on real data sets obtained from organizations in the steel retail environment. The actual data on inventory holding, ordering and transportation costs of distributors and retailers are used as inputs. Decisions like choosing the correct set of distribution centers and keeping optimal regular and safe stock inventory levels are arrived at by applying practical constraints in the supply chain. The model developed assists in the effective and efficient distribution of the products manufactured from the optimal location at a minimal cost.
Furthermore, the research paper by Mantrala and Raman (1999) reveals that in the buy-back contract, the manufacturer agrees to buy back all the unsold units from the retailer at a price agreed upon previously at the end of the season. According to Yao et al. (2008) and Zhou and Wang (2009) that in a revenue-sharing contract, the retailer shares some of his revenues with the manufacturer in return for a discount on the wholesale price. Moreover, the sales rebate mechanism directly incentivizes the retailer to increase sales using a rebate paid by the manufacturer for any item sold above a certain quantity. However, in the research paper by Lau and Wang (2009); Lee (2001); and Pan et al. (2009) say that in the markdown money policy, at the end of the season, the unsold units are sold at discounted prices. Furthermore, the availability of wide ranges of varieties, consumer preferences, market trends, technology and shorter product life cycles have resulted in a volatile and unpredictable market for cold products (Frings, 2008). Multiple decision-makers in the supply chain will lack coordination, as agents tend to have different incentives and objectives, resulting in double marginalization (Spengler, 1950). Thus, a coordinated supply chain maximizes the profitability of the entire supply chain (Liu et al., 2002).
However, the contractual aim of coordinating the forecast information and production decision for a two-echelon supply chain will result in successful coordination at each channel. Some literature materials are available to study the return policies for channel coordination, including Lariviere (1999) and Mondal and Giri (2022). Using a game theoretical analysis framework, Whang (2009) presented a model for markdown competition in which two retailers compete in the retail market. The study found that the markdown policy directly impacts their preceding inventory decisions. (2007) considered a setting in which a retailer uses a pronounced markdown pricing mechanism to sell a finite amount of products. Moreover, Emmons and Gilbert (1998) studied the role of return policies in a manufacturer-retailer channel's pricing and inventory decisions. Under the return policy, the retailer commits to both the retail price of the product and the ordered quantity from the manufacturer before the selling season, and at the end of the selling season, the retailer can return unsold units to the manufacturer with a refund. Pioneered by Markowitz in the 1950s, the mean-variance formulation became a fundamental finance risk management theory. The mean variance approach, as what Buzacott et al. (2011) have mentioned, aims at providing an implementable, useful and approximate solutions.
The subsequent sections of this paper are organized as follows. Section 3 provides a detailed description of the industrial cases and outlines the methodology for supply chain coordination. Section 4 is about the results and analysis where different business cases are examined. Section 5 describes industrial cases that demonstrate the feasibility of applying the proposed model in real-world situations, accompanied by a discussion of the notable findings. Managerial implications are presented in Section 6. Finally, Section 7 serves as the conclusion, offering recommendations and suggestions for future work.
3. Methodology
The research methodology for investigating supply chain coordination schemes in the context of the cold chain industry involves following cases:
3.1 Case 0 or no coordination
No coordination or each entity behaves as a unique or individual business unit for no coordination of supply chain (SC) benefit (e.g. see Table 1). In this setting, the manufacturer charges a unit price to the retailer, keeping in mind that profit is included. Retailer purchases the number of units (optimum order quantity) according to expected demand before the start of the selling season. The retailer sets the unit retail price of the product to get a maximum net income at the end of the selling season. In a coordination situation, both the member/components of the supply chain try to maximize their earnings, which is experienced as “Double Marginalization.” Due to this, it fails to coordinate the Supply chain.
3.2 Case 1 to 5 (coordination contracts)
Because they are the part/components of the supply chain (Figure 2), unifying all the benefits toward the common goal is a supply chain benefit. A contract (Coordination Scheme) coordinates the actions of supply chain members, improving the profitability of each member of the supply chain (Table 1).
3.2.1 Case 1 wholesale price contract.
The manufacturer charges a unit wholesale price to the retailer in the wholesale price setting. Even so, the retailer decides the optimal order quantity according to the product's wholesale cost, keeping in view that he is a member/component of the supply chain. It is most ordinarily applied in practice because it is uncomplicated. To coordinate the supply chain, the manufacturer must sell the product at a price equal to the product's manufacturing cost; this leads to no profit or zero profit.
3.2.2 Case 2 buy-back contract.
Under this contract, the manufacturer sells the product to the retailer at wholesale cost and allows the retailer to return the remaining units of the product at the end of the selling season. The buy-back price of the product is less than or equal to the wholesale price of the product. This setting motivates the retailer to increase the order quantity, ultimately increasing the supply chain's profit.
3.2.3 Case 3 revenue sharing contract.
Under this contract, the manufacturer sells the product to the retailer at wholesale price. The wholesale price of the product is nearly equal to the price of the product (Manufacturing cost). Also, the retail price of the product is determined by the manufacturer. Retailer shares a proportion of revenue generated at the end of the selling season. According to wholesale price and percentage of revenue sharing, retailers decide the optimal order quantity to maximize the supply chain profit.
3.2.4 Case 4 markdown money policy.
In markdown money setting, the manufacturer offers the retailer an incentive called markdown money (Bin Shen, 2013) at the end of the selling season. The retailer decides the optimal order quantity according to the wholesale price and markdown money incentive. At the end of the selling season, the manufacturer can subsidize the left-over inventory at the retailer's end.
3.2.5 Case 5 sales rebate contract.
In this coordination, the manufacturer offers sales rebates to retailers to coordinate the supply chain. It is a payment from the manufacturer to the retailer based on the number of units of a product sold to the end consumer at the end of the product's selling season. It is obvious that the measure of sales rebate that can be offered heavily depends on the retailer’s performance and the magnitude of sales push the manufacturer desires to accomplish.
4. Results and analysis
The mathematical model for supply chain coordination schemes is formulated based on a clear definition of the problem, emphasizing the intricacies of the cold chain industry. This step involves identifying key variables, constraints and objectives relevant to the coordination of the supply chain.
4.1 Mathematical model description for assessment of coordination schemes
The schemes are analyzed per the research paper (Arshinder et al., 2009; Choi and Choi, 2008; Govindan et al., 2012) (Table 2). A description of numerical example has been stated for assessing six cases (Table 3). The parameters (Table 4) considered for profit calculation are the marginal cost of goods produced by the manufacturer, the marginal cost of the retailer and for both members, the goodwill and salvage costs, the wholesale price, optimal order quantity and the expected sales.
And p > w > Sm;w > Cm, β > Sm and w > β > Sm > 0
The end customer demand is randomly distributed and denoted by random variable x, with a probability density function f(x) and corresponding cumulative density function F(x) and
S(q): Expected sales:
Decision variable for buy-back contract:
β: The buy-back price received by the retailer from the supplier for the unsold units at the end of the period under the buy-back contract with β < w
Decision variable for revenue sharing contract:
W: New wholesale price
φ: Revenue sharing fraction with φϵ(0,1)
Decision variable for markdown money policy (MMP):
Bm: Markdown price/unit
Decision variable for sales rebate contract:
Um: Sales rebate/unit
Tm: Target sales (in units)
Assumptions
There is one supplier and one retailer in the supply chain (a two-echelon supply chain is considered for dissertation).
The product has a short life cycle (news boy type of product) with no replenishment possibility.
One period is considered a time frame.
The demand is normally distributed within given mean and standard deviations N(μ, σ2).
The make-to-order supply chain is a process in which the manufacturer produces an exact number of units ordered by the retailer, which is very common in the cold chain industry.
Any unmet demand is considered lost, and goodwill cost per unit is applied.
The remaining stock can be salvaged by both members at the end of the selling season.
In a buy-back contract, if there is any left-over inventory at the retailer, the entire amount can be returned to the supplier at a fraction of the wholesale price.
In a revenue-sharing contract, each participant is allocated a share of the total revenue generated by the retailer.
The maximum commitment of the manufacturer in a quantity flexibility contract is the optimal order quantity of the supply chain.
Although the supplier has infinite capacity, no inventory is held during the selling season, and production occurs according to optimal order quantity. The only expectation is quantity flexibility, where the retailer orders less than the optimal order quantity, and in case of high demand, the supplier commits to providing units to the optimal order quantity.
The mentioned points are very realistic in the cold chain perishable product industry:
Retail price is fixed during the selling season.
The market demand for the product is stochastic, and retailers and manufacturers cannot control it.
The market demand and cost structure of manufacturers and retailers are common information.
Evaluation criteria
The criteria which are used to evaluate the different contracts are as follows (Shen et al., 2013):
profit before coordination (PBC);
expected profit after coordination (EP);
standard deviation of profit and (SDP); and
coefficient variation of profit (CVP).
These factors are monetary units.
The profit functions of two members depend upon the evaluation criteria as follows:
where Pr represents the profit realized by the retailer.
4.2 Profit functions
4.2.1 No coordination (case 0).
If the supply chain members do not coordinate with each other (decentralized supply chain), they only want to maximize their profits, and the retailer will place the order of that quantity so that profit will be maximized.
Profit function of manufacturer/supplier:
Profit function of retailer/buyer:
Optimal order quantity of no coordination (decentralized) supply chain can be obtained by differentiating the retailer’s profit function. The optimum order quantity will be as follows:
4.2.2 Coordination contracts (Case 1 to Case 5).
Here, we consider a centralized decision system where the manufacturer and the buyer belong to one firm. The firm's objective is to maximize the expected total profit of the system by choosing the appropriate supply chain coordination contract (Kanda and Deshmukh, 2009).
4.2.2.1 Case 1: wholesale price contract.
The optimal order quantity the retailer places equals the supply chain's optimal order quantity. The wholesale price contract coordinates the channel only if the manufacturer earns a non-positive profit. So, the manufacturer always sets a higher wholesale price. Due to this, the wholesale-price contract is generally not considered a coordinating contract. For details, refer to Gerard P. Cachon (2004).
Manufacturer profit function:
Retailer profit function:
The optimal order quantity of the supply chain can be obtained by differentiating the total supply chain profit, i.e.
4.2.2.2 Case 2: Buy-back contract.
A buy-back contract between manufacturer and retailer depicts that the manufacturer buys back from the retailer any unsold units at the end of the selling season at a price β < w. In this way, the manufacturer takes responsibility (share risk) for unsold units. The profit functions are as follows:
Manufacturer profit function:
Retailer profit function:
With
4.2.2.3 Case 3: Revenue sharing contract.
A revenue-sharing contract depicts that the retailer purchases the goods at a discounted price before the demand is observed but commits to sharing a fraction of the revenue realized at the end of selling se with the suppliers. The manufacturer reduces the wholesale price from W to Wand, and the retailer shares the fraction. (φ) Of the revenue generated from the sales. The profit functions are as follows:
Manufacturer profit function:
Retailer profit function:
As per the joint decision, the optimal order quantity of the retailer must be equal to the optimal order quantity of the coordinated supply chain. Thus, the optimal order quantity:
4.2.2.4 Case 4: markdown money policy (MMP).
In MMP, the manufacturer provides the retailer with a unit markdown price Bm At the end of the selling season, each unit of left-over inventory leaves the left-over inventory to the retailer to liquidate.
Manufacturer profit function:
Retailer profit function:
4.2.2.5 Case 5: sales rebate contract.
In a sales rebate contract, the retailer will get a rebate. (Um) from the manufacturer for each unit of the product sold beyond the target sales(Tm). Under this contract, the manufacturer decides the wholesale price, sales rebate and target sale before the selling season to the retailer, according to which retailer purchases the required amount of product(QSC) (Chun-Hung Chiu et al., 2012).
Manufacturer profit function:
Retailer profit function:
With the supply chain model proposed above, we can drive Profit, EP, SDP and CVP for the whole supply chain, manufacturer and retailer. Π(q) is a key variable for SDP, and it equals Var(q − x)+(T. M. Choi et.al., 2007; Choi et al., 2019). The goodwill costs for the retailer and manufacturer are ignored for calculation simplification.where
4.3 Description of numerical example for assessment of six cases
A numerical example is considered for a two-echelon supply chain having one manufacturer and one retailer. For the evaluation of the proposed model, input data is taken from (Arshinder et al. (2009) and presented in Input data given below (Table 5). Decision variables and optimal order quantity are presented (Table 6). Decision variables are found with the help of simulation using an Excel sheet.
We find that:
4.4 Comparison among the six cases
The performance of six cases (Table 8) is assessed by solving the illustrative example (Table 7) and applying the respective models of coordination schemes. The schemes are compared by their EP, SDP and Coefficient of variation of profits and elaborated graphically (Figure 3, Figure 4 and Figure 5) in the following subsections. Finally, based on a comparison of the schemes we stated, we give a ranking to assess the performance of six cases (Table 9).
5. Discussion
The analysis of the result considering the performance of the manufacturer, retailer and supply chain of all the six cases following includes a summarization of the performance of three stakeholders expressing them in terms of expected profit (EP), standard deviation of profit (SDP) and coefficient of variation of profit.
5.1 Comparison according to the expected profit and standard deviation of profit of the manufacturer
At the manufacturer's end, the revenue sharing contract gives maximum profit and no coordination, the setting gives the least profit also, the manufacturer bears less risk in a revenue sharing contract than wholesale and no coordination situation, but the risk is more than the rest of the contracts (i.e. Buy-back, Markdown money and sales rebate). The Wholesale price contract also gives better profit than other contracts, except the Revenue sharing contract, but risk sharing is maximized, so it is not more beneficial to go for wholesale price in the sentiment of the manufacturer. A buy-back contract gives sufficient profit to the manufacturer as profit is greater than the sales rebate, and MMP and no coordination contracts also carry a higher risk than sales rebate and MMP.
5.2 Comparison according to the expected profit and standard deviation of profit of retailer
With the help of the revenue sharing setting, the retailer gets maximum profit and shares more risk than the manufacturer, thus getting more profit. Retailers exert great effort to sell the product to the maximum extent to get the maximum share of revenue generated from selling. In the sales rebate contract, the retailer also gives his utmost effort to obtain a sales rebate from the manufacturer. Buy-back and MMP give less profit than sales rebate and revenue-sharing contracts and face less risk sharing. No coordination gives the least profit and faces more risk to get that profit.
5.3 Comparison according to the expected profit and standard deviation of profit of the supply chain
In the position of SC as a whole, the revenue sharing contract is most suited to coordinate the SC of the textile (apparel) industry and also faces less risk than other contracts except the sales rebate contract. Revenue sharing contract gives maximum gain to both parties, and risk sharing is nearly equal, whereas No coordination setting gives the least profit to SC chain and retailer and higher risk than other contracts except MMP, which is why the least order quantity from the retail side to the manufacturer. Wholesale price and sales rebate contracts coordinate the SC equally and realize more net income than other contracts, except revenue sharing contracts also face less risk than other contracts. MMP gives more profit than no coordination situation but faces higher risk than no coordination situation.
6. Managerial implication
This study reveals impactful findings for the cold chain industry regarding contractual coordination mechanisms. The foremost implication is the emergence of revenue-sharing contracts as the most desirable approach. This model fosters robust collaboration, aligning manufacturer and retailer goals and demonstrably leads to efficient operations and reduced risk compared to other studied contracts. Considering these merits, cold chain partnerships should prioritize the implementation of revenue-sharing models to achieve enhanced trust, operational optimization and profitability gains. While sales rebate contracts offer potentially higher profits relative to no coordination, they simultaneously entail significantly greater risk. Therefore, meticulously evaluating risk tolerance and business goals is crucial before pursuing such contracts. Revenue sharing presents a more balanced alternative with lower inherent risk and potentially greater long-term benefits.
Furthermore, the limitations of existing contractual schemes must be acknowledged. Further research is imperative to achieve optimal cold chain coordination. This encompasses the investigation of diverse contingencies and scenarios encountered within the cold chain environment, assessing the suitability of different contracts under various conditions and uncertainties and conducting detailed sensitivity analyses to comprehend the impact of contract terms and external factors on supply chain performance. Moreover, supply chain coordination is an ongoing process, necessitating continuous adaptation and refinement of contractual choices. These demands are staying abreast of new research, data and evolving market dynamics. Collaborative efforts with research institutions and industry players are also crucial for developing and implementing increasingly effective coordination mechanisms tailored to the specific needs of the cold chain. By adopting these insights and pursuing further research, cold chain industry managers can unlock the potential for enhanced coordination, optimized operations and a more resilient and profitable ecosystem for all stakeholders.
7. Conclusion
Supply chain contracts are instruments to help achieve supply chain coordination. Contracts are now commonly adopted in each sector of the industry. In this paper, a detailed study has been carried out, analyzing the comparison of performances of no coordination, wholesale price contract, buy-back contract, revenue sharing contract, Markdown money policy and sales rebate contract in the cold chain industry, with the use of secondary data from Kanda and Deshmukh (2005). While analyzing, it is found that a revenue-sharing contract is most suitable for manufacturers, retailers and the whole supply chain. It is interesting to observe that the risk level for achieving coordination by the contract is also lower than other contracts considered for this study. It is further realized that a sales rebate contract offers more profit than the no coordination situation but faces a high risk of achieving that gain.
It is further submitted that the schemes are still not comprehensive enough to extract all relevant insights into supply chain coordination. The study may be further extended to encompass various contingencies and scenarios in the cold supply chain to assess the contracts' suitability under various situations. A detailed sensitivity analysis in its pursuit is expected to enrich this domain of supply chain coordination.
Figures
Figure 1.
A schematic presentation of organizing the study
Figure 2.
Supply chain
Figure 3.
Comparison of expected profit
Figure 4.
Comparison of standard deviation of profit
Figure 5.
Comparison of coefficient variation of profit
Outline of coordination schemes
| Contracts | Producer | Retailer | Supply chain | |||
|---|---|---|---|---|---|---|
| Advantage | Disadvantage | Advantage | Disadvantage | Advantage | Disadvantage | |
| Case 0 (No coordination) | Facing lowest risk | Low order quantity from the retailer side | High profit margin | High risk | Virtually, there is no overall advantage supply chain (SC) | Expected demand for the product decreases |
| Free to set the wholesale price of the product to maximize own profit | High inventory carrying cost | Free to set a retail price to maximize own profit | Low product demand due to high price | Low supply chain profit | ||
| High profit margin | High product cost | |||||
| Case 1 (Whole sale price contracts) | Low risk | Low or zero profit | Capture maximum profit share of the SC | High risk | Profit increases | Both members of SC did not benefit equally, so the SC was not coordinated |
| High order quantity from the retailer | Can able to order a greater number of units of the product | Inventory carrying cost increases | Efficiency of the SC increases | |||
| Double marginalization reduces | ||||||
| Case 2 (Buy back contracts) | Profit increases | Risk sharing increase | Reduces risk of overstocking | Inventory carrying cost increases | Profit increases/overall cost reduction | Win-win situation is not achievable |
| Order quantity increases | Increases salvage value per unit in the form of payback | Product availability increases | May increase the retail price | |||
| Increases the level of product availability | Better coordination between producer and retailer | |||||
| Case 3 (Revenue Sharing Contracts) | Profit increases | Problem to decide the percentage of profit sharing | Lower wholesale price | Inventory carrying cost increases | Profit increases/overall cost reduction | Win-win situation is not achievable |
| Encourages retailer to order larger quantities | Applicability is limited to high value products | Profit increases | Due to the high cost of the product order quantity is less | Better coordination between producer and retailer | ||
| More space for negotiation | Increases the level of product availability | |||||
| Lower risk sharing | ||||||
| Case 4 (Markdown money policy) | Significant percentage increase in earnings | Require more careful deliberation | Net income increases | Inventory carrying cost increases | Profit increases/overall cost reduction | More complicated than wholesale price contract |
| Encourages retailer to order larger quantities | Increases the degree of product accessibility | High risk sharing | Simplicity and ease of implementation | |||
| Can use preannounce d markdown pricing to sell finite amount of product | Significant effect on pushing order quantity up | |||||
| Case 5 (Sale rebate contracts) | Net income increases | Due to rebate net income may decrease | Ordering cost reduces | To avoid significant markdown under stocking may be set up | Better marketing of merchandise | Win-win situation is not manageable |
| This reduces the risk factors | Net income increases | The high sales effort to receive a sales rebate | Enhance the supply chain performance | |||
| Encourages retailer to order larger quantities | Increases the level of product availability | |||||
| Push retailer to sell a greater number of units | Lower risk | |||||
Model description for assessment of coordination schemes
| Notation | Description |
|---|---|
| Manufacturer/ supplier | |
| Cm | Production cost/unit |
| Gm | Goodwill cost/unit |
| Sm | Salvage cost/unit |
| W | Wholesale price/unit |
| W | Wholesale price/unit for revenue sharing contract |
| Bm | Markdown price/unit |
| Um | Sales rebate/unit |
| Tm | Target sales |
| Retailer/buyer | |
| Cr | Marginal cost/unit |
| P | Retail price/unit |
| Gr | Goodwill cost/unit |
| Sr | Salvage cost/unit |
| Q | Optimal order quantity (no coordination) |
| Qsc | Optimal order quantity of coordinated supply chain |
| Others | |
| M | For manufacturer |
| R | For retailer |
| WSP | Wholesale price contract |
| BB | Buy-back contract |
| RS | Revenue sharing contract |
| QF | Quantity flexibility contract |
| MMP | Markdown money policy |
| SR | Sales rebate contract |
| EP | Expected profit after coordination |
| PBC | Profit before coordination |
| SDP | Standard deviation of profit |
| S(q) | Expected sales |
| CVP | Coefficient variation of profit |
Extension of T.-M. Choi and Choi (2008), T. M. Choi et al. (2019) and Shen et al. (2013)
| Member of supply chain | ||
|---|---|---|
| No coordination | ||
| Profit | Manufacturer | (w − Cm)q |
| Retailer | (p − w − Cr)q − (p − Sr)(q − x)+ | |
| Supply chain | (p − Cm − Cr)q − (p − Sr)(q − x)+ | |
| EP | Manufacturer | (w − Cm)q |
| Retailer | (p − w − Cr)q − (p − Sr)(q – s(q)) | |
| Supply chain | (p − Cm − Cr)q − (p − Sr)(q – S(q)) | |
| SDP | Manufacturer | 0 |
| Retailer |
|
|
| Supply chain |
|
|
| Wholesale price contract | ||
| Profit | Manufacturer | (w − Cm)qsc |
| Retailer | (p − w − Cr)qsc − (p − Sr)(qsc − x)+ | |
| Supply chain | (p − Cm − Cr)qsc − (p − Sr)(qsc − x)+ | |
| EP | Manufacturer | (w − Cm)qsc |
| Retailer | (p − w − Cr)qsc − (p − Sr) (qsc – s(qsc)) | |
| Supply chain | (p − Cm − Cr)qsc − (p − Sr) (qsc – s(qsc)) | |
| SDP | Manufacturer | 0 |
| Retailer |
|
|
| Supply chain |
|
|
| Buy-back contract | ||
| Profit | Manufacturer | (w – Cm)qsc − (β – Sm)(qsc − x)+ |
| Retailer | (p – Cr – w)qsc − (p – β)(qsc − x)+ | |
| Supply chain | (p – Cm – Cr)qsc − (p – Sm)(qsc − x)+ | |
| EP | Manufacturer | (w – Cm)qsc – (β – Sm)(qsc – S(qsc)) |
| Retailer | (p – Cr – w)qsc – (p – β)(qsc – S(qsc)) | |
| Supply chain | (p – Cm – Cr)qsc – (p – Sm)(qsc – S(qsc)) | |
| SDP | Manufacturer |
|
| Retailer |
|
|
| Supply chain |
|
|
| Revenue sharing contract | ||
| Profit | Manufacturer | (W – Cm + p(1 – φ))Qsc – {(p(1 – φ) – Sr)(Qsc – x)+} |
| Retailer | (p – W – Cr)qsc – (φp – Sr)(qsc – x)+ | |
| Supply chain | (p(2 – φ) – Cm – Cr)Qsc – (p – 2Sr)(Qsc – x)+ | |
| EP | Manufacturer | (W – Cm + p(1 – φ))Qsc – {(p(1 – φ) – Sr)(Qsc – S(Qsc))} |
| Retailer | (p – W – Cr)qsc – (φp – Sr)(qsc – S(qsc)) | |
| Supply chain | (p(2 – φ) – Cm – Cr)Qsc – (p – 2Sr)(Qsc – S(Qsc)) | |
| SDP | Manufacturer |
|
| Retailer |
|
|
| Supply chain |
|
|
| Markdown money policy | ||
| Profit | Manufacturer | (w – Cm)qsc – Bm(qsc – x)+ |
| Retailer | (p – w – Cr)qsc – (p – Bm)(qsc – x)+ | |
| Supply chains | (p – Cm – Cr)qsc – p(qsc – x)+ | |
| EP | Manufacturer | (w – Cm)qsc – Bm(qsc – S(qsc)) |
| Retailer | (p – w – Cr)qsc – (p – Bm)(qsc – s(qsc)) | |
| Supply chain | (p – Cm – Cr)qsc – p(qsc – S(qsc)) | |
| SDP | Manufacturer |
|
| Retailer |
|
|
| Supply chain |
|
|
| Sales rebate contract | ||
| Profit | Manufacturer | (w – Cm)qsc – Um(qsc – Tm)+ |
| Retailer | (p – w – Cr)qsc – (p – Sr)(qsc – S(qsc)) + Um(qsc – Tm)+ | |
| Supply chain | (p – Cm – Cr)qsc – (p – Sr)(qsc – s(qsc)) | |
| EP | Manufacturer |
|
| Retailer |
|
|
| Supply chain | (p – Cm – Cr)qsc – (p – Sr)(qsc – s(qsc)) | |
| SDP | Manufacturer |
|
| Retailer |
|
|
| Supply chain |
|
|
Description of numerical example for assessment of six cases
| Contract | Member | Coefficient of variation |
|---|---|---|
| Case 0 – No coordination | Manufacturer | 0 |
| Retailer |
|
|
| Supply chain |
|
|
| Case 1 – Wholesale price contract | Manufacturer | 0 |
| Retailer |
|
|
| Supply chain |
|
|
| Case 2 – Buyback contract | Manufacturer |
|
| Retailer |
|
|
| Supply chain |
|
|
| Case 3 – Revenue sharing contract | Manufacturer |
|
| Retailer |
|
|
| Supply chain |
|
|
| Case 4 – Markdown money Policy | Manufacturer |
|
| Retailer |
|
|
| Supply chain |
|
|
| Case 5 – Sales rebate contract | Manufacturer |
|
| Retailer |
|
|
| Supply chain |
|
|
Input data
| Manufacturer | Retailer | Others |
|---|---|---|
| Cm = 12 | Cr = 2 | Demand∼N (100,302) |
| Gm =8 | p = 30 | μ = 100 |
| Sm = 9 | Gr = 14 | σ = 30 |
| w = 20 | Sr =10 |
Decision variables and optimal order quantity
| Contract | Decision variables | |||||||
|---|---|---|---|---|---|---|---|---|
| Optimum order quantity | β | W | φ | Bm | Um | Tm | ||
| Case 0 | No coordination | 111.35 | ||||||
| Case 1 | Wholesale price | 139.35 | ||||||
| Case 2 | Buy-back | 139.35 | 15 | |||||
| Case 3 | Revenue sharing | 139.35 | 10.1 | 0.6 | ||||
| Case 4 | Markdown money policy | 139.35 | 15 | |||||
| Case 5 | Sales rebate | 135.95 | 8.84 | 124.31 | ||||
Extension of T.-M. Choi and Choi (2008), T. M. Choi et al. (2019) and Shen et al. (2013)
| Member of supply chain | ||
|---|---|---|
| No coordination | ||
| EP | Manufacturer | 890.8 |
| Retailer | 105.6 | |
| Supply chain | 996.4 | |
| SDP | Manufacturer | 0 |
| Retailer | 1064.8 | |
| Supply chain | 1064.8 | |
| Wholesale price contract | ||
| EP | Manufacturer | 1114.8 |
| Retailer | 847.26 | |
| Supply chain | 1962.06 | |
| SDP | Manufacturer | 0 |
| Retailer | 821 | |
| Supply chain | 821 | |
| Buy-back contract | ||
| EP | Manufacturer | 1034.538 |
| Retailer | 914.145 | |
| Supply chain | 1948.683 | |
| SDP | Manufacturer | 246.3 |
| Retailer | 615.75 | |
| Supply chain | 862.05 | |
| Revenue sharing contract | ||
| EP | Manufacturer | 1380.681 |
| Retailer | 2387.349 | |
| Supply chain | 3768.03 | |
| SDP | Manufacturer | 82.1 |
| Retailer | 328.4 | |
| Supply chain | 410.5 | |
| Markdown money policy | ||
| EP | Manufacturer | 914.145 |
| Retailer | 914.145 | |
| Supply chain | 1828.29 | |
| SDP | Manufacturer | 615.75 |
| Retailer | 615.75 | |
| Supply chain | 1231.5 | |
| Sales rebate contract | ||
| EP | Manufacturer | 994.69 |
| Retailer | 967.37 | |
| Supply chain | 1962.06 | |
| SDP | Manufacturer | 280.06 |
| Retailer | 32.53 | |
| Supply chain | 312.59 | |
Assessment of performance of six cases
| Contract | Member | Coefficient of variation |
|---|---|---|
| Case 0 – No coordination | Manufacturer | 0.00 |
| Retailer | 10.08 | |
| Supply chain | 1.06 | |
| Case 1 – Wholesale price contract | Manufacturer | 0.00 |
| Retailer | 0.969 | |
| Supply chain | 0.418 | |
| Case 2 – Buyback contract | Manufacturer | 0.238 |
| Retailer | 0.673 | |
| Supply chain | 0.442 | |
| Case 3 – Revenue sharing contract | Manufacturer | 0.059 |
| Retailer | 0.137 | |
| Supply chain | 0.108 | |
| Case 4 – Markdown money policy | Manufacturer | 0.673 |
| Retailer | 0.673 | |
| Supply chain | 0.673 | |
| Case 5 – Sales rebate contract | Manufacturer | 0.281 |
| Retailer | 0.033 | |
| Supply chain | 6.27 | |
Ranking of coordination contracts
| Coordination contracts | EP | SDP | CV | Overall rank | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| M | R | SC | M | R | S | M | R | SC | ||
| No coordination | Rank6 | Rank5 | Rank 5 | Rank 1 | Rank 5 | Rank 5 | Rank 1 | Rank 5 | Rank 5 | Rank 5 |
| Wholesale price | Rank2 | Rank4 | Rank 2 | Rank 1 | Rank 4 | Rank 3 | Rank 1 | Rank 3 | Rank 2 | Rank 2 |
| Buy-back | Rank3 | Rank3 | Rank 3 | Rank 3 | Rank 3 | Rank 4 | Rank 3 | Rank 2 | Rank 3 | Rank 3 |
| Revenue sharing | Rank1 | Rank1 | Rank 1 | Rank 2 | Rank 2 | Rank 2 | Rank 2 | Rank 1 | Rank 1 | Rank 1 |
| Markdown money policy | Rank5 | Rank3 | Rank 4 | Rank 5 | Rank 3 | Rank 6 | Rank 5 | Rank 2 | Rank 4 | Rank 4 |
| Sales rebate | Rank4 | Rank2 | Rank 2 | Rank 4 | Rank 1 | Rank 1 | Rank 4 | Rank 4 | Rank 6 | Rank 6 |
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Further reading
Elmaghraby, W., Gülcü, A. and Keskinocak, P. (2007), “Designing optimal preannounced markdowns in the presence of rational customers with multiunit demands”, Manufacturing and Service Operations Management, Vol. 10 No. 1, pp. 126-148, doi: 10.1287/MSOM.1070.0157.